From 850c7b170e5de0f5051573f58f561855f854f558 Mon Sep 17 00:00:00 2001 From: Ere Oh Date: Mon, 21 Oct 2024 18:25:29 +0000 Subject: [PATCH] v0.6.0 --- .../pull_request_template.md | 15 +- .gitignore | 33 +- README.md | 109 ++- docker-compose.yml | 2 +- ...ts.conf.NarrowbandCleanEbNoTrainConfig.rst | 37 + ....conf.NarrowbandCleanEbNoTrainQAConfig.rst | 37 + ...sets.conf.NarrowbandCleanEbNoValConfig.rst | 37 + ...ts.conf.NarrowbandCleanEbNoValQAConfig.rst | 37 + ...tasets.conf.NarrowbandCleanTrainConfig.rst | 37 + ...sets.conf.NarrowbandCleanTrainQAConfig.rst | 37 + ...datasets.conf.NarrowbandCleanValConfig.rst | 37 + ...tasets.conf.NarrowbandCleanValQAConfig.rst | 37 + ...orchsig.datasets.conf.NarrowbandConfig.rst | 37 + ...conf.NarrowbandImpairedEbNoTrainConfig.rst | 37 + ...nf.NarrowbandImpairedEbNoTrainQAConfig.rst | 37 + ...s.conf.NarrowbandImpairedEbNoValConfig.rst | 37 + ...conf.NarrowbandImpairedEbNoValQAConfig.rst | 37 + ...ets.conf.NarrowbandImpairedTrainConfig.rst | 37 + 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examples/05_yolo.yaml create mode 100644 examples/06_yolo.yaml create mode 100644 examples/07_example_narrowband_yolo.ipynb create mode 100644 examples/07_yolo.yaml create mode 100644 examples/08_example_optuna_yolo.ipynb create mode 100644 examples/08_yolo_optuna.yaml create mode 100644 examples/09_example_synthetic_spectrogram_dataset.ipynb create mode 100644 examples/10_example_yolo_annotation_tool.ipynb create mode 100755 gr-spectrumdetect/README.md create mode 100755 gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train.py create mode 100755 gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train_scipy.py create mode 100755 gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val.py create mode 100755 gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val_scipy.py create mode 100755 gr-spectrumdetect/examples/wideband_yolo.yaml rename scripts/{generate_sig53.py => generate_narrowband.py} (78%) rename scripts/{generate_wideband_sig53.py => generate_wideband.py} (77%) create mode 100644 scripts/test_generate_narrowband_scripts.py delete mode 100644 scripts/test_generate_sig53_scripts.py create mode 100644 scripts/test_generate_wideband_scripts.py delete mode 100644 scripts/test_generate_wideband_sig53_scripts.py create mode 100755 scripts/train_narrowband.py delete mode 100755 scripts/train_sig53.py mode change 100755 => 100644 torchsig/datasets/sig53.py create mode 100755 torchsig/datasets/torchsig_narrowband.py create mode 100755 torchsig/datasets/torchsig_wideband.py mode change 100755 => 100644 torchsig/datasets/wideband_sig53.py create mode 100644 torchsig/image_datasets/annotation_tools/yolo_annotation_example.ipynb create mode 100644 torchsig/image_datasets/annotation_tools/yolo_annotation_tool.py create mode 100644 torchsig/image_datasets/transforms/denoising.py create mode 100644 torchsig/models/iq_models/inceptiontime/__init__.py create mode 100644 torchsig/models/iq_models/inceptiontime/inceptiontime.py create mode 100644 torchsig/utils/narrowband_trainer.py create mode 100644 torchsig/utils/optuna/PyTorchLightningCallback.py create mode 100644 torchsig/utils/optuna/tuner.py diff --git a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md index e1304e6..87d5847 100644 --- a/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md +++ b/.github/PULL_REQUEST_TEMPLATE/pull_request_template.md @@ -7,18 +7,11 @@ Describe your changes here specifying if the change is a bug fix, enhancement, n Describe how you tested and verified your changes here (changes captured in existing tests, built and ran new tests, etc.). ## Before Submitting -- [ ] Check mypy locally - - `pip3 install mypy==1.2.0` - - `mypy --ignore-missing-imports torchsig` - - Address any error messages -- [ ] Lint check locally - - `pip3 install flake8` - - `flake8 --select=E9,F63,F7,F82 torchsig` - - Address any error messages +- [ ] Check for bugs/errors +- [ ] Run example notebooks + - `examples/` + - Ensure all notebooks run successfully. - [ ] Run formatter if needed - `pip3 install git+https://github.com/GooeeIOT/pyfmt.git` - `pyfmt torchsig` -- [ ] Run test suite locally - - `pytest --ignore-glob=*_figures.py --ignore-glob=*_benchmark.py` - - Ensure tests are successful prior to submitting PR diff --git a/.gitignore b/.gitignore index 07f4985..5feb87f 100644 --- a/.gitignore +++ b/.gitignore @@ -1,17 +1,28 @@ +# Common Python Files __pycache__/ -*.pyc -*.mdb -*.swp -torchsig.egg-info .vscode/ -*.png build/ checkpoints/ lightning_logs/ -*.pt -*.jpg -*.benchmarks/ -*.ipynb_checkpoints/ dist/ -examples/*.ipynb_checkpoints/ -gr-spectrumdetect/examples/*.pt +**.ipynb_checkpoints** +**.pytest_cache +*.benchmarks/ + +**.pyc +**.mdb +**.lmdb +**.swp +**.png +**.txt +**.npy +**.jpg +**.pt* +**.pth + +# TorchSig +torchsig.egg-info +examples/runs +examples/datasets +examples/yolo +examples/plots diff --git a/README.md b/README.md index 374e343..1678c7f 100755 --- a/README.md +++ b/README.md @@ -6,74 +6,61 @@ ----- -![build](https://github.com/torchDSP/torchsig/actions/workflows/pip_build.yml/badge.svg?branch=37-automate-install-tests) +[TorchSig](https://torchsig.com) is an open-source signal processing machine learning toolkit based on the PyTorch data handling pipeline. The user-friendly toolkit simplifies common digital signal processing operations, augmentations, and transformations when dealing with both real and complex-valued signals. TorchSig streamlines the integration process of these signals processing tools building on PyTorch, enabling faster and easier development and research for machine learning techniques applied to signals data, particularly within (but not limited to) the radio frequency domain. An example dataset, TorchSigNarrowband, based on many unique communication signal modulations is included to accelerate the field of modulation classification. Additionally, an example wideband dataset, TorchSigWideband, is also included that extends TorchSigNarrowband with larger data example sizes containing multiple signals enabling accelerated research in the fields of wideband signal detection and recognition. -[TorchSig](https://torchsig.com) is an open-source signal processing machine learning toolkit based on the PyTorch data handling pipeline. The user-friendly toolkit simplifies common digital signal processing operations, augmentations, and transformations when dealing with both real and complex-valued signals. TorchSig streamlines the integration process of these signals processing tools building on PyTorch, enabling faster and easier development and research for machine learning techniques applied to signals data, particularly within (but not limited to) the radio frequency domain. An example dataset, Sig53, based on many unique communication signal modulations is included to accelerate the field of modulation classification. Additionally, an example wideband dataset, WidebandSig53, is also included that extends Sig53 with larger data example sizes containing multiple signals enabling accelerated research in the fields of wideband signal detection and recognition. +# Getting Started -*TorchSig is currently in beta* +## Prerequisites +- Ubuntu ≥ 20.04 +- Hard drive storage with: + - ≥ 500 GB for Narrowband + - ≥ 10 TB for Wideband +- CPU with ≥ 4 cores +- GPU with ≥ 16 GB storage (reccomended) +- Python ≥ 3.9 -## Key Features ---- -TorchSig provides many useful tools to facilitate and accelerate research on signals processing machine learning technologies: -- The `SignalData` class and its `SignalMetadata` objects enable signals objects and meta data to be seamlessly handled and operated on throughout the TorchSig infrastructure. -- The `Sig53` Dataset is a state-of-the-art static modulations-based RF dataset meant to serve as the next baseline for RFML classification development & evaluation. -- The `ModulationsDataset` class synthetically creates, augments, and transforms the largest communications signals modulations dataset to date in a generic, flexible fashion. -- The `WidebandSig53` Dataset is a state-of-the-art static wideband RF signals dataset meant to serve as the baseline for RFML signal detection and recognition development & evaluation. -- The `WidebandModulationsDataset` class synthetically creates, augments, and transforms the largest wideband communications signals dataset in a generic, flexible fashion. -- Numerous signals processing transforms enable existing ML techniques to be employed on the signals data, streamline domain-specific signals augmentations in signals processing machine learning experiments, and signals-specific data transformations to speed up the field of expert feature signals processing machine learning integration. -- TorchSig also includes a model API similar to open source code in other ML domains, where several state-of-the-art convolutional and transformer-based neural architectures have been adapted to the signals domain and pretrained on the `Sig53` and `WidebandSig53` datasets. These models can be easily used for follow-on research in the form of additional hyperparameter tuning, out-of-the-box comparative analysis/evaluations, and/or fine-tuning to custom datasets. - - -## Documentation ---- -Documentation can be found [online](https://torchsig.readthedocs.io/en/latest/) or built locally by following the instructions below. -``` -cd docs -pip install -r docs-requirements.txt -make html -firefox build/html/index.html -``` +We highly reccomend Ubuntu or using a Docker container. ## Installation ---- -Clone the `torchsig` repository and simply install using the following commands: +Clone the `torchsig` repository and install using the following commands: ``` +git clone https://github.com/TorchDSP/torchsig.git cd torchsig pip install . ``` -## Generating the Datasets -If you'd like to generate the named datasets without messing with your current Python environment, you can build the development container and use it to generate data at the location of your choosing. +# Usage +## Generating the Datasets with Command Line +To create the narrowband dataset: ``` -docker build -t torchsig -f Dockerfile . -docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_sig53.py --root=/workspace/code/torchsig/data --all=True +python3 ./scripts/generate_narrowband.py --root ./examples/datasets --all --num-workers=4 +``` +To create the wideband dataset: +``` +python3 ./scripts/generate_wideband.py --root ./examples/datasets --all --num-workers=4 ``` -For the wideband dataset, you can do: +## Generating the Datasets in Docker +Docker can be used to generate the datasets without modifying your current Python environment. Build a Docker container: ``` docker build -t torchsig -f Dockerfile . -docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_wideband_sig53.py --root=/workspace/code/torchsig/data --all=True ``` -If you do not need to use Docker, you can also just generate using the regular command-line interface - +To create the narrowband dataset with the Docker container: ``` -python3 torchsig/scripts/generate_sig53.py --root=torchsig/data --all=True +docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_narrowband.py --root=/workspace/code/torchsig/data --all ``` -or for the wideband dataset: - +To create the wideband dataset with the Docker container: ``` -python3 torchsig/scripts/generate_wideband_sig53.py --root=torchsig/data --all=True +docker run -u $(id -u ${USER}):$(id -g ${USER}) -v `pwd`:/workspace/code/torchsig torchsig python3 torchsig/scripts/generate_wideband.py --root=/workspace/code/torchsig/data --all ``` -Then, be sure to point scripts looking for ```root``` to ```torchsig/data```. - -## Using the Dockerfile -If you have Docker installed along with compatible GPUs and drivers, you can try: +## Jupyter Notebook Examples with Docker and GPUs +The example jupyter notebooks can be run within Docker with GPU support, try the command: ``` docker build -t torchsig -f Dockerfile . @@ -83,13 +70,41 @@ docker exec torchsig_workspace jupyter notebook --allow-root --ip=0.0.0.0 --no-b Then use the URL in the output in your browser to run the examples and notebooks. -## License ---- + +# Key Features +TorchSig provides many useful tools to facilitate and accelerate research on signals processing machine learning technologies: +- The `SignalData` class and its `SignalMetadata` objects enable signals objects and meta data to be seamlessly handled and operated on throughout the TorchSig infrastructure. +- The `TorchSigNarrowband` Dataset is a state-of-the-art static modulations-based RF dataset meant to serve as the next baseline for RFML classification development & evaluation. +- The `ModulationsDataset` class synthetically creates, augments, and transforms the largest communications signals modulations dataset to date in a generic, flexible fashion. +- The `TorchSigWideband` Dataset is a state-of-the-art static wideband RF signals dataset meant to serve as the baseline for RFML signal detection and recognition development & evaluation. +- The `WidebandModulationsDataset` class synthetically creates, augments, and transforms the largest wideband communications signals dataset in a generic, flexible fashion. +- Numerous signals processing transforms enable existing ML techniques to be employed on the signals data, streamline domain-specific signals augmentations in signals processing machine learning experiments, and signals-specific data transformations to speed up the field of expert feature signals processing machine learning integration. +- TorchSig also includes a model API similar to open source code in other ML domains, where several state-of-the-art convolutional and transformer-based neural architectures have been adapted to the signals domain and pretrained on the `TorchSigNarrowband` and `TorchSigWideband` datasets. These models can be easily used for follow-on research in the form of additional hyperparameter tuning, out-of-the-box comparative analysis/evaluations, and/or fine-tuning to custom datasets. + + +# Documentation +Documentation can be found [online](https://torchsig.readthedocs.io/en/latest/) or built locally by following the instructions below. +``` +cd docs +pip install -r docs-requirements.txt +make html +firefox build/html/index.html +``` + + +# License TorchSig is released under the MIT License. The MIT license is a popular open-source software license enabling free use, redistribution, and modifications, even for commercial purposes, provided the license is included in all copies or substantial portions of the software. TorchSig has no connection to MIT, other than through the use of this license. +# Publications +| Title | Year | Cite (APA) | +| ----- | ---- | ---------- | +| [TorchSig: A GNU Radio Block and New Spectrogram Tools for Augmenting ML Training](https://events.gnuradio.org/event/24/contributions/628/attachments/190/473/TorchSig_GRCon2024_paper.pdf) | 2024 | Vallance, P., Oh, E., Mullins, J., Gulati, M., Hoffman, J., & Carrick, M. (2024, September). TorchSig: A GNU Radio Block and New Spectrogram Tools for Augmenting ML Training. In Proceedings of the GNU Radio Conference (Vol. 9, No. 1). | +| [Large Scale Radio Frequency Wideband Signal Detection & Recognition](https://doi.org/10.48550/arXiv.2211.10335)| 2022 | Boegner, L., Vanhoy, G., Vallance, P., Gulati, M., Feitzinger, D., Comar, B., & Miller, R. D. (2022). Large Scale Radio Frequency Wideband Signal Detection & Recognition. arXiv preprint arXiv:2211.10335. | +| [Large Scale Radio Frequency Signal Classification](https://doi.org/10.48550/arXiv.2207.09918) | 2022 | Boegner, L., Gulati, M., Vanhoy, G., Vallance, P., Comar, B., Kokalj-Filipovic, S., ... & Miller, R. D. (2022). Large Scale Radio Frequency Signal Classification. arXiv preprint arXiv:2207.09918. | + + +# Citing TorchSig -## Citing TorchSig ---- Please cite TorchSig if you use it for your research or business. ```bibtext @@ -103,4 +118,4 @@ Please cite TorchSig if you use it for your research or business. note={arXiv:2207.09918} url={https://arxiv.org/abs/2207.09918} } -``` \ No newline at end of file +``` diff --git a/docker-compose.yml b/docker-compose.yml index fd3fa44..0b5ea72 100755 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -2,7 +2,7 @@ name: torch_sig_container_${PROJECT_NAME} services: torchsig_service: build: . - image: torchsig_github:v0.5.0 + image: torchsig_github container_name: torchsig_${PROJECT_NAME} stdin_open: true tty: true diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoTrainConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoTrainConfig.rst new file mode 100644 index 0000000..aadb3db --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoTrainConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanEbNoTrainConfig +===================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanEbNoTrainConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanEbNoTrainConfig.eb_no + ~NarrowbandCleanEbNoTrainConfig.include_snr + ~NarrowbandCleanEbNoTrainConfig.level + ~NarrowbandCleanEbNoTrainConfig.name + ~NarrowbandCleanEbNoTrainConfig.num_iq_samples + ~NarrowbandCleanEbNoTrainConfig.num_samples + ~NarrowbandCleanEbNoTrainConfig.seed + ~NarrowbandCleanEbNoTrainConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoTrainQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoTrainQAConfig.rst new file mode 100644 index 0000000..3fc8f57 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoTrainQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanEbNoTrainQAConfig +======================================================= + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanEbNoTrainQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanEbNoTrainQAConfig.eb_no + ~NarrowbandCleanEbNoTrainQAConfig.include_snr + ~NarrowbandCleanEbNoTrainQAConfig.level + ~NarrowbandCleanEbNoTrainQAConfig.name + ~NarrowbandCleanEbNoTrainQAConfig.num_iq_samples + ~NarrowbandCleanEbNoTrainQAConfig.num_samples + ~NarrowbandCleanEbNoTrainQAConfig.seed + ~NarrowbandCleanEbNoTrainQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoValConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoValConfig.rst new file mode 100644 index 0000000..1297ecb --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoValConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanEbNoValConfig +=================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanEbNoValConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanEbNoValConfig.eb_no + ~NarrowbandCleanEbNoValConfig.include_snr + ~NarrowbandCleanEbNoValConfig.level + ~NarrowbandCleanEbNoValConfig.name + ~NarrowbandCleanEbNoValConfig.num_iq_samples + ~NarrowbandCleanEbNoValConfig.num_samples + ~NarrowbandCleanEbNoValConfig.seed + ~NarrowbandCleanEbNoValConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoValQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoValQAConfig.rst new file mode 100644 index 0000000..06c9984 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanEbNoValQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanEbNoValQAConfig +===================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanEbNoValQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanEbNoValQAConfig.eb_no + ~NarrowbandCleanEbNoValQAConfig.include_snr + ~NarrowbandCleanEbNoValQAConfig.level + ~NarrowbandCleanEbNoValQAConfig.name + ~NarrowbandCleanEbNoValQAConfig.num_iq_samples + ~NarrowbandCleanEbNoValQAConfig.num_samples + ~NarrowbandCleanEbNoValQAConfig.seed + ~NarrowbandCleanEbNoValQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanTrainConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanTrainConfig.rst new file mode 100644 index 0000000..0a91273 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanTrainConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanTrainConfig +================================================= + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanTrainConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanTrainConfig.eb_no + ~NarrowbandCleanTrainConfig.include_snr + ~NarrowbandCleanTrainConfig.level + ~NarrowbandCleanTrainConfig.name + ~NarrowbandCleanTrainConfig.num_iq_samples + ~NarrowbandCleanTrainConfig.num_samples + ~NarrowbandCleanTrainConfig.seed + ~NarrowbandCleanTrainConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanTrainQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanTrainQAConfig.rst new file mode 100644 index 0000000..cff86d2 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanTrainQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanTrainQAConfig +=================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanTrainQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanTrainQAConfig.eb_no + ~NarrowbandCleanTrainQAConfig.include_snr + ~NarrowbandCleanTrainQAConfig.level + ~NarrowbandCleanTrainQAConfig.name + ~NarrowbandCleanTrainQAConfig.num_iq_samples + ~NarrowbandCleanTrainQAConfig.num_samples + ~NarrowbandCleanTrainQAConfig.seed + ~NarrowbandCleanTrainQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanValConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanValConfig.rst new file mode 100644 index 0000000..0c65968 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanValConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanValConfig +=============================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanValConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanValConfig.eb_no + ~NarrowbandCleanValConfig.include_snr + ~NarrowbandCleanValConfig.level + ~NarrowbandCleanValConfig.name + ~NarrowbandCleanValConfig.num_iq_samples + ~NarrowbandCleanValConfig.num_samples + ~NarrowbandCleanValConfig.seed + ~NarrowbandCleanValConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanValQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanValQAConfig.rst new file mode 100644 index 0000000..939931d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandCleanValQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandCleanValQAConfig +================================================= + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandCleanValQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandCleanValQAConfig.eb_no + ~NarrowbandCleanValQAConfig.include_snr + ~NarrowbandCleanValQAConfig.level + ~NarrowbandCleanValQAConfig.name + ~NarrowbandCleanValQAConfig.num_iq_samples + ~NarrowbandCleanValQAConfig.num_samples + ~NarrowbandCleanValQAConfig.seed + ~NarrowbandCleanValQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandConfig.rst new file mode 100644 index 0000000..9d5f27d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandConfig +======================================= + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandConfig.include_snr + ~NarrowbandConfig.num_iq_samples + ~NarrowbandConfig.use_class_idx + ~NarrowbandConfig.name + ~NarrowbandConfig.num_samples + ~NarrowbandConfig.level + ~NarrowbandConfig.seed + ~NarrowbandConfig.eb_no + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoTrainConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoTrainConfig.rst new file mode 100644 index 0000000..9ef525a --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoTrainConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedEbNoTrainConfig +======================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedEbNoTrainConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedEbNoTrainConfig.eb_no + ~NarrowbandImpairedEbNoTrainConfig.include_snr + ~NarrowbandImpairedEbNoTrainConfig.level + ~NarrowbandImpairedEbNoTrainConfig.name + ~NarrowbandImpairedEbNoTrainConfig.num_iq_samples + ~NarrowbandImpairedEbNoTrainConfig.num_samples + ~NarrowbandImpairedEbNoTrainConfig.seed + ~NarrowbandImpairedEbNoTrainConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoTrainQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoTrainQAConfig.rst new file mode 100644 index 0000000..87a7d3e --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoTrainQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedEbNoTrainQAConfig +========================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedEbNoTrainQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedEbNoTrainQAConfig.eb_no + ~NarrowbandImpairedEbNoTrainQAConfig.include_snr + ~NarrowbandImpairedEbNoTrainQAConfig.level + ~NarrowbandImpairedEbNoTrainQAConfig.name + ~NarrowbandImpairedEbNoTrainQAConfig.num_iq_samples + ~NarrowbandImpairedEbNoTrainQAConfig.num_samples + ~NarrowbandImpairedEbNoTrainQAConfig.seed + ~NarrowbandImpairedEbNoTrainQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoValConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoValConfig.rst new file mode 100644 index 0000000..1047dc4 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoValConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedEbNoValConfig +====================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedEbNoValConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedEbNoValConfig.eb_no + ~NarrowbandImpairedEbNoValConfig.include_snr + ~NarrowbandImpairedEbNoValConfig.level + ~NarrowbandImpairedEbNoValConfig.name + ~NarrowbandImpairedEbNoValConfig.num_iq_samples + ~NarrowbandImpairedEbNoValConfig.num_samples + ~NarrowbandImpairedEbNoValConfig.seed + ~NarrowbandImpairedEbNoValConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoValQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoValQAConfig.rst new file mode 100644 index 0000000..0840f02 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedEbNoValQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedEbNoValQAConfig +======================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedEbNoValQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedEbNoValQAConfig.eb_no + ~NarrowbandImpairedEbNoValQAConfig.include_snr + ~NarrowbandImpairedEbNoValQAConfig.level + ~NarrowbandImpairedEbNoValQAConfig.name + ~NarrowbandImpairedEbNoValQAConfig.num_iq_samples + ~NarrowbandImpairedEbNoValQAConfig.num_samples + ~NarrowbandImpairedEbNoValQAConfig.seed + ~NarrowbandImpairedEbNoValQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedTrainConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedTrainConfig.rst new file mode 100644 index 0000000..49ab507 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedTrainConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedTrainConfig +==================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedTrainConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedTrainConfig.eb_no + ~NarrowbandImpairedTrainConfig.include_snr + ~NarrowbandImpairedTrainConfig.level + ~NarrowbandImpairedTrainConfig.name + ~NarrowbandImpairedTrainConfig.num_iq_samples + ~NarrowbandImpairedTrainConfig.num_samples + ~NarrowbandImpairedTrainConfig.seed + ~NarrowbandImpairedTrainConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedTrainQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedTrainQAConfig.rst new file mode 100644 index 0000000..a24fa9e --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedTrainQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedTrainQAConfig +====================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedTrainQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedTrainQAConfig.eb_no + ~NarrowbandImpairedTrainQAConfig.include_snr + ~NarrowbandImpairedTrainQAConfig.level + ~NarrowbandImpairedTrainQAConfig.name + ~NarrowbandImpairedTrainQAConfig.num_iq_samples + ~NarrowbandImpairedTrainQAConfig.num_samples + ~NarrowbandImpairedTrainQAConfig.seed + ~NarrowbandImpairedTrainQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedValConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedValConfig.rst new file mode 100644 index 0000000..2928f06 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedValConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedValConfig +================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedValConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedValConfig.eb_no + ~NarrowbandImpairedValConfig.include_snr + ~NarrowbandImpairedValConfig.level + ~NarrowbandImpairedValConfig.name + ~NarrowbandImpairedValConfig.num_iq_samples + ~NarrowbandImpairedValConfig.num_samples + ~NarrowbandImpairedValConfig.seed + ~NarrowbandImpairedValConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedValQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedValQAConfig.rst new file mode 100644 index 0000000..6eb9b33 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.NarrowbandImpairedValQAConfig.rst @@ -0,0 +1,37 @@ +torchsig.datasets.conf.NarrowbandImpairedValQAConfig +==================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: NarrowbandImpairedValQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandImpairedValQAConfig.eb_no + ~NarrowbandImpairedValQAConfig.include_snr + ~NarrowbandImpairedValQAConfig.level + ~NarrowbandImpairedValQAConfig.name + ~NarrowbandImpairedValQAConfig.num_iq_samples + ~NarrowbandImpairedValQAConfig.num_samples + ~NarrowbandImpairedValQAConfig.seed + ~NarrowbandImpairedValQAConfig.use_class_idx + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandCleanTrainConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanTrainConfig.rst new file mode 100644 index 0000000..16f1cc3 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanTrainConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandCleanTrainConfig +=============================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandCleanTrainConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandCleanTrainConfig.level + ~WidebandCleanTrainConfig.name + ~WidebandCleanTrainConfig.num_iq_samples + ~WidebandCleanTrainConfig.num_samples + ~WidebandCleanTrainConfig.overlap_prob + ~WidebandCleanTrainConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandCleanTrainQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanTrainQAConfig.rst new file mode 100644 index 0000000..67c79da --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanTrainQAConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandCleanTrainQAConfig +================================================= + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandCleanTrainQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandCleanTrainQAConfig.level + ~WidebandCleanTrainQAConfig.name + ~WidebandCleanTrainQAConfig.num_iq_samples + ~WidebandCleanTrainQAConfig.num_samples + ~WidebandCleanTrainQAConfig.overlap_prob + ~WidebandCleanTrainQAConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandCleanValConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanValConfig.rst new file mode 100644 index 0000000..8eb0367 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanValConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandCleanValConfig +============================================= + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandCleanValConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandCleanValConfig.level + ~WidebandCleanValConfig.name + ~WidebandCleanValConfig.num_iq_samples + ~WidebandCleanValConfig.num_samples + ~WidebandCleanValConfig.overlap_prob + ~WidebandCleanValConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandCleanValQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanValQAConfig.rst new file mode 100644 index 0000000..abfc87a --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandCleanValQAConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandCleanValQAConfig +=============================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandCleanValQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandCleanValQAConfig.level + ~WidebandCleanValQAConfig.name + ~WidebandCleanValQAConfig.num_iq_samples + ~WidebandCleanValQAConfig.num_samples + ~WidebandCleanValQAConfig.overlap_prob + ~WidebandCleanValQAConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandConfig.rst new file mode 100644 index 0000000..d54b309 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandConfig +===================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandConfig.num_iq_samples + ~WidebandConfig.overlap_prob + ~WidebandConfig.name + ~WidebandConfig.num_samples + ~WidebandConfig.level + ~WidebandConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedTrainConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedTrainConfig.rst new file mode 100644 index 0000000..0cec555 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedTrainConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandImpairedTrainConfig +================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandImpairedTrainConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandImpairedTrainConfig.level + ~WidebandImpairedTrainConfig.name + ~WidebandImpairedTrainConfig.num_iq_samples + ~WidebandImpairedTrainConfig.num_samples + ~WidebandImpairedTrainConfig.overlap_prob + ~WidebandImpairedTrainConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedTrainQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedTrainQAConfig.rst new file mode 100644 index 0000000..b552602 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedTrainQAConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandImpairedTrainQAConfig +==================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandImpairedTrainQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandImpairedTrainQAConfig.level + ~WidebandImpairedTrainQAConfig.name + ~WidebandImpairedTrainQAConfig.num_iq_samples + ~WidebandImpairedTrainQAConfig.num_samples + ~WidebandImpairedTrainQAConfig.overlap_prob + ~WidebandImpairedTrainQAConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedValConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedValConfig.rst new file mode 100644 index 0000000..3d57188 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedValConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandImpairedValConfig +================================================ + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandImpairedValConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandImpairedValConfig.level + ~WidebandImpairedValConfig.name + ~WidebandImpairedValConfig.num_iq_samples + ~WidebandImpairedValConfig.num_samples + ~WidebandImpairedValConfig.overlap_prob + ~WidebandImpairedValConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedValQAConfig.rst b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedValQAConfig.rst new file mode 100644 index 0000000..2a2bd44 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.WidebandImpairedValQAConfig.rst @@ -0,0 +1,35 @@ +torchsig.datasets.conf.WidebandImpairedValQAConfig +================================================== + +.. currentmodule:: torchsig.datasets.conf + +.. autoclass:: WidebandImpairedValQAConfig + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandImpairedValQAConfig.level + ~WidebandImpairedValQAConfig.name + ~WidebandImpairedValQAConfig.num_iq_samples + ~WidebandImpairedValQAConfig.num_samples + ~WidebandImpairedValQAConfig.overlap_prob + ~WidebandImpairedValQAConfig.seed + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.conf.rst b/docs/_autosummary/torchsig.datasets.conf.rst new file mode 100644 index 0000000..ee2ab50 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.conf.rst @@ -0,0 +1,57 @@ +torchsig.datasets.conf +====================== + +.. automodule:: torchsig.datasets.conf + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + NarrowbandCleanEbNoTrainConfig + NarrowbandCleanEbNoTrainQAConfig + NarrowbandCleanEbNoValConfig + NarrowbandCleanEbNoValQAConfig + NarrowbandCleanTrainConfig + NarrowbandCleanTrainQAConfig + NarrowbandCleanValConfig + NarrowbandCleanValQAConfig + NarrowbandConfig + NarrowbandImpairedEbNoTrainConfig + NarrowbandImpairedEbNoTrainQAConfig + NarrowbandImpairedEbNoValConfig + NarrowbandImpairedEbNoValQAConfig + NarrowbandImpairedTrainConfig + NarrowbandImpairedTrainQAConfig + NarrowbandImpairedValConfig + NarrowbandImpairedValQAConfig + WidebandCleanTrainConfig + WidebandCleanTrainQAConfig + WidebandCleanValConfig + WidebandCleanValQAConfig + WidebandConfig + WidebandImpairedTrainConfig + WidebandImpairedTrainQAConfig + WidebandImpairedValConfig + WidebandImpairedValQAConfig + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.datamodules.NarrowbandDataModule.rst b/docs/_autosummary/torchsig.datasets.datamodules.NarrowbandDataModule.rst new file mode 100644 index 0000000..294c7d9 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.datamodules.NarrowbandDataModule.rst @@ -0,0 +1,60 @@ +torchsig.datasets.datamodules.NarrowbandDataModule +================================================== + +.. currentmodule:: torchsig.datasets.datamodules + +.. autoclass:: NarrowbandDataModule + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~NarrowbandDataModule.from_datasets + ~NarrowbandDataModule.load_from_checkpoint + ~NarrowbandDataModule.load_state_dict + ~NarrowbandDataModule.on_after_batch_transfer + ~NarrowbandDataModule.on_before_batch_transfer + ~NarrowbandDataModule.on_exception + ~NarrowbandDataModule.predict_dataloader + ~NarrowbandDataModule.prepare_data + ~NarrowbandDataModule.save_hyperparameters + ~NarrowbandDataModule.setup + ~NarrowbandDataModule.state_dict + ~NarrowbandDataModule.teardown + ~NarrowbandDataModule.test_dataloader + ~NarrowbandDataModule.train_dataloader + ~NarrowbandDataModule.transfer_batch_to_device + ~NarrowbandDataModule.val_dataloader + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~NarrowbandDataModule.CHECKPOINT_HYPER_PARAMS_KEY + ~NarrowbandDataModule.CHECKPOINT_HYPER_PARAMS_NAME + ~NarrowbandDataModule.CHECKPOINT_HYPER_PARAMS_TYPE + ~NarrowbandDataModule.class_list + ~NarrowbandDataModule.hparams + ~NarrowbandDataModule.hparams_initial + ~NarrowbandDataModule.name + ~NarrowbandDataModule.clean + ~NarrowbandDataModule.train_config + ~NarrowbandDataModule.val_config + ~NarrowbandDataModule.train + ~NarrowbandDataModule.val + ~NarrowbandDataModule.data_path + ~NarrowbandDataModule.train_path + ~NarrowbandDataModule.val_path + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.datamodules.TorchSigDataModule.rst b/docs/_autosummary/torchsig.datasets.datamodules.TorchSigDataModule.rst new file mode 100644 index 0000000..b4cdae4 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.datamodules.TorchSigDataModule.rst @@ -0,0 +1,59 @@ +torchsig.datasets.datamodules.TorchSigDataModule +================================================ + +.. currentmodule:: torchsig.datasets.datamodules + +.. autoclass:: TorchSigDataModule + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TorchSigDataModule.from_datasets + ~TorchSigDataModule.load_from_checkpoint + ~TorchSigDataModule.load_state_dict + ~TorchSigDataModule.on_after_batch_transfer + ~TorchSigDataModule.on_before_batch_transfer + ~TorchSigDataModule.on_exception + ~TorchSigDataModule.predict_dataloader + ~TorchSigDataModule.prepare_data + ~TorchSigDataModule.save_hyperparameters + ~TorchSigDataModule.setup + ~TorchSigDataModule.state_dict + ~TorchSigDataModule.teardown + ~TorchSigDataModule.test_dataloader + ~TorchSigDataModule.train_dataloader + ~TorchSigDataModule.transfer_batch_to_device + ~TorchSigDataModule.val_dataloader + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~TorchSigDataModule.CHECKPOINT_HYPER_PARAMS_KEY + ~TorchSigDataModule.CHECKPOINT_HYPER_PARAMS_NAME + ~TorchSigDataModule.CHECKPOINT_HYPER_PARAMS_TYPE + ~TorchSigDataModule.hparams + ~TorchSigDataModule.hparams_initial + ~TorchSigDataModule.name + ~TorchSigDataModule.clean + ~TorchSigDataModule.train_config + ~TorchSigDataModule.val_config + ~TorchSigDataModule.train + ~TorchSigDataModule.val + ~TorchSigDataModule.data_path + ~TorchSigDataModule.train_path + ~TorchSigDataModule.val_path + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.datamodules.WidebandDataModule.rst b/docs/_autosummary/torchsig.datasets.datamodules.WidebandDataModule.rst new file mode 100644 index 0000000..886a977 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.datamodules.WidebandDataModule.rst @@ -0,0 +1,59 @@ +torchsig.datasets.datamodules.WidebandDataModule +================================================ + +.. currentmodule:: torchsig.datasets.datamodules + +.. autoclass:: WidebandDataModule + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~WidebandDataModule.from_datasets + ~WidebandDataModule.load_from_checkpoint + ~WidebandDataModule.load_state_dict + ~WidebandDataModule.on_after_batch_transfer + ~WidebandDataModule.on_before_batch_transfer + ~WidebandDataModule.on_exception + ~WidebandDataModule.predict_dataloader + ~WidebandDataModule.prepare_data + ~WidebandDataModule.save_hyperparameters + ~WidebandDataModule.setup + ~WidebandDataModule.state_dict + ~WidebandDataModule.teardown + ~WidebandDataModule.test_dataloader + ~WidebandDataModule.train_dataloader + ~WidebandDataModule.transfer_batch_to_device + ~WidebandDataModule.val_dataloader + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandDataModule.CHECKPOINT_HYPER_PARAMS_KEY + ~WidebandDataModule.CHECKPOINT_HYPER_PARAMS_NAME + ~WidebandDataModule.CHECKPOINT_HYPER_PARAMS_TYPE + ~WidebandDataModule.hparams + ~WidebandDataModule.hparams_initial + ~WidebandDataModule.name + ~WidebandDataModule.clean + ~WidebandDataModule.train_config + ~WidebandDataModule.val_config + ~WidebandDataModule.train + ~WidebandDataModule.val + ~WidebandDataModule.data_path + ~WidebandDataModule.train_path + ~WidebandDataModule.val_path + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.datamodules.rst b/docs/_autosummary/torchsig.datasets.datamodules.rst new file mode 100644 index 0000000..2a6f10b --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.datamodules.rst @@ -0,0 +1,34 @@ +torchsig.datasets.datamodules +============================= + +.. automodule:: torchsig.datasets.datamodules + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + NarrowbandDataModule + TorchSigDataModule + WidebandDataModule + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.file_datasets.CSVFileInterpreter.rst b/docs/_autosummary/torchsig.datasets.file_datasets.CSVFileInterpreter.rst new file mode 100644 index 0000000..67354b6 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.file_datasets.CSVFileInterpreter.rst @@ -0,0 +1,25 @@ +torchsig.datasets.file\_datasets.CSVFileInterpreter +=================================================== + +.. currentmodule:: torchsig.datasets.file_datasets + +.. autoclass:: CSVFileInterpreter + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~CSVFileInterpreter.convert_to_signalburst + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.file_datasets.FileBurstSourceDataset.rst b/docs/_autosummary/torchsig.datasets.file_datasets.FileBurstSourceDataset.rst new file mode 100644 index 0000000..6848fb6 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.file_datasets.FileBurstSourceDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.file\_datasets.FileBurstSourceDataset +======================================================= + +.. currentmodule:: torchsig.datasets.file_datasets + +.. autoclass:: FileBurstSourceDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.file_datasets.SigMFInterpreter.rst b/docs/_autosummary/torchsig.datasets.file_datasets.SigMFInterpreter.rst new file mode 100644 index 0000000..3bf4d4e --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.file_datasets.SigMFInterpreter.rst @@ -0,0 +1,25 @@ +torchsig.datasets.file\_datasets.SigMFInterpreter +================================================= + +.. currentmodule:: torchsig.datasets.file_datasets + +.. autoclass:: SigMFInterpreter + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SigMFInterpreter.convert_to_signalburst + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.file_datasets.TargetInterpreter.rst b/docs/_autosummary/torchsig.datasets.file_datasets.TargetInterpreter.rst new file mode 100644 index 0000000..2ce263d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.file_datasets.TargetInterpreter.rst @@ -0,0 +1,25 @@ +torchsig.datasets.file\_datasets.TargetInterpreter +================================================== + +.. currentmodule:: torchsig.datasets.file_datasets + +.. autoclass:: TargetInterpreter + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TargetInterpreter.convert_to_signalburst + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.file_datasets.WidebandFileSignalBurst.rst b/docs/_autosummary/torchsig.datasets.file_datasets.WidebandFileSignalBurst.rst new file mode 100644 index 0000000..f2ed879 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.file_datasets.WidebandFileSignalBurst.rst @@ -0,0 +1,25 @@ +torchsig.datasets.file\_datasets.WidebandFileSignalBurst +======================================================== + +.. currentmodule:: torchsig.datasets.file_datasets + +.. autoclass:: WidebandFileSignalBurst + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~WidebandFileSignalBurst.generate_iq + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.file_datasets.rst b/docs/_autosummary/torchsig.datasets.file_datasets.rst new file mode 100644 index 0000000..9328d88 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.file_datasets.rst @@ -0,0 +1,36 @@ +torchsig.datasets.file\_datasets +================================ + +.. automodule:: torchsig.datasets.file_datasets + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + CSVFileInterpreter + FileBurstSourceDataset + SigMFInterpreter + TargetInterpreter + WidebandFileSignalBurst + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.modulations.ModulationsDataset.rst b/docs/_autosummary/torchsig.datasets.modulations.ModulationsDataset.rst new file mode 100644 index 0000000..eede955 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.modulations.ModulationsDataset.rst @@ -0,0 +1,34 @@ +torchsig.datasets.modulations.ModulationsDataset +================================================ + +.. currentmodule:: torchsig.datasets.modulations + +.. autoclass:: ModulationsDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ModulationsDataset.cumsum + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ModulationsDataset.cummulative_sizes + ~ModulationsDataset.default_classes + ~ModulationsDataset.datasets + ~ModulationsDataset.cumulative_sizes + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.modulations.rst b/docs/_autosummary/torchsig.datasets.modulations.rst new file mode 100644 index 0000000..0f4152e --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.modulations.rst @@ -0,0 +1,32 @@ +torchsig.datasets.modulations +============================= + +.. automodule:: torchsig.datasets.modulations + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + ModulationsDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.radioml.RadioML2016.rst b/docs/_autosummary/torchsig.datasets.radioml.RadioML2016.rst new file mode 100644 index 0000000..35c9a99 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.radioml.RadioML2016.rst @@ -0,0 +1,24 @@ +torchsig.datasets.radioml.RadioML2016 +===================================== + +.. currentmodule:: torchsig.datasets.radioml + +.. autoclass:: RadioML2016 + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.radioml.RadioML2018.rst b/docs/_autosummary/torchsig.datasets.radioml.RadioML2018.rst new file mode 100644 index 0000000..b139176 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.radioml.RadioML2018.rst @@ -0,0 +1,24 @@ +torchsig.datasets.radioml.RadioML2018 +===================================== + +.. currentmodule:: torchsig.datasets.radioml + +.. autoclass:: RadioML2018 + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.radioml.rst b/docs/_autosummary/torchsig.datasets.radioml.rst new file mode 100644 index 0000000..fc479aa --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.radioml.rst @@ -0,0 +1,33 @@ +torchsig.datasets.radioml +========================= + +.. automodule:: torchsig.datasets.radioml + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + RadioML2016 + RadioML2018 + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.rst b/docs/_autosummary/torchsig.datasets.rst new file mode 100644 index 0000000..2dd2637 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.rst @@ -0,0 +1,41 @@ +torchsig.datasets +================= + +.. automodule:: torchsig.datasets + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + conf + datamodules + file_datasets + modulations + radioml + sig53 + signal_classes + synthetic + torchsig_narrowband + torchsig_wideband + wideband + wideband_sig53 + diff --git a/docs/_autosummary/torchsig.datasets.sig53.Sig53.rst b/docs/_autosummary/torchsig.datasets.sig53.Sig53.rst new file mode 100644 index 0000000..4250721 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.sig53.Sig53.rst @@ -0,0 +1,26 @@ +torchsig.datasets.sig53.Sig53 +============================= + +.. currentmodule:: torchsig.datasets.sig53 + +.. autoclass:: Sig53 + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Sig53.convert_idx_to_name + ~Sig53.convert_name_to_idx + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.sig53.rst b/docs/_autosummary/torchsig.datasets.sig53.rst new file mode 100644 index 0000000..431639d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.sig53.rst @@ -0,0 +1,32 @@ +torchsig.datasets.sig53 +======================= + +.. automodule:: torchsig.datasets.sig53 + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + Sig53 + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.signal_classes.radioml2018.rst b/docs/_autosummary/torchsig.datasets.signal_classes.radioml2018.rst new file mode 100644 index 0000000..bf3aa56 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.signal_classes.radioml2018.rst @@ -0,0 +1,30 @@ +torchsig.datasets.signal\_classes.radioml2018 +============================================= + +.. currentmodule:: torchsig.datasets.signal_classes + +.. autoclass:: radioml2018 + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~radioml2018.family_class_list + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.signal_classes.rst b/docs/_autosummary/torchsig.datasets.signal_classes.rst new file mode 100644 index 0000000..9f4c444 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.signal_classes.rst @@ -0,0 +1,34 @@ +torchsig.datasets.signal\_classes +================================= + +.. automodule:: torchsig.datasets.signal_classes + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + radioml2018 + sig53 + torchsig_signals + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.signal_classes.sig53.rst b/docs/_autosummary/torchsig.datasets.signal_classes.sig53.rst new file mode 100644 index 0000000..4089383 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.signal_classes.sig53.rst @@ -0,0 +1,31 @@ +torchsig.datasets.signal\_classes.sig53 +======================================= + +.. currentmodule:: torchsig.datasets.signal_classes + +.. autoclass:: sig53 + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~sig53.class_list + ~sig53.family_dict + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.signal_classes.torchsig_signals.rst b/docs/_autosummary/torchsig.datasets.signal_classes.torchsig_signals.rst new file mode 100644 index 0000000..4a1d1cf --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.signal_classes.torchsig_signals.rst @@ -0,0 +1,40 @@ +torchsig.datasets.signal\_classes.torchsig\_signals +=================================================== + +.. currentmodule:: torchsig.datasets.signal_classes + +.. autoclass:: torchsig_signals + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~torchsig_signals.am_signals + ~torchsig_signals.chirpss_signals + ~torchsig_signals.class_list + ~torchsig_signals.constellation_signals + ~torchsig_signals.family_dict + ~torchsig_signals.fm_signals + ~torchsig_signals.fsk_signals + ~torchsig_signals.lfm_signals + ~torchsig_signals.name + ~torchsig_signals.ofdm_signals + ~torchsig_signals.ofdm_subcarrier_modulations + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.AMDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.AMDataset.rst new file mode 100644 index 0000000..172b33a --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.AMDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.AMDataset +===================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: AMDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.ChirpSSDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.ChirpSSDataset.rst new file mode 100644 index 0000000..0e7ca56 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.ChirpSSDataset.rst @@ -0,0 +1,26 @@ +torchsig.datasets.synthetic.ChirpSSDataset +========================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: ChirpSSDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ChirpSSDataset.chirp + ~ChirpSSDataset.get_symbol_map + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.ConstellationBasebandModulator.rst b/docs/_autosummary/torchsig.datasets.synthetic.ConstellationBasebandModulator.rst new file mode 100644 index 0000000..76793f3 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.ConstellationBasebandModulator.rst @@ -0,0 +1,6 @@ +torchsig.datasets.synthetic.ConstellationBasebandModulator +========================================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autofunction:: ConstellationBasebandModulator \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.ConstellationDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.ConstellationDataset.rst new file mode 100644 index 0000000..9b57537 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.ConstellationDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.ConstellationDataset +================================================ + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: ConstellationDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.FMDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.FMDataset.rst new file mode 100644 index 0000000..5d443e7 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.FMDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.FMDataset +===================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: FMDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.FSKBasebandModulator.rst b/docs/_autosummary/torchsig.datasets.synthetic.FSKBasebandModulator.rst new file mode 100644 index 0000000..27c368c --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.FSKBasebandModulator.rst @@ -0,0 +1,6 @@ +torchsig.datasets.synthetic.FSKBasebandModulator +================================================ + +.. currentmodule:: torchsig.datasets.synthetic + +.. autofunction:: FSKBasebandModulator \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.FSKDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.FSKDataset.rst new file mode 100644 index 0000000..e02ffef --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.FSKDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.FSKDataset +====================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: FSKDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.LFMDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.LFMDataset.rst new file mode 100644 index 0000000..d2724b6 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.LFMDataset.rst @@ -0,0 +1,26 @@ +torchsig.datasets.synthetic.LFMDataset +====================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: LFMDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~LFMDataset.chirp + ~LFMDataset.get_symbol_map + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.ModulateNarrowbandDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.ModulateNarrowbandDataset.rst new file mode 100644 index 0000000..9d567f5 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.ModulateNarrowbandDataset.rst @@ -0,0 +1,33 @@ +torchsig.datasets.synthetic.ModulateNarrowbandDataset +===================================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: ModulateNarrowbandDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ModulateNarrowbandDataset.cumsum + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ModulateNarrowbandDataset.cummulative_sizes + ~ModulateNarrowbandDataset.datasets + ~ModulateNarrowbandDataset.cumulative_sizes + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.OFDMDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.OFDMDataset.rst new file mode 100644 index 0000000..a89b69a --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.OFDMDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.OFDMDataset +======================================= + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: OFDMDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.SyntheticDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.SyntheticDataset.rst new file mode 100644 index 0000000..c960e73 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.SyntheticDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.SyntheticDataset +============================================ + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: SyntheticDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.ToneDataset.rst b/docs/_autosummary/torchsig.datasets.synthetic.ToneDataset.rst new file mode 100644 index 0000000..125bdc1 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.ToneDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.synthetic.ToneDataset +======================================= + +.. currentmodule:: torchsig.datasets.synthetic + +.. autoclass:: ToneDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.getFSKFreqMap.rst b/docs/_autosummary/torchsig.datasets.synthetic.getFSKFreqMap.rst new file mode 100644 index 0000000..00785b3 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.getFSKFreqMap.rst @@ -0,0 +1,6 @@ +torchsig.datasets.synthetic.getFSKFreqMap +========================================= + +.. currentmodule:: torchsig.datasets.synthetic + +.. autofunction:: getFSKFreqMap \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.getFSKModIndex.rst b/docs/_autosummary/torchsig.datasets.synthetic.getFSKModIndex.rst new file mode 100644 index 0000000..d8d57f5 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.getFSKModIndex.rst @@ -0,0 +1,6 @@ +torchsig.datasets.synthetic.getFSKModIndex +========================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autofunction:: getFSKModIndex \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.remove_corners.rst b/docs/_autosummary/torchsig.datasets.synthetic.remove_corners.rst new file mode 100644 index 0000000..d94a68c --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.remove_corners.rst @@ -0,0 +1,6 @@ +torchsig.datasets.synthetic.remove\_corners +=========================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autofunction:: remove_corners \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.synthetic.rst b/docs/_autosummary/torchsig.datasets.synthetic.rst new file mode 100644 index 0000000..8e5833d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.rst @@ -0,0 +1,54 @@ +torchsig.datasets.synthetic +=========================== + +.. automodule:: torchsig.datasets.synthetic + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + ConstellationBasebandModulator + FSKBasebandModulator + getFSKFreqMap + getFSKModIndex + remove_corners + upconversionAntiAliasingFilter + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + AMDataset + ChirpSSDataset + ConstellationDataset + FMDataset + FSKDataset + LFMDataset + ModulateNarrowbandDataset + OFDMDataset + SyntheticDataset + ToneDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.synthetic.upconversionAntiAliasingFilter.rst b/docs/_autosummary/torchsig.datasets.synthetic.upconversionAntiAliasingFilter.rst new file mode 100644 index 0000000..3dd4a7e --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.synthetic.upconversionAntiAliasingFilter.rst @@ -0,0 +1,6 @@ +torchsig.datasets.synthetic.upconversionAntiAliasingFilter +========================================================== + +.. currentmodule:: torchsig.datasets.synthetic + +.. autofunction:: upconversionAntiAliasingFilter \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.torchsig_narrowband.TorchSigNarrowband.rst b/docs/_autosummary/torchsig.datasets.torchsig_narrowband.TorchSigNarrowband.rst new file mode 100644 index 0000000..c119a69 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.torchsig_narrowband.TorchSigNarrowband.rst @@ -0,0 +1,26 @@ +torchsig.datasets.torchsig\_narrowband.TorchSigNarrowband +========================================================= + +.. currentmodule:: torchsig.datasets.torchsig_narrowband + +.. autoclass:: TorchSigNarrowband + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TorchSigNarrowband.convert_idx_to_name + ~TorchSigNarrowband.convert_name_to_idx + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.torchsig_narrowband.rst b/docs/_autosummary/torchsig.datasets.torchsig_narrowband.rst new file mode 100644 index 0000000..432e69d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.torchsig_narrowband.rst @@ -0,0 +1,32 @@ +torchsig.datasets.torchsig\_narrowband +====================================== + +.. automodule:: torchsig.datasets.torchsig_narrowband + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + TorchSigNarrowband + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.torchsig_wideband.TorchSigWideband.rst b/docs/_autosummary/torchsig.datasets.torchsig_wideband.TorchSigWideband.rst new file mode 100644 index 0000000..625594c --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.torchsig_wideband.TorchSigWideband.rst @@ -0,0 +1,24 @@ +torchsig.datasets.torchsig\_wideband.TorchSigWideband +===================================================== + +.. currentmodule:: torchsig.datasets.torchsig_wideband + +.. autoclass:: TorchSigWideband + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.torchsig_wideband.rst b/docs/_autosummary/torchsig.datasets.torchsig_wideband.rst new file mode 100644 index 0000000..48c5033 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.torchsig_wideband.rst @@ -0,0 +1,32 @@ +torchsig.datasets.torchsig\_wideband +==================================== + +.. automodule:: torchsig.datasets.torchsig_wideband + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + TorchSigWideband + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.wideband.BurstSourceDataset.rst b/docs/_autosummary/torchsig.datasets.wideband.BurstSourceDataset.rst new file mode 100644 index 0000000..2f68826 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.BurstSourceDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.wideband.BurstSourceDataset +============================================= + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: BurstSourceDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.FileSignalBurst.rst b/docs/_autosummary/torchsig.datasets.wideband.FileSignalBurst.rst new file mode 100644 index 0000000..a4392de --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.FileSignalBurst.rst @@ -0,0 +1,25 @@ +torchsig.datasets.wideband.FileSignalBurst +========================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: FileSignalBurst + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~FileSignalBurst.generate_iq + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.Interferers.rst b/docs/_autosummary/torchsig.datasets.wideband.Interferers.rst new file mode 100644 index 0000000..9cad483 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.Interferers.rst @@ -0,0 +1,28 @@ +torchsig.datasets.wideband.Interferers +====================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: Interferers + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Interferers.convert_to_signal + ~Interferers.parameters + ~Interferers.transform_data + ~Interferers.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.ModulatedSignalBurst.rst b/docs/_autosummary/torchsig.datasets.wideband.ModulatedSignalBurst.rst new file mode 100644 index 0000000..9be147f --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.ModulatedSignalBurst.rst @@ -0,0 +1,25 @@ +torchsig.datasets.wideband.ModulatedSignalBurst +=============================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: ModulatedSignalBurst + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ModulatedSignalBurst.generate_iq + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.RandomSignalInsertion.rst b/docs/_autosummary/torchsig.datasets.wideband.RandomSignalInsertion.rst new file mode 100644 index 0000000..417afad --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.RandomSignalInsertion.rst @@ -0,0 +1,34 @@ +torchsig.datasets.wideband.RandomSignalInsertion +================================================ + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: RandomSignalInsertion + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomSignalInsertion.convert_to_signal + ~RandomSignalInsertion.parameters + ~RandomSignalInsertion.transform_data + ~RandomSignalInsertion.transform_meta + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~RandomSignalInsertion.default_modulation_list + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.ShapedNoiseSignalBurst.rst b/docs/_autosummary/torchsig.datasets.wideband.ShapedNoiseSignalBurst.rst new file mode 100644 index 0000000..1f07747 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.ShapedNoiseSignalBurst.rst @@ -0,0 +1,25 @@ +torchsig.datasets.wideband.ShapedNoiseSignalBurst +================================================= + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: ShapedNoiseSignalBurst + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ShapedNoiseSignalBurst.generate_iq + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.SignalBurst.rst b/docs/_autosummary/torchsig.datasets.wideband.SignalBurst.rst new file mode 100644 index 0000000..5ed855f --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.SignalBurst.rst @@ -0,0 +1,25 @@ +torchsig.datasets.wideband.SignalBurst +====================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: SignalBurst + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SignalBurst.generate_iq + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.SignalOfInterestSignalBurst.rst b/docs/_autosummary/torchsig.datasets.wideband.SignalOfInterestSignalBurst.rst new file mode 100644 index 0000000..4437b7d --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.SignalOfInterestSignalBurst.rst @@ -0,0 +1,25 @@ +torchsig.datasets.wideband.SignalOfInterestSignalBurst +====================================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: SignalOfInterestSignalBurst + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SignalOfInterestSignalBurst.generate_iq + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.SyntheticBurstSourceDataset.rst b/docs/_autosummary/torchsig.datasets.wideband.SyntheticBurstSourceDataset.rst new file mode 100644 index 0000000..f0e70da --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.SyntheticBurstSourceDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.wideband.SyntheticBurstSourceDataset +====================================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: SyntheticBurstSourceDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.WidebandDataset.rst b/docs/_autosummary/torchsig.datasets.wideband.WidebandDataset.rst new file mode 100644 index 0000000..2569f47 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.WidebandDataset.rst @@ -0,0 +1,24 @@ +torchsig.datasets.wideband.WidebandDataset +========================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: WidebandDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.WidebandModulationsDataset.rst b/docs/_autosummary/torchsig.datasets.wideband.WidebandModulationsDataset.rst new file mode 100644 index 0000000..df2fab8 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.WidebandModulationsDataset.rst @@ -0,0 +1,32 @@ +torchsig.datasets.wideband.WidebandModulationsDataset +===================================================== + +.. currentmodule:: torchsig.datasets.wideband + +.. autoclass:: WidebandModulationsDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~WidebandModulationsDataset.iter_cf_bw + ~WidebandModulationsDataset.ret_transforms + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~WidebandModulationsDataset.default_modulations + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband.rst b/docs/_autosummary/torchsig.datasets.wideband.rst new file mode 100644 index 0000000..54c644b --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband.rst @@ -0,0 +1,42 @@ +torchsig.datasets.wideband +========================== + +.. automodule:: torchsig.datasets.wideband + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + BurstSourceDataset + FileSignalBurst + Interferers + ModulatedSignalBurst + RandomSignalInsertion + ShapedNoiseSignalBurst + SignalBurst + SignalOfInterestSignalBurst + SyntheticBurstSourceDataset + WidebandDataset + WidebandModulationsDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.datasets.wideband_sig53.WidebandSig53.rst b/docs/_autosummary/torchsig.datasets.wideband_sig53.WidebandSig53.rst new file mode 100644 index 0000000..0a95077 --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband_sig53.WidebandSig53.rst @@ -0,0 +1,24 @@ +torchsig.datasets.wideband\_sig53.WidebandSig53 +=============================================== + +.. currentmodule:: torchsig.datasets.wideband_sig53 + +.. autoclass:: WidebandSig53 + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.datasets.wideband_sig53.rst b/docs/_autosummary/torchsig.datasets.wideband_sig53.rst new file mode 100644 index 0000000..406371e --- /dev/null +++ b/docs/_autosummary/torchsig.datasets.wideband_sig53.rst @@ -0,0 +1,32 @@ +torchsig.datasets.wideband\_sig53 +================================= + +.. automodule:: torchsig.datasets.wideband_sig53 + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + WidebandSig53 + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.dataset_generation.batched_write_yolo_synthetic_dataset.rst b/docs/_autosummary/torchsig.image_datasets.dataset_generation.batched_write_yolo_synthetic_dataset.rst new file mode 100644 index 0000000..62c179a --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.dataset_generation.batched_write_yolo_synthetic_dataset.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.dataset\_generation.batched\_write\_yolo\_synthetic\_dataset +===================================================================================== + +.. currentmodule:: torchsig.image_datasets.dataset_generation + +.. autofunction:: batched_write_yolo_synthetic_dataset \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.dataset_generation.rst b/docs/_autosummary/torchsig.image_datasets.dataset_generation.rst new file mode 100644 index 0000000..650dd54 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.dataset_generation.rst @@ -0,0 +1,32 @@ +torchsig.image\_datasets.dataset\_generation +============================================ + +.. automodule:: torchsig.image_datasets.dataset_generation + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + batched_write_yolo_synthetic_dataset + save_yolo_data + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.dataset_generation.save_yolo_data.rst b/docs/_autosummary/torchsig.image_datasets.dataset_generation.save_yolo_data.rst new file mode 100644 index 0000000..9ec5dd5 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.dataset_generation.save_yolo_data.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.dataset\_generation.save\_yolo\_data +============================================================= + +.. currentmodule:: torchsig.image_datasets.dataset_generation + +.. autofunction:: save_yolo_data \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.composites.ConcatDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.composites.ConcatDataset.rst new file mode 100644 index 0000000..42a96b6 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.composites.ConcatDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.composites.ConcatDataset +========================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.composites + +.. autoclass:: ConcatDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ConcatDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.composites.rst b/docs/_autosummary/torchsig.image_datasets.datasets.composites.rst new file mode 100644 index 0000000..d35c664 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.composites.rst @@ -0,0 +1,32 @@ +torchsig.image\_datasets.datasets.composites +============================================ + +.. automodule:: torchsig.image_datasets.datasets.composites + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + ConcatDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.ImageDirectoryDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.ImageDirectoryDataset.rst new file mode 100644 index 0000000..524d461 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.ImageDirectoryDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.ImageDirectoryDataset +=============================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autoclass:: ImageDirectoryDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ImageDirectoryDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.LazyImageDirectoryDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.LazyImageDirectoryDataset.rst new file mode 100644 index 0000000..4ad2b5c --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.LazyImageDirectoryDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.LazyImageDirectoryDataset +=================================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autoclass:: LazyImageDirectoryDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~LazyImageDirectoryDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.SOIExtractorDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.SOIExtractorDataset.rst new file mode 100644 index 0000000..aa0f7eb --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.SOIExtractorDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.SOIExtractorDataset +============================================================================= + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autoclass:: SOIExtractorDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SOIExtractorDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_bounding_boxes.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_bounding_boxes.rst new file mode 100644 index 0000000..f699f3c --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_bounding_boxes.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.extract\_bounding\_boxes +================================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autofunction:: extract_bounding_boxes \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_bounding_boxes_from_image.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_bounding_boxes_from_image.rst new file mode 100644 index 0000000..32edda5 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_bounding_boxes_from_image.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.extract\_bounding\_boxes\_from\_image +=============================================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autofunction:: extract_bounding_boxes_from_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_sois.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_sois.rst new file mode 100644 index 0000000..b8282b8 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.extract_sois.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.extract\_sois +======================================================================= + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autofunction:: extract_sois \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.isolate_soi.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.isolate_soi.rst new file mode 100644 index 0000000..60035b7 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.isolate_soi.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.isolate\_soi +====================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autofunction:: isolate_soi \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.load_image_grey.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.load_image_grey.rst new file mode 100644 index 0000000..0236f1b --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.load_image_grey.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.load\_image\_grey +=========================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autofunction:: load_image_grey \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.load_image_rgb.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.load_image_rgb.rst new file mode 100644 index 0000000..07585da --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.load_image_rgb.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets.load\_image\_rgb +========================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.file_loading_datasets + +.. autofunction:: load_image_rgb \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.rst b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.rst new file mode 100644 index 0000000..3313d83 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.file_loading_datasets.rst @@ -0,0 +1,47 @@ +torchsig.image\_datasets.datasets.file\_loading\_datasets +========================================================= + +.. automodule:: torchsig.image_datasets.datasets.file_loading_datasets + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + extract_bounding_boxes + extract_bounding_boxes_from_image + extract_sois + isolate_soi + load_image_grey + load_image_rgb + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + ImageDirectoryDataset + LazyImageDirectoryDataset + SOIExtractorDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.CFGSignalProtocolDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.CFGSignalProtocolDataset.rst new file mode 100644 index 0000000..5fcaf8f --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.CFGSignalProtocolDataset.rst @@ -0,0 +1,33 @@ +torchsig.image\_datasets.datasets.protocols.CFGSignalProtocolDataset +==================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autoclass:: CFGSignalProtocolDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~CFGSignalProtocolDataset.add_rule + ~CFGSignalProtocolDataset.combine_products + ~CFGSignalProtocolDataset.compose_data + ~CFGSignalProtocolDataset.format_blank_image + ~CFGSignalProtocolDataset.get_random_product + ~CFGSignalProtocolDataset.get_subproduct_list + ~CFGSignalProtocolDataset.get_token_product + ~CFGSignalProtocolDataset.next + ~CFGSignalProtocolDataset.set_initial_token + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.FrequencyHoppingDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.FrequencyHoppingDataset.rst new file mode 100644 index 0000000..3c3bcf9 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.FrequencyHoppingDataset.rst @@ -0,0 +1,28 @@ +torchsig.image\_datasets.datasets.protocols.FrequencyHoppingDataset +=================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autoclass:: FrequencyHoppingDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~FrequencyHoppingDataset.compose_data + ~FrequencyHoppingDataset.format_blank_image + ~FrequencyHoppingDataset.generate_hopping_signal + ~FrequencyHoppingDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.VerticalCFGSignalProtocolDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.VerticalCFGSignalProtocolDataset.rst new file mode 100644 index 0000000..701a941 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.VerticalCFGSignalProtocolDataset.rst @@ -0,0 +1,33 @@ +torchsig.image\_datasets.datasets.protocols.VerticalCFGSignalProtocolDataset +============================================================================ + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autoclass:: VerticalCFGSignalProtocolDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~VerticalCFGSignalProtocolDataset.add_rule + ~VerticalCFGSignalProtocolDataset.combine_products + ~VerticalCFGSignalProtocolDataset.compose_data + ~VerticalCFGSignalProtocolDataset.format_blank_image + ~VerticalCFGSignalProtocolDataset.get_random_product + ~VerticalCFGSignalProtocolDataset.get_subproduct_list + ~VerticalCFGSignalProtocolDataset.get_token_product + ~VerticalCFGSignalProtocolDataset.next + ~VerticalCFGSignalProtocolDataset.set_initial_token + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOCFGSignalProtocolDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOCFGSignalProtocolDataset.rst new file mode 100644 index 0000000..084f39a --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOCFGSignalProtocolDataset.rst @@ -0,0 +1,33 @@ +torchsig.image\_datasets.datasets.protocols.YOLOCFGSignalProtocolDataset +======================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autoclass:: YOLOCFGSignalProtocolDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOCFGSignalProtocolDataset.add_rule + ~YOLOCFGSignalProtocolDataset.combine_products + ~YOLOCFGSignalProtocolDataset.compose_data + ~YOLOCFGSignalProtocolDataset.format_blank_image + ~YOLOCFGSignalProtocolDataset.get_random_product + ~YOLOCFGSignalProtocolDataset.get_subproduct_list + ~YOLOCFGSignalProtocolDataset.get_token_product + ~YOLOCFGSignalProtocolDataset.next + ~YOLOCFGSignalProtocolDataset.set_initial_token + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOFrequencyHoppingDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOFrequencyHoppingDataset.rst new file mode 100644 index 0000000..dc24b18 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOFrequencyHoppingDataset.rst @@ -0,0 +1,28 @@ +torchsig.image\_datasets.datasets.protocols.YOLOFrequencyHoppingDataset +======================================================================= + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autoclass:: YOLOFrequencyHoppingDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOFrequencyHoppingDataset.compose_data + ~YOLOFrequencyHoppingDataset.format_blank_image + ~YOLOFrequencyHoppingDataset.generate_hopping_signal + ~YOLOFrequencyHoppingDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOVerticalCFGSignalProtocolDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOVerticalCFGSignalProtocolDataset.rst new file mode 100644 index 0000000..823896f --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.YOLOVerticalCFGSignalProtocolDataset.rst @@ -0,0 +1,33 @@ +torchsig.image\_datasets.datasets.protocols.YOLOVerticalCFGSignalProtocolDataset +================================================================================ + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autoclass:: YOLOVerticalCFGSignalProtocolDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOVerticalCFGSignalProtocolDataset.add_rule + ~YOLOVerticalCFGSignalProtocolDataset.combine_products + ~YOLOVerticalCFGSignalProtocolDataset.compose_data + ~YOLOVerticalCFGSignalProtocolDataset.format_blank_image + ~YOLOVerticalCFGSignalProtocolDataset.get_random_product + ~YOLOVerticalCFGSignalProtocolDataset.get_subproduct_list + ~YOLOVerticalCFGSignalProtocolDataset.get_token_product + ~YOLOVerticalCFGSignalProtocolDataset.next + ~YOLOVerticalCFGSignalProtocolDataset.set_initial_token + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.random_hopping.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.random_hopping.rst new file mode 100644 index 0000000..5b8afbc --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.random_hopping.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.protocols.random\_hopping +=========================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.protocols + +.. autofunction:: random_hopping \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.protocols.rst b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.rst new file mode 100644 index 0000000..6acf0a3 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.protocols.rst @@ -0,0 +1,45 @@ +torchsig.image\_datasets.datasets.protocols +=========================================== + +.. automodule:: torchsig.image_datasets.datasets.protocols + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + random_hopping + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + CFGSignalProtocolDataset + FrequencyHoppingDataset + VerticalCFGSignalProtocolDataset + YOLOCFGSignalProtocolDataset + YOLOFrequencyHoppingDataset + YOLOVerticalCFGSignalProtocolDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.rst b/docs/_autosummary/torchsig.image_datasets.datasets.rst new file mode 100644 index 0000000..00dd88f --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.rst @@ -0,0 +1,34 @@ +torchsig.image\_datasets.datasets +================================= + +.. automodule:: torchsig.image_datasets.datasets + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + composites + file_loading_datasets + protocols + synthetic_signals + yolo_datasets + diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.GeneratorFunctionDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.GeneratorFunctionDataset.rst new file mode 100644 index 0000000..711a1e3 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.GeneratorFunctionDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.GeneratorFunctionDataset +============================================================================= + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autoclass:: GeneratorFunctionDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~GeneratorFunctionDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.chirp_generator_function.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.chirp_generator_function.rst new file mode 100644 index 0000000..f8c63cc --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.chirp_generator_function.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.chirp\_generator\_function +=============================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: chirp_generator_function \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_chirp.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_chirp.rst new file mode 100644 index 0000000..f8ecc10 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_chirp.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.generate\_chirp +==================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: generate_chirp \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_rectangle_signal.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_rectangle_signal.rst new file mode 100644 index 0000000..c744cf8 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_rectangle_signal.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.generate\_rectangle\_signal +================================================================================ + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: generate_rectangle_signal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_repeated_signal.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_repeated_signal.rst new file mode 100644 index 0000000..5d5fa82 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_repeated_signal.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.generate\_repeated\_signal +=============================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: generate_repeated_signal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_tone.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_tone.rst new file mode 100644 index 0000000..1136ff6 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.generate_tone.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.generate\_tone +=================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: generate_tone \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.rectangle_signal_generator_function.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.rectangle_signal_generator_function.rst new file mode 100644 index 0000000..274245f --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.rectangle_signal_generator_function.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.rectangle\_signal\_generator\_function +=========================================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: rectangle_signal_generator_function \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.repeated_signal_generator_function.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.repeated_signal_generator_function.rst new file mode 100644 index 0000000..b903f02 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.repeated_signal_generator_function.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.repeated\_signal\_generator\_function +========================================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: repeated_signal_generator_function \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.rst new file mode 100644 index 0000000..70a1a23 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.rst @@ -0,0 +1,47 @@ +torchsig.image\_datasets.datasets.synthetic\_signals +==================================================== + +.. automodule:: torchsig.image_datasets.datasets.synthetic_signals + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + chirp_generator_function + generate_chirp + generate_rectangle_signal + generate_repeated_signal + generate_tone + rectangle_signal_generator_function + repeated_signal_generator_function + tone_generator_function + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + GeneratorFunctionDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.tone_generator_function.rst b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.tone_generator_function.rst new file mode 100644 index 0000000..620ac6f --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.synthetic_signals.tone_generator_function.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.synthetic\_signals.tone\_generator\_function +============================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.synthetic_signals + +.. autofunction:: tone_generator_function \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLODatasetAdapter.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLODatasetAdapter.rst new file mode 100644 index 0000000..1851f86 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLODatasetAdapter.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.YOLODatasetAdapter +=================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autoclass:: YOLODatasetAdapter + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLODatum.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLODatum.rst new file mode 100644 index 0000000..f498180 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLODatum.rst @@ -0,0 +1,37 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.YOLODatum +========================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autoclass:: YOLODatum + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLODatum.append_labels + ~YOLODatum.append_yolo_labels + ~YOLODatum.compose_yolo_data + ~YOLODatum.has_labels + ~YOLODatum.size + ~YOLODatum.transpose_yolo_labels + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~YOLODatum.labels + ~YOLODatum.shape + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOFileDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOFileDataset.rst new file mode 100644 index 0000000..014df74 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOFileDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.YOLOFileDataset +================================================================ + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autoclass:: YOLOFileDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOFileDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOImageCompositeDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOImageCompositeDataset.rst new file mode 100644 index 0000000..456942f --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOImageCompositeDataset.rst @@ -0,0 +1,27 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.YOLOImageCompositeDataset +========================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autoclass:: YOLOImageCompositeDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOImageCompositeDataset.add_component + ~YOLOImageCompositeDataset.add_component_to_image_and_labels + ~YOLOImageCompositeDataset.get_components_to_add + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOImageCompositeDatasetComponent.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOImageCompositeDatasetComponent.rst new file mode 100644 index 0000000..3abf542 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOImageCompositeDatasetComponent.rst @@ -0,0 +1,26 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.YOLOImageCompositeDatasetComponent +=================================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autoclass:: YOLOImageCompositeDatasetComponent + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOImageCompositeDatasetComponent.get_components_to_add + ~YOLOImageCompositeDatasetComponent.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOSOIExtractorDataset.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOSOIExtractorDataset.rst new file mode 100644 index 0000000..8637ee5 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.YOLOSOIExtractorDataset.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.YOLOSOIExtractorDataset +======================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autoclass:: YOLOSOIExtractorDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YOLOSOIExtractorDataset.next + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.extract_yolo_boxes.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.extract_yolo_boxes.rst new file mode 100644 index 0000000..f203eb6 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.extract_yolo_boxes.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.extract\_yolo\_boxes +===================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autofunction:: extract_yolo_boxes \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.read_yolo_datum.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.read_yolo_datum.rst new file mode 100644 index 0000000..85f6718 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.read_yolo_datum.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.read\_yolo\_datum +================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autofunction:: read_yolo_datum \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.rst new file mode 100644 index 0000000..ef6cc55 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.rst @@ -0,0 +1,48 @@ +torchsig.image\_datasets.datasets.yolo\_datasets +================================================ + +.. automodule:: torchsig.image_datasets.datasets.yolo_datasets + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + extract_yolo_boxes + read_yolo_datum + yolo_box_on_image + yolo_to_pixels_on_image + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + YOLODatasetAdapter + YOLODatum + YOLOFileDataset + YOLOImageCompositeDataset + YOLOImageCompositeDatasetComponent + YOLOSOIExtractorDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.yolo_box_on_image.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.yolo_box_on_image.rst new file mode 100644 index 0000000..fd29fae --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.yolo_box_on_image.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.yolo\_box\_on\_image +===================================================================== + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autofunction:: yolo_box_on_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.yolo_to_pixels_on_image.rst b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.yolo_to_pixels_on_image.rst new file mode 100644 index 0000000..c2e2f8a --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.datasets.yolo_datasets.yolo_to_pixels_on_image.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.datasets.yolo\_datasets.yolo\_to\_pixels\_on\_image +============================================================================ + +.. currentmodule:: torchsig.image_datasets.datasets.yolo_datasets + +.. autofunction:: yolo_to_pixels_on_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.generate_dataset.add_falling_edge.rst b/docs/_autosummary/torchsig.image_datasets.generate_dataset.add_falling_edge.rst new file mode 100644 index 0000000..8fec176 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.generate_dataset.add_falling_edge.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.generate\_dataset.add\_falling\_edge +============================================================= + +.. currentmodule:: torchsig.image_datasets.generate_dataset + +.. autofunction:: add_falling_edge \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.generate_dataset.clamp_max_by_std.rst b/docs/_autosummary/torchsig.image_datasets.generate_dataset.clamp_max_by_std.rst new file mode 100644 index 0000000..e1ccc33 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.generate_dataset.clamp_max_by_std.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.generate\_dataset.clamp\_max\_by\_std +============================================================== + +.. currentmodule:: torchsig.image_datasets.generate_dataset + +.. autofunction:: clamp_max_by_std \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.generate_dataset.main.rst b/docs/_autosummary/torchsig.image_datasets.generate_dataset.main.rst new file mode 100644 index 0000000..c91305c --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.generate_dataset.main.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.generate\_dataset.main +=============================================== + +.. currentmodule:: torchsig.image_datasets.generate_dataset + +.. autofunction:: main \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.generate_dataset.rst b/docs/_autosummary/torchsig.image_datasets.generate_dataset.rst new file mode 100644 index 0000000..fe0a959 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.generate_dataset.rst @@ -0,0 +1,34 @@ +torchsig.image\_datasets.generate\_dataset +========================================== + +.. automodule:: torchsig.image_datasets.generate_dataset + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + add_falling_edge + clamp_max_by_std + main + threshold_mod_signal + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.generate_dataset.threshold_mod_signal.rst b/docs/_autosummary/torchsig.image_datasets.generate_dataset.threshold_mod_signal.rst new file mode 100644 index 0000000..43e867a --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.generate_dataset.threshold_mod_signal.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.generate\_dataset.threshold\_mod\_signal +================================================================= + +.. currentmodule:: torchsig.image_datasets.generate_dataset + +.. autofunction:: threshold_mod_signal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.plotting.plotting.plot_yolo_boxes_on_image.rst b/docs/_autosummary/torchsig.image_datasets.plotting.plotting.plot_yolo_boxes_on_image.rst new file mode 100644 index 0000000..473151a --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.plotting.plotting.plot_yolo_boxes_on_image.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.plotting.plotting.plot\_yolo\_boxes\_on\_image +======================================================================= + +.. currentmodule:: torchsig.image_datasets.plotting.plotting + +.. autofunction:: plot_yolo_boxes_on_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.plotting.plotting.plot_yolo_datum.rst b/docs/_autosummary/torchsig.image_datasets.plotting.plotting.plot_yolo_datum.rst new file mode 100644 index 0000000..53a400b --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.plotting.plotting.plot_yolo_datum.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.plotting.plotting.plot\_yolo\_datum +============================================================ + +.. currentmodule:: torchsig.image_datasets.plotting.plotting + +.. autofunction:: plot_yolo_datum \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.plotting.plotting.rst b/docs/_autosummary/torchsig.image_datasets.plotting.plotting.rst new file mode 100644 index 0000000..2d42732 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.plotting.plotting.rst @@ -0,0 +1,32 @@ +torchsig.image\_datasets.plotting.plotting +========================================== + +.. automodule:: torchsig.image_datasets.plotting.plotting + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + plot_yolo_boxes_on_image + plot_yolo_datum + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.plotting.rst b/docs/_autosummary/torchsig.image_datasets.plotting.rst new file mode 100644 index 0000000..612b0f9 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.plotting.rst @@ -0,0 +1,30 @@ +torchsig.image\_datasets.plotting +================================= + +.. automodule:: torchsig.image_datasets.plotting + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + plotting + diff --git a/docs/_autosummary/torchsig.image_datasets.rst b/docs/_autosummary/torchsig.image_datasets.rst new file mode 100644 index 0000000..6a2f4ee --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.rst @@ -0,0 +1,34 @@ +torchsig.image\_datasets +======================== + +.. automodule:: torchsig.image_datasets + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + dataset_generation + datasets + generate_dataset + plotting + transforms + diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.denoising.isolate_foreground_signal.rst b/docs/_autosummary/torchsig.image_datasets.transforms.denoising.isolate_foreground_signal.rst new file mode 100644 index 0000000..16ba408 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.denoising.isolate_foreground_signal.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.transforms.denoising.isolate\_foreground\_signal +========================================================================= + +.. currentmodule:: torchsig.image_datasets.transforms.denoising + +.. autofunction:: isolate_foreground_signal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.denoising.normalize_image.rst b/docs/_autosummary/torchsig.image_datasets.transforms.denoising.normalize_image.rst new file mode 100644 index 0000000..1d8eea1 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.denoising.normalize_image.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.transforms.denoising.normalize\_image +============================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.denoising + +.. autofunction:: normalize_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.denoising.rst b/docs/_autosummary/torchsig.image_datasets.transforms.denoising.rst new file mode 100644 index 0000000..1aa5526 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.denoising.rst @@ -0,0 +1,32 @@ +torchsig.image\_datasets.transforms.denoising +============================================= + +.. automodule:: torchsig.image_datasets.transforms.denoising + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + isolate_foreground_signal + normalize_image + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.BlurTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.BlurTransform.rst new file mode 100644 index 0000000..b7416c2 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.BlurTransform.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.transforms.impairments.BlurTransform +============================================================= + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: BlurTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.GaussianNoiseTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.GaussianNoiseTransform.rst new file mode 100644 index 0000000..8507143 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.GaussianNoiseTransform.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.transforms.impairments.GaussianNoiseTransform +====================================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: GaussianNoiseTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomGaussianNoiseTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomGaussianNoiseTransform.rst new file mode 100644 index 0000000..2bf4d62 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomGaussianNoiseTransform.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.transforms.impairments.RandomGaussianNoiseTransform +============================================================================ + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: RandomGaussianNoiseTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomImageResizeTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomImageResizeTransform.rst new file mode 100644 index 0000000..90268e7 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomImageResizeTransform.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.transforms.impairments.RandomImageResizeTransform +========================================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: RandomImageResizeTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomRippleNoiseTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomRippleNoiseTransform.rst new file mode 100644 index 0000000..d905219 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RandomRippleNoiseTransform.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.transforms.impairments.RandomRippleNoiseTransform +========================================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: RandomRippleNoiseTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RippleNoiseTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RippleNoiseTransform.rst new file mode 100644 index 0000000..d0fbc10 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.RippleNoiseTransform.rst @@ -0,0 +1,25 @@ +torchsig.image\_datasets.transforms.impairments.RippleNoiseTransform +==================================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: RippleNoiseTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RippleNoiseTransform.update_mesh_spacing + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.ScaleTransform.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.ScaleTransform.rst new file mode 100644 index 0000000..4b946d0 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.ScaleTransform.rst @@ -0,0 +1,24 @@ +torchsig.image\_datasets.transforms.impairments.ScaleTransform +============================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autoclass:: ScaleTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.pad_border.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.pad_border.rst new file mode 100644 index 0000000..47009f5 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.pad_border.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.transforms.impairments.pad\_border +=========================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autofunction:: pad_border \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.rst new file mode 100644 index 0000000..2be0f58 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.rst @@ -0,0 +1,47 @@ +torchsig.image\_datasets.transforms.impairments +=============================================== + +.. automodule:: torchsig.image_datasets.transforms.impairments + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + pad_border + scale_dynamic_range + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + BlurTransform + GaussianNoiseTransform + RandomGaussianNoiseTransform + RandomImageResizeTransform + RandomRippleNoiseTransform + RippleNoiseTransform + ScaleTransform + + + + + + + + + diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.impairments.scale_dynamic_range.rst b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.scale_dynamic_range.rst new file mode 100644 index 0000000..80aaf96 --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.impairments.scale_dynamic_range.rst @@ -0,0 +1,6 @@ +torchsig.image\_datasets.transforms.impairments.scale\_dynamic\_range +===================================================================== + +.. currentmodule:: torchsig.image_datasets.transforms.impairments + +.. autofunction:: scale_dynamic_range \ No newline at end of file diff --git a/docs/_autosummary/torchsig.image_datasets.transforms.rst b/docs/_autosummary/torchsig.image_datasets.transforms.rst new file mode 100644 index 0000000..892cb4b --- /dev/null +++ b/docs/_autosummary/torchsig.image_datasets.transforms.rst @@ -0,0 +1,31 @@ +torchsig.image\_datasets.transforms +=================================== + +.. automodule:: torchsig.image_datasets.transforms + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + denoising + impairments + diff --git a/docs/_autosummary/torchsig.models.iq_models.densenet.densenet1d.DenseNet1d.rst b/docs/_autosummary/torchsig.models.iq_models.densenet.densenet1d.DenseNet1d.rst new file mode 100644 index 0000000..01e01b1 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.densenet.densenet1d.DenseNet1d.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.densenet.densenet1d.DenseNet1d +========================================================= + +.. currentmodule:: torchsig.models.iq_models.densenet.densenet1d + +.. autofunction:: DenseNet1d \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.densenet.densenet1d.rst b/docs/_autosummary/torchsig.models.iq_models.densenet.densenet1d.rst new file mode 100644 index 0000000..77c5fd6 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.densenet.densenet1d.rst @@ -0,0 +1,31 @@ +torchsig.models.iq\_models.densenet.densenet1d +============================================== + +.. automodule:: torchsig.models.iq_models.densenet.densenet1d + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + DenseNet1d + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.iq_models.densenet.rst b/docs/_autosummary/torchsig.models.iq_models.densenet.rst new file mode 100644 index 0000000..a3698fa --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.densenet.rst @@ -0,0 +1,30 @@ +torchsig.models.iq\_models.densenet +=================================== + +.. automodule:: torchsig.models.iq_models.densenet + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + densenet1d + diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.FastGlobalAvgPool1d.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.FastGlobalAvgPool1d.rst new file mode 100644 index 0000000..a57cb62 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.FastGlobalAvgPool1d.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.FastGlobalAvgPool1d +======================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autoclass:: FastGlobalAvgPool1d + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~FastGlobalAvgPool1d.add_module + ~FastGlobalAvgPool1d.apply + ~FastGlobalAvgPool1d.bfloat16 + ~FastGlobalAvgPool1d.buffers + ~FastGlobalAvgPool1d.children + ~FastGlobalAvgPool1d.compile + ~FastGlobalAvgPool1d.cpu + ~FastGlobalAvgPool1d.cuda + ~FastGlobalAvgPool1d.double + ~FastGlobalAvgPool1d.eval + ~FastGlobalAvgPool1d.extra_repr + ~FastGlobalAvgPool1d.float + ~FastGlobalAvgPool1d.forward + ~FastGlobalAvgPool1d.get_buffer + ~FastGlobalAvgPool1d.get_extra_state + ~FastGlobalAvgPool1d.get_parameter + ~FastGlobalAvgPool1d.get_submodule + ~FastGlobalAvgPool1d.half + ~FastGlobalAvgPool1d.ipu + ~FastGlobalAvgPool1d.load_state_dict + ~FastGlobalAvgPool1d.modules + ~FastGlobalAvgPool1d.named_buffers + ~FastGlobalAvgPool1d.named_children + ~FastGlobalAvgPool1d.named_modules + ~FastGlobalAvgPool1d.named_parameters + ~FastGlobalAvgPool1d.parameters + ~FastGlobalAvgPool1d.register_backward_hook + ~FastGlobalAvgPool1d.register_buffer + ~FastGlobalAvgPool1d.register_forward_hook + ~FastGlobalAvgPool1d.register_forward_pre_hook + ~FastGlobalAvgPool1d.register_full_backward_hook + ~FastGlobalAvgPool1d.register_full_backward_pre_hook + ~FastGlobalAvgPool1d.register_load_state_dict_post_hook + ~FastGlobalAvgPool1d.register_module + ~FastGlobalAvgPool1d.register_parameter + ~FastGlobalAvgPool1d.register_state_dict_pre_hook + ~FastGlobalAvgPool1d.requires_grad_ + ~FastGlobalAvgPool1d.set_extra_state + ~FastGlobalAvgPool1d.share_memory + ~FastGlobalAvgPool1d.state_dict + ~FastGlobalAvgPool1d.to + ~FastGlobalAvgPool1d.to_empty + ~FastGlobalAvgPool1d.train + ~FastGlobalAvgPool1d.type + ~FastGlobalAvgPool1d.xpu + ~FastGlobalAvgPool1d.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~FastGlobalAvgPool1d.T_destination + ~FastGlobalAvgPool1d.call_super_init + ~FastGlobalAvgPool1d.dump_patches + ~FastGlobalAvgPool1d.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.GBN.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.GBN.rst new file mode 100644 index 0000000..b7ed5dd --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.GBN.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.GBN +======================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autoclass:: GBN + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~GBN.add_module + ~GBN.apply + ~GBN.bfloat16 + ~GBN.buffers + ~GBN.children + ~GBN.compile + ~GBN.cpu + ~GBN.cuda + ~GBN.double + ~GBN.eval + ~GBN.extra_repr + ~GBN.float + ~GBN.forward + ~GBN.get_buffer + ~GBN.get_extra_state + ~GBN.get_parameter + ~GBN.get_submodule + ~GBN.half + ~GBN.ipu + ~GBN.load_state_dict + ~GBN.modules + ~GBN.named_buffers + ~GBN.named_children + ~GBN.named_modules + ~GBN.named_parameters + ~GBN.parameters + ~GBN.register_backward_hook + ~GBN.register_buffer + ~GBN.register_forward_hook + ~GBN.register_forward_pre_hook + ~GBN.register_full_backward_hook + ~GBN.register_full_backward_pre_hook + ~GBN.register_load_state_dict_post_hook + ~GBN.register_module + ~GBN.register_parameter + ~GBN.register_state_dict_pre_hook + ~GBN.requires_grad_ + ~GBN.set_extra_state + ~GBN.share_memory + ~GBN.state_dict + ~GBN.to + ~GBN.to_empty + ~GBN.train + ~GBN.type + ~GBN.xpu + ~GBN.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~GBN.T_destination + ~GBN.call_super_init + ~GBN.dump_patches + ~GBN.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.SqueezeExcite.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.SqueezeExcite.rst new file mode 100644 index 0000000..d8de5ba --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.SqueezeExcite.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.SqueezeExcite +================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autoclass:: SqueezeExcite + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SqueezeExcite.add_module + ~SqueezeExcite.apply + ~SqueezeExcite.bfloat16 + ~SqueezeExcite.buffers + ~SqueezeExcite.children + ~SqueezeExcite.compile + ~SqueezeExcite.cpu + ~SqueezeExcite.cuda + ~SqueezeExcite.double + ~SqueezeExcite.eval + ~SqueezeExcite.extra_repr + ~SqueezeExcite.float + ~SqueezeExcite.forward + ~SqueezeExcite.get_buffer + ~SqueezeExcite.get_extra_state + ~SqueezeExcite.get_parameter + ~SqueezeExcite.get_submodule + ~SqueezeExcite.half + ~SqueezeExcite.ipu + ~SqueezeExcite.load_state_dict + ~SqueezeExcite.modules + ~SqueezeExcite.named_buffers + ~SqueezeExcite.named_children + ~SqueezeExcite.named_modules + ~SqueezeExcite.named_parameters + ~SqueezeExcite.parameters + ~SqueezeExcite.register_backward_hook + ~SqueezeExcite.register_buffer + ~SqueezeExcite.register_forward_hook + ~SqueezeExcite.register_forward_pre_hook + ~SqueezeExcite.register_full_backward_hook + ~SqueezeExcite.register_full_backward_pre_hook + ~SqueezeExcite.register_load_state_dict_post_hook + ~SqueezeExcite.register_module + ~SqueezeExcite.register_parameter + ~SqueezeExcite.register_state_dict_pre_hook + ~SqueezeExcite.requires_grad_ + ~SqueezeExcite.set_extra_state + ~SqueezeExcite.share_memory + ~SqueezeExcite.state_dict + ~SqueezeExcite.to + ~SqueezeExcite.to_empty + ~SqueezeExcite.train + ~SqueezeExcite.type + ~SqueezeExcite.xpu + ~SqueezeExcite.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~SqueezeExcite.T_destination + ~SqueezeExcite.call_super_init + ~SqueezeExcite.dump_patches + ~SqueezeExcite.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.create_effnet.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.create_effnet.rst new file mode 100644 index 0000000..09de258 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.create_effnet.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.create\_effnet +=================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: create_effnet \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b0.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b0.rst new file mode 100644 index 0000000..a92c20b --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b0.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.efficientnet\_b0 +===================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: efficientnet_b0 \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b2.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b2.rst new file mode 100644 index 0000000..196cedc --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b2.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.efficientnet\_b2 +===================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: efficientnet_b2 \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b4.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b4.rst new file mode 100644 index 0000000..b0edd1d --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.efficientnet_b4.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.efficientnet\_b4 +===================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: efficientnet_b4 \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_bn.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_bn.rst new file mode 100644 index 0000000..ebf88ef --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_bn.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.replace\_bn +================================================================ + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: replace_bn \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_conv_effnet.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_conv_effnet.rst new file mode 100644 index 0000000..d40edf3 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_conv_effnet.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.replace\_conv\_effnet +========================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: replace_conv_effnet \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_se.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_se.rst new file mode 100644 index 0000000..cc13168 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.replace_se.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet.replace\_se +================================================================ + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet + +.. autofunction:: replace_se \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.rst new file mode 100644 index 0000000..77bd3ed --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet.rst @@ -0,0 +1,48 @@ +torchsig.models.iq\_models.efficientnet.efficientnet +==================================================== + +.. automodule:: torchsig.models.iq_models.efficientnet.efficientnet + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + create_effnet + efficientnet_b0 + efficientnet_b2 + efficientnet_b4 + replace_bn + replace_conv_effnet + replace_se + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + FastGlobalAvgPool1d + GBN + SqueezeExcite + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet1d.EfficientNet1d.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet1d.EfficientNet1d.rst new file mode 100644 index 0000000..6b18ceb --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet1d.EfficientNet1d.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.efficientnet.efficientnet1d.EfficientNet1d +===================================================================== + +.. currentmodule:: torchsig.models.iq_models.efficientnet.efficientnet1d + +.. autofunction:: EfficientNet1d \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet1d.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet1d.rst new file mode 100644 index 0000000..1f6d30c --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.efficientnet1d.rst @@ -0,0 +1,31 @@ +torchsig.models.iq\_models.efficientnet.efficientnet1d +====================================================== + +.. automodule:: torchsig.models.iq_models.efficientnet.efficientnet1d + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + EfficientNet1d + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.iq_models.efficientnet.rst b/docs/_autosummary/torchsig.models.iq_models.efficientnet.rst new file mode 100644 index 0000000..8717430 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.efficientnet.rst @@ -0,0 +1,31 @@ +torchsig.models.iq\_models.efficientnet +======================================= + +.. automodule:: torchsig.models.iq_models.efficientnet + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + efficientnet + efficientnet1d + diff --git a/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.ClassifierMetrics.rst b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.ClassifierMetrics.rst new file mode 100644 index 0000000..b69cf59 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.ClassifierMetrics.rst @@ -0,0 +1,69 @@ +torchsig.models.iq\_models.inceptiontime.inceptiontime.ClassifierMetrics +======================================================================== + +.. currentmodule:: torchsig.models.iq_models.inceptiontime.inceptiontime + +.. autoclass:: ClassifierMetrics + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ClassifierMetrics.load_state_dict + ~ClassifierMetrics.on_after_backward + ~ClassifierMetrics.on_before_backward + ~ClassifierMetrics.on_before_optimizer_step + ~ClassifierMetrics.on_before_zero_grad + ~ClassifierMetrics.on_exception + ~ClassifierMetrics.on_fit_end + ~ClassifierMetrics.on_fit_start + ~ClassifierMetrics.on_load_checkpoint + ~ClassifierMetrics.on_predict_batch_end + ~ClassifierMetrics.on_predict_batch_start + ~ClassifierMetrics.on_predict_end + ~ClassifierMetrics.on_predict_epoch_end + ~ClassifierMetrics.on_predict_epoch_start + ~ClassifierMetrics.on_predict_start + ~ClassifierMetrics.on_sanity_check_end + ~ClassifierMetrics.on_sanity_check_start + ~ClassifierMetrics.on_save_checkpoint + ~ClassifierMetrics.on_test_batch_end + ~ClassifierMetrics.on_test_batch_start + ~ClassifierMetrics.on_test_end + ~ClassifierMetrics.on_test_epoch_end + ~ClassifierMetrics.on_test_epoch_start + ~ClassifierMetrics.on_test_start + ~ClassifierMetrics.on_train_batch_end + ~ClassifierMetrics.on_train_batch_start + ~ClassifierMetrics.on_train_end + ~ClassifierMetrics.on_train_epoch_end + ~ClassifierMetrics.on_train_epoch_start + ~ClassifierMetrics.on_train_start + ~ClassifierMetrics.on_validation_batch_end + ~ClassifierMetrics.on_validation_batch_start + ~ClassifierMetrics.on_validation_end + ~ClassifierMetrics.on_validation_epoch_end + ~ClassifierMetrics.on_validation_epoch_start + ~ClassifierMetrics.on_validation_start + ~ClassifierMetrics.setup + ~ClassifierMetrics.state_dict + ~ClassifierMetrics.teardown + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ClassifierMetrics.state_key + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.FocalLoss.rst b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.FocalLoss.rst new file mode 100644 index 0000000..75b24df --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.FocalLoss.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.inceptiontime.inceptiontime.FocalLoss +================================================================ + +.. currentmodule:: torchsig.models.iq_models.inceptiontime.inceptiontime + +.. autoclass:: FocalLoss + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~FocalLoss.add_module + ~FocalLoss.apply + ~FocalLoss.bfloat16 + ~FocalLoss.buffers + ~FocalLoss.children + ~FocalLoss.compile + ~FocalLoss.cpu + ~FocalLoss.cuda + ~FocalLoss.double + ~FocalLoss.eval + ~FocalLoss.extra_repr + ~FocalLoss.float + ~FocalLoss.forward + ~FocalLoss.get_buffer + ~FocalLoss.get_extra_state + ~FocalLoss.get_parameter + ~FocalLoss.get_submodule + ~FocalLoss.half + ~FocalLoss.ipu + ~FocalLoss.load_state_dict + ~FocalLoss.modules + ~FocalLoss.named_buffers + ~FocalLoss.named_children + ~FocalLoss.named_modules + ~FocalLoss.named_parameters + ~FocalLoss.parameters + ~FocalLoss.register_backward_hook + ~FocalLoss.register_buffer + ~FocalLoss.register_forward_hook + ~FocalLoss.register_forward_pre_hook + ~FocalLoss.register_full_backward_hook + ~FocalLoss.register_full_backward_pre_hook + ~FocalLoss.register_load_state_dict_post_hook + ~FocalLoss.register_module + ~FocalLoss.register_parameter + ~FocalLoss.register_state_dict_pre_hook + ~FocalLoss.requires_grad_ + ~FocalLoss.set_extra_state + ~FocalLoss.share_memory + ~FocalLoss.state_dict + ~FocalLoss.to + ~FocalLoss.to_empty + ~FocalLoss.train + ~FocalLoss.type + ~FocalLoss.xpu + ~FocalLoss.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~FocalLoss.T_destination + ~FocalLoss.call_super_init + ~FocalLoss.dump_patches + ~FocalLoss.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.InceptionModule.rst b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.InceptionModule.rst new file mode 100644 index 0000000..489686d --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.InceptionModule.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.inceptiontime.inceptiontime.InceptionModule +====================================================================== + +.. currentmodule:: torchsig.models.iq_models.inceptiontime.inceptiontime + +.. autoclass:: InceptionModule + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~InceptionModule.add_module + ~InceptionModule.apply + ~InceptionModule.bfloat16 + ~InceptionModule.buffers + ~InceptionModule.children + ~InceptionModule.compile + ~InceptionModule.cpu + ~InceptionModule.cuda + ~InceptionModule.double + ~InceptionModule.eval + ~InceptionModule.extra_repr + ~InceptionModule.float + ~InceptionModule.forward + ~InceptionModule.get_buffer + ~InceptionModule.get_extra_state + ~InceptionModule.get_parameter + ~InceptionModule.get_submodule + ~InceptionModule.half + ~InceptionModule.ipu + ~InceptionModule.load_state_dict + ~InceptionModule.modules + ~InceptionModule.named_buffers + ~InceptionModule.named_children + ~InceptionModule.named_modules + ~InceptionModule.named_parameters + ~InceptionModule.parameters + ~InceptionModule.register_backward_hook + ~InceptionModule.register_buffer + ~InceptionModule.register_forward_hook + ~InceptionModule.register_forward_pre_hook + ~InceptionModule.register_full_backward_hook + ~InceptionModule.register_full_backward_pre_hook + ~InceptionModule.register_load_state_dict_post_hook + ~InceptionModule.register_module + ~InceptionModule.register_parameter + ~InceptionModule.register_state_dict_pre_hook + ~InceptionModule.requires_grad_ + ~InceptionModule.set_extra_state + ~InceptionModule.share_memory + ~InceptionModule.state_dict + ~InceptionModule.to + ~InceptionModule.to_empty + ~InceptionModule.train + ~InceptionModule.type + ~InceptionModule.xpu + ~InceptionModule.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~InceptionModule.T_destination + ~InceptionModule.call_super_init + ~InceptionModule.dump_patches + ~InceptionModule.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.InceptionTime.rst b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.InceptionTime.rst new file mode 100644 index 0000000..d5727e2 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.InceptionTime.rst @@ -0,0 +1,176 @@ +torchsig.models.iq\_models.inceptiontime.inceptiontime.InceptionTime +==================================================================== + +.. currentmodule:: torchsig.models.iq_models.inceptiontime.inceptiontime + +.. autoclass:: InceptionTime + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~InceptionTime.add_module + ~InceptionTime.all_gather + ~InceptionTime.apply + ~InceptionTime.backward + ~InceptionTime.bfloat16 + ~InceptionTime.buffers + ~InceptionTime.children + ~InceptionTime.clip_gradients + ~InceptionTime.compile + ~InceptionTime.configure_callbacks + ~InceptionTime.configure_gradient_clipping + ~InceptionTime.configure_model + ~InceptionTime.configure_optimizers + ~InceptionTime.configure_sharded_model + ~InceptionTime.cpu + ~InceptionTime.cuda + ~InceptionTime.double + ~InceptionTime.eval + ~InceptionTime.extra_repr + ~InceptionTime.float + ~InceptionTime.forward + ~InceptionTime.freeze + ~InceptionTime.get_buffer + ~InceptionTime.get_extra_state + ~InceptionTime.get_parameter + ~InceptionTime.get_submodule + ~InceptionTime.half + ~InceptionTime.ipu + ~InceptionTime.load_from_checkpoint + ~InceptionTime.load_state_dict + ~InceptionTime.log + ~InceptionTime.log_dict + ~InceptionTime.lr_scheduler_step + ~InceptionTime.lr_schedulers + ~InceptionTime.manual_backward + ~InceptionTime.modules + ~InceptionTime.named_buffers + ~InceptionTime.named_children + ~InceptionTime.named_modules + ~InceptionTime.named_parameters + ~InceptionTime.on_after_backward + ~InceptionTime.on_after_batch_transfer + ~InceptionTime.on_before_backward + ~InceptionTime.on_before_batch_transfer + ~InceptionTime.on_before_optimizer_step + ~InceptionTime.on_before_zero_grad + ~InceptionTime.on_fit_end + ~InceptionTime.on_fit_start + ~InceptionTime.on_load_checkpoint + ~InceptionTime.on_predict_batch_end + ~InceptionTime.on_predict_batch_start + ~InceptionTime.on_predict_end + ~InceptionTime.on_predict_epoch_end + ~InceptionTime.on_predict_epoch_start + ~InceptionTime.on_predict_model_eval + ~InceptionTime.on_predict_start + ~InceptionTime.on_save_checkpoint + ~InceptionTime.on_test_batch_end + ~InceptionTime.on_test_batch_start + ~InceptionTime.on_test_end + ~InceptionTime.on_test_epoch_end + ~InceptionTime.on_test_epoch_start + ~InceptionTime.on_test_model_eval + ~InceptionTime.on_test_model_train + ~InceptionTime.on_test_start + ~InceptionTime.on_train_batch_end + ~InceptionTime.on_train_batch_start + ~InceptionTime.on_train_end + ~InceptionTime.on_train_epoch_end + ~InceptionTime.on_train_epoch_start + ~InceptionTime.on_train_start + ~InceptionTime.on_validation_batch_end + ~InceptionTime.on_validation_batch_start + ~InceptionTime.on_validation_end + ~InceptionTime.on_validation_epoch_end + ~InceptionTime.on_validation_epoch_start + ~InceptionTime.on_validation_model_eval + ~InceptionTime.on_validation_model_train + ~InceptionTime.on_validation_model_zero_grad + ~InceptionTime.on_validation_start + ~InceptionTime.optimizer_step + ~InceptionTime.optimizer_zero_grad + ~InceptionTime.optimizers + ~InceptionTime.parameters + ~InceptionTime.predict_dataloader + ~InceptionTime.predict_step + ~InceptionTime.prepare_data + ~InceptionTime.print + ~InceptionTime.register_backward_hook + ~InceptionTime.register_buffer + ~InceptionTime.register_forward_hook + ~InceptionTime.register_forward_pre_hook + ~InceptionTime.register_full_backward_hook + ~InceptionTime.register_full_backward_pre_hook + ~InceptionTime.register_load_state_dict_post_hook + ~InceptionTime.register_module + ~InceptionTime.register_parameter + ~InceptionTime.register_state_dict_pre_hook + ~InceptionTime.requires_grad_ + ~InceptionTime.save_hyperparameters + ~InceptionTime.set_extra_state + ~InceptionTime.setup + ~InceptionTime.share_memory + ~InceptionTime.state_dict + ~InceptionTime.teardown + ~InceptionTime.test_dataloader + ~InceptionTime.test_step + ~InceptionTime.to + ~InceptionTime.to_empty + ~InceptionTime.to_onnx + ~InceptionTime.to_torchscript + ~InceptionTime.toggle_optimizer + ~InceptionTime.train + ~InceptionTime.train_dataloader + ~InceptionTime.training_step + ~InceptionTime.transfer_batch_to_device + ~InceptionTime.type + ~InceptionTime.unfreeze + ~InceptionTime.untoggle_optimizer + ~InceptionTime.val_dataloader + ~InceptionTime.validation_step + ~InceptionTime.xpu + ~InceptionTime.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~InceptionTime.CHECKPOINT_HYPER_PARAMS_KEY + ~InceptionTime.CHECKPOINT_HYPER_PARAMS_NAME + ~InceptionTime.CHECKPOINT_HYPER_PARAMS_TYPE + ~InceptionTime.T_destination + ~InceptionTime.automatic_optimization + ~InceptionTime.call_super_init + ~InceptionTime.current_epoch + ~InceptionTime.device + ~InceptionTime.device_mesh + ~InceptionTime.dtype + ~InceptionTime.dump_patches + ~InceptionTime.example_input_array + ~InceptionTime.fabric + ~InceptionTime.global_rank + ~InceptionTime.global_step + ~InceptionTime.hparams + ~InceptionTime.hparams_initial + ~InceptionTime.local_rank + ~InceptionTime.logger + ~InceptionTime.loggers + ~InceptionTime.on_gpu + ~InceptionTime.strict_loading + ~InceptionTime.trainer + ~InceptionTime.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.rst b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.rst new file mode 100644 index 0000000..c0717c2 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.inceptiontime.rst @@ -0,0 +1,35 @@ +torchsig.models.iq\_models.inceptiontime.inceptiontime +====================================================== + +.. automodule:: torchsig.models.iq_models.inceptiontime.inceptiontime + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + ClassifierMetrics + FocalLoss + InceptionModule + InceptionTime + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.iq_models.inceptiontime.rst b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.rst new file mode 100644 index 0000000..9d7b52a --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.inceptiontime.rst @@ -0,0 +1,30 @@ +torchsig.models.iq\_models.inceptiontime +======================================== + +.. automodule:: torchsig.models.iq_models.inceptiontime + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + inceptiontime + diff --git a/docs/_autosummary/torchsig.models.iq_models.rst b/docs/_autosummary/torchsig.models.iq_models.rst new file mode 100644 index 0000000..677f7a2 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.rst @@ -0,0 +1,33 @@ +torchsig.models.iq\_models +========================== + +.. automodule:: torchsig.models.iq_models + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + densenet + efficientnet + inceptiontime + xcit + diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.rst new file mode 100644 index 0000000..bc4b837 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.rst @@ -0,0 +1,31 @@ +torchsig.models.iq\_models.xcit +=============================== + +.. automodule:: torchsig.models.iq_models.xcit + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + xcit + xcit1d + diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.Chunker.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.Chunker.rst new file mode 100644 index 0000000..ccde6b8 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.Chunker.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit.Chunker +============================================ + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit + +.. autoclass:: Chunker + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Chunker.add_module + ~Chunker.apply + ~Chunker.bfloat16 + ~Chunker.buffers + ~Chunker.children + ~Chunker.compile + ~Chunker.cpu + ~Chunker.cuda + ~Chunker.double + ~Chunker.eval + ~Chunker.extra_repr + ~Chunker.float + ~Chunker.forward + ~Chunker.get_buffer + ~Chunker.get_extra_state + ~Chunker.get_parameter + ~Chunker.get_submodule + ~Chunker.half + ~Chunker.ipu + ~Chunker.load_state_dict + ~Chunker.modules + ~Chunker.named_buffers + ~Chunker.named_children + ~Chunker.named_modules + ~Chunker.named_parameters + ~Chunker.parameters + ~Chunker.register_backward_hook + ~Chunker.register_buffer + ~Chunker.register_forward_hook + ~Chunker.register_forward_pre_hook + ~Chunker.register_full_backward_hook + ~Chunker.register_full_backward_pre_hook + ~Chunker.register_load_state_dict_post_hook + ~Chunker.register_module + ~Chunker.register_parameter + ~Chunker.register_state_dict_pre_hook + ~Chunker.requires_grad_ + ~Chunker.set_extra_state + ~Chunker.share_memory + ~Chunker.state_dict + ~Chunker.to + ~Chunker.to_empty + ~Chunker.train + ~Chunker.type + ~Chunker.xpu + ~Chunker.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Chunker.T_destination + ~Chunker.call_super_init + ~Chunker.dump_patches + ~Chunker.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.ConvDownSampler.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.ConvDownSampler.rst new file mode 100644 index 0000000..8aecf1d --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.ConvDownSampler.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit.ConvDownSampler +==================================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit + +.. autoclass:: ConvDownSampler + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ConvDownSampler.add_module + ~ConvDownSampler.apply + ~ConvDownSampler.bfloat16 + ~ConvDownSampler.buffers + ~ConvDownSampler.children + ~ConvDownSampler.compile + ~ConvDownSampler.cpu + ~ConvDownSampler.cuda + ~ConvDownSampler.double + ~ConvDownSampler.eval + ~ConvDownSampler.extra_repr + ~ConvDownSampler.float + ~ConvDownSampler.forward + ~ConvDownSampler.get_buffer + ~ConvDownSampler.get_extra_state + ~ConvDownSampler.get_parameter + ~ConvDownSampler.get_submodule + ~ConvDownSampler.half + ~ConvDownSampler.ipu + ~ConvDownSampler.load_state_dict + ~ConvDownSampler.modules + ~ConvDownSampler.named_buffers + ~ConvDownSampler.named_children + ~ConvDownSampler.named_modules + ~ConvDownSampler.named_parameters + ~ConvDownSampler.parameters + ~ConvDownSampler.register_backward_hook + ~ConvDownSampler.register_buffer + ~ConvDownSampler.register_forward_hook + ~ConvDownSampler.register_forward_pre_hook + ~ConvDownSampler.register_full_backward_hook + ~ConvDownSampler.register_full_backward_pre_hook + ~ConvDownSampler.register_load_state_dict_post_hook + ~ConvDownSampler.register_module + ~ConvDownSampler.register_parameter + ~ConvDownSampler.register_state_dict_pre_hook + ~ConvDownSampler.requires_grad_ + ~ConvDownSampler.set_extra_state + ~ConvDownSampler.share_memory + ~ConvDownSampler.state_dict + ~ConvDownSampler.to + ~ConvDownSampler.to_empty + ~ConvDownSampler.train + ~ConvDownSampler.type + ~ConvDownSampler.xpu + ~ConvDownSampler.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ConvDownSampler.T_destination + ~ConvDownSampler.call_super_init + ~ConvDownSampler.dump_patches + ~ConvDownSampler.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.XCiT.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.XCiT.rst new file mode 100644 index 0000000..8302032 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.XCiT.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit.XCiT +========================================= + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit + +.. autoclass:: XCiT + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~XCiT.add_module + ~XCiT.apply + ~XCiT.bfloat16 + ~XCiT.buffers + ~XCiT.children + ~XCiT.compile + ~XCiT.cpu + ~XCiT.cuda + ~XCiT.double + ~XCiT.eval + ~XCiT.extra_repr + ~XCiT.float + ~XCiT.forward + ~XCiT.get_buffer + ~XCiT.get_extra_state + ~XCiT.get_parameter + ~XCiT.get_submodule + ~XCiT.half + ~XCiT.ipu + ~XCiT.load_state_dict + ~XCiT.modules + ~XCiT.named_buffers + ~XCiT.named_children + ~XCiT.named_modules + ~XCiT.named_parameters + ~XCiT.parameters + ~XCiT.register_backward_hook + ~XCiT.register_buffer + ~XCiT.register_forward_hook + ~XCiT.register_forward_pre_hook + ~XCiT.register_full_backward_hook + ~XCiT.register_full_backward_pre_hook + ~XCiT.register_load_state_dict_post_hook + ~XCiT.register_module + ~XCiT.register_parameter + ~XCiT.register_state_dict_pre_hook + ~XCiT.requires_grad_ + ~XCiT.set_extra_state + ~XCiT.share_memory + ~XCiT.state_dict + ~XCiT.to + ~XCiT.to_empty + ~XCiT.train + ~XCiT.type + ~XCiT.xpu + ~XCiT.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~XCiT.T_destination + ~XCiT.call_super_init + ~XCiT.dump_patches + ~XCiT.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.rst new file mode 100644 index 0000000..161f3d1 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.rst @@ -0,0 +1,43 @@ +torchsig.models.iq\_models.xcit.xcit +==================================== + +.. automodule:: torchsig.models.iq_models.xcit.xcit + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + xcit_nano + xcit_tiny12 + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + Chunker + ConvDownSampler + XCiT + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.xcit_nano.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.xcit_nano.rst new file mode 100644 index 0000000..a45c0a6 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.xcit_nano.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.xcit.xcit.xcit\_nano +=============================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit + +.. autofunction:: xcit_nano \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.xcit_tiny12.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.xcit_tiny12.rst new file mode 100644 index 0000000..0317cf7 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit.xcit_tiny12.rst @@ -0,0 +1,6 @@ +torchsig.models.iq\_models.xcit.xcit.xcit\_tiny12 +================================================= + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit + +.. autofunction:: xcit_tiny12 \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.Chunker.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.Chunker.rst new file mode 100644 index 0000000..ac8dc4c --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.Chunker.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit1d.Chunker +============================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: Chunker + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Chunker.add_module + ~Chunker.apply + ~Chunker.bfloat16 + ~Chunker.buffers + ~Chunker.children + ~Chunker.compile + ~Chunker.cpu + ~Chunker.cuda + ~Chunker.double + ~Chunker.eval + ~Chunker.extra_repr + ~Chunker.float + ~Chunker.forward + ~Chunker.get_buffer + ~Chunker.get_extra_state + ~Chunker.get_parameter + ~Chunker.get_submodule + ~Chunker.half + ~Chunker.ipu + ~Chunker.load_state_dict + ~Chunker.modules + ~Chunker.named_buffers + ~Chunker.named_children + ~Chunker.named_modules + ~Chunker.named_parameters + ~Chunker.parameters + ~Chunker.register_backward_hook + ~Chunker.register_buffer + ~Chunker.register_forward_hook + ~Chunker.register_forward_pre_hook + ~Chunker.register_full_backward_hook + ~Chunker.register_full_backward_pre_hook + ~Chunker.register_load_state_dict_post_hook + ~Chunker.register_module + ~Chunker.register_parameter + ~Chunker.register_state_dict_pre_hook + ~Chunker.requires_grad_ + ~Chunker.set_extra_state + ~Chunker.share_memory + ~Chunker.state_dict + ~Chunker.to + ~Chunker.to_empty + ~Chunker.train + ~Chunker.type + ~Chunker.xpu + ~Chunker.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Chunker.T_destination + ~Chunker.call_super_init + ~Chunker.dump_patches + ~Chunker.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.ClassifierMetrics.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.ClassifierMetrics.rst new file mode 100644 index 0000000..9608156 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.ClassifierMetrics.rst @@ -0,0 +1,69 @@ +torchsig.models.iq\_models.xcit.xcit1d.ClassifierMetrics +======================================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: ClassifierMetrics + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ClassifierMetrics.load_state_dict + ~ClassifierMetrics.on_after_backward + ~ClassifierMetrics.on_before_backward + ~ClassifierMetrics.on_before_optimizer_step + ~ClassifierMetrics.on_before_zero_grad + ~ClassifierMetrics.on_exception + ~ClassifierMetrics.on_fit_end + ~ClassifierMetrics.on_fit_start + ~ClassifierMetrics.on_load_checkpoint + ~ClassifierMetrics.on_predict_batch_end + ~ClassifierMetrics.on_predict_batch_start + ~ClassifierMetrics.on_predict_end + ~ClassifierMetrics.on_predict_epoch_end + ~ClassifierMetrics.on_predict_epoch_start + ~ClassifierMetrics.on_predict_start + ~ClassifierMetrics.on_sanity_check_end + ~ClassifierMetrics.on_sanity_check_start + ~ClassifierMetrics.on_save_checkpoint + ~ClassifierMetrics.on_test_batch_end + ~ClassifierMetrics.on_test_batch_start + ~ClassifierMetrics.on_test_end + ~ClassifierMetrics.on_test_epoch_end + ~ClassifierMetrics.on_test_epoch_start + ~ClassifierMetrics.on_test_start + ~ClassifierMetrics.on_train_batch_end + ~ClassifierMetrics.on_train_batch_start + ~ClassifierMetrics.on_train_end + ~ClassifierMetrics.on_train_epoch_end + ~ClassifierMetrics.on_train_epoch_start + ~ClassifierMetrics.on_train_start + ~ClassifierMetrics.on_validation_batch_end + ~ClassifierMetrics.on_validation_batch_start + ~ClassifierMetrics.on_validation_end + ~ClassifierMetrics.on_validation_epoch_end + ~ClassifierMetrics.on_validation_epoch_start + ~ClassifierMetrics.on_validation_start + ~ClassifierMetrics.setup + ~ClassifierMetrics.state_dict + ~ClassifierMetrics.teardown + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ClassifierMetrics.state_key + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.ConvDownSampler.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.ConvDownSampler.rst new file mode 100644 index 0000000..9d47e27 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.ConvDownSampler.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit1d.ConvDownSampler +====================================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: ConvDownSampler + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ConvDownSampler.add_module + ~ConvDownSampler.apply + ~ConvDownSampler.bfloat16 + ~ConvDownSampler.buffers + ~ConvDownSampler.children + ~ConvDownSampler.compile + ~ConvDownSampler.cpu + ~ConvDownSampler.cuda + ~ConvDownSampler.double + ~ConvDownSampler.eval + ~ConvDownSampler.extra_repr + ~ConvDownSampler.float + ~ConvDownSampler.forward + ~ConvDownSampler.get_buffer + ~ConvDownSampler.get_extra_state + ~ConvDownSampler.get_parameter + ~ConvDownSampler.get_submodule + ~ConvDownSampler.half + ~ConvDownSampler.ipu + ~ConvDownSampler.load_state_dict + ~ConvDownSampler.modules + ~ConvDownSampler.named_buffers + ~ConvDownSampler.named_children + ~ConvDownSampler.named_modules + ~ConvDownSampler.named_parameters + ~ConvDownSampler.parameters + ~ConvDownSampler.register_backward_hook + ~ConvDownSampler.register_buffer + ~ConvDownSampler.register_forward_hook + ~ConvDownSampler.register_forward_pre_hook + ~ConvDownSampler.register_full_backward_hook + ~ConvDownSampler.register_full_backward_pre_hook + ~ConvDownSampler.register_load_state_dict_post_hook + ~ConvDownSampler.register_module + ~ConvDownSampler.register_parameter + ~ConvDownSampler.register_state_dict_pre_hook + ~ConvDownSampler.requires_grad_ + ~ConvDownSampler.set_extra_state + ~ConvDownSampler.share_memory + ~ConvDownSampler.state_dict + ~ConvDownSampler.to + ~ConvDownSampler.to_empty + ~ConvDownSampler.train + ~ConvDownSampler.type + ~ConvDownSampler.xpu + ~ConvDownSampler.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ConvDownSampler.T_destination + ~ConvDownSampler.call_super_init + ~ConvDownSampler.dump_patches + ~ConvDownSampler.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.FocalLoss.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.FocalLoss.rst new file mode 100644 index 0000000..a5f682c --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.FocalLoss.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit1d.FocalLoss +================================================ + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: FocalLoss + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~FocalLoss.add_module + ~FocalLoss.apply + ~FocalLoss.bfloat16 + ~FocalLoss.buffers + ~FocalLoss.children + ~FocalLoss.compile + ~FocalLoss.cpu + ~FocalLoss.cuda + ~FocalLoss.double + ~FocalLoss.eval + ~FocalLoss.extra_repr + ~FocalLoss.float + ~FocalLoss.forward + ~FocalLoss.get_buffer + ~FocalLoss.get_extra_state + ~FocalLoss.get_parameter + ~FocalLoss.get_submodule + ~FocalLoss.half + ~FocalLoss.ipu + ~FocalLoss.load_state_dict + ~FocalLoss.modules + ~FocalLoss.named_buffers + ~FocalLoss.named_children + ~FocalLoss.named_modules + ~FocalLoss.named_parameters + ~FocalLoss.parameters + ~FocalLoss.register_backward_hook + ~FocalLoss.register_buffer + ~FocalLoss.register_forward_hook + ~FocalLoss.register_forward_pre_hook + ~FocalLoss.register_full_backward_hook + ~FocalLoss.register_full_backward_pre_hook + ~FocalLoss.register_load_state_dict_post_hook + ~FocalLoss.register_module + ~FocalLoss.register_parameter + ~FocalLoss.register_state_dict_pre_hook + ~FocalLoss.requires_grad_ + ~FocalLoss.set_extra_state + ~FocalLoss.share_memory + ~FocalLoss.state_dict + ~FocalLoss.to + ~FocalLoss.to_empty + ~FocalLoss.train + ~FocalLoss.type + ~FocalLoss.xpu + ~FocalLoss.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~FocalLoss.T_destination + ~FocalLoss.call_super_init + ~FocalLoss.dump_patches + ~FocalLoss.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.PositionalEncoding1D.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.PositionalEncoding1D.rst new file mode 100644 index 0000000..aa7b200 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.PositionalEncoding1D.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit1d.PositionalEncoding1D +=========================================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: PositionalEncoding1D + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~PositionalEncoding1D.add_module + ~PositionalEncoding1D.apply + ~PositionalEncoding1D.bfloat16 + ~PositionalEncoding1D.buffers + ~PositionalEncoding1D.children + ~PositionalEncoding1D.compile + ~PositionalEncoding1D.cpu + ~PositionalEncoding1D.cuda + ~PositionalEncoding1D.double + ~PositionalEncoding1D.eval + ~PositionalEncoding1D.extra_repr + ~PositionalEncoding1D.float + ~PositionalEncoding1D.forward + ~PositionalEncoding1D.get_buffer + ~PositionalEncoding1D.get_extra_state + ~PositionalEncoding1D.get_parameter + ~PositionalEncoding1D.get_submodule + ~PositionalEncoding1D.half + ~PositionalEncoding1D.ipu + ~PositionalEncoding1D.load_state_dict + ~PositionalEncoding1D.modules + ~PositionalEncoding1D.named_buffers + ~PositionalEncoding1D.named_children + ~PositionalEncoding1D.named_modules + ~PositionalEncoding1D.named_parameters + ~PositionalEncoding1D.parameters + ~PositionalEncoding1D.register_backward_hook + ~PositionalEncoding1D.register_buffer + ~PositionalEncoding1D.register_forward_hook + ~PositionalEncoding1D.register_forward_pre_hook + ~PositionalEncoding1D.register_full_backward_hook + ~PositionalEncoding1D.register_full_backward_pre_hook + ~PositionalEncoding1D.register_load_state_dict_post_hook + ~PositionalEncoding1D.register_module + ~PositionalEncoding1D.register_parameter + ~PositionalEncoding1D.register_state_dict_pre_hook + ~PositionalEncoding1D.requires_grad_ + ~PositionalEncoding1D.set_extra_state + ~PositionalEncoding1D.share_memory + ~PositionalEncoding1D.state_dict + ~PositionalEncoding1D.to + ~PositionalEncoding1D.to_empty + ~PositionalEncoding1D.train + ~PositionalEncoding1D.type + ~PositionalEncoding1D.xpu + ~PositionalEncoding1D.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~PositionalEncoding1D.T_destination + ~PositionalEncoding1D.call_super_init + ~PositionalEncoding1D.dump_patches + ~PositionalEncoding1D.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.XCiT1d.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.XCiT1d.rst new file mode 100644 index 0000000..26cbafa --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.XCiT1d.rst @@ -0,0 +1,79 @@ +torchsig.models.iq\_models.xcit.xcit1d.XCiT1d +============================================= + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: XCiT1d + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~XCiT1d.add_module + ~XCiT1d.apply + ~XCiT1d.bfloat16 + ~XCiT1d.buffers + ~XCiT1d.children + ~XCiT1d.compile + ~XCiT1d.cpu + ~XCiT1d.cuda + ~XCiT1d.double + ~XCiT1d.eval + ~XCiT1d.extra_repr + ~XCiT1d.float + ~XCiT1d.forward + ~XCiT1d.get_buffer + ~XCiT1d.get_extra_state + ~XCiT1d.get_parameter + ~XCiT1d.get_submodule + ~XCiT1d.half + ~XCiT1d.ipu + ~XCiT1d.load_state_dict + ~XCiT1d.modules + ~XCiT1d.named_buffers + ~XCiT1d.named_children + ~XCiT1d.named_modules + ~XCiT1d.named_parameters + ~XCiT1d.parameters + ~XCiT1d.register_backward_hook + ~XCiT1d.register_buffer + ~XCiT1d.register_forward_hook + ~XCiT1d.register_forward_pre_hook + ~XCiT1d.register_full_backward_hook + ~XCiT1d.register_full_backward_pre_hook + ~XCiT1d.register_load_state_dict_post_hook + ~XCiT1d.register_module + ~XCiT1d.register_parameter + ~XCiT1d.register_state_dict_pre_hook + ~XCiT1d.requires_grad_ + ~XCiT1d.set_extra_state + ~XCiT1d.share_memory + ~XCiT1d.state_dict + ~XCiT1d.to + ~XCiT1d.to_empty + ~XCiT1d.train + ~XCiT1d.type + ~XCiT1d.xpu + ~XCiT1d.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~XCiT1d.T_destination + ~XCiT1d.call_super_init + ~XCiT1d.dump_patches + ~XCiT1d.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.XCiTClassifier.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.XCiTClassifier.rst new file mode 100644 index 0000000..3658b0b --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.XCiTClassifier.rst @@ -0,0 +1,176 @@ +torchsig.models.iq\_models.xcit.xcit1d.XCiTClassifier +===================================================== + +.. currentmodule:: torchsig.models.iq_models.xcit.xcit1d + +.. autoclass:: XCiTClassifier + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~XCiTClassifier.add_module + ~XCiTClassifier.all_gather + ~XCiTClassifier.apply + ~XCiTClassifier.backward + ~XCiTClassifier.bfloat16 + ~XCiTClassifier.buffers + ~XCiTClassifier.children + ~XCiTClassifier.clip_gradients + ~XCiTClassifier.compile + ~XCiTClassifier.configure_callbacks + ~XCiTClassifier.configure_gradient_clipping + ~XCiTClassifier.configure_model + ~XCiTClassifier.configure_optimizers + ~XCiTClassifier.configure_sharded_model + ~XCiTClassifier.cpu + ~XCiTClassifier.cuda + ~XCiTClassifier.double + ~XCiTClassifier.eval + ~XCiTClassifier.extra_repr + ~XCiTClassifier.float + ~XCiTClassifier.forward + ~XCiTClassifier.freeze + ~XCiTClassifier.get_buffer + ~XCiTClassifier.get_extra_state + ~XCiTClassifier.get_parameter + ~XCiTClassifier.get_submodule + ~XCiTClassifier.half + ~XCiTClassifier.ipu + ~XCiTClassifier.load_from_checkpoint + ~XCiTClassifier.load_state_dict + ~XCiTClassifier.log + ~XCiTClassifier.log_dict + ~XCiTClassifier.lr_scheduler_step + ~XCiTClassifier.lr_schedulers + ~XCiTClassifier.manual_backward + ~XCiTClassifier.modules + ~XCiTClassifier.named_buffers + ~XCiTClassifier.named_children + ~XCiTClassifier.named_modules + ~XCiTClassifier.named_parameters + ~XCiTClassifier.on_after_backward + ~XCiTClassifier.on_after_batch_transfer + ~XCiTClassifier.on_before_backward + ~XCiTClassifier.on_before_batch_transfer + ~XCiTClassifier.on_before_optimizer_step + ~XCiTClassifier.on_before_zero_grad + ~XCiTClassifier.on_fit_end + ~XCiTClassifier.on_fit_start + ~XCiTClassifier.on_load_checkpoint + ~XCiTClassifier.on_predict_batch_end + ~XCiTClassifier.on_predict_batch_start + ~XCiTClassifier.on_predict_end + ~XCiTClassifier.on_predict_epoch_end + ~XCiTClassifier.on_predict_epoch_start + ~XCiTClassifier.on_predict_model_eval + ~XCiTClassifier.on_predict_start + ~XCiTClassifier.on_save_checkpoint + ~XCiTClassifier.on_test_batch_end + ~XCiTClassifier.on_test_batch_start + ~XCiTClassifier.on_test_end + ~XCiTClassifier.on_test_epoch_end + ~XCiTClassifier.on_test_epoch_start + ~XCiTClassifier.on_test_model_eval + ~XCiTClassifier.on_test_model_train + ~XCiTClassifier.on_test_start + ~XCiTClassifier.on_train_batch_end + ~XCiTClassifier.on_train_batch_start + ~XCiTClassifier.on_train_end + ~XCiTClassifier.on_train_epoch_end + ~XCiTClassifier.on_train_epoch_start + ~XCiTClassifier.on_train_start + ~XCiTClassifier.on_validation_batch_end + ~XCiTClassifier.on_validation_batch_start + ~XCiTClassifier.on_validation_end + ~XCiTClassifier.on_validation_epoch_end + ~XCiTClassifier.on_validation_epoch_start + ~XCiTClassifier.on_validation_model_eval + ~XCiTClassifier.on_validation_model_train + ~XCiTClassifier.on_validation_model_zero_grad + ~XCiTClassifier.on_validation_start + ~XCiTClassifier.optimizer_step + ~XCiTClassifier.optimizer_zero_grad + ~XCiTClassifier.optimizers + ~XCiTClassifier.parameters + ~XCiTClassifier.predict_dataloader + ~XCiTClassifier.predict_step + ~XCiTClassifier.prepare_data + ~XCiTClassifier.print + ~XCiTClassifier.register_backward_hook + ~XCiTClassifier.register_buffer + ~XCiTClassifier.register_forward_hook + ~XCiTClassifier.register_forward_pre_hook + ~XCiTClassifier.register_full_backward_hook + ~XCiTClassifier.register_full_backward_pre_hook + ~XCiTClassifier.register_load_state_dict_post_hook + ~XCiTClassifier.register_module + ~XCiTClassifier.register_parameter + ~XCiTClassifier.register_state_dict_pre_hook + ~XCiTClassifier.requires_grad_ + ~XCiTClassifier.save_hyperparameters + ~XCiTClassifier.set_extra_state + ~XCiTClassifier.setup + ~XCiTClassifier.share_memory + ~XCiTClassifier.state_dict + ~XCiTClassifier.teardown + ~XCiTClassifier.test_dataloader + ~XCiTClassifier.test_step + ~XCiTClassifier.to + ~XCiTClassifier.to_empty + ~XCiTClassifier.to_onnx + ~XCiTClassifier.to_torchscript + ~XCiTClassifier.toggle_optimizer + ~XCiTClassifier.train + ~XCiTClassifier.train_dataloader + ~XCiTClassifier.training_step + ~XCiTClassifier.transfer_batch_to_device + ~XCiTClassifier.type + ~XCiTClassifier.unfreeze + ~XCiTClassifier.untoggle_optimizer + ~XCiTClassifier.val_dataloader + ~XCiTClassifier.validation_step + ~XCiTClassifier.xpu + ~XCiTClassifier.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~XCiTClassifier.CHECKPOINT_HYPER_PARAMS_KEY + ~XCiTClassifier.CHECKPOINT_HYPER_PARAMS_NAME + ~XCiTClassifier.CHECKPOINT_HYPER_PARAMS_TYPE + ~XCiTClassifier.T_destination + ~XCiTClassifier.automatic_optimization + ~XCiTClassifier.call_super_init + ~XCiTClassifier.current_epoch + ~XCiTClassifier.device + ~XCiTClassifier.device_mesh + ~XCiTClassifier.dtype + ~XCiTClassifier.dump_patches + ~XCiTClassifier.example_input_array + ~XCiTClassifier.fabric + ~XCiTClassifier.global_rank + ~XCiTClassifier.global_step + ~XCiTClassifier.hparams + ~XCiTClassifier.hparams_initial + ~XCiTClassifier.local_rank + ~XCiTClassifier.logger + ~XCiTClassifier.loggers + ~XCiTClassifier.on_gpu + ~XCiTClassifier.strict_loading + ~XCiTClassifier.trainer + ~XCiTClassifier.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.rst b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.rst new file mode 100644 index 0000000..7e6c593 --- /dev/null +++ b/docs/_autosummary/torchsig.models.iq_models.xcit.xcit1d.rst @@ -0,0 +1,38 @@ +torchsig.models.iq\_models.xcit.xcit1d +====================================== + +.. automodule:: torchsig.models.iq_models.xcit.xcit1d + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + Chunker + ClassifierMetrics + ConvDownSampler + FocalLoss + PositionalEncoding1D + XCiT1d + XCiTClassifier + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.rst b/docs/_autosummary/torchsig.models.rst new file mode 100644 index 0000000..ee0b4e2 --- /dev/null +++ b/docs/_autosummary/torchsig.models.rst @@ -0,0 +1,31 @@ +torchsig.models +=============== + +.. automodule:: torchsig.models + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + iq_models + spectrogram_models + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.HungarianMatcher.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.HungarianMatcher.rst new file mode 100644 index 0000000..a3be500 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.HungarianMatcher.rst @@ -0,0 +1,80 @@ +torchsig.models.spectrogram\_models.detr.criterion.HungarianMatcher +=================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autoclass:: HungarianMatcher + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~HungarianMatcher.add_module + ~HungarianMatcher.apply + ~HungarianMatcher.bfloat16 + ~HungarianMatcher.buffers + ~HungarianMatcher.children + ~HungarianMatcher.compile + ~HungarianMatcher.cpu + ~HungarianMatcher.cuda + ~HungarianMatcher.double + ~HungarianMatcher.eval + ~HungarianMatcher.extra_repr + ~HungarianMatcher.float + ~HungarianMatcher.forward + ~HungarianMatcher.get_buffer + ~HungarianMatcher.get_extra_state + ~HungarianMatcher.get_parameter + ~HungarianMatcher.get_submodule + ~HungarianMatcher.half + ~HungarianMatcher.ipu + ~HungarianMatcher.load_state_dict + ~HungarianMatcher.memory_efficient_forward + ~HungarianMatcher.modules + ~HungarianMatcher.named_buffers + ~HungarianMatcher.named_children + ~HungarianMatcher.named_modules + ~HungarianMatcher.named_parameters + ~HungarianMatcher.parameters + ~HungarianMatcher.register_backward_hook + ~HungarianMatcher.register_buffer + ~HungarianMatcher.register_forward_hook + ~HungarianMatcher.register_forward_pre_hook + ~HungarianMatcher.register_full_backward_hook + ~HungarianMatcher.register_full_backward_pre_hook + ~HungarianMatcher.register_load_state_dict_post_hook + ~HungarianMatcher.register_module + ~HungarianMatcher.register_parameter + ~HungarianMatcher.register_state_dict_pre_hook + ~HungarianMatcher.requires_grad_ + ~HungarianMatcher.set_extra_state + ~HungarianMatcher.share_memory + ~HungarianMatcher.state_dict + ~HungarianMatcher.to + ~HungarianMatcher.to_empty + ~HungarianMatcher.train + ~HungarianMatcher.type + ~HungarianMatcher.xpu + ~HungarianMatcher.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~HungarianMatcher.T_destination + ~HungarianMatcher.call_super_init + ~HungarianMatcher.dump_patches + ~HungarianMatcher.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.NestedTensor.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.NestedTensor.rst new file mode 100644 index 0000000..fedc373 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.NestedTensor.rst @@ -0,0 +1,26 @@ +torchsig.models.spectrogram\_models.detr.criterion.NestedTensor +=============================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autoclass:: NestedTensor + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~NestedTensor.decompose + ~NestedTensor.to + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.SetCriterion.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.SetCriterion.rst new file mode 100644 index 0000000..d7194fd --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.SetCriterion.rst @@ -0,0 +1,82 @@ +torchsig.models.spectrogram\_models.detr.criterion.SetCriterion +=============================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autoclass:: SetCriterion + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SetCriterion.add_module + ~SetCriterion.apply + ~SetCriterion.bfloat16 + ~SetCriterion.buffers + ~SetCriterion.children + ~SetCriterion.compile + ~SetCriterion.cpu + ~SetCriterion.cuda + ~SetCriterion.double + ~SetCriterion.eval + ~SetCriterion.extra_repr + ~SetCriterion.float + ~SetCriterion.forward + ~SetCriterion.get_buffer + ~SetCriterion.get_extra_state + ~SetCriterion.get_loss + ~SetCriterion.get_parameter + ~SetCriterion.get_submodule + ~SetCriterion.half + ~SetCriterion.ipu + ~SetCriterion.load_state_dict + ~SetCriterion.loss_labels + ~SetCriterion.loss_masks + ~SetCriterion.modules + ~SetCriterion.named_buffers + ~SetCriterion.named_children + ~SetCriterion.named_modules + ~SetCriterion.named_parameters + ~SetCriterion.parameters + ~SetCriterion.register_backward_hook + ~SetCriterion.register_buffer + ~SetCriterion.register_forward_hook + ~SetCriterion.register_forward_pre_hook + ~SetCriterion.register_full_backward_hook + ~SetCriterion.register_full_backward_pre_hook + ~SetCriterion.register_load_state_dict_post_hook + ~SetCriterion.register_module + ~SetCriterion.register_parameter + ~SetCriterion.register_state_dict_pre_hook + ~SetCriterion.requires_grad_ + ~SetCriterion.set_extra_state + ~SetCriterion.share_memory + ~SetCriterion.state_dict + ~SetCriterion.to + ~SetCriterion.to_empty + ~SetCriterion.train + ~SetCriterion.type + ~SetCriterion.xpu + ~SetCriterion.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~SetCriterion.T_destination + ~SetCriterion.call_super_init + ~SetCriterion.dump_patches + ~SetCriterion.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.batch_dice_loss.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.batch_dice_loss.rst new file mode 100644 index 0000000..2c54a33 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.batch_dice_loss.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.batch\_dice\_loss +==================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: batch_dice_loss \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.batch_sigmoid_ce_loss.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.batch_sigmoid_ce_loss.rst new file mode 100644 index 0000000..579a923 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.batch_sigmoid_ce_loss.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.batch\_sigmoid\_ce\_loss +=========================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: batch_sigmoid_ce_loss \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.calculate_uncertainty.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.calculate_uncertainty.rst new file mode 100644 index 0000000..cf3dd12 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.calculate_uncertainty.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.calculate\_uncertainty +========================================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: calculate_uncertainty \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.dice_loss.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.dice_loss.rst new file mode 100644 index 0000000..54620b3 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.dice_loss.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.dice\_loss +============================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: dice_loss \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.get_uncertain_point_coords_with_randomness.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.get_uncertain_point_coords_with_randomness.rst new file mode 100644 index 0000000..f3c2b2d --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.get_uncertain_point_coords_with_randomness.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.get\_uncertain\_point\_coords\_with\_randomness +================================================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: get_uncertain_point_coords_with_randomness \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.get_world_size.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.get_world_size.rst new file mode 100644 index 0000000..2fadc1f --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.get_world_size.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.get\_world\_size +=================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: get_world_size \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.is_dist_avail_and_initialized.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.is_dist_avail_and_initialized.rst new file mode 100644 index 0000000..dd57b0d --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.is_dist_avail_and_initialized.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.is\_dist\_avail\_and\_initialized +==================================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: is_dist_avail_and_initialized \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.nested_tensor_from_tensor_list.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.nested_tensor_from_tensor_list.rst new file mode 100644 index 0000000..d7a5566 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.nested_tensor_from_tensor_list.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.nested\_tensor\_from\_tensor\_list +===================================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: nested_tensor_from_tensor_list \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.point_sample.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.point_sample.rst new file mode 100644 index 0000000..9c078a6 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.point_sample.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.point\_sample +================================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: point_sample \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.rst new file mode 100644 index 0000000..76e8c75 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.rst @@ -0,0 +1,51 @@ +torchsig.models.spectrogram\_models.detr.criterion +================================================== + +.. automodule:: torchsig.models.spectrogram_models.detr.criterion + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + batch_dice_loss + batch_sigmoid_ce_loss + calculate_uncertainty + dice_loss + get_uncertain_point_coords_with_randomness + get_world_size + is_dist_avail_and_initialized + nested_tensor_from_tensor_list + point_sample + sigmoid_ce_loss + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + HungarianMatcher + NestedTensor + SetCriterion + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.sigmoid_ce_loss.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.sigmoid_ce_loss.rst new file mode 100644 index 0000000..a5bf33a --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.criterion.sigmoid_ce_loss.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.criterion.sigmoid\_ce\_loss +==================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.criterion + +.. autofunction:: sigmoid_ce_loss \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b0_nano.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b0_nano.rst new file mode 100644 index 0000000..b1dcc21 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b0_nano.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.detr.detr\_b0\_nano +============================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr + +.. autofunction:: detr_b0_nano \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b0_nano_mod_family.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b0_nano_mod_family.rst new file mode 100644 index 0000000..ca1d143 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b0_nano_mod_family.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.detr.detr\_b0\_nano\_mod\_family +========================================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr + +.. autofunction:: detr_b0_nano_mod_family \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b2_nano.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b2_nano.rst new file mode 100644 index 0000000..9486582 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b2_nano.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.detr.detr\_b2\_nano +============================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr + +.. autofunction:: detr_b2_nano \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b2_nano_mod_family.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b2_nano_mod_family.rst new file mode 100644 index 0000000..48e16ab --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b2_nano_mod_family.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.detr.detr\_b2\_nano\_mod\_family +========================================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr + +.. autofunction:: detr_b2_nano_mod_family \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b4_nano.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b4_nano.rst new file mode 100644 index 0000000..7d1f90e --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b4_nano.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.detr.detr\_b4\_nano +============================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr + +.. autofunction:: detr_b4_nano \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b4_nano_mod_family.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b4_nano_mod_family.rst new file mode 100644 index 0000000..e3334f7 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.detr_b4_nano_mod_family.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.detr.detr\_b4\_nano\_mod\_family +========================================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr + +.. autofunction:: detr_b4_nano_mod_family \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.rst new file mode 100644 index 0000000..38517cc --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.detr.rst @@ -0,0 +1,36 @@ +torchsig.models.spectrogram\_models.detr.detr +============================================= + +.. automodule:: torchsig.models.spectrogram_models.detr.detr + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + detr_b0_nano + detr_b0_nano_mod_family + detr_b2_nano + detr_b2_nano_mod_family + detr_b4_nano + detr_b4_nano_mod_family + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.Chunker.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.Chunker.rst new file mode 100644 index 0000000..7976191 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.Chunker.rst @@ -0,0 +1,79 @@ +torchsig.models.spectrogram\_models.detr.modules.Chunker +======================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: Chunker + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Chunker.add_module + ~Chunker.apply + ~Chunker.bfloat16 + ~Chunker.buffers + ~Chunker.children + ~Chunker.compile + ~Chunker.cpu + ~Chunker.cuda + ~Chunker.double + ~Chunker.eval + ~Chunker.extra_repr + ~Chunker.float + ~Chunker.forward + ~Chunker.get_buffer + ~Chunker.get_extra_state + ~Chunker.get_parameter + ~Chunker.get_submodule + ~Chunker.half + ~Chunker.ipu + ~Chunker.load_state_dict + ~Chunker.modules + ~Chunker.named_buffers + ~Chunker.named_children + ~Chunker.named_modules + ~Chunker.named_parameters + ~Chunker.parameters + ~Chunker.register_backward_hook + ~Chunker.register_buffer + ~Chunker.register_forward_hook + ~Chunker.register_forward_pre_hook + ~Chunker.register_full_backward_hook + ~Chunker.register_full_backward_pre_hook + ~Chunker.register_load_state_dict_post_hook + ~Chunker.register_module + ~Chunker.register_parameter + ~Chunker.register_state_dict_pre_hook + ~Chunker.requires_grad_ + ~Chunker.set_extra_state + ~Chunker.share_memory + ~Chunker.state_dict + ~Chunker.to + ~Chunker.to_empty + ~Chunker.train + ~Chunker.type + ~Chunker.xpu + ~Chunker.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Chunker.T_destination + ~Chunker.call_super_init + ~Chunker.dump_patches + ~Chunker.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.ConvDownSampler.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.ConvDownSampler.rst new file mode 100644 index 0000000..dbd5345 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.ConvDownSampler.rst @@ -0,0 +1,79 @@ +torchsig.models.spectrogram\_models.detr.modules.ConvDownSampler +================================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: ConvDownSampler + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ConvDownSampler.add_module + ~ConvDownSampler.apply + ~ConvDownSampler.bfloat16 + ~ConvDownSampler.buffers + ~ConvDownSampler.children + ~ConvDownSampler.compile + ~ConvDownSampler.cpu + ~ConvDownSampler.cuda + ~ConvDownSampler.double + ~ConvDownSampler.eval + ~ConvDownSampler.extra_repr + ~ConvDownSampler.float + ~ConvDownSampler.forward + ~ConvDownSampler.get_buffer + ~ConvDownSampler.get_extra_state + ~ConvDownSampler.get_parameter + ~ConvDownSampler.get_submodule + ~ConvDownSampler.half + ~ConvDownSampler.ipu + ~ConvDownSampler.load_state_dict + ~ConvDownSampler.modules + ~ConvDownSampler.named_buffers + ~ConvDownSampler.named_children + ~ConvDownSampler.named_modules + ~ConvDownSampler.named_parameters + ~ConvDownSampler.parameters + ~ConvDownSampler.register_backward_hook + ~ConvDownSampler.register_buffer + ~ConvDownSampler.register_forward_hook + ~ConvDownSampler.register_forward_pre_hook + ~ConvDownSampler.register_full_backward_hook + ~ConvDownSampler.register_full_backward_pre_hook + ~ConvDownSampler.register_load_state_dict_post_hook + ~ConvDownSampler.register_module + ~ConvDownSampler.register_parameter + ~ConvDownSampler.register_state_dict_pre_hook + ~ConvDownSampler.requires_grad_ + ~ConvDownSampler.set_extra_state + ~ConvDownSampler.share_memory + ~ConvDownSampler.state_dict + ~ConvDownSampler.to + ~ConvDownSampler.to_empty + ~ConvDownSampler.train + ~ConvDownSampler.type + ~ConvDownSampler.xpu + ~ConvDownSampler.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ConvDownSampler.T_destination + ~ConvDownSampler.call_super_init + ~ConvDownSampler.dump_patches + ~ConvDownSampler.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.DETRModel.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.DETRModel.rst new file mode 100644 index 0000000..7bb7141 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.DETRModel.rst @@ -0,0 +1,79 @@ +torchsig.models.spectrogram\_models.detr.modules.DETRModel +========================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: DETRModel + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DETRModel.add_module + ~DETRModel.apply + ~DETRModel.bfloat16 + ~DETRModel.buffers + ~DETRModel.children + ~DETRModel.compile + ~DETRModel.cpu + ~DETRModel.cuda + ~DETRModel.double + ~DETRModel.eval + ~DETRModel.extra_repr + ~DETRModel.float + ~DETRModel.forward + ~DETRModel.get_buffer + ~DETRModel.get_extra_state + ~DETRModel.get_parameter + ~DETRModel.get_submodule + ~DETRModel.half + ~DETRModel.ipu + ~DETRModel.load_state_dict + ~DETRModel.modules + ~DETRModel.named_buffers + ~DETRModel.named_children + ~DETRModel.named_modules + ~DETRModel.named_parameters + ~DETRModel.parameters + ~DETRModel.register_backward_hook + ~DETRModel.register_buffer + ~DETRModel.register_forward_hook + ~DETRModel.register_forward_pre_hook + ~DETRModel.register_full_backward_hook + ~DETRModel.register_full_backward_pre_hook + ~DETRModel.register_load_state_dict_post_hook + ~DETRModel.register_module + ~DETRModel.register_parameter + ~DETRModel.register_state_dict_pre_hook + ~DETRModel.requires_grad_ + ~DETRModel.set_extra_state + ~DETRModel.share_memory + ~DETRModel.state_dict + ~DETRModel.to + ~DETRModel.to_empty + ~DETRModel.train + ~DETRModel.type + ~DETRModel.xpu + ~DETRModel.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~DETRModel.T_destination + ~DETRModel.call_super_init + ~DETRModel.dump_patches + ~DETRModel.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.HungarianMatcher.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.HungarianMatcher.rst new file mode 100644 index 0000000..2f043e1 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.HungarianMatcher.rst @@ -0,0 +1,79 @@ +torchsig.models.spectrogram\_models.detr.modules.HungarianMatcher +================================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: HungarianMatcher + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~HungarianMatcher.add_module + ~HungarianMatcher.apply + ~HungarianMatcher.bfloat16 + ~HungarianMatcher.buffers + ~HungarianMatcher.children + ~HungarianMatcher.compile + ~HungarianMatcher.cpu + ~HungarianMatcher.cuda + ~HungarianMatcher.double + ~HungarianMatcher.eval + ~HungarianMatcher.extra_repr + ~HungarianMatcher.float + ~HungarianMatcher.forward + ~HungarianMatcher.get_buffer + ~HungarianMatcher.get_extra_state + ~HungarianMatcher.get_parameter + ~HungarianMatcher.get_submodule + ~HungarianMatcher.half + ~HungarianMatcher.ipu + ~HungarianMatcher.load_state_dict + ~HungarianMatcher.modules + ~HungarianMatcher.named_buffers + ~HungarianMatcher.named_children + ~HungarianMatcher.named_modules + ~HungarianMatcher.named_parameters + ~HungarianMatcher.parameters + ~HungarianMatcher.register_backward_hook + ~HungarianMatcher.register_buffer + ~HungarianMatcher.register_forward_hook + ~HungarianMatcher.register_forward_pre_hook + ~HungarianMatcher.register_full_backward_hook + ~HungarianMatcher.register_full_backward_pre_hook + ~HungarianMatcher.register_load_state_dict_post_hook + ~HungarianMatcher.register_module + ~HungarianMatcher.register_parameter + ~HungarianMatcher.register_state_dict_pre_hook + ~HungarianMatcher.requires_grad_ + ~HungarianMatcher.set_extra_state + ~HungarianMatcher.share_memory + ~HungarianMatcher.state_dict + ~HungarianMatcher.to + ~HungarianMatcher.to_empty + ~HungarianMatcher.train + ~HungarianMatcher.type + ~HungarianMatcher.xpu + ~HungarianMatcher.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~HungarianMatcher.T_destination + ~HungarianMatcher.call_super_init + ~HungarianMatcher.dump_patches + ~HungarianMatcher.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.MLP.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.MLP.rst new file mode 100644 index 0000000..0423caa --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.MLP.rst @@ -0,0 +1,79 @@ +torchsig.models.spectrogram\_models.detr.modules.MLP +==================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: MLP + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~MLP.add_module + ~MLP.apply + ~MLP.bfloat16 + ~MLP.buffers + ~MLP.children + ~MLP.compile + ~MLP.cpu + ~MLP.cuda + ~MLP.double + ~MLP.eval + ~MLP.extra_repr + ~MLP.float + ~MLP.forward + ~MLP.get_buffer + ~MLP.get_extra_state + ~MLP.get_parameter + ~MLP.get_submodule + ~MLP.half + ~MLP.ipu + ~MLP.load_state_dict + ~MLP.modules + ~MLP.named_buffers + ~MLP.named_children + ~MLP.named_modules + ~MLP.named_parameters + ~MLP.parameters + ~MLP.register_backward_hook + ~MLP.register_buffer + ~MLP.register_forward_hook + ~MLP.register_forward_pre_hook + ~MLP.register_full_backward_hook + ~MLP.register_full_backward_pre_hook + ~MLP.register_load_state_dict_post_hook + ~MLP.register_module + ~MLP.register_parameter + ~MLP.register_state_dict_pre_hook + ~MLP.requires_grad_ + ~MLP.set_extra_state + ~MLP.share_memory + ~MLP.state_dict + ~MLP.to + ~MLP.to_empty + ~MLP.train + ~MLP.type + ~MLP.xpu + ~MLP.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~MLP.T_destination + ~MLP.call_super_init + ~MLP.dump_patches + ~MLP.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.SetCriterion.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.SetCriterion.rst new file mode 100644 index 0000000..bd66c24 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.SetCriterion.rst @@ -0,0 +1,84 @@ +torchsig.models.spectrogram\_models.detr.modules.SetCriterion +============================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: SetCriterion + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SetCriterion.add_module + ~SetCriterion.apply + ~SetCriterion.bfloat16 + ~SetCriterion.buffers + ~SetCriterion.children + ~SetCriterion.compile + ~SetCriterion.cpu + ~SetCriterion.cuda + ~SetCriterion.double + ~SetCriterion.eval + ~SetCriterion.extra_repr + ~SetCriterion.float + ~SetCriterion.forward + ~SetCriterion.get_buffer + ~SetCriterion.get_extra_state + ~SetCriterion.get_loss + ~SetCriterion.get_parameter + ~SetCriterion.get_submodule + ~SetCriterion.half + ~SetCriterion.ipu + ~SetCriterion.load_state_dict + ~SetCriterion.loss_boxes + ~SetCriterion.loss_cardinality + ~SetCriterion.loss_labels + ~SetCriterion.loss_masks + ~SetCriterion.modules + ~SetCriterion.named_buffers + ~SetCriterion.named_children + ~SetCriterion.named_modules + ~SetCriterion.named_parameters + ~SetCriterion.parameters + ~SetCriterion.register_backward_hook + ~SetCriterion.register_buffer + ~SetCriterion.register_forward_hook + ~SetCriterion.register_forward_pre_hook + ~SetCriterion.register_full_backward_hook + ~SetCriterion.register_full_backward_pre_hook + ~SetCriterion.register_load_state_dict_post_hook + ~SetCriterion.register_module + ~SetCriterion.register_parameter + ~SetCriterion.register_state_dict_pre_hook + ~SetCriterion.requires_grad_ + ~SetCriterion.set_extra_state + ~SetCriterion.share_memory + ~SetCriterion.state_dict + ~SetCriterion.to + ~SetCriterion.to_empty + ~SetCriterion.train + ~SetCriterion.type + ~SetCriterion.xpu + ~SetCriterion.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~SetCriterion.T_destination + ~SetCriterion.call_super_init + ~SetCriterion.dump_patches + ~SetCriterion.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.XCiT.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.XCiT.rst new file mode 100644 index 0000000..1d48f9d --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.XCiT.rst @@ -0,0 +1,79 @@ +torchsig.models.spectrogram\_models.detr.modules.XCiT +===================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autoclass:: XCiT + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~XCiT.add_module + ~XCiT.apply + ~XCiT.bfloat16 + ~XCiT.buffers + ~XCiT.children + ~XCiT.compile + ~XCiT.cpu + ~XCiT.cuda + ~XCiT.double + ~XCiT.eval + ~XCiT.extra_repr + ~XCiT.float + ~XCiT.forward + ~XCiT.get_buffer + ~XCiT.get_extra_state + ~XCiT.get_parameter + ~XCiT.get_submodule + ~XCiT.half + ~XCiT.ipu + ~XCiT.load_state_dict + ~XCiT.modules + ~XCiT.named_buffers + ~XCiT.named_children + ~XCiT.named_modules + ~XCiT.named_parameters + ~XCiT.parameters + ~XCiT.register_backward_hook + ~XCiT.register_buffer + ~XCiT.register_forward_hook + ~XCiT.register_forward_pre_hook + ~XCiT.register_full_backward_hook + ~XCiT.register_full_backward_pre_hook + ~XCiT.register_load_state_dict_post_hook + ~XCiT.register_module + ~XCiT.register_parameter + ~XCiT.register_state_dict_pre_hook + ~XCiT.requires_grad_ + ~XCiT.set_extra_state + ~XCiT.share_memory + ~XCiT.state_dict + ~XCiT.to + ~XCiT.to_empty + ~XCiT.train + ~XCiT.type + ~XCiT.xpu + ~XCiT.zero_grad + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~XCiT.T_destination + ~XCiT.call_super_init + ~XCiT.dump_patches + ~XCiT.training + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.create_detr.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.create_detr.rst new file mode 100644 index 0000000..5215b26 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.create_detr.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.modules.create\_detr +============================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.modules + +.. autofunction:: create_detr \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.rst new file mode 100644 index 0000000..8daaa41 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.modules.rst @@ -0,0 +1,46 @@ +torchsig.models.spectrogram\_models.detr.modules +================================================ + +.. automodule:: torchsig.models.spectrogram_models.detr.modules + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + create_detr + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + Chunker + ConvDownSampler + DETRModel + HungarianMatcher + MLP + SetCriterion + XCiT + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.rst new file mode 100644 index 0000000..ffca79d --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.rst @@ -0,0 +1,33 @@ +torchsig.models.spectrogram\_models.detr +======================================== + +.. automodule:: torchsig.models.spectrogram_models.detr + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + criterion + detr + modules + utils + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.accuracy.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.accuracy.rst new file mode 100644 index 0000000..5e53516 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.accuracy.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.accuracy +======================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: accuracy \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.box_cxcywh_to_xyxy.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.box_cxcywh_to_xyxy.rst new file mode 100644 index 0000000..c97e2a8 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.box_cxcywh_to_xyxy.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.box\_cxcywh\_to\_xyxy +==================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: box_cxcywh_to_xyxy \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.box_iou.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.box_iou.rst new file mode 100644 index 0000000..87653df --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.box_iou.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.box\_iou +======================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: box_iou \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.drop_classifier.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.drop_classifier.rst new file mode 100644 index 0000000..f5d1052 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.drop_classifier.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.drop\_classifier +=============================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: drop_classifier \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.find_output_features.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.find_output_features.rst new file mode 100644 index 0000000..df9d5a8 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.find_output_features.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.find\_output\_features +===================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: find_output_features \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.format_preds.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.format_preds.rst new file mode 100644 index 0000000..ebfec05 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.format_preds.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.format\_preds +============================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: format_preds \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.format_targets.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.format_targets.rst new file mode 100644 index 0000000..5d1b1ac --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.format_targets.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.format\_targets +============================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: format_targets \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.generalized_box_iou.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.generalized_box_iou.rst new file mode 100644 index 0000000..6fc67dc --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.generalized_box_iou.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.generalized\_box\_iou +==================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: generalized_box_iou \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.get_world_size.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.get_world_size.rst new file mode 100644 index 0000000..aa18083 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.get_world_size.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.get\_world\_size +=============================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: get_world_size \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.is_dist_avail_and_initialized.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.is_dist_avail_and_initialized.rst new file mode 100644 index 0000000..d7a1c6a --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.is_dist_avail_and_initialized.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.is\_dist\_avail\_and\_initialized +================================================================================ + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: is_dist_avail_and_initialized \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.rst new file mode 100644 index 0000000..231d30f --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.rst @@ -0,0 +1,41 @@ +torchsig.models.spectrogram\_models.detr.utils +============================================== + +.. automodule:: torchsig.models.spectrogram_models.detr.utils + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + accuracy + box_cxcywh_to_xyxy + box_iou + drop_classifier + find_output_features + format_preds + format_targets + generalized_box_iou + get_world_size + is_dist_avail_and_initialized + xcit_name_to_timm_name + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.xcit_name_to_timm_name.rst b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.xcit_name_to_timm_name.rst new file mode 100644 index 0000000..c67ac4c --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.detr.utils.xcit_name_to_timm_name.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.detr.utils.xcit\_name\_to\_timm\_name +========================================================================= + +.. currentmodule:: torchsig.models.spectrogram_models.detr.utils + +.. autofunction:: xcit_name_to_timm_name \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.efficientnet2d.EfficientNet2d.rst b/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.efficientnet2d.EfficientNet2d.rst new file mode 100644 index 0000000..9904452 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.efficientnet2d.EfficientNet2d.rst @@ -0,0 +1,6 @@ +torchsig.models.spectrogram\_models.efficientnet.efficientnet2d.EfficientNet2d +============================================================================== + +.. currentmodule:: torchsig.models.spectrogram_models.efficientnet.efficientnet2d + +.. autofunction:: EfficientNet2d \ No newline at end of file diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.efficientnet2d.rst b/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.efficientnet2d.rst new file mode 100644 index 0000000..7dfd401 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.efficientnet2d.rst @@ -0,0 +1,31 @@ +torchsig.models.spectrogram\_models.efficientnet.efficientnet2d +=============================================================== + +.. automodule:: torchsig.models.spectrogram_models.efficientnet.efficientnet2d + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + EfficientNet2d + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.rst b/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.rst new file mode 100644 index 0000000..2699b4b --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.efficientnet.rst @@ -0,0 +1,30 @@ +torchsig.models.spectrogram\_models.efficientnet +================================================ + +.. automodule:: torchsig.models.spectrogram_models.efficientnet + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + efficientnet2d + diff --git a/docs/_autosummary/torchsig.models.spectrogram_models.rst b/docs/_autosummary/torchsig.models.spectrogram_models.rst new file mode 100644 index 0000000..7d187f4 --- /dev/null +++ b/docs/_autosummary/torchsig.models.spectrogram_models.rst @@ -0,0 +1,31 @@ +torchsig.models.spectrogram\_models +=================================== + +.. automodule:: torchsig.models.spectrogram_models + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + detr + efficientnet + diff --git a/docs/_autosummary/torchsig.rst b/docs/_autosummary/torchsig.rst new file mode 100644 index 0000000..e36f74a --- /dev/null +++ b/docs/_autosummary/torchsig.rst @@ -0,0 +1,34 @@ +torchsig +======== + +.. automodule:: torchsig + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + datasets + image_datasets + models + transforms + utils + diff --git a/docs/_autosummary/torchsig.transforms.functional.add_slope.rst b/docs/_autosummary/torchsig.transforms.functional.add_slope.rst new file mode 100644 index 0000000..a345563 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.add_slope.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.add\_slope +========================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: add_slope \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.agc.rst b/docs/_autosummary/torchsig.transforms.functional.agc.rst new file mode 100644 index 0000000..2b94ee4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.agc.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.agc +================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: agc \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.amplitude_reversal.rst b/docs/_autosummary/torchsig.transforms.functional.amplitude_reversal.rst new file mode 100644 index 0000000..499171e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.amplitude_reversal.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.amplitude\_reversal +================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: amplitude_reversal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.awgn.rst b/docs/_autosummary/torchsig.transforms.functional.awgn.rst new file mode 100644 index 0000000..9abde54 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.awgn.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.awgn +=================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: awgn \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.channel_swap.rst b/docs/_autosummary/torchsig.transforms.functional.channel_swap.rst new file mode 100644 index 0000000..ffd94d3 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.channel_swap.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.channel\_swap +============================================ + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: channel_swap \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.clip.rst b/docs/_autosummary/torchsig.transforms.functional.clip.rst new file mode 100644 index 0000000..1eb246e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.clip.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.clip +=================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: clip \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.complex_magnitude.rst b/docs/_autosummary/torchsig.transforms.functional.complex_magnitude.rst new file mode 100644 index 0000000..b120c21 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.complex_magnitude.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.complex\_magnitude +================================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: complex_magnitude \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.complex_to_2d.rst b/docs/_autosummary/torchsig.transforms.functional.complex_to_2d.rst new file mode 100644 index 0000000..a7c304a --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.complex_to_2d.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.complex\_to\_2d +============================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: complex_to_2d \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.continuous_wavelet_transform.rst b/docs/_autosummary/torchsig.transforms.functional.continuous_wavelet_transform.rst new file mode 100644 index 0000000..214a91b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.continuous_wavelet_transform.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.continuous\_wavelet\_transform +============================================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: continuous_wavelet_transform \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.cut_out.rst b/docs/_autosummary/torchsig.transforms.functional.cut_out.rst new file mode 100644 index 0000000..d233486 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.cut_out.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.cut\_out +======================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: cut_out \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.discrete_fourier_transform.rst b/docs/_autosummary/torchsig.transforms.functional.discrete_fourier_transform.rst new file mode 100644 index 0000000..4909d3c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.discrete_fourier_transform.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.discrete\_fourier\_transform +=========================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: discrete_fourier_transform \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.drop_samples.rst b/docs/_autosummary/torchsig.transforms.functional.drop_samples.rst new file mode 100644 index 0000000..9b9a38a --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.drop_samples.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.drop\_samples +============================================ + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: drop_samples \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.drop_spec_samples.rst b/docs/_autosummary/torchsig.transforms.functional.drop_spec_samples.rst new file mode 100644 index 0000000..9fa3a14 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.drop_spec_samples.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.drop\_spec\_samples +================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: drop_spec_samples \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.fractional_shift.rst b/docs/_autosummary/torchsig.transforms.functional.fractional_shift.rst new file mode 100644 index 0000000..caeb007 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.fractional_shift.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.fractional\_shift +================================================ + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: fractional_shift \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.freq_shift.rst b/docs/_autosummary/torchsig.transforms.functional.freq_shift.rst new file mode 100644 index 0000000..5baf40c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.freq_shift.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.freq\_shift +========================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: freq_shift \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.freq_shift_avoid_aliasing.rst b/docs/_autosummary/torchsig.transforms.functional.freq_shift_avoid_aliasing.rst new file mode 100644 index 0000000..928b598 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.freq_shift_avoid_aliasing.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.freq\_shift\_avoid\_aliasing +=========================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: freq_shift_avoid_aliasing \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.imag.rst b/docs/_autosummary/torchsig.transforms.functional.imag.rst new file mode 100644 index 0000000..00fd104 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.imag.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.imag +=================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: imag \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.impulsive_interference.rst b/docs/_autosummary/torchsig.transforms.functional.impulsive_interference.rst new file mode 100644 index 0000000..ceebc2a --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.impulsive_interference.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.impulsive\_interference +====================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: impulsive_interference \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.interleave_complex.rst b/docs/_autosummary/torchsig.transforms.functional.interleave_complex.rst new file mode 100644 index 0000000..f12ea0e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.interleave_complex.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.interleave\_complex +================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: interleave_complex \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.iq_imbalance.rst b/docs/_autosummary/torchsig.transforms.functional.iq_imbalance.rst new file mode 100644 index 0000000..e1f58df --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.iq_imbalance.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.iq\_imbalance +============================================ + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: iq_imbalance \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.mag_rescale.rst b/docs/_autosummary/torchsig.transforms.functional.mag_rescale.rst new file mode 100644 index 0000000..3b9e489 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.mag_rescale.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.mag\_rescale +=========================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: mag_rescale \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.make_sinc_filter.rst b/docs/_autosummary/torchsig.transforms.functional.make_sinc_filter.rst new file mode 100644 index 0000000..80acad4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.make_sinc_filter.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.make\_sinc\_filter +================================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: make_sinc_filter \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.normalize.rst b/docs/_autosummary/torchsig.transforms.functional.normalize.rst new file mode 100644 index 0000000..b702ab1 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.normalize.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.normalize +======================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: normalize \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.patch_shuffle.rst b/docs/_autosummary/torchsig.transforms.functional.patch_shuffle.rst new file mode 100644 index 0000000..6856482 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.patch_shuffle.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.patch\_shuffle +============================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: patch_shuffle \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.phase_offset.rst b/docs/_autosummary/torchsig.transforms.functional.phase_offset.rst new file mode 100644 index 0000000..968b926 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.phase_offset.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.phase\_offset +============================================ + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: phase_offset \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.quantize.rst b/docs/_autosummary/torchsig.transforms.functional.quantize.rst new file mode 100644 index 0000000..493d18b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.quantize.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.quantize +======================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: quantize \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.random_convolve.rst b/docs/_autosummary/torchsig.transforms.functional.random_convolve.rst new file mode 100644 index 0000000..f2bac46 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.random_convolve.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.random\_convolve +=============================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: random_convolve \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.rayleigh_fading.rst b/docs/_autosummary/torchsig.transforms.functional.rayleigh_fading.rst new file mode 100644 index 0000000..cc6265c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.rayleigh_fading.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.rayleigh\_fading +=============================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: rayleigh_fading \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.real.rst b/docs/_autosummary/torchsig.transforms.functional.real.rst new file mode 100644 index 0000000..cdd0139 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.real.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.real +=================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: real \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.resample.rst b/docs/_autosummary/torchsig.transforms.functional.resample.rst new file mode 100644 index 0000000..d91a399 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.resample.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.resample +======================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: resample \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.roll_off.rst b/docs/_autosummary/torchsig.transforms.functional.roll_off.rst new file mode 100644 index 0000000..293da39 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.roll_off.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.roll\_off +======================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: roll_off \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.rst b/docs/_autosummary/torchsig.transforms.functional.rst new file mode 100644 index 0000000..f4a77e3 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.rst @@ -0,0 +1,74 @@ +torchsig.transforms.functional +============================== + +.. automodule:: torchsig.transforms.functional + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + add_slope + agc + amplitude_reversal + awgn + channel_swap + clip + complex_magnitude + complex_to_2d + continuous_wavelet_transform + cut_out + discrete_fourier_transform + drop_samples + drop_spec_samples + fractional_shift + freq_shift + freq_shift_avoid_aliasing + imag + impulsive_interference + interleave_complex + iq_imbalance + mag_rescale + make_sinc_filter + normalize + patch_shuffle + phase_offset + quantize + random_convolve + rayleigh_fading + real + resample + roll_off + spec_patch_shuffle + spec_translate + spectral_inversion + spectrogram + spectrogram_image + time_crop + time_reversal + time_shift + time_varying_awgn + to_distribution + uniform_continuous_distribution + uniform_discrete_distribution + wrapped_phase + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.transforms.functional.spec_patch_shuffle.rst b/docs/_autosummary/torchsig.transforms.functional.spec_patch_shuffle.rst new file mode 100644 index 0000000..6896278 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.spec_patch_shuffle.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.spec\_patch\_shuffle +=================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: spec_patch_shuffle \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.spec_translate.rst b/docs/_autosummary/torchsig.transforms.functional.spec_translate.rst new file mode 100644 index 0000000..3ccde74 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.spec_translate.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.spec\_translate +============================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: spec_translate \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.spectral_inversion.rst b/docs/_autosummary/torchsig.transforms.functional.spectral_inversion.rst new file mode 100644 index 0000000..6736719 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.spectral_inversion.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.spectral\_inversion +================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: spectral_inversion \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.spectrogram.rst b/docs/_autosummary/torchsig.transforms.functional.spectrogram.rst new file mode 100644 index 0000000..953fcfc --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.spectrogram.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.spectrogram +========================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: spectrogram \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.spectrogram_image.rst b/docs/_autosummary/torchsig.transforms.functional.spectrogram_image.rst new file mode 100644 index 0000000..2677166 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.spectrogram_image.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.spectrogram\_image +================================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: spectrogram_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.time_crop.rst b/docs/_autosummary/torchsig.transforms.functional.time_crop.rst new file mode 100644 index 0000000..0c4a4ce --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.time_crop.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.time\_crop +========================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: time_crop \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.time_reversal.rst b/docs/_autosummary/torchsig.transforms.functional.time_reversal.rst new file mode 100644 index 0000000..5e6f4ab --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.time_reversal.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.time\_reversal +============================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: time_reversal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.time_shift.rst b/docs/_autosummary/torchsig.transforms.functional.time_shift.rst new file mode 100644 index 0000000..92f2317 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.time_shift.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.time\_shift +========================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: time_shift \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.time_varying_awgn.rst b/docs/_autosummary/torchsig.transforms.functional.time_varying_awgn.rst new file mode 100644 index 0000000..52ee64c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.time_varying_awgn.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.time\_varying\_awgn +================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: time_varying_awgn \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.to_distribution.rst b/docs/_autosummary/torchsig.transforms.functional.to_distribution.rst new file mode 100644 index 0000000..7449a80 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.to_distribution.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.to\_distribution +=============================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: to_distribution \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.uniform_continuous_distribution.rst b/docs/_autosummary/torchsig.transforms.functional.uniform_continuous_distribution.rst new file mode 100644 index 0000000..6d20614 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.uniform_continuous_distribution.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.uniform\_continuous\_distribution +================================================================ + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: uniform_continuous_distribution \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.uniform_discrete_distribution.rst b/docs/_autosummary/torchsig.transforms.functional.uniform_discrete_distribution.rst new file mode 100644 index 0000000..d7b9ceb --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.uniform_discrete_distribution.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.uniform\_discrete\_distribution +============================================================== + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: uniform_discrete_distribution \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.functional.wrapped_phase.rst b/docs/_autosummary/torchsig.transforms.functional.wrapped_phase.rst new file mode 100644 index 0000000..ff440dc --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.functional.wrapped_phase.rst @@ -0,0 +1,6 @@ +torchsig.transforms.functional.wrapped\_phase +============================================= + +.. currentmodule:: torchsig.transforms.functional + +.. autofunction:: wrapped_phase \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.rst b/docs/_autosummary/torchsig.transforms.rst new file mode 100644 index 0000000..1d530da --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.rst @@ -0,0 +1,32 @@ +torchsig.transforms +=================== + +.. automodule:: torchsig.transforms + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + functional + target_transforms + transforms + diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescPassThrough.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescPassThrough.rst new file mode 100644 index 0000000..832bc6b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescPassThrough.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescPassThrough +====================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescPassThrough + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToAnchorBoxes.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToAnchorBoxes.rst new file mode 100644 index 0000000..f8731c7 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToAnchorBoxes.rst @@ -0,0 +1,25 @@ +torchsig.transforms.target\_transforms.DescToAnchorBoxes +======================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToAnchorBoxes + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DescToAnchorBoxes.iou + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBox.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBox.rst new file mode 100644 index 0000000..61f81ca --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBox.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToBBox +================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBBox + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxDict.rst new file mode 100644 index 0000000..da9f0f8 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxDict.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToBBoxDict +===================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBBoxDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxFamilyDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxFamilyDict.rst new file mode 100644 index 0000000..cfb3229 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxFamilyDict.rst @@ -0,0 +1,30 @@ +torchsig.transforms.target\_transforms.DescToBBoxFamilyDict +=========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBBoxFamilyDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~DescToBBoxFamilyDict.class_family_dict + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxSignalDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxSignalDict.rst new file mode 100644 index 0000000..c81bcc5 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxSignalDict.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToBBoxSignalDict +=========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBBoxSignalDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxYoloDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxYoloDict.rst new file mode 100644 index 0000000..6d10bd8 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxYoloDict.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToBBoxYoloDict +========================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBBoxYoloDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxYoloSignalDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxYoloSignalDict.rst new file mode 100644 index 0000000..a71c77e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBBoxYoloSignalDict.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToBBoxYoloSignalDict +=============================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBBoxYoloSignalDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToBinary.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBinary.rst new file mode 100644 index 0000000..8bf9962 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToBinary.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToBinary +=================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToBinary + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassEncoding.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassEncoding.rst new file mode 100644 index 0000000..47319f7 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassEncoding.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToClassEncoding +========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToClassEncoding + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassIndex.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassIndex.rst new file mode 100644 index 0000000..97aafe7 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassIndex.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToClassIndex +======================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToClassIndex + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassIndexSNR.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassIndexSNR.rst new file mode 100644 index 0000000..92c571e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassIndexSNR.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToClassIndexSNR +========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToClassIndexSNR + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassName.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassName.rst new file mode 100644 index 0000000..a18c7d9 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassName.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToClassName +====================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToClassName + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassNameSNR.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassNameSNR.rst new file mode 100644 index 0000000..1e59062 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToClassNameSNR.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToClassNameSNR +========================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToClassNameSNR + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToCustom.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToCustom.rst new file mode 100644 index 0000000..a3d28c6 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToCustom.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToCustom +=================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToCustom + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToFamilyName.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToFamilyName.rst new file mode 100644 index 0000000..dc0170e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToFamilyName.rst @@ -0,0 +1,30 @@ +torchsig.transforms.target\_transforms.DescToFamilyName +======================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToFamilyName + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~DescToFamilyName.class_family_dict + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToInstMaskDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToInstMaskDict.rst new file mode 100644 index 0000000..d5cd87d --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToInstMaskDict.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToInstMaskDict +========================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToInstMaskDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToListTuple.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToListTuple.rst new file mode 100644 index 0000000..5f0900d --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToListTuple.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToListTuple +====================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToListTuple + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToMask.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMask.rst new file mode 100644 index 0000000..0ca04f8 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMask.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToMask +================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToMask + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskClass.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskClass.rst new file mode 100644 index 0000000..5027820 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskClass.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToMaskClass +====================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToMaskClass + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskFamily.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskFamily.rst new file mode 100644 index 0000000..971418a --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskFamily.rst @@ -0,0 +1,30 @@ +torchsig.transforms.target\_transforms.DescToMaskFamily +======================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToMaskFamily + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~DescToMaskFamily.class_family_dict + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskSignal.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskSignal.rst new file mode 100644 index 0000000..16dccc4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToMaskSignal.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToMaskSignal +======================================================= + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToMaskSignal + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToSemanticClass.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToSemanticClass.rst new file mode 100644 index 0000000..8d0f6d4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToSemanticClass.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToSemanticClass +========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToSemanticClass + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToSignalFamilyInstMaskDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToSignalFamilyInstMaskDict.rst new file mode 100644 index 0000000..c66f65c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToSignalFamilyInstMaskDict.rst @@ -0,0 +1,30 @@ +torchsig.transforms.target\_transforms.DescToSignalFamilyInstMaskDict +===================================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToSignalFamilyInstMaskDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~DescToSignalFamilyInstMaskDict.class_family_dict + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToSignalInstMaskDict.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToSignalInstMaskDict.rst new file mode 100644 index 0000000..77add38 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToSignalInstMaskDict.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToSignalInstMaskDict +=============================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToSignalInstMaskDict + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToWeightedCutMix.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToWeightedCutMix.rst new file mode 100644 index 0000000..ac12ac2 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToWeightedCutMix.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToWeightedCutMix +=========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToWeightedCutMix + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.DescToWeightedMixUp.rst b/docs/_autosummary/torchsig.transforms.target_transforms.DescToWeightedMixUp.rst new file mode 100644 index 0000000..d8b5863 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.DescToWeightedMixUp.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.DescToWeightedMixUp +========================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: DescToWeightedMixUp + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.LabelSmoothing.rst b/docs/_autosummary/torchsig.transforms.target_transforms.LabelSmoothing.rst new file mode 100644 index 0000000..e060a9f --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.LabelSmoothing.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.LabelSmoothing +===================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: LabelSmoothing + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.ListTupleToDesc.rst b/docs/_autosummary/torchsig.transforms.target_transforms.ListTupleToDesc.rst new file mode 100644 index 0000000..568a643 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.ListTupleToDesc.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.ListTupleToDesc +====================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: ListTupleToDesc + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.ListTupleToYOLO.rst b/docs/_autosummary/torchsig.transforms.target_transforms.ListTupleToYOLO.rst new file mode 100644 index 0000000..478435a --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.ListTupleToYOLO.rst @@ -0,0 +1,24 @@ +torchsig.transforms.target\_transforms.ListTupleToYOLO +====================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autoclass:: ListTupleToYOLO + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.generate_mask.rst b/docs/_autosummary/torchsig.transforms.target_transforms.generate_mask.rst new file mode 100644 index 0000000..be5d4a2 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.generate_mask.rst @@ -0,0 +1,6 @@ +torchsig.transforms.target\_transforms.generate\_mask +===================================================== + +.. currentmodule:: torchsig.transforms.target_transforms + +.. autofunction:: generate_mask \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.target_transforms.rst b/docs/_autosummary/torchsig.transforms.target_transforms.rst new file mode 100644 index 0000000..81ccf6e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.target_transforms.rst @@ -0,0 +1,69 @@ +torchsig.transforms.target\_transforms +====================================== + +.. automodule:: torchsig.transforms.target_transforms + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + generate_mask + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + DescPassThrough + DescToAnchorBoxes + DescToBBox + DescToBBoxDict + DescToBBoxFamilyDict + DescToBBoxSignalDict + DescToBBoxYoloDict + DescToBBoxYoloSignalDict + DescToBinary + DescToClassEncoding + DescToClassIndex + DescToClassIndexSNR + DescToClassName + DescToClassNameSNR + DescToCustom + DescToFamilyName + DescToInstMaskDict + DescToListTuple + DescToMask + DescToMaskClass + DescToMaskFamily + DescToMaskSignal + DescToSemanticClass + DescToSignalFamilyInstMaskDict + DescToSignalInstMaskDict + DescToWeightedCutMix + DescToWeightedMixUp + LabelSmoothing + ListTupleToDesc + ListTupleToYOLO + + + + + + + + + diff --git a/docs/_autosummary/torchsig.transforms.transforms.AddNoise.rst b/docs/_autosummary/torchsig.transforms.transforms.AddNoise.rst new file mode 100644 index 0000000..347a614 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.AddNoise.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.AddNoise +======================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: AddNoise + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~AddNoise.convert_to_signal + ~AddNoise.parameters + ~AddNoise.transform_data + ~AddNoise.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.AddSlope.rst b/docs/_autosummary/torchsig.transforms.transforms.AddSlope.rst new file mode 100644 index 0000000..194982e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.AddSlope.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.AddSlope +======================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: AddSlope + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~AddSlope.convert_to_signal + ~AddSlope.parameters + ~AddSlope.transform_data + ~AddSlope.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.AmplitudeReversal.rst b/docs/_autosummary/torchsig.transforms.transforms.AmplitudeReversal.rst new file mode 100644 index 0000000..d030904 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.AmplitudeReversal.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.AmplitudeReversal +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: AmplitudeReversal + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~AmplitudeReversal.convert_to_signal + ~AmplitudeReversal.parameters + ~AmplitudeReversal.transform_data + ~AmplitudeReversal.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.AutomaticGainControl.rst b/docs/_autosummary/torchsig.transforms.transforms.AutomaticGainControl.rst new file mode 100644 index 0000000..33d226e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.AutomaticGainControl.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.AutomaticGainControl +=================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: AutomaticGainControl + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~AutomaticGainControl.convert_to_signal + ~AutomaticGainControl.parameters + ~AutomaticGainControl.transform_data + ~AutomaticGainControl.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ChannelConcatIQDFT.rst b/docs/_autosummary/torchsig.transforms.transforms.ChannelConcatIQDFT.rst new file mode 100644 index 0000000..072fec7 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ChannelConcatIQDFT.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ChannelConcatIQDFT +================================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ChannelConcatIQDFT + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ChannelConcatIQDFT.convert_to_signal + ~ChannelConcatIQDFT.parameters + ~ChannelConcatIQDFT.transform_data + ~ChannelConcatIQDFT.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ChannelSwap.rst b/docs/_autosummary/torchsig.transforms.transforms.ChannelSwap.rst new file mode 100644 index 0000000..ef8fa3f --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ChannelSwap.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ChannelSwap +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ChannelSwap + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ChannelSwap.convert_to_signal + ~ChannelSwap.parameters + ~ChannelSwap.transform_data + ~ChannelSwap.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Clip.rst b/docs/_autosummary/torchsig.transforms.transforms.Clip.rst new file mode 100644 index 0000000..dd241da --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Clip.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Clip +=================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Clip + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Clip.convert_to_signal + ~Clip.parameters + ~Clip.transform_data + ~Clip.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ComplexMagnitude.rst b/docs/_autosummary/torchsig.transforms.transforms.ComplexMagnitude.rst new file mode 100644 index 0000000..9b8b79c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ComplexMagnitude.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ComplexMagnitude +=============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ComplexMagnitude + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ComplexMagnitude.convert_to_signal + ~ComplexMagnitude.parameters + ~ComplexMagnitude.transform_data + ~ComplexMagnitude.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ComplexTo2D.rst b/docs/_autosummary/torchsig.transforms.transforms.ComplexTo2D.rst new file mode 100644 index 0000000..b508a96 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ComplexTo2D.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ComplexTo2D +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ComplexTo2D + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ComplexTo2D.convert_to_signal + ~ComplexTo2D.parameters + ~ComplexTo2D.transform_data + ~ComplexTo2D.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Compose.rst b/docs/_autosummary/torchsig.transforms.transforms.Compose.rst new file mode 100644 index 0000000..bbe726a --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Compose.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.Compose +====================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Compose + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Concatenate.rst b/docs/_autosummary/torchsig.transforms.transforms.Concatenate.rst new file mode 100644 index 0000000..0022190 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Concatenate.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Concatenate +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Concatenate + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Concatenate.convert_to_signal + ~Concatenate.parameters + ~Concatenate.transform_data + ~Concatenate.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ContinuousWavelet.rst b/docs/_autosummary/torchsig.transforms.transforms.ContinuousWavelet.rst new file mode 100644 index 0000000..1d3d875 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ContinuousWavelet.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ContinuousWavelet +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ContinuousWavelet + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ContinuousWavelet.convert_to_signal + ~ContinuousWavelet.parameters + ~ContinuousWavelet.transform_data + ~ContinuousWavelet.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.CutOut.rst b/docs/_autosummary/torchsig.transforms.transforms.CutOut.rst new file mode 100644 index 0000000..bdc24f9 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.CutOut.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.CutOut +===================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: CutOut + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~CutOut.convert_to_signal + ~CutOut.parameters + ~CutOut.transform_data + ~CutOut.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.DatasetBasebandCutMix.rst b/docs/_autosummary/torchsig.transforms.transforms.DatasetBasebandCutMix.rst new file mode 100644 index 0000000..ce07056 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.DatasetBasebandCutMix.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.DatasetBasebandCutMix +==================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: DatasetBasebandCutMix + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetBasebandCutMix.convert_to_signal + ~DatasetBasebandCutMix.parameters + ~DatasetBasebandCutMix.transform_data + ~DatasetBasebandCutMix.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.DatasetBasebandMixUp.rst b/docs/_autosummary/torchsig.transforms.transforms.DatasetBasebandMixUp.rst new file mode 100644 index 0000000..6f09e5e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.DatasetBasebandMixUp.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.DatasetBasebandMixUp +=================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: DatasetBasebandMixUp + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetBasebandMixUp.convert_to_signal + ~DatasetBasebandMixUp.parameters + ~DatasetBasebandMixUp.transform_data + ~DatasetBasebandMixUp.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.DatasetWidebandCutMix.rst b/docs/_autosummary/torchsig.transforms.transforms.DatasetWidebandCutMix.rst new file mode 100644 index 0000000..f154a72 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.DatasetWidebandCutMix.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.DatasetWidebandCutMix +==================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: DatasetWidebandCutMix + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetWidebandCutMix.convert_to_signal + ~DatasetWidebandCutMix.parameters + ~DatasetWidebandCutMix.transform_data + ~DatasetWidebandCutMix.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.DatasetWidebandMixUp.rst b/docs/_autosummary/torchsig.transforms.transforms.DatasetWidebandMixUp.rst new file mode 100644 index 0000000..74fc7b0 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.DatasetWidebandMixUp.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.DatasetWidebandMixUp +=================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: DatasetWidebandMixUp + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetWidebandMixUp.convert_to_signal + ~DatasetWidebandMixUp.parameters + ~DatasetWidebandMixUp.transform_data + ~DatasetWidebandMixUp.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.DiscreteFourierTransform.rst b/docs/_autosummary/torchsig.transforms.transforms.DiscreteFourierTransform.rst new file mode 100644 index 0000000..421e4e4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.DiscreteFourierTransform.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.DiscreteFourierTransform +======================================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: DiscreteFourierTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DiscreteFourierTransform.convert_to_signal + ~DiscreteFourierTransform.parameters + ~DiscreteFourierTransform.transform_data + ~DiscreteFourierTransform.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.FixedRandom.rst b/docs/_autosummary/torchsig.transforms.transforms.FixedRandom.rst new file mode 100644 index 0000000..10433a4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.FixedRandom.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.FixedRandom +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: FixedRandom + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.GainDrift.rst b/docs/_autosummary/torchsig.transforms.transforms.GainDrift.rst new file mode 100644 index 0000000..bab2e9b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.GainDrift.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.GainDrift +======================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: GainDrift + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~GainDrift.convert_to_signal + ~GainDrift.parameters + ~GainDrift.transform_data + ~GainDrift.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.IQImbalance.rst b/docs/_autosummary/torchsig.transforms.transforms.IQImbalance.rst new file mode 100644 index 0000000..3f581fe --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.IQImbalance.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.IQImbalance +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: IQImbalance + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~IQImbalance.convert_to_signal + ~IQImbalance.parameters + ~IQImbalance.transform_data + ~IQImbalance.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Identity.rst b/docs/_autosummary/torchsig.transforms.transforms.Identity.rst new file mode 100644 index 0000000..ede5863 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Identity.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.Identity +======================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Identity + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Imag.rst b/docs/_autosummary/torchsig.transforms.transforms.Imag.rst new file mode 100644 index 0000000..07b2638 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Imag.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Imag +=================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Imag + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Imag.convert_to_signal + ~Imag.parameters + ~Imag.transform_data + ~Imag.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ImpulseInterferer.rst b/docs/_autosummary/torchsig.transforms.transforms.ImpulseInterferer.rst new file mode 100644 index 0000000..c5f8efb --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ImpulseInterferer.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ImpulseInterferer +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ImpulseInterferer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ImpulseInterferer.convert_to_signal + ~ImpulseInterferer.parameters + ~ImpulseInterferer.transform_data + ~ImpulseInterferer.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.InterleaveComplex.rst b/docs/_autosummary/torchsig.transforms.transforms.InterleaveComplex.rst new file mode 100644 index 0000000..a90a51e --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.InterleaveComplex.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.InterleaveComplex +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: InterleaveComplex + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~InterleaveComplex.convert_to_signal + ~InterleaveComplex.parameters + ~InterleaveComplex.transform_data + ~InterleaveComplex.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Lambda.rst b/docs/_autosummary/torchsig.transforms.transforms.Lambda.rst new file mode 100644 index 0000000..1e68a4b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Lambda.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.Lambda +===================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Lambda + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.LocalOscillatorDrift.rst b/docs/_autosummary/torchsig.transforms.transforms.LocalOscillatorDrift.rst new file mode 100644 index 0000000..0626398 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.LocalOscillatorDrift.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.LocalOscillatorDrift +=================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: LocalOscillatorDrift + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~LocalOscillatorDrift.convert_to_signal + ~LocalOscillatorDrift.parameters + ~LocalOscillatorDrift.transform_data + ~LocalOscillatorDrift.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Normalize.rst b/docs/_autosummary/torchsig.transforms.transforms.Normalize.rst new file mode 100644 index 0000000..d174737 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Normalize.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Normalize +======================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Normalize + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Normalize.convert_to_signal + ~Normalize.parameters + ~Normalize.transform_data + ~Normalize.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.PatchShuffle.rst b/docs/_autosummary/torchsig.transforms.transforms.PatchShuffle.rst new file mode 100644 index 0000000..c94c6d7 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.PatchShuffle.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.PatchShuffle +=========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: PatchShuffle + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~PatchShuffle.convert_to_signal + ~PatchShuffle.parameters + ~PatchShuffle.transform_data + ~PatchShuffle.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Quantize.rst b/docs/_autosummary/torchsig.transforms.transforms.Quantize.rst new file mode 100644 index 0000000..bd643d9 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Quantize.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Quantize +======================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Quantize + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Quantize.convert_to_signal + ~Quantize.parameters + ~Quantize.transform_data + ~Quantize.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandAugment.rst b/docs/_autosummary/torchsig.transforms.transforms.RandAugment.rst new file mode 100644 index 0000000..67f3ae4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandAugment.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RandAugment +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandAugment + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandAugment.convert_to_signal + ~RandAugment.parameters + ~RandAugment.transform_data + ~RandAugment.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandChoice.rst b/docs/_autosummary/torchsig.transforms.transforms.RandChoice.rst new file mode 100644 index 0000000..d4c51ab --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandChoice.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RandChoice +========================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandChoice + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandChoice.convert_to_signal + ~RandChoice.parameters + ~RandChoice.transform_data + ~RandChoice.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomApply.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomApply.rst new file mode 100644 index 0000000..29b3462 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomApply.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.RandomApply +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomApply + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomConvolve.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomConvolve.rst new file mode 100644 index 0000000..e18e5d4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomConvolve.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RandomConvolve +============================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomConvolve + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomConvolve.convert_to_signal + ~RandomConvolve.parameters + ~RandomConvolve.transform_data + ~RandomConvolve.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomDelayedFrequencyShift.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomDelayedFrequencyShift.rst new file mode 100644 index 0000000..1a6aec2 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomDelayedFrequencyShift.rst @@ -0,0 +1,31 @@ +torchsig.transforms.transforms.RandomDelayedFrequencyShift +========================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomDelayedFrequencyShift + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomDelayedFrequencyShift.clip_frequency + ~RandomDelayedFrequencyShift.convert_to_signal + ~RandomDelayedFrequencyShift.parameters + ~RandomDelayedFrequencyShift.shift_frequency + ~RandomDelayedFrequencyShift.transform_data + ~RandomDelayedFrequencyShift.transform_meta + ~RandomDelayedFrequencyShift.will_alias + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomDropSamples.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomDropSamples.rst new file mode 100644 index 0000000..1a07ce4 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomDropSamples.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RandomDropSamples +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomDropSamples + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomDropSamples.convert_to_signal + ~RandomDropSamples.parameters + ~RandomDropSamples.transform_data + ~RandomDropSamples.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomFrequencyShift.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomFrequencyShift.rst new file mode 100644 index 0000000..e1df437 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomFrequencyShift.rst @@ -0,0 +1,29 @@ +torchsig.transforms.transforms.RandomFrequencyShift +=================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomFrequencyShift + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomFrequencyShift.check_freq_bounds + ~RandomFrequencyShift.convert_to_signal + ~RandomFrequencyShift.parameters + ~RandomFrequencyShift.transform_data + ~RandomFrequencyShift.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomMagRescale.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomMagRescale.rst new file mode 100644 index 0000000..d487bbe --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomMagRescale.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RandomMagRescale +=============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomMagRescale + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomMagRescale.convert_to_signal + ~RandomMagRescale.parameters + ~RandomMagRescale.transform_data + ~RandomMagRescale.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomPhaseShift.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomPhaseShift.rst new file mode 100644 index 0000000..2002224 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomPhaseShift.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RandomPhaseShift +=============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomPhaseShift + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomPhaseShift.convert_to_signal + ~RandomPhaseShift.parameters + ~RandomPhaseShift.transform_data + ~RandomPhaseShift.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomResample.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomResample.rst new file mode 100644 index 0000000..ae38b5c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomResample.rst @@ -0,0 +1,30 @@ +torchsig.transforms.transforms.RandomResample +============================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomResample + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomResample.check_bounds + ~RandomResample.check_time_freq_bounds + ~RandomResample.convert_to_signal + ~RandomResample.parameters + ~RandomResample.transform_data + ~RandomResample.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RandomTimeShift.rst b/docs/_autosummary/torchsig.transforms.transforms.RandomTimeShift.rst new file mode 100644 index 0000000..44b5c4b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RandomTimeShift.rst @@ -0,0 +1,29 @@ +torchsig.transforms.transforms.RandomTimeShift +============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RandomTimeShift + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RandomTimeShift.check_time_bounds + ~RandomTimeShift.convert_to_signal + ~RandomTimeShift.parameters + ~RandomTimeShift.transform_data + ~RandomTimeShift.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RayleighFadingChannel.rst b/docs/_autosummary/torchsig.transforms.transforms.RayleighFadingChannel.rst new file mode 100644 index 0000000..92b5aad --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RayleighFadingChannel.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RayleighFadingChannel +==================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RayleighFadingChannel + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RayleighFadingChannel.convert_to_signal + ~RayleighFadingChannel.parameters + ~RayleighFadingChannel.transform_data + ~RayleighFadingChannel.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Real.rst b/docs/_autosummary/torchsig.transforms.transforms.Real.rst new file mode 100644 index 0000000..383d59f --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Real.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Real +=================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Real + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Real.convert_to_signal + ~Real.parameters + ~Real.transform_data + ~Real.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.ReshapeTransform.rst b/docs/_autosummary/torchsig.transforms.transforms.ReshapeTransform.rst new file mode 100644 index 0000000..6a9c289 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.ReshapeTransform.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.ReshapeTransform +=============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: ReshapeTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ReshapeTransform.convert_to_signal + ~ReshapeTransform.parameters + ~ReshapeTransform.transform_data + ~ReshapeTransform.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.RollOff.rst b/docs/_autosummary/torchsig.transforms.transforms.RollOff.rst new file mode 100644 index 0000000..40e021d --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.RollOff.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.RollOff +====================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: RollOff + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RollOff.convert_to_signal + ~RollOff.parameters + ~RollOff.transform_data + ~RollOff.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SignalTransform.rst b/docs/_autosummary/torchsig.transforms.transforms.SignalTransform.rst new file mode 100644 index 0000000..c0d5308 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SignalTransform.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SignalTransform +============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SignalTransform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SignalTransform.convert_to_signal + ~SignalTransform.parameters + ~SignalTransform.transform_data + ~SignalTransform.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectralInversion.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectralInversion.rst new file mode 100644 index 0000000..e4023d0 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectralInversion.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SpectralInversion +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectralInversion + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectralInversion.convert_to_signal + ~SpectralInversion.parameters + ~SpectralInversion.transform_data + ~SpectralInversion.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Spectrogram.rst b/docs/_autosummary/torchsig.transforms.transforms.Spectrogram.rst new file mode 100644 index 0000000..b63336d --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Spectrogram.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.Spectrogram +========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Spectrogram + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Spectrogram.convert_to_signal + ~Spectrogram.parameters + ~Spectrogram.transform_data + ~Spectrogram.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectrogramDropSamples.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramDropSamples.rst new file mode 100644 index 0000000..5a55ef5 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramDropSamples.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SpectrogramDropSamples +===================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectrogramDropSamples + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectrogramDropSamples.convert_to_signal + ~SpectrogramDropSamples.parameters + ~SpectrogramDropSamples.transform_data + ~SpectrogramDropSamples.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectrogramImage.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramImage.rst new file mode 100644 index 0000000..560b286 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramImage.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SpectrogramImage +=============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectrogramImage + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectrogramImage.convert_to_signal + ~SpectrogramImage.parameters + ~SpectrogramImage.transform_data + ~SpectrogramImage.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectrogramMosaicDownsample.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramMosaicDownsample.rst new file mode 100644 index 0000000..bc57d9d --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramMosaicDownsample.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SpectrogramMosaicDownsample +========================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectrogramMosaicDownsample + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectrogramMosaicDownsample.convert_to_signal + ~SpectrogramMosaicDownsample.parameters + ~SpectrogramMosaicDownsample.transform_data + ~SpectrogramMosaicDownsample.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectrogramPatchShuffle.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramPatchShuffle.rst new file mode 100644 index 0000000..c09c9f8 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramPatchShuffle.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SpectrogramPatchShuffle +====================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectrogramPatchShuffle + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectrogramPatchShuffle.convert_to_signal + ~SpectrogramPatchShuffle.parameters + ~SpectrogramPatchShuffle.transform_data + ~SpectrogramPatchShuffle.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectrogramRandomResizeCrop.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramRandomResizeCrop.rst new file mode 100644 index 0000000..a3c969b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramRandomResizeCrop.rst @@ -0,0 +1,35 @@ +torchsig.transforms.transforms.SpectrogramRandomResizeCrop +========================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectrogramRandomResizeCrop + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectrogramRandomResizeCrop.convert_to_signal + ~SpectrogramRandomResizeCrop.meta_crop_height + ~SpectrogramRandomResizeCrop.meta_crop_width + ~SpectrogramRandomResizeCrop.meta_pad_width + ~SpectrogramRandomResizeCrop.pad_func + ~SpectrogramRandomResizeCrop.pad_spec + ~SpectrogramRandomResizeCrop.pad_spec_complex + ~SpectrogramRandomResizeCrop.parameters + ~SpectrogramRandomResizeCrop.spec_to_complex + ~SpectrogramRandomResizeCrop.transform_data + ~SpectrogramRandomResizeCrop.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.SpectrogramTranslation.rst b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramTranslation.rst new file mode 100644 index 0000000..cca9122 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.SpectrogramTranslation.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.SpectrogramTranslation +===================================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: SpectrogramTranslation + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SpectrogramTranslation.convert_to_signal + ~SpectrogramTranslation.parameters + ~SpectrogramTranslation.transform_data + ~SpectrogramTranslation.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.TargetConcatenate.rst b/docs/_autosummary/torchsig.transforms.transforms.TargetConcatenate.rst new file mode 100644 index 0000000..d1e7d11 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.TargetConcatenate.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.TargetConcatenate +================================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: TargetConcatenate + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.TargetSNR.rst b/docs/_autosummary/torchsig.transforms.transforms.TargetSNR.rst new file mode 100644 index 0000000..8d75c19 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.TargetSNR.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.TargetSNR +======================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: TargetSNR + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TargetSNR.convert_to_signal + ~TargetSNR.parameters + ~TargetSNR.transform_data + ~TargetSNR.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.TimeCrop.rst b/docs/_autosummary/torchsig.transforms.transforms.TimeCrop.rst new file mode 100644 index 0000000..c4b53fe --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.TimeCrop.rst @@ -0,0 +1,29 @@ +torchsig.transforms.transforms.TimeCrop +======================================= + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: TimeCrop + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TimeCrop.check_time_bounds + ~TimeCrop.convert_to_signal + ~TimeCrop.parameters + ~TimeCrop.transform_data + ~TimeCrop.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.TimeReversal.rst b/docs/_autosummary/torchsig.transforms.transforms.TimeReversal.rst new file mode 100644 index 0000000..3d7d07b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.TimeReversal.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.TimeReversal +=========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: TimeReversal + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TimeReversal.convert_to_signal + ~TimeReversal.parameters + ~TimeReversal.transform_data + ~TimeReversal.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.TimeVaryingNoise.rst b/docs/_autosummary/torchsig.transforms.transforms.TimeVaryingNoise.rst new file mode 100644 index 0000000..68ee5a3 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.TimeVaryingNoise.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.TimeVaryingNoise +=============================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: TimeVaryingNoise + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TimeVaryingNoise.convert_to_signal + ~TimeVaryingNoise.parameters + ~TimeVaryingNoise.transform_data + ~TimeVaryingNoise.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.Transform.rst b/docs/_autosummary/torchsig.transforms.transforms.Transform.rst new file mode 100644 index 0000000..a83d711 --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.Transform.rst @@ -0,0 +1,24 @@ +torchsig.transforms.transforms.Transform +======================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: Transform + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.WrappedPhase.rst b/docs/_autosummary/torchsig.transforms.transforms.WrappedPhase.rst new file mode 100644 index 0000000..31b392b --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.WrappedPhase.rst @@ -0,0 +1,28 @@ +torchsig.transforms.transforms.WrappedPhase +=========================================== + +.. currentmodule:: torchsig.transforms.transforms + +.. autoclass:: WrappedPhase + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~WrappedPhase.convert_to_signal + ~WrappedPhase.parameters + ~WrappedPhase.transform_data + ~WrappedPhase.transform_meta + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.find_nearest.rst b/docs/_autosummary/torchsig.transforms.transforms.find_nearest.rst new file mode 100644 index 0000000..e47881f --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.find_nearest.rst @@ -0,0 +1,6 @@ +torchsig.transforms.transforms.find\_nearest +============================================ + +.. currentmodule:: torchsig.transforms.transforms + +.. autofunction:: find_nearest \ No newline at end of file diff --git a/docs/_autosummary/torchsig.transforms.transforms.rst b/docs/_autosummary/torchsig.transforms.transforms.rst new file mode 100644 index 0000000..8632c6c --- /dev/null +++ b/docs/_autosummary/torchsig.transforms.transforms.rst @@ -0,0 +1,100 @@ +torchsig.transforms.transforms +============================== + +.. automodule:: torchsig.transforms.transforms + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + find_nearest + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + AddNoise + AddSlope + AmplitudeReversal + AutomaticGainControl + ChannelConcatIQDFT + ChannelSwap + Clip + ComplexMagnitude + ComplexTo2D + Compose + Concatenate + ContinuousWavelet + CutOut + DatasetBasebandCutMix + DatasetBasebandMixUp + DatasetWidebandCutMix + DatasetWidebandMixUp + DiscreteFourierTransform + FixedRandom + GainDrift + IQImbalance + Identity + Imag + ImpulseInterferer + InterleaveComplex + Lambda + LocalOscillatorDrift + Normalize + PatchShuffle + Quantize + RandAugment + RandChoice + RandomApply + RandomConvolve + RandomDelayedFrequencyShift + RandomDropSamples + RandomFrequencyShift + RandomMagRescale + RandomPhaseShift + RandomResample + RandomTimeShift + RayleighFadingChannel + Real + ReshapeTransform + RollOff + SignalTransform + SpectralInversion + Spectrogram + SpectrogramDropSamples + SpectrogramImage + SpectrogramMosaicDownsample + SpectrogramPatchShuffle + SpectrogramRandomResizeCrop + SpectrogramTranslation + TargetConcatenate + TargetSNR + TimeCrop + TimeReversal + TimeVaryingNoise + Transform + WrappedPhase + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.classify_transforms.PLL.rst b/docs/_autosummary/torchsig.utils.classify_transforms.PLL.rst new file mode 100644 index 0000000..9e32389 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.classify_transforms.PLL.rst @@ -0,0 +1,28 @@ +torchsig.utils.classify\_transforms.PLL +======================================= + +.. currentmodule:: torchsig.utils.classify_transforms + +.. autoclass:: PLL + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~PLL.correct_signal + ~PLL.loop_filter + ~PLL.phase_detector + ~PLL.vco + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.classify_transforms.complex_iq_to_heatmap.rst b/docs/_autosummary/torchsig.utils.classify_transforms.complex_iq_to_heatmap.rst new file mode 100644 index 0000000..04786e3 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.classify_transforms.complex_iq_to_heatmap.rst @@ -0,0 +1,6 @@ +torchsig.utils.classify\_transforms.complex\_iq\_to\_heatmap +============================================================ + +.. currentmodule:: torchsig.utils.classify_transforms + +.. autofunction:: complex_iq_to_heatmap \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.classify_transforms.real_imag_vstacked_cwt_image.rst b/docs/_autosummary/torchsig.utils.classify_transforms.real_imag_vstacked_cwt_image.rst new file mode 100644 index 0000000..6b8440c --- /dev/null +++ b/docs/_autosummary/torchsig.utils.classify_transforms.real_imag_vstacked_cwt_image.rst @@ -0,0 +1,6 @@ +torchsig.utils.classify\_transforms.real\_imag\_vstacked\_cwt\_image +==================================================================== + +.. currentmodule:: torchsig.utils.classify_transforms + +.. autofunction:: real_imag_vstacked_cwt_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.classify_transforms.rst b/docs/_autosummary/torchsig.utils.classify_transforms.rst new file mode 100644 index 0000000..8d2b973 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.classify_transforms.rst @@ -0,0 +1,43 @@ +torchsig.utils.classify\_transforms +=================================== + +.. automodule:: torchsig.utils.classify_transforms + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + complex_iq_to_heatmap + real_imag_vstacked_cwt_image + spectrogram_image + upsample_iq + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + PLL + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.classify_transforms.spectrogram_image.rst b/docs/_autosummary/torchsig.utils.classify_transforms.spectrogram_image.rst new file mode 100644 index 0000000..e41f3af --- /dev/null +++ b/docs/_autosummary/torchsig.utils.classify_transforms.spectrogram_image.rst @@ -0,0 +1,6 @@ +torchsig.utils.classify\_transforms.spectrogram\_image +====================================================== + +.. currentmodule:: torchsig.utils.classify_transforms + +.. autofunction:: spectrogram_image \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.classify_transforms.upsample_iq.rst b/docs/_autosummary/torchsig.utils.classify_transforms.upsample_iq.rst new file mode 100644 index 0000000..6c0f552 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.classify_transforms.upsample_iq.rst @@ -0,0 +1,6 @@ +torchsig.utils.classify\_transforms.upsample\_iq +================================================ + +.. currentmodule:: torchsig.utils.classify_transforms + +.. autofunction:: upsample_iq \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.cm_plotter.plot_confusion_matrix.rst b/docs/_autosummary/torchsig.utils.cm_plotter.plot_confusion_matrix.rst new file mode 100644 index 0000000..a923ebc --- /dev/null +++ b/docs/_autosummary/torchsig.utils.cm_plotter.plot_confusion_matrix.rst @@ -0,0 +1,6 @@ +torchsig.utils.cm\_plotter.plot\_confusion\_matrix +================================================== + +.. currentmodule:: torchsig.utils.cm_plotter + +.. autofunction:: plot_confusion_matrix \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.cm_plotter.rst b/docs/_autosummary/torchsig.utils.cm_plotter.rst new file mode 100644 index 0000000..0094972 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.cm_plotter.rst @@ -0,0 +1,31 @@ +torchsig.utils.cm\_plotter +========================== + +.. automodule:: torchsig.utils.cm_plotter + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + plot_confusion_matrix + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.dataset.SignalDataset.rst b/docs/_autosummary/torchsig.utils.dataset.SignalDataset.rst new file mode 100644 index 0000000..c0c4e13 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dataset.SignalDataset.rst @@ -0,0 +1,24 @@ +torchsig.utils.dataset.SignalDataset +==================================== + +.. currentmodule:: torchsig.utils.dataset + +.. autoclass:: SignalDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dataset.SignalFileDataset.rst b/docs/_autosummary/torchsig.utils.dataset.SignalFileDataset.rst new file mode 100644 index 0000000..ecbaa96 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dataset.SignalFileDataset.rst @@ -0,0 +1,24 @@ +torchsig.utils.dataset.SignalFileDataset +======================================== + +.. currentmodule:: torchsig.utils.dataset + +.. autoclass:: SignalFileDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dataset.SignalTensorDataset.rst b/docs/_autosummary/torchsig.utils.dataset.SignalTensorDataset.rst new file mode 100644 index 0000000..776f6d3 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dataset.SignalTensorDataset.rst @@ -0,0 +1,30 @@ +torchsig.utils.dataset.SignalTensorDataset +========================================== + +.. currentmodule:: torchsig.utils.dataset + +.. autoclass:: SignalTensorDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~SignalTensorDataset.tensors + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dataset.collate_fn.rst b/docs/_autosummary/torchsig.utils.dataset.collate_fn.rst new file mode 100644 index 0000000..5de5eb6 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dataset.collate_fn.rst @@ -0,0 +1,6 @@ +torchsig.utils.dataset.collate\_fn +================================== + +.. currentmodule:: torchsig.utils.dataset + +.. autofunction:: collate_fn \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dataset.rst b/docs/_autosummary/torchsig.utils.dataset.rst new file mode 100644 index 0000000..18d99ab --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dataset.rst @@ -0,0 +1,42 @@ +torchsig.utils.dataset +====================== + +.. automodule:: torchsig.utils.dataset + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + collate_fn + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + SignalDataset + SignalFileDataset + SignalTensorDataset + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.dsp.calculate_exponential_filter.rst b/docs/_autosummary/torchsig.utils.dsp.calculate_exponential_filter.rst new file mode 100644 index 0000000..6e25181 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.calculate_exponential_filter.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.calculate\_exponential\_filter +================================================= + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: calculate_exponential_filter \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.convolve.rst b/docs/_autosummary/torchsig.utils.dsp.convolve.rst new file mode 100644 index 0000000..e8cca8d --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.convolve.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.convolve +=========================== + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: convolve \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.estimate_filter_length.rst b/docs/_autosummary/torchsig.utils.dsp.estimate_filter_length.rst new file mode 100644 index 0000000..1b2eb81 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.estimate_filter_length.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.estimate\_filter\_length +=========================================== + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: estimate_filter_length \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.gaussian_taps.rst b/docs/_autosummary/torchsig.utils.dsp.gaussian_taps.rst new file mode 100644 index 0000000..98e298c --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.gaussian_taps.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.gaussian\_taps +================================= + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: gaussian_taps \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.low_pass.rst b/docs/_autosummary/torchsig.utils.dsp.low_pass.rst new file mode 100644 index 0000000..9c349e9 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.low_pass.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.low\_pass +============================ + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: low_pass \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.polyphase_prototype_filter.rst b/docs/_autosummary/torchsig.utils.dsp.polyphase_prototype_filter.rst new file mode 100644 index 0000000..552df1c --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.polyphase_prototype_filter.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.polyphase\_prototype\_filter +=============================================== + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: polyphase_prototype_filter \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.rational_rate_resampler.rst b/docs/_autosummary/torchsig.utils.dsp.rational_rate_resampler.rst new file mode 100644 index 0000000..2151bd0 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.rational_rate_resampler.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.rational\_rate\_resampler +============================================ + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: rational_rate_resampler \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.rrc_taps.rst b/docs/_autosummary/torchsig.utils.dsp.rrc_taps.rst new file mode 100644 index 0000000..64139f6 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.rrc_taps.rst @@ -0,0 +1,6 @@ +torchsig.utils.dsp.rrc\_taps +============================ + +.. currentmodule:: torchsig.utils.dsp + +.. autofunction:: rrc_taps \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.dsp.rst b/docs/_autosummary/torchsig.utils.dsp.rst new file mode 100644 index 0000000..a70148a --- /dev/null +++ b/docs/_autosummary/torchsig.utils.dsp.rst @@ -0,0 +1,38 @@ +torchsig.utils.dsp +================== + +.. automodule:: torchsig.utils.dsp + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + calculate_exponential_filter + convolve + estimate_filter_length + gaussian_taps + low_pass + polyphase_prototype_filter + rational_rate_resampler + rrc_taps + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.index.indexer_from_folders_sigmf.rst b/docs/_autosummary/torchsig.utils.index.indexer_from_folders_sigmf.rst new file mode 100644 index 0000000..d29a4c4 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.index.indexer_from_folders_sigmf.rst @@ -0,0 +1,6 @@ +torchsig.utils.index.indexer\_from\_folders\_sigmf +================================================== + +.. currentmodule:: torchsig.utils.index + +.. autofunction:: indexer_from_folders_sigmf \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.index.indexer_from_pickle.rst b/docs/_autosummary/torchsig.utils.index.indexer_from_pickle.rst new file mode 100644 index 0000000..0ced01c --- /dev/null +++ b/docs/_autosummary/torchsig.utils.index.indexer_from_pickle.rst @@ -0,0 +1,6 @@ +torchsig.utils.index.indexer\_from\_pickle +========================================== + +.. currentmodule:: torchsig.utils.index + +.. autofunction:: indexer_from_pickle \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.index.rst b/docs/_autosummary/torchsig.utils.index.rst new file mode 100644 index 0000000..cd38a15 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.index.rst @@ -0,0 +1,33 @@ +torchsig.utils.index +==================== + +.. automodule:: torchsig.utils.index + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + indexer_from_folders_sigmf + indexer_from_pickle + save_index + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.index.save_index.rst b/docs/_autosummary/torchsig.utils.index.save_index.rst new file mode 100644 index 0000000..2a45b00 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.index.save_index.rst @@ -0,0 +1,6 @@ +torchsig.utils.index.save\_index +================================ + +.. currentmodule:: torchsig.utils.index + +.. autofunction:: save_index \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.narrowband_trainer.MetricsLogger.rst b/docs/_autosummary/torchsig.utils.narrowband_trainer.MetricsLogger.rst new file mode 100644 index 0000000..51d38c0 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.narrowband_trainer.MetricsLogger.rst @@ -0,0 +1,69 @@ +torchsig.utils.narrowband\_trainer.MetricsLogger +================================================ + +.. currentmodule:: torchsig.utils.narrowband_trainer + +.. autoclass:: MetricsLogger + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~MetricsLogger.load_state_dict + ~MetricsLogger.on_after_backward + ~MetricsLogger.on_before_backward + ~MetricsLogger.on_before_optimizer_step + ~MetricsLogger.on_before_zero_grad + ~MetricsLogger.on_exception + ~MetricsLogger.on_fit_end + ~MetricsLogger.on_fit_start + ~MetricsLogger.on_load_checkpoint + ~MetricsLogger.on_predict_batch_end + ~MetricsLogger.on_predict_batch_start + ~MetricsLogger.on_predict_end + ~MetricsLogger.on_predict_epoch_end + ~MetricsLogger.on_predict_epoch_start + ~MetricsLogger.on_predict_start + ~MetricsLogger.on_sanity_check_end + ~MetricsLogger.on_sanity_check_start + ~MetricsLogger.on_save_checkpoint + ~MetricsLogger.on_test_batch_end + ~MetricsLogger.on_test_batch_start + ~MetricsLogger.on_test_end + ~MetricsLogger.on_test_epoch_end + ~MetricsLogger.on_test_epoch_start + ~MetricsLogger.on_test_start + ~MetricsLogger.on_train_batch_end + ~MetricsLogger.on_train_batch_start + ~MetricsLogger.on_train_end + ~MetricsLogger.on_train_epoch_end + ~MetricsLogger.on_train_epoch_start + ~MetricsLogger.on_train_start + ~MetricsLogger.on_validation_batch_end + ~MetricsLogger.on_validation_batch_start + ~MetricsLogger.on_validation_end + ~MetricsLogger.on_validation_epoch_end + ~MetricsLogger.on_validation_epoch_start + ~MetricsLogger.on_validation_start + ~MetricsLogger.setup + ~MetricsLogger.state_dict + ~MetricsLogger.teardown + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~MetricsLogger.state_key + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.narrowband_trainer.NarrowbandTrainer.rst b/docs/_autosummary/torchsig.utils.narrowband_trainer.NarrowbandTrainer.rst new file mode 100644 index 0000000..c00c621 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.narrowband_trainer.NarrowbandTrainer.rst @@ -0,0 +1,31 @@ +torchsig.utils.narrowband\_trainer.NarrowbandTrainer +==================================================== + +.. currentmodule:: torchsig.utils.narrowband_trainer + +.. autoclass:: NarrowbandTrainer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~NarrowbandTrainer.plot_confusion_matrix + ~NarrowbandTrainer.plot_metrics + ~NarrowbandTrainer.predict + ~NarrowbandTrainer.prepare_data + ~NarrowbandTrainer.prepare_model + ~NarrowbandTrainer.train + ~NarrowbandTrainer.validate + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.narrowband_trainer.rst b/docs/_autosummary/torchsig.utils.narrowband_trainer.rst new file mode 100644 index 0000000..89b4a3f --- /dev/null +++ b/docs/_autosummary/torchsig.utils.narrowband_trainer.rst @@ -0,0 +1,33 @@ +torchsig.utils.narrowband\_trainer +================================== + +.. automodule:: torchsig.utils.narrowband_trainer + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + MetricsLogger + NarrowbandTrainer + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.reader.reader_from_sigmf.rst b/docs/_autosummary/torchsig.utils.reader.reader_from_sigmf.rst new file mode 100644 index 0000000..8398590 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.reader.reader_from_sigmf.rst @@ -0,0 +1,6 @@ +torchsig.utils.reader.reader\_from\_sigmf +========================================= + +.. currentmodule:: torchsig.utils.reader + +.. autofunction:: reader_from_sigmf \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.reader.rst b/docs/_autosummary/torchsig.utils.reader.rst new file mode 100644 index 0000000..b5b0ad9 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.reader.rst @@ -0,0 +1,31 @@ +torchsig.utils.reader +===================== + +.. automodule:: torchsig.utils.reader + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + reader_from_sigmf + + + + + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.rst b/docs/_autosummary/torchsig.utils.rst new file mode 100644 index 0000000..27e2893 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.rst @@ -0,0 +1,43 @@ +torchsig.utils +============== + +.. automodule:: torchsig.utils + + + + + + + + + + + + + + + + + + + +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: + + classify_transforms + cm_plotter + dataset + dsp + index + narrowband_trainer + reader + types + visualize + writer + yolo_classify + yolo_train + yolo_val + yolo_validator + diff --git a/docs/_autosummary/torchsig.utils.types.ModulatedRFMetadata.rst b/docs/_autosummary/torchsig.utils.types.ModulatedRFMetadata.rst new file mode 100644 index 0000000..6e25c47 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.ModulatedRFMetadata.rst @@ -0,0 +1,56 @@ +torchsig.utils.types.ModulatedRFMetadata +======================================== + +.. currentmodule:: torchsig.utils.types + +.. autoclass:: ModulatedRFMetadata + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ModulatedRFMetadata.clear + ~ModulatedRFMetadata.copy + ~ModulatedRFMetadata.fromkeys + ~ModulatedRFMetadata.get + ~ModulatedRFMetadata.items + ~ModulatedRFMetadata.keys + ~ModulatedRFMetadata.pop + ~ModulatedRFMetadata.popitem + ~ModulatedRFMetadata.setdefault + ~ModulatedRFMetadata.update + ~ModulatedRFMetadata.values + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ModulatedRFMetadata.sample_rate + ~ModulatedRFMetadata.num_samples + ~ModulatedRFMetadata.complex + ~ModulatedRFMetadata.lower_freq + ~ModulatedRFMetadata.upper_freq + ~ModulatedRFMetadata.center_freq + ~ModulatedRFMetadata.bandwidth + ~ModulatedRFMetadata.start + ~ModulatedRFMetadata.stop + ~ModulatedRFMetadata.duration + ~ModulatedRFMetadata.snr + ~ModulatedRFMetadata.bits_per_symbol + ~ModulatedRFMetadata.samples_per_symbol + ~ModulatedRFMetadata.excess_bandwidth + ~ModulatedRFMetadata.class_name + ~ModulatedRFMetadata.class_index + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.RFMetadata.rst b/docs/_autosummary/torchsig.utils.types.RFMetadata.rst new file mode 100644 index 0000000..00720c5 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.RFMetadata.rst @@ -0,0 +1,50 @@ +torchsig.utils.types.RFMetadata +=============================== + +.. currentmodule:: torchsig.utils.types + +.. autoclass:: RFMetadata + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~RFMetadata.clear + ~RFMetadata.copy + ~RFMetadata.fromkeys + ~RFMetadata.get + ~RFMetadata.items + ~RFMetadata.keys + ~RFMetadata.pop + ~RFMetadata.popitem + ~RFMetadata.setdefault + ~RFMetadata.update + ~RFMetadata.values + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~RFMetadata.sample_rate + ~RFMetadata.num_samples + ~RFMetadata.complex + ~RFMetadata.lower_freq + ~RFMetadata.upper_freq + ~RFMetadata.center_freq + ~RFMetadata.bandwidth + ~RFMetadata.start + ~RFMetadata.stop + ~RFMetadata.duration + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.Signal.rst b/docs/_autosummary/torchsig.utils.types.Signal.rst new file mode 100644 index 0000000..f5bf5ed --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.Signal.rst @@ -0,0 +1,42 @@ +torchsig.utils.types.Signal +=========================== + +.. currentmodule:: torchsig.utils.types + +.. autoclass:: Signal + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Signal.clear + ~Signal.copy + ~Signal.fromkeys + ~Signal.get + ~Signal.items + ~Signal.keys + ~Signal.pop + ~Signal.popitem + ~Signal.setdefault + ~Signal.update + ~Signal.values + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~Signal.data + ~Signal.metadata + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.SignalCapture.rst b/docs/_autosummary/torchsig.utils.types.SignalCapture.rst new file mode 100644 index 0000000..eb9d733 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.SignalCapture.rst @@ -0,0 +1,24 @@ +torchsig.utils.types.SignalCapture +================================== + +.. currentmodule:: torchsig.utils.types + +.. autoclass:: SignalCapture + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.SignalData.rst b/docs/_autosummary/torchsig.utils.types.SignalData.rst new file mode 100644 index 0000000..a5e5dbd --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.SignalData.rst @@ -0,0 +1,41 @@ +torchsig.utils.types.SignalData +=============================== + +.. currentmodule:: torchsig.utils.types + +.. autoclass:: SignalData + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SignalData.clear + ~SignalData.copy + ~SignalData.fromkeys + ~SignalData.get + ~SignalData.items + ~SignalData.keys + ~SignalData.pop + ~SignalData.popitem + ~SignalData.setdefault + ~SignalData.update + ~SignalData.values + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~SignalData.samples + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.SignalMetadata.rst b/docs/_autosummary/torchsig.utils.types.SignalMetadata.rst new file mode 100644 index 0000000..3e55ab6 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.SignalMetadata.rst @@ -0,0 +1,42 @@ +torchsig.utils.types.SignalMetadata +=================================== + +.. currentmodule:: torchsig.utils.types + +.. autoclass:: SignalMetadata + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~SignalMetadata.clear + ~SignalMetadata.copy + ~SignalMetadata.fromkeys + ~SignalMetadata.get + ~SignalMetadata.items + ~SignalMetadata.keys + ~SignalMetadata.pop + ~SignalMetadata.popitem + ~SignalMetadata.setdefault + ~SignalMetadata.update + ~SignalMetadata.values + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~SignalMetadata.sample_rate + ~SignalMetadata.num_samples + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.create_modulated_rf_metadata.rst b/docs/_autosummary/torchsig.utils.types.create_modulated_rf_metadata.rst new file mode 100644 index 0000000..44188b2 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.create_modulated_rf_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.create\_modulated\_rf\_metadata +==================================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: create_modulated_rf_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.create_rf_metadata.rst b/docs/_autosummary/torchsig.utils.types.create_rf_metadata.rst new file mode 100644 index 0000000..1ac1b9b --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.create_rf_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.create\_rf\_metadata +========================================= + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: create_rf_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.create_signal.rst b/docs/_autosummary/torchsig.utils.types.create_signal.rst new file mode 100644 index 0000000..316c689 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.create_signal.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.create\_signal +=================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: create_signal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.create_signal_data.rst b/docs/_autosummary/torchsig.utils.types.create_signal_data.rst new file mode 100644 index 0000000..4549b56 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.create_signal_data.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.create\_signal\_data +========================================= + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: create_signal_data \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.create_signal_metadata.rst b/docs/_autosummary/torchsig.utils.types.create_signal_metadata.rst new file mode 100644 index 0000000..cd8df08 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.create_signal_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.create\_signal\_metadata +============================================= + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: create_signal_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.data_shape.rst b/docs/_autosummary/torchsig.utils.types.data_shape.rst new file mode 100644 index 0000000..1e2aa45 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.data_shape.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.data\_shape +================================ + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: data_shape \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.has_modulated_rf_metadata.rst b/docs/_autosummary/torchsig.utils.types.has_modulated_rf_metadata.rst new file mode 100644 index 0000000..c70c815 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.has_modulated_rf_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.has\_modulated\_rf\_metadata +================================================= + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: has_modulated_rf_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.has_rf_metadata.rst b/docs/_autosummary/torchsig.utils.types.has_rf_metadata.rst new file mode 100644 index 0000000..95eb1f2 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.has_rf_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.has\_rf\_metadata +====================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: has_rf_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.is_rf_metadata.rst b/docs/_autosummary/torchsig.utils.types.is_rf_metadata.rst new file mode 100644 index 0000000..e79ac42 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.is_rf_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.is\_rf\_metadata +===================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: is_rf_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.is_rf_modulated_metadata.rst b/docs/_autosummary/torchsig.utils.types.is_rf_modulated_metadata.rst new file mode 100644 index 0000000..d2ae5f3 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.is_rf_modulated_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.is\_rf\_modulated\_metadata +================================================ + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: is_rf_modulated_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.is_signal.rst b/docs/_autosummary/torchsig.utils.types.is_signal.rst new file mode 100644 index 0000000..1506d10 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.is_signal.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.is\_signal +=============================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: is_signal \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.is_signal_data.rst b/docs/_autosummary/torchsig.utils.types.is_signal_data.rst new file mode 100644 index 0000000..bea4e46 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.is_signal_data.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.is\_signal\_data +===================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: is_signal_data \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.is_signal_metadata.rst b/docs/_autosummary/torchsig.utils.types.is_signal_metadata.rst new file mode 100644 index 0000000..6050a61 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.is_signal_metadata.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.is\_signal\_metadata +========================================= + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: is_signal_metadata \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.meta_bound_frequency.rst b/docs/_autosummary/torchsig.utils.types.meta_bound_frequency.rst new file mode 100644 index 0000000..04f0b35 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.meta_bound_frequency.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.meta\_bound\_frequency +=========================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: meta_bound_frequency \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.meta_pad_height.rst b/docs/_autosummary/torchsig.utils.types.meta_pad_height.rst new file mode 100644 index 0000000..1f1db3a --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.meta_pad_height.rst @@ -0,0 +1,6 @@ +torchsig.utils.types.meta\_pad\_height +====================================== + +.. currentmodule:: torchsig.utils.types + +.. autofunction:: meta_pad_height \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.types.rst b/docs/_autosummary/torchsig.utils.types.rst new file mode 100644 index 0000000..89357c8 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.types.rst @@ -0,0 +1,59 @@ +torchsig.utils.types +==================== + +.. automodule:: torchsig.utils.types + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + create_modulated_rf_metadata + create_rf_metadata + create_signal + create_signal_data + create_signal_metadata + data_shape + has_modulated_rf_metadata + has_rf_metadata + is_rf_metadata + is_rf_modulated_metadata + is_signal + is_signal_data + is_signal_metadata + meta_bound_frequency + meta_pad_height + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + ModulatedRFMetadata + RFMetadata + Signal + SignalCapture + SignalData + SignalMetadata + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.visualize.AnchorBoxVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.AnchorBoxVisualizer.rst new file mode 100644 index 0000000..89d1348 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.AnchorBoxVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.AnchorBoxVisualizer +============================================ + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: AnchorBoxVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.BoundingBoxVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.BoundingBoxVisualizer.rst new file mode 100644 index 0000000..b02d19e --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.BoundingBoxVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.BoundingBoxVisualizer +============================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: BoundingBoxVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.ConstellationVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.ConstellationVisualizer.rst new file mode 100644 index 0000000..f14f837 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.ConstellationVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.ConstellationVisualizer +================================================ + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: ConstellationVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.IQVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.IQVisualizer.rst new file mode 100644 index 0000000..7683864 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.IQVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.IQVisualizer +===================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: IQVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.ImageVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.ImageVisualizer.rst new file mode 100644 index 0000000..875ddb1 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.ImageVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.ImageVisualizer +======================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: ImageVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.MaskClassVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.MaskClassVisualizer.rst new file mode 100644 index 0000000..04f5c7d --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.MaskClassVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.MaskClassVisualizer +============================================ + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: MaskClassVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.MaskVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.MaskVisualizer.rst new file mode 100644 index 0000000..c5816d3 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.MaskVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.MaskVisualizer +======================================= + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: MaskVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.PSDVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.PSDVisualizer.rst new file mode 100644 index 0000000..17957fe --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.PSDVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.PSDVisualizer +====================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: PSDVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.SemanticMaskClassVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.SemanticMaskClassVisualizer.rst new file mode 100644 index 0000000..47f1d00 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.SemanticMaskClassVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.SemanticMaskClassVisualizer +==================================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: SemanticMaskClassVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.SpectrogramVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.SpectrogramVisualizer.rst new file mode 100644 index 0000000..41483b9 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.SpectrogramVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.SpectrogramVisualizer +============================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: SpectrogramVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.TimeSeriesVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.TimeSeriesVisualizer.rst new file mode 100644 index 0000000..329fcd1 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.TimeSeriesVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.TimeSeriesVisualizer +============================================= + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: TimeSeriesVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.Visualizer.rst b/docs/_autosummary/torchsig.utils.visualize.Visualizer.rst new file mode 100644 index 0000000..33c9844 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.Visualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.Visualizer +=================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: Visualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.WaveletVisualizer.rst b/docs/_autosummary/torchsig.utils.visualize.WaveletVisualizer.rst new file mode 100644 index 0000000..449c218 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.WaveletVisualizer.rst @@ -0,0 +1,24 @@ +torchsig.utils.visualize.WaveletVisualizer +========================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autoclass:: WaveletVisualizer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.binary_label_format.rst b/docs/_autosummary/torchsig.utils.visualize.binary_label_format.rst new file mode 100644 index 0000000..6a639ab --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.binary_label_format.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.binary\_label\_format +============================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: binary_label_format \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.complex_spectrogram_to_magnitude.rst b/docs/_autosummary/torchsig.utils.visualize.complex_spectrogram_to_magnitude.rst new file mode 100644 index 0000000..410dd0a --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.complex_spectrogram_to_magnitude.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.complex\_spectrogram\_to\_magnitude +============================================================ + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: complex_spectrogram_to_magnitude \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.iq_to_complex_magnitude.rst b/docs/_autosummary/torchsig.utils.visualize.iq_to_complex_magnitude.rst new file mode 100644 index 0000000..7281147 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.iq_to_complex_magnitude.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.iq\_to\_complex\_magnitude +=================================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: iq_to_complex_magnitude \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.magnitude_spectrogram.rst b/docs/_autosummary/torchsig.utils.visualize.magnitude_spectrogram.rst new file mode 100644 index 0000000..150222f --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.magnitude_spectrogram.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.magnitude\_spectrogram +=============================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: magnitude_spectrogram \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.mask_class_to_outline.rst b/docs/_autosummary/torchsig.utils.visualize.mask_class_to_outline.rst new file mode 100644 index 0000000..c82d079 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.mask_class_to_outline.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.mask\_class\_to\_outline +================================================= + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: mask_class_to_outline \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.mask_to_outline.rst b/docs/_autosummary/torchsig.utils.visualize.mask_to_outline.rst new file mode 100644 index 0000000..c34475b --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.mask_to_outline.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.mask\_to\_outline +========================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: mask_to_outline \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.mask_to_outline_overlap.rst b/docs/_autosummary/torchsig.utils.visualize.mask_to_outline_overlap.rst new file mode 100644 index 0000000..eede90e --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.mask_to_outline_overlap.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.mask\_to\_outline\_overlap +=================================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: mask_to_outline_overlap \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.multihot_label_format.rst b/docs/_autosummary/torchsig.utils.visualize.multihot_label_format.rst new file mode 100644 index 0000000..459b44f --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.multihot_label_format.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.multihot\_label\_format +================================================ + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: multihot_label_format \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.onehot_label_format.rst b/docs/_autosummary/torchsig.utils.visualize.onehot_label_format.rst new file mode 100644 index 0000000..0a19714 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.onehot_label_format.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.onehot\_label\_format +============================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: onehot_label_format \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.overlay_mask.rst b/docs/_autosummary/torchsig.utils.visualize.overlay_mask.rst new file mode 100644 index 0000000..79ff6a2 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.overlay_mask.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.overlay\_mask +====================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: overlay_mask \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.visualize.rst b/docs/_autosummary/torchsig.utils.visualize.rst new file mode 100644 index 0000000..aae8fb6 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.rst @@ -0,0 +1,62 @@ +torchsig.utils.visualize +======================== + +.. automodule:: torchsig.utils.visualize + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + binary_label_format + complex_spectrogram_to_magnitude + iq_to_complex_magnitude + magnitude_spectrogram + mask_class_to_outline + mask_to_outline + mask_to_outline_overlap + multihot_label_format + onehot_label_format + overlay_mask + two_channel_to_complex + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + AnchorBoxVisualizer + BoundingBoxVisualizer + ConstellationVisualizer + IQVisualizer + ImageVisualizer + MaskClassVisualizer + MaskVisualizer + PSDVisualizer + SemanticMaskClassVisualizer + SpectrogramVisualizer + TimeSeriesVisualizer + Visualizer + WaveletVisualizer + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.visualize.two_channel_to_complex.rst b/docs/_autosummary/torchsig.utils.visualize.two_channel_to_complex.rst new file mode 100644 index 0000000..3d64505 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.visualize.two_channel_to_complex.rst @@ -0,0 +1,6 @@ +torchsig.utils.visualize.two\_channel\_to\_complex +================================================== + +.. currentmodule:: torchsig.utils.visualize + +.. autofunction:: two_channel_to_complex \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.writer.DatasetCreator.rst b/docs/_autosummary/torchsig.utils.writer.DatasetCreator.rst new file mode 100644 index 0000000..1ce1783 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.writer.DatasetCreator.rst @@ -0,0 +1,25 @@ +torchsig.utils.writer.DatasetCreator +==================================== + +.. currentmodule:: torchsig.utils.writer + +.. autoclass:: DatasetCreator + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetCreator.create + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.writer.DatasetLoader.rst b/docs/_autosummary/torchsig.utils.writer.DatasetLoader.rst new file mode 100644 index 0000000..a850321 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.writer.DatasetLoader.rst @@ -0,0 +1,25 @@ +torchsig.utils.writer.DatasetLoader +=================================== + +.. currentmodule:: torchsig.utils.writer + +.. autoclass:: DatasetLoader + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetLoader.worker_init_fn + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.writer.DatasetWriter.rst b/docs/_autosummary/torchsig.utils.writer.DatasetWriter.rst new file mode 100644 index 0000000..d1f7313 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.writer.DatasetWriter.rst @@ -0,0 +1,26 @@ +torchsig.utils.writer.DatasetWriter +=================================== + +.. currentmodule:: torchsig.utils.writer + +.. autoclass:: DatasetWriter + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~DatasetWriter.exists + ~DatasetWriter.write + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.writer.LMDBDatasetWriter.rst b/docs/_autosummary/torchsig.utils.writer.LMDBDatasetWriter.rst new file mode 100644 index 0000000..24cdf80 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.writer.LMDBDatasetWriter.rst @@ -0,0 +1,26 @@ +torchsig.utils.writer.LMDBDatasetWriter +======================================= + +.. currentmodule:: torchsig.utils.writer + +.. autoclass:: LMDBDatasetWriter + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~LMDBDatasetWriter.exists + ~LMDBDatasetWriter.write + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.writer.rst b/docs/_autosummary/torchsig.utils.writer.rst new file mode 100644 index 0000000..d81752a --- /dev/null +++ b/docs/_autosummary/torchsig.utils.writer.rst @@ -0,0 +1,35 @@ +torchsig.utils.writer +===================== + +.. automodule:: torchsig.utils.writer + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + DatasetCreator + DatasetLoader + DatasetWriter + LMDBDatasetWriter + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.yolo_classify.TorchsigClassificationDataset.rst b/docs/_autosummary/torchsig.utils.yolo_classify.TorchsigClassificationDataset.rst new file mode 100644 index 0000000..39470b8 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_classify.TorchsigClassificationDataset.rst @@ -0,0 +1,25 @@ +torchsig.utils.yolo\_classify.TorchsigClassificationDataset +=========================================================== + +.. currentmodule:: torchsig.utils.yolo_classify + +.. autoclass:: TorchsigClassificationDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TorchsigClassificationDataset.spectrogram_image + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_classify.YoloClassifyTrainer.rst b/docs/_autosummary/torchsig.utils.yolo_classify.YoloClassifyTrainer.rst new file mode 100644 index 0000000..01204fb --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_classify.YoloClassifyTrainer.rst @@ -0,0 +1,52 @@ +torchsig.utils.yolo\_classify.YoloClassifyTrainer +================================================= + +.. currentmodule:: torchsig.utils.yolo_classify + +.. autoclass:: YoloClassifyTrainer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~YoloClassifyTrainer.add_callback + ~YoloClassifyTrainer.build_dataset + ~YoloClassifyTrainer.build_optimizer + ~YoloClassifyTrainer.build_targets + ~YoloClassifyTrainer.check_resume + ~YoloClassifyTrainer.final_eval + ~YoloClassifyTrainer.get_dataloader + ~YoloClassifyTrainer.get_dataset + ~YoloClassifyTrainer.get_model + ~YoloClassifyTrainer.get_validator + ~YoloClassifyTrainer.label_loss_items + ~YoloClassifyTrainer.on_plot + ~YoloClassifyTrainer.optimizer_step + ~YoloClassifyTrainer.plot_metrics + ~YoloClassifyTrainer.plot_training_labels + ~YoloClassifyTrainer.plot_training_samples + ~YoloClassifyTrainer.preprocess_batch + ~YoloClassifyTrainer.progress_string + ~YoloClassifyTrainer.read_results_csv + ~YoloClassifyTrainer.resume_training + ~YoloClassifyTrainer.run_callbacks + ~YoloClassifyTrainer.save_metrics + ~YoloClassifyTrainer.save_model + ~YoloClassifyTrainer.set_callback + ~YoloClassifyTrainer.set_model_attributes + ~YoloClassifyTrainer.setup_model + ~YoloClassifyTrainer.train + ~YoloClassifyTrainer.validate + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_classify.rst b/docs/_autosummary/torchsig.utils.yolo_classify.rst new file mode 100644 index 0000000..4d9933c --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_classify.rst @@ -0,0 +1,33 @@ +torchsig.utils.yolo\_classify +============================= + +.. automodule:: torchsig.utils.yolo_classify + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + TorchsigClassificationDataset + YoloClassifyTrainer + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.yolo_train.TorchsigDataset.rst b/docs/_autosummary/torchsig.utils.yolo_train.TorchsigDataset.rst new file mode 100644 index 0000000..d9d5345 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.TorchsigDataset.rst @@ -0,0 +1,38 @@ +torchsig.utils.yolo\_train.TorchsigDataset +========================================== + +.. currentmodule:: torchsig.utils.yolo_train + +.. autoclass:: TorchsigDataset + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~TorchsigDataset.build_transforms + ~TorchsigDataset.cache_images + ~TorchsigDataset.cache_images_to_disk + ~TorchsigDataset.cache_labels + ~TorchsigDataset.check_cache_ram + ~TorchsigDataset.close_mosaic + ~TorchsigDataset.collate_fn + ~TorchsigDataset.get_image_and_label + ~TorchsigDataset.get_img_files + ~TorchsigDataset.get_labels + ~TorchsigDataset.load_image + ~TorchsigDataset.set_rectangle + ~TorchsigDataset.update_labels + ~TorchsigDataset.update_labels_info + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_train.Yolo_Trainer.rst b/docs/_autosummary/torchsig.utils.yolo_train.Yolo_Trainer.rst new file mode 100644 index 0000000..6c3936b --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.Yolo_Trainer.rst @@ -0,0 +1,52 @@ +torchsig.utils.yolo\_train.Yolo\_Trainer +======================================== + +.. currentmodule:: torchsig.utils.yolo_train + +.. autoclass:: Yolo_Trainer + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~Yolo_Trainer.add_callback + ~Yolo_Trainer.build_dataset + ~Yolo_Trainer.build_optimizer + ~Yolo_Trainer.build_targets + ~Yolo_Trainer.check_resume + ~Yolo_Trainer.final_eval + ~Yolo_Trainer.get_dataloader + ~Yolo_Trainer.get_dataset + ~Yolo_Trainer.get_model + ~Yolo_Trainer.get_validator + ~Yolo_Trainer.label_loss_items + ~Yolo_Trainer.on_plot + ~Yolo_Trainer.optimizer_step + ~Yolo_Trainer.plot_metrics + ~Yolo_Trainer.plot_training_labels + ~Yolo_Trainer.plot_training_samples + ~Yolo_Trainer.preprocess_batch + ~Yolo_Trainer.progress_string + ~Yolo_Trainer.read_results_csv + ~Yolo_Trainer.resume_training + ~Yolo_Trainer.run_callbacks + ~Yolo_Trainer.save_metrics + ~Yolo_Trainer.save_model + ~Yolo_Trainer.set_callback + ~Yolo_Trainer.set_model_attributes + ~Yolo_Trainer.setup_model + ~Yolo_Trainer.train + ~Yolo_Trainer.validate + + + + + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_train.build_torchsig_dataset.rst b/docs/_autosummary/torchsig.utils.yolo_train.build_torchsig_dataset.rst new file mode 100644 index 0000000..3e60b62 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.build_torchsig_dataset.rst @@ -0,0 +1,6 @@ +torchsig.utils.yolo\_train.build\_torchsig\_dataset +=================================================== + +.. currentmodule:: torchsig.utils.yolo_train + +.. autofunction:: build_torchsig_dataset \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_train.load_dataset_cache_file.rst b/docs/_autosummary/torchsig.utils.yolo_train.load_dataset_cache_file.rst new file mode 100644 index 0000000..4127e99 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.load_dataset_cache_file.rst @@ -0,0 +1,6 @@ +torchsig.utils.yolo\_train.load\_dataset\_cache\_file +===================================================== + +.. currentmodule:: torchsig.utils.yolo_train + +.. autofunction:: load_dataset_cache_file \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_train.process_batch.rst b/docs/_autosummary/torchsig.utils.yolo_train.process_batch.rst new file mode 100644 index 0000000..a22db1b --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.process_batch.rst @@ -0,0 +1,6 @@ +torchsig.utils.yolo\_train.process\_batch +========================================= + +.. currentmodule:: torchsig.utils.yolo_train + +.. autofunction:: process_batch \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_train.rst b/docs/_autosummary/torchsig.utils.yolo_train.rst new file mode 100644 index 0000000..22119c8 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.rst @@ -0,0 +1,44 @@ +torchsig.utils.yolo\_train +========================== + +.. automodule:: torchsig.utils.yolo_train + + + + + + + + .. rubric:: Functions + + .. autosummary:: + :toctree: + :nosignatures: + + build_torchsig_dataset + load_dataset_cache_file + process_batch + save_dataset_cache_file + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + TorchsigDataset + Yolo_Trainer + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.yolo_train.save_dataset_cache_file.rst b/docs/_autosummary/torchsig.utils.yolo_train.save_dataset_cache_file.rst new file mode 100644 index 0000000..7894f99 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_train.save_dataset_cache_file.rst @@ -0,0 +1,6 @@ +torchsig.utils.yolo\_train.save\_dataset\_cache\_file +===================================================== + +.. currentmodule:: torchsig.utils.yolo_train + +.. autofunction:: save_dataset_cache_file \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_val.ClassificationValidator.rst b/docs/_autosummary/torchsig.utils.yolo_val.ClassificationValidator.rst new file mode 100644 index 0000000..268c6ae --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_val.ClassificationValidator.rst @@ -0,0 +1,49 @@ +torchsig.utils.yolo\_val.ClassificationValidator +================================================ + +.. currentmodule:: torchsig.utils.yolo_val + +.. autoclass:: ClassificationValidator + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~ClassificationValidator.add_callback + ~ClassificationValidator.build_dataset + ~ClassificationValidator.check_stats + ~ClassificationValidator.eval_json + ~ClassificationValidator.finalize_metrics + ~ClassificationValidator.get_dataloader + ~ClassificationValidator.get_desc + ~ClassificationValidator.get_stats + ~ClassificationValidator.init_metrics + ~ClassificationValidator.match_predictions + ~ClassificationValidator.on_plot + ~ClassificationValidator.plot_predictions + ~ClassificationValidator.plot_val_samples + ~ClassificationValidator.postprocess + ~ClassificationValidator.pred_to_json + ~ClassificationValidator.preprocess + ~ClassificationValidator.print_results + ~ClassificationValidator.run_callbacks + ~ClassificationValidator.update_metrics + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~ClassificationValidator.metric_keys + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_val.rst b/docs/_autosummary/torchsig.utils.yolo_val.rst new file mode 100644 index 0000000..3ebd7a3 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_val.rst @@ -0,0 +1,32 @@ +torchsig.utils.yolo\_val +======================== + +.. automodule:: torchsig.utils.yolo_val + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + ClassificationValidator + + + + + + + + + diff --git a/docs/_autosummary/torchsig.utils.yolo_validator.BaseValidator.rst b/docs/_autosummary/torchsig.utils.yolo_validator.BaseValidator.rst new file mode 100644 index 0000000..bb5e205 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_validator.BaseValidator.rst @@ -0,0 +1,49 @@ +torchsig.utils.yolo\_validator.BaseValidator +============================================ + +.. currentmodule:: torchsig.utils.yolo_validator + +.. autoclass:: BaseValidator + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + + + .. rubric:: Methods + + .. autosummary:: + :nosignatures: + + ~BaseValidator.add_callback + ~BaseValidator.build_dataset + ~BaseValidator.check_stats + ~BaseValidator.eval_json + ~BaseValidator.finalize_metrics + ~BaseValidator.get_dataloader + ~BaseValidator.get_desc + ~BaseValidator.get_stats + ~BaseValidator.init_metrics + ~BaseValidator.match_predictions + ~BaseValidator.on_plot + ~BaseValidator.plot_predictions + ~BaseValidator.plot_val_samples + ~BaseValidator.postprocess + ~BaseValidator.pred_to_json + ~BaseValidator.preprocess + ~BaseValidator.print_results + ~BaseValidator.run_callbacks + ~BaseValidator.update_metrics + + + + + + .. rubric:: Attributes + + .. autosummary:: + + ~BaseValidator.metric_keys + + \ No newline at end of file diff --git a/docs/_autosummary/torchsig.utils.yolo_validator.rst b/docs/_autosummary/torchsig.utils.yolo_validator.rst new file mode 100644 index 0000000..fa01fc4 --- /dev/null +++ b/docs/_autosummary/torchsig.utils.yolo_validator.rst @@ -0,0 +1,32 @@ +torchsig.utils.yolo\_validator +============================== + +.. automodule:: torchsig.utils.yolo_validator + + + + + + + + + + + + .. rubric:: Classes + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + + BaseValidator + + + + + + + + + diff --git a/docs/_templates/custom_class_template.rst b/docs/_templates/custom_class_template.rst new file mode 100644 index 0000000..d64b80d --- /dev/null +++ b/docs/_templates/custom_class_template.rst @@ -0,0 +1,34 @@ +{{ fullname | escape | underline}} + +.. currentmodule:: {{ module }} + +.. autoclass:: {{ objname }} + :members: + :show-inheritance: + :inherited-members: + :special-members: __call__, __add__, __mul__ + + {% block methods %} + {% if methods %} + .. rubric:: {{ _('Methods') }} + + .. autosummary:: + :nosignatures: + {% for item in methods %} + {%- if not item.startswith('_') %} + ~{{ name }}.{{ item }} + {%- endif -%} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block attributes %} + {% if attributes %} + .. rubric:: {{ _('Attributes') }} + + .. autosummary:: + {% for item in attributes %} + ~{{ name }}.{{ item }} + {%- endfor %} + {% endif %} + {% endblock %} \ No newline at end of file diff --git a/docs/_templates/custom_module_template.rst b/docs/_templates/custom_module_template.rst new file mode 100644 index 0000000..1985007 --- /dev/null +++ b/docs/_templates/custom_module_template.rst @@ -0,0 +1,66 @@ +{{ fullname | escape | underline}} + +.. automodule:: {{ fullname }} + + {% block attributes %} + {% if attributes %} + .. rubric:: Module attributes + + .. autosummary:: + :toctree: + {% for item in attributes %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block functions %} + {% if functions %} + .. rubric:: {{ _('Functions') }} + + .. autosummary:: + :toctree: + :nosignatures: + {% for item in functions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block classes %} + {% if classes %} + .. rubric:: {{ _('Classes') }} + + .. autosummary:: + :toctree: + :template: custom_class_template.rst + :nosignatures: + {% for item in classes %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + + {% block exceptions %} + {% if exceptions %} + .. rubric:: {{ _('Exceptions') }} + + .. autosummary:: + :toctree: + {% for item in exceptions %} + {{ item }} + {%- endfor %} + {% endif %} + {% endblock %} + +{% block modules %} +{% if modules %} +.. autosummary:: + :toctree: + :template: custom_module_template.rst + :recursive: +{% for item in modules %} + {{ item }} +{%- endfor %} +{% endif %} +{% endblock %} \ No newline at end of file diff --git a/docs/api.rst b/docs/api.rst new file mode 100755 index 0000000..93f01f1 --- /dev/null +++ b/docs/api.rst @@ -0,0 +1,14 @@ +.. torchsig documentation master file, created by + sphinx-quickstart + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +API +=========== + +.. autosummary:: + :toctree: _autosummary + :template: custom_module_template.rst + :recursive: + + torchsig diff --git a/docs/conf.py b/docs/conf.py index 55ec423..11f081c 100755 --- a/docs/conf.py +++ b/docs/conf.py @@ -35,9 +35,14 @@ 'sphinx.ext.todo', 'sphinx.ext.coverage', 'sphinx.ext.mathjax', - 'sphinx.ext.viewcode' + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', # allows autodoc from Google Style docstrings + 'sphinx.ext.autosummary' ] +autosummary_generate = True +autodoc_member_order = 'bysource' + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -59,9 +64,9 @@ # built documents. # # The short X.Y version. -version = '0.1' +version = '0.6' # The full version, including alpha/beta/rc tags. -release = '0.1.0' +release = '0.6.0' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. diff --git a/docs/datasets.rst b/docs/datasets.rst index 465c22b..cd815ee 100755 --- a/docs/datasets.rst +++ b/docs/datasets.rst @@ -19,109 +19,72 @@ All the datasets have almost similar API. They all have a common argument: ``transform`` to transform the input data. -.. currentmodule:: torchsig.datasets.sig53 +TorchSigNarrowband +---------------- +.. automodule:: torchsig.datasets.torchsig_narrowband + :members: + :undoc-members: + :show-inheritance: -Sig53 -~~~~~~~~~~~~~~ - -.. autoclass:: Sig53 - - -.. currentmodule:: torchsig.datasets.wideband_sig53 - -WidebandSig53 -~~~~~~~~~~~~~~ - -.. autoclass:: WidebandSig53 - - -.. currentmodule:: torchsig.datasets.modulations - -ModulationsDataset -~~~~~~~~~~~~~~~~~~~~ - -.. autoclass:: ModulationsDataset - +TorchSigWideband +---------------- +.. automodule:: torchsig.datasets.torchsig_wideband + :members: + :undoc-members: + :show-inheritance: -.. currentmodule:: torchsig.datasets.wideband -WidebandModulationsDataset -~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: WidebandModulationsDataset +Modulations Dataset +------------------ +.. automodule:: torchsig.datasets.modulations + :members: + :undoc-members: + :show-inheritance: -.. currentmodule:: torchsig.datasets.synthetic -DigitalModulationDataset -~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: DigitalModulationDataset +Wideband Datasets +------------------ +.. automodule:: torchsig.datasets.wideband + :members: + :undoc-members: + :show-inheritance: -ConstellationDataset -~~~~~~~~~~~~~~~~~~~~~~~ +Synthetic Datasets +------------------ +.. automodule:: torchsig.datasets.synthetic + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: ConstellationDataset - - -OFDMDataset -~~~~~~~~~~~~~~ - -.. autoclass:: OFDMDataset - - -FSKDataset -~~~~~~~~~~~~~~ - -.. autoclass:: FSKDataset - - -AMDataset -~~~~~~~~~~~~~~ - -.. autoclass:: AMDataset - - -FMDataset -~~~~~~~~~~~~~~ - -.. autoclass:: FMDataset - - -.. currentmodule:: torchsig.datasets.wideband - -WidebandDataset -~~~~~~~~~~~~~~~~~~ - -.. autoclass:: WidebandDataset - - -SyntheticBurstSourceDataset -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autoclass:: SyntheticBurstSourceDataset - - -.. currentmodule:: torchsig.datasets.file_datasets - - -FileBurstSourceDataset -~~~~~~~~~~~~~~~~~~~~~~~~ - -.. autoclass:: FileBurstSourceDataset +Radio ML Datasets +------------------ .. currentmodule:: torchsig.datasets.radioml - -RadioML2016 +Radio ML 2016 ~~~~~~~~~~~~~~ - .. autoclass:: RadioML2016 + :members: + :undoc-members: + :show-inheritance: - -RadioML2018 +Radio ML 2018 ~~~~~~~~~~~~~~ +.. autoclass:: RadioML2018 + :members: + :undoc-members: + :show-inheritance: + + -.. autoclass:: RadioML2018 \ No newline at end of file +File Datasets +--------------- +.. automodule:: torchsig.datasets.file_datasets + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/docs-requirements.txt b/docs/docs-requirements.txt index e3b40a8..f417f7b 100755 --- a/docs/docs-requirements.txt +++ b/docs/docs-requirements.txt @@ -1,6 +1,6 @@ -better-apidoc>=0.3.1 +better-apidoc>=0.3.2 numba recommonmark>=0.7.1 six -sphinx-rtd-theme>=0.4.3 -Sphinx>=3.4.3 \ No newline at end of file +sphinx-rtd-theme==2.0.0 +Sphinx>=7.4.7 \ No newline at end of file diff --git a/docs/image_datasets.rst b/docs/image_datasets.rst new file mode 100755 index 0000000..6359594 --- /dev/null +++ b/docs/image_datasets.rst @@ -0,0 +1,63 @@ +Image Datasets +====================== + +.. currentmodule:: torchsig.image_datasets + +Synthetic spectrogram datasets (not from I/Q data) and tools for signal spectrogram detection and classification. Read more in the `Torchsig GNU Radio Conference 2024 publication `_. + +.. contents:: Image Datasets + :local: + +Datasets +---------------- + +.. automodule:: torchsig.image_datasets.datasets.synthetic_signals + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: torchsig.image_datasets.datasets.file_loading_datasets + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: torchsig.image_datasets.datasets.composites + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: torchsig.image_datasets.datasets.protocols + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: torchsig.image_datasets.datasets.yolo_datasets + :members: + :undoc-members: + :show-inheritance: + +Plotting +---------------- +.. automodule:: torchsig.image_datasets.plotting.plotting + :members: + :undoc-members: + :show-inheritance: + +Transforms +---------------- +.. automodule:: torchsig.image_datasets.transforms.denoising + :members: + :undoc-members: + :show-inheritance: + +.. automodule:: torchsig.image_datasets.transforms.impairments + :members: + :undoc-members: + :show-inheritance: + +Annotation Tools +---------------- +.. automodule:: torchsig.image_datasets.annotation_tools.yolo_annotation_tool + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index 4152609..6cc5fb7 100755 --- a/docs/index.rst +++ b/docs/index.rst @@ -13,12 +13,23 @@ TorchSig :mod:`TorchSig` is an open-source signals processing machine learning toolkit. .. toctree:: + :maxdepth: 2 + :caption: Contents: datasets + image_datasets transforms + target_transforms models utils + api -.. automodule:: torchsig - :members: +.. .. automodule:: torchsig +.. :members: + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` diff --git a/docs/models.rst b/docs/models.rst index ad6b8c0..14a9b94 100755 --- a/docs/models.rst +++ b/docs/models.rst @@ -42,28 +42,28 @@ Spectrogram Models ------------------ -YOLOv5 -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. YOLOv5 +.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.models.spectrogram_models.yolov5 +.. .. currentmodule:: torchsig.models.spectrogram_models.yolov5 -.. autoclass:: yolov5p +.. .. autoclass:: yolov5p -.. autoclass:: yolov5n +.. .. autoclass:: yolov5n -.. autoclass:: yolov5s +.. .. autoclass:: yolov5s -.. autoclass:: yolov5p_mod_family +.. .. autoclass:: yolov5p_mod_family -.. autoclass:: yolov5n_mod_family +.. .. autoclass:: yolov5n_mod_family -.. autoclass:: yolov5s_mod_family +.. .. autoclass:: yolov5s_mod_family DETR ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.models.spectrogram_models.detr +.. currentmodule:: torchsig.models.spectrogram_models.detr.detr .. autoclass:: detr_b0_nano @@ -78,37 +78,37 @@ DETR .. autoclass:: detr_b4_nano_mod_family -PSPNet -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. PSPNet +.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.models.spectrogram_models.pspnet +.. .. currentmodule:: torchsig.models.spectrogram_models.pspnet -.. autoclass:: pspnet_b0 +.. .. autoclass:: pspnet_b0 -.. autoclass:: pspnet_b2 +.. .. autoclass:: pspnet_b2 -.. autoclass:: pspnet_b4 +.. .. autoclass:: pspnet_b4 -.. autoclass:: pspnet_b0_mod_family +.. .. autoclass:: pspnet_b0_mod_family -.. autoclass:: pspnet_b2_mod_family +.. .. autoclass:: pspnet_b2_mod_family -.. autoclass:: pspnet_b4_mod_family +.. .. autoclass:: pspnet_b4_mod_family -Mask2Former -~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ +.. Mask2Former +.. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.models.spectrogram_models.mask2former +.. .. currentmodule:: torchsig.models.spectrogram_models.mask2former -.. autoclass:: mask2former_b0 +.. .. autoclass:: mask2former_b0 -.. autoclass:: mask2former_b2 +.. .. autoclass:: mask2former_b2 -.. autoclass:: mask2former_b4 +.. .. autoclass:: mask2former_b4 -.. autoclass:: mask2former_b0_mod_family +.. .. autoclass:: mask2former_b0_mod_family -.. autoclass:: mask2former_b2_mod_family +.. .. autoclass:: mask2former_b2_mod_family -.. autoclass:: mask2former_b4_mod_family +.. .. autoclass:: mask2former_b4_mod_family diff --git a/docs/target_transforms.rst b/docs/target_transforms.rst new file mode 100755 index 0000000..87b6d17 --- /dev/null +++ b/docs/target_transforms.rst @@ -0,0 +1,17 @@ +Target Transforms +====================== + +.. currentmodule:: torchsig.transforms.target_transforms + +Target transforms are common signal transformations. They can be chained together using :class:`Compose`. +Additionally, there is the :mod:`torchsig.transforms.target_transforms` module. +Functional transforms give fine-grained control over the transformations. +This is useful if you have to build a more complex transformation pipeline + +.. contents:: Target Transforms + :local: + +.. automodule:: torchsig.transforms.target_transforms + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/docs/transforms.rst b/docs/transforms.rst index e9e83be..8716cb3 100755 --- a/docs/transforms.rst +++ b/docs/transforms.rst @@ -1,7 +1,7 @@ Transforms ====================== -.. currentmodule:: torchsig.transforms +.. currentmodule:: torchsig.transforms.tranforms Transforms are common signal transformations. They can be chained together using :class:`Compose`. Additionally, there is the :mod:`torchsig.transforms.functional` module. @@ -12,360 +12,15 @@ This is useful if you have to build a more complex transformation pipeline :local: Transforms ----------- -.. currentmodule:: torchsig.transforms.transforms - -Transform -^^^^^^^^^ -.. autoclass:: Transform - -Compose -^^^^^^^^^ -.. autoclass:: Compose - -Identity -^^^^^^^^^ -.. autoclass:: Identity - -Lambda -^^^^^^^^^ -.. autoclass:: Lambda - -FixedRandom -^^^^^^^^^^^^^ -.. autoclass:: FixedRandom - -RandomApply -^^^^^^^^^^^^^ -.. autoclass:: RandomApply - -SignalTransform -^^^^^^^^^^^^^^^^^ -.. autoclass:: SignalTransform - -Concatenate -^^^^^^^^^^^^^ -.. autoclass:: Concatenate - -TargetConcatenate -^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: TargetConcatenate - -InterleaveComplex -^^^^^^^^^^^^^^^^^ -.. autoclass:: InterleaveComplex - -ComplexTo2D -^^^^^^^^^^^^^ -.. autoclass:: ComplexTo2D - -Real -^^^^^^^^^ -.. autoclass:: Real - -Imag -^^^^^^^^^ -.. autoclass:: Imag - -ComplexMagnitude -^^^^^^^^^^^^^^^^^ -.. autoclass:: ComplexMagnitude - -WrappedPhase -^^^^^^^^^^^^^ -.. autoclass:: WrappedPhase - -DiscreteFourierTransform -^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DiscreteFourierTransform - -ChannelConcatIQDFT -^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: ChannelConcatIQDFT - -Spectrogram -^^^^^^^^^^^^^ -.. autoclass:: Spectrogram - -ContinuousWavelet -^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: ContinuousWavelet - -ReshapeTransform -^^^^^^^^^^^^^^^^^ -.. autoclass:: ReshapeTransform - -RandAugment -^^^^^^^^^^^^^ -.. autoclass:: RandAugment - -Normalize -^^^^^^^^^ -.. autoclass:: Normalize - - -Augmentations -------------- -.. currentmodule:: torchsig.transforms.transforms - -DatasetBasebandMixUp -^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DatasetBasebandMixUp - -DatasetBasebandCutMix -^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DatasetBasebandCutMix - -CutOut -^^^^^^^^^ -.. autoclass:: CutOut - -PatchShuffle -^^^^^^^^^^^^^ -.. autoclass:: PatchShuffle - -DatasetWidebandMixUp -^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DatasetWidebandMixUp - -DatasetWidebandCutMix -^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DatasetWidebandCutMix - -SpectrogramRandomResizeCrop -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectrogramRandomResizeCrop - -RandomResample -^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomResample - -RandomTimeShift -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomTimeShift - -TimeCrop -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: TimeCrop - -TimeReversal -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: TimeReversal - -AmplitudeReversal -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: AmplitudeReversal - -RandomFrequencyShift -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomFrequencyShift - -RandomDelayedFrequencyShift -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomDelayedFrequencyShift - -LocalOscillatorDrift -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: LocalOscillatorDrift - -GainDrift -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: GainDrift - -AutomaticGainControl -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: AutomaticGainControl - -IQImbalance -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: IQImbalance - -RollOff -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RollOff - -AddSlope -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: AddSlope - -SpectralInversion -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectralInversion - -ChannelSwap -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: ChannelSwap - -RandomMagRescale -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomMagRescale - -RandomDropSamples -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomDropSamples - -Quantize -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: Quantize - -Clip -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: Clip - -RandomConvolve -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomConvolve - -TargetSNR -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: TargetSNR - -AddNoise -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: AddNoise - -TimeVaryingNoise -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: TimeVaryingNoise - -RayleighFadingChannel -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RayleighFadingChannel - -ImpulseInterferer -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: ImpulseInterferer - -RandomPhaseShift -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: RandomPhaseShift - -SpectrogramDropSamples -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectrogramDropSamples - -SpectrogramPatchShuffle -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectrogramPatchShuffle - -SpectrogramTranslation -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectrogramTranslation - -SpectrogramMosaicCrop -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectrogramMosaicCrop - -SpectrogramMosaicDownsample -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: SpectrogramMosaicDownsample - - -Target Transforms ------------------ -.. currentmodule:: torchsig.transforms.target_transforms - -DescToClassName -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToClassName - -DescToClassNameSNR -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToClassNameSNR - -DescToClassIndex -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToClassIndex - -DescToClassIndexSNR -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToClassIndexSNR - -DescToMask -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToMask - -DescToMaskSignal -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToMaskSignal - -DescToMaskFamily -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToMaskFamily - -DescToMaskClass -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToMaskClass - -DescToSemanticClass -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToSemanticClass - -DescToBBox -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToBBox - -DescToAnchorBoxes -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToAnchorBoxes - -DescPassThrough -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescPassThrough - -DescToBinary -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToBinary - -DescToCustom -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToCustom - -DescToClassEncoding -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToClassEncoding - -DescToWeightedMixUp -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToWeightedMixUp - -DescToWeightedCutMix -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToWeightedCutMix - -DescToBBoxDict -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToBBoxDict - -DescToBBoxSignalDict -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToBBoxSignalDict - -DescToBBoxFamilyDict -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToBBoxFamilyDict - -DescToInstMaskDict -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToInstMaskDict - -DescToSignalInstMaskDict -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToSignalInstMaskDict - -DescToSignalFamilyInstMaskDict -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToSignalFamilyInstMaskDict - -DescToListTuple -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: DescToListTuple - -ListTupleToDesc -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: ListTupleToDesc - -LabelSmoothing -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -.. autoclass:: LabelSmoothing - +------------------ +.. automodule:: torchsig.transforms.transforms + :members: + :undoc-members: + :show-inheritance: + +Functional Transforms +------------------ +.. automodule:: torchsig.transforms.functional + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/utils.rst b/docs/utils.rst index e75742d..6f03132 100755 --- a/docs/utils.rst +++ b/docs/utils.rst @@ -11,57 +11,83 @@ The following utilities are available: :local: -Signal Datasets +Dataset Utils ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.utils.dataset +.. automodule:: torchsig.utils.dataset + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: SignalDataset +.. automodule:: torchsig.utils.writer + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: SignalFileDataset +.. automodule:: torchsig.utils.reader + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: SignalTensorDataset +.. automodule:: torchsig.utils.index + :members: + :undoc-members: + :show-inheritance: Signal Types ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.utils.types +.. automodule:: torchsig.utils.types + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: SignalDescription - -.. autoclass:: SignalData - -.. autoclass:: SignalCapture - - -Signal Visualizers +YOLO Utils ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. currentmodule:: torchsig.utils.visualize - -.. autoclass:: Visualizer +.. automodule:: torchsig.utils.yolo_classify + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: SpectrogramVisualizer +.. automodule:: torchsig.utils.yolo_train + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: WaveletVisualizer +.. automodule:: torchsig.utils.yolo_val + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: ConstellationVisualizer +.. automodule:: torchsig.utils.yolo_validator + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: IQVisualizer +.. automodule:: torchsig.utils.narrowband_trainer + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: TimeSeriesVisualizer - -.. autoclass:: ImageVisualizer - -.. autoclass:: PSDVisualizer - -.. autoclass:: MaskVisualizer +Signal Visualizers/Plotters +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: MaskClassVisualizer +.. automodule:: torchsig.utils.visualize + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: SemanticMaskClassVisualizer +.. automodule:: torchsig.utils.cm_plotter + :members: + :undoc-members: + :show-inheritance: -.. autoclass:: BoundingBoxVisualizer +Miscellaneous +~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ -.. autoclass:: AnchorBoxVisualizer \ No newline at end of file +.. automodule:: torchsig.utils.dsp + :members: + :undoc-members: + :show-inheritance: \ No newline at end of file diff --git a/examples/00_example_narrowband_dataset.ipynb b/examples/00_example_narrowband_dataset.ipynb new file mode 100644 index 0000000..f48af88 --- /dev/null +++ b/examples/00_example_narrowband_dataset.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "ae75a385-88ca-4cdf-b430-b9928adffbd3", + "metadata": {}, + "source": [ + "# Example 00 - The Official TorchSig Narrowband Dataset\n", + "This notebook walks through an example of how the official TorchSig Narrowband dataset can be instantiated and analyzed.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "f634e735-56bc-459f-a04f-5c4eadc5f8dd", + "metadata": {}, + "source": [ + "## Import Libraries\n", + "First, import all the necessary public libraries as well as a few classes from the `torchsig` toolkit." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ec50ccc1-1b10-45be-8bb2-48c623e3d579", + "metadata": {}, + "outputs": [], + "source": [ + "from torchsig.utils.visualize import IQVisualizer, SpectrogramVisualizer\n", + "from torchsig.datasets.torchsig_narrowband import TorchSigNarrowband\n", + "from torchsig.utils.dataset import SignalDataset\n", + "from torchsig.datasets import conf\n", + "from torch.utils.data import DataLoader\n", + "from torch.nn import Identity\n", + "from matplotlib import pyplot as plt\n", + "from tqdm import tqdm\n", + "import numpy as np\n", + "import os\n", + "\n", + "from torchsig.datasets.datamodules import NarrowbandDataModule\n", + "from torchsig.transforms.target_transforms import DescToClassIndexSNR" + ] + }, + { + "cell_type": "markdown", + "id": "a901decc-9a77-4070-9d8d-ceb43af5d4af", + "metadata": {}, + "source": [ + "### Instantiate TorchSigNarrowband Dataset\n", + "To instantiate the TorchSigNarrowband dataset, several parameters are given to the imported `NarrowbandDataModule` class. These paramters are:\n", + "- `root` - A string to specify the root directory of where to instantiate and/or read an existing TorchSigNarrowband dataset\n", + "- `impaired` - A boolean to specify if the TorchSigNarrowband dataset should be the clean version or the impaired version\n", + "- `qa` - A boolean to specify whether to generate a small subset of TorchSigNarrowband (True), or the full dataset (False), default is True\n", + "- `eb_no` - A boolean specifying if the SNR should be defined as Eb/No if True (making higher order modulations more powerful) or as Es/No if False (Defualt: False)\n", + "- `transform` - Optionally, pass in any data transforms here if the dataset will be used in an ML training pipeline\n", + "- `target_transform` ~ Optionally, pass in any target transforms here if the dataset will be used in an ML training pipeline\n", + "\n", + "A combination of the `impaired` and the `qa` booleans determines which of the four (4) distinct TorchSigNarrowband datasets will be instantiated:\n", + "| `impaired` | `qa` | Result |\n", + "| ---------- | ---- | ------- |\n", + "| `False` | `False` | Clean datasets of train=1.06M examples and val=5.3M examples |\n", + "| `False` | `True` | Clean datasets of train=10600 examples and val=1060 examples |\n", + "| `True` | `False` | Impaired datasets of train=1.06M examples and val=5.3M examples |\n", + "| `True` | `True` | Impaired datasets of train=10600 examples and val=1060 examples |\n", + "\n", + "The final option of the impaired validation set is the dataset to be used when reporting any results with the official TorchSigNarrowband dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "627e5be9-dd69-4df3-ab77-9bf1e35e6390", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate TorchSigNarrowband DataModule\n", + "root = \"./datasets/narrowband\"\n", + "class_list = list(TorchSigNarrowband._idx_to_name_dict.values())\n", + "num_workers = 4\n", + "impaired = False\n", + "\n", + "datamodule = NarrowbandDataModule(\n", + " root=root,\n", + " impaired=impaired,\n", + " transform=Identity(),\n", + " target_transform=DescToClassIndexSNR(class_list),\n", + " num_workers=num_workers\n", + ")\n", + "datamodule.prepare_data()\n", + "datamodule.setup(\"fit\")\n", + "narrowband_train = datamodule.train\n", + "\n", + "# Retrieve a sample and print out information\n", + "idx = np.random.randint(len(narrowband_train))\n", + "data, (label, snr) = narrowband_train[idx]\n", + "print(\"Dataset length: {}\".format(len(narrowband_train)))\n", + "print(\"Data shape: {}\".format(data.shape))\n", + "print(\"Label Index: {}\".format(label))\n", + "print(\"Label Class: {}\".format(TorchSigNarrowband.convert_idx_to_name(label)))\n", + "print(\"SNR: {}\".format(snr))" + ] + }, + { + "cell_type": "markdown", + "id": "0815da97-fb34-4dcd-9c3d-560d573e1f27", + "metadata": {}, + "source": [ + "## Plot Subset to Verify\n", + "The `IQVisualizer` and the `SpectrogramVisualizer` can be passed a `Dataloader` and plot visualizations of the dataset. The `batch_size` of the `DataLoader` determines how many examples to plot for each iteration over the visualizer. Note that the dataset itself can be indexed and plotted sequentially using any familiar python plotting tools as an alternative plotting method to using the `torchsig` `Visualizer` as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bf58bd2-b157-4f33-be6a-af1dd7f5ba26", + "metadata": {}, + "outputs": [], + "source": [ + "# For plotting, omit the SNR values\n", + "class DataWrapper(SignalDataset):\n", + " def __init__(self, dataset):\n", + " self.dataset = dataset\n", + " super().__init__(dataset)\n", + "\n", + " def __getitem__(self, idx):\n", + " x, (y, z) = self.dataset[idx]\n", + " return x, y\n", + "\n", + " def __len__(self) -> int:\n", + " return len(self.dataset)\n", + "\n", + "\n", + "plot_dataset = DataWrapper(narrowband_train)\n", + "\n", + "data_loader = DataLoader(dataset=plot_dataset, batch_size=16, shuffle=True)\n", + "\n", + "# Transform the plotting titles from the class index to the name\n", + "def target_idx_to_name(tensor: np.ndarray) -> list:\n", + " batch_size = tensor.shape[0]\n", + " label = []\n", + " for idx in range(batch_size):\n", + " label.append(TorchSigNarrowband.convert_idx_to_name(int(tensor[idx])))\n", + " return label\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "39311b83-3900-4e47-9d90-0f78353f07ed", + "metadata": {}, + "outputs": [], + "source": [ + "visualizer = IQVisualizer(\n", + " data_loader=data_loader,\n", + " visualize_transform=None,\n", + " visualize_target_transform=target_idx_to_name,\n", + ")\n", + "\n", + "for figure in iter(visualizer):\n", + " figure.set_size_inches(14, 9)\n", + " # plt.savefig(f\"{figure_dir}/00_iq_data.png\")\n", + " break" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad8b620f-3b50-4bb8-bf75-821ec1af37fe", + "metadata": {}, + "outputs": [], + "source": [ + "# Repeat but plot the spectrograms for a new random sampling of the data\n", + "visualizer = SpectrogramVisualizer(\n", + " data_loader=data_loader,\n", + " nfft=1024,\n", + " visualize_transform=None,\n", + " visualize_target_transform=target_idx_to_name,\n", + ")\n", + "\n", + "for figure in iter(visualizer):\n", + " figure.set_size_inches(14, 9)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "49cadba0-d6f8-4dbe-9702-60c67aa1a855", + "metadata": {}, + "source": [ + "## Analyze Dataset\n", + "The dataset can also be analyzed at the macro level for details such as the distribution of classes and SNR values. This exercise is performed below to show the nearly uniform distribution across each." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcc62b77-811e-4a2c-a20a-ded5d61080f2", + "metadata": {}, + "outputs": [], + "source": [ + "# Loop through the dataset recording classes and SNRs\n", + "class_counter_dict = {\n", + " class_name: 0 for class_name in list(TorchSigNarrowband._idx_to_name_dict.values())\n", + "}\n", + "all_snrs = []\n", + "\n", + "for idx in tqdm(range(len(narrowband_train))):\n", + " data, (modulation, snr) = narrowband_train[idx]\n", + " class_counter_dict[TorchSigNarrowband.convert_idx_to_name(modulation)] += 1\n", + " all_snrs.append(snr)\n", + "\n", + "\n", + "# Plot the distribution of classes\n", + "class_names = list(class_counter_dict.keys())\n", + "num_classes = list(class_counter_dict.values())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3c9baa8-d7e5-422d-9703-9e701adfd028", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(9, 9))\n", + "plt.pie(num_classes, labels=class_names)\n", + "plt.title(\"Class Distribution Pie Chart\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4a0fdaf4-8c48-48ca-a8bd-42cd8da3b5ac", + "metadata": {}, + "outputs": [], + "source": [ + "plt.figure(figsize=(11, 4))\n", + "plt.bar(class_names, num_classes)\n", + "plt.xticks(rotation=90)\n", + "plt.title(\"Class Distribution Bar Chart\")\n", + "plt.xlabel(\"Modulation Class Name\")\n", + "plt.ylabel(\"Counts\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "501a871b-8b65-4fa4-a51e-e16853270f08", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the distribution of SNR values\n", + "plt.figure(figsize=(11, 4))\n", + "plt.hist(x=all_snrs, bins=100)\n", + "plt.title(\"SNR Distribution\")\n", + "plt.xlabel(\"SNR Bins (dB)\")\n", + "plt.ylabel(\"Counts\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/01_example_modulations_dataset.ipynb b/examples/01_example_modulations_dataset.ipynb index bbf6192..800709c 100644 --- a/examples/01_example_modulations_dataset.ipynb +++ b/examples/01_example_modulations_dataset.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Example 01 - Modulations Dataset\n", - "This notebook steps through an example of how to use `torchsig` to instantiate a `SignalDataset` containing 53 unique modulations. The notebook then plots the signals using `Visualizers` for both the IQ and Spectrogram representations of the dataset. The end of the notebook then shows how the instantiated dataset can be saved to an LMDB static dataset for standalone research, experimentation, and/or analysis.\n", + "This notebook steps through an example of how to use `torchsig` to instantiate a `SignalDataset` containing 50+ unique modulations. The notebook then plots the signals using `Visualizers` for both the IQ and Spectrogram representations of the dataset. The end of the notebook then shows how the instantiated dataset can be saved to an LMDB static dataset for standalone research, experimentation, and/or analysis.\n", "\n", "---" ] @@ -47,7 +47,7 @@ "source": [ "----\n", "### Instantiate Modulations Dataset\n", - "Next, instantiate the `ModulationsDataset` by passing in the desired classes, a boolean specifying whether to use the class name or index as the label, the desired level of signal impairments/augmentations, the number of IQ samples per example, and the total number of samples. Note that the total number of samples will be divided evenly among the class list (for example, `num_samples=5300` will result in 100x samples of each of the 53 modulation classes). Also note that the classes input parameter can be omitted if all classes are desired.\n", + "Next, instantiate the `ModulationsDataset` by passing in the desired classes, a boolean specifying whether to use the class name or index as the label, the desired level of signal impairments/augmentations, the number of IQ samples per example, and the total number of samples. Note that the total number of samples will be divided evenly among the class list (for example, `num_samples=5300` will result in 100x samples of each of the 50+ modulation classes). Also note that the classes input parameter can be omitted if all classes are desired.\n", "\n", "If all classes are included at `level=0` (clean signals), all signals will occupy roughly half of the returned signal bandwidth except for the FSK and MSK modulations. These two subfamilies do not contain any pulse shaping, and as such are returned at roughly 1/8th occupied bandwidth for the main lobe. At the higher impairment levels, there is a randomized low pass filter applied at the 8x oversampled rate to suppress the sidelobes prior to downsampling to roughly the same half bandwidth target as the remaining signals.\n", "\n", @@ -115,6 +115,14 @@ " \"ofdm-1024\",\n", " \"ofdm-1200\",\n", " \"ofdm-2048\",\n", + " \"fm\",\n", + " \"am-dsb-sc\",\n", + " \"am-dsb\",\n", + " \"am-lsb\",\n", + " \"am-usb\",\n", + " \"lfm_data\",\n", + " \"lfm_radar\",\n", + " \"chirpss\",\n", "]\n", "num_classes = len(classes)\n", "level = 0\n", @@ -228,7 +236,7 @@ "source": [ "---\n", "## Save Data to LMDB\n", - "As a final exercise for this example notebook, the dataset can be saved to an LMDB static dataset for offline use. Note this is similar to how the static Sig53 dataset is generated and saved to serve as a static performance evaluation dataset." + "As a final exercise for this example notebook, the dataset can be saved to an LMDB static dataset for offline use. Note this is similar to how the static TorchSigNarrowband dataset is generated and saved to serve as a static performance evaluation dataset." ] }, { diff --git a/examples/02_example_narrowband_classifier.ipynb b/examples/02_example_narrowband_classifier.ipynb new file mode 100644 index 0000000..8fd8442 --- /dev/null +++ b/examples/02_example_narrowband_classifier.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e278e6c5-bde9-4912-a6f0-5a580a9e70b1", + "metadata": {}, + "source": [ + "# Example 02 - TorchSig Narrowband Classifier\n", + "This notebook walks through a simple example of how to use the clean TorchSig Narrowband Dataset and Trainer. You can train from scratch or load a pre-trained supported model, and evaluate the trained network's performance. Note that the experiment and the results herein are not to be interpreted with any significant value but rather serve simply as a practical example of how the `torchsig` dataset and tools can be used and integrated within a typical [PyTorch](https://pytorch.org/) and/or [PyTorch Lightning](https://www.pytorchlightning.ai/) workflow.\n", + "\n", + "----" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "43f0ec09-17a4-4cd8-b1e2-196b1400e14d", + "metadata": {}, + "outputs": [], + "source": [ + "# TorchSig imports\n", + "from torchsig.transforms.target_transforms import DescToClassIndex\n", + "from torchsig.transforms.transforms import (\n", + " RandomPhaseShift,\n", + " Normalize,\n", + " ComplexTo2D,\n", + " Compose,\n", + ")\n", + "from torchsig.utils.narrowband_trainer import NarrowbandTrainer\n", + "from torchsig.datasets.torchsig_narrowband import TorchSigNarrowband\n", + "from torchsig.datasets.datamodules import NarrowbandDataModule\n", + "import numpy as np\n", + "import cv2\n", + "import os\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "markdown", + "id": "90b68819-5d6d-4ce2-9e41-22babdaaf754", + "metadata": {}, + "source": [ + "----\n", + "### Instantiate TorchSigNarrowband Dataset\n", + "Here, we instantiate the TorchSigNarrowband clean training dataset and the TorchSigNarrowband clean validation dataset. We demonstrate how to compose multiple TorchSig transforms together, using a data impairment with a random phase shift that uniformly samples a phase offset between -1 pi and +1 pi. The next transform normalizes the complex tensor, and the final transform converts the complex data to a real-valued tensor with the real and imaginary parts as two channels. We additionally provide a target transform that maps the `SignalMetadata` objects, that are part of `SignalData` objects, to a desired format for the model we will train. In this case, we use the `DescToClassIndex` target transform to map class names to their indices within an ordered class list. Finally, we sample from our datasets and print details in order to confirm functionality.\n", + "\n", + "For more details on the TorchSigNarrowband dataset instantiations, please see `00_example_narrowband_dataset.ipynb`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7775b0b-57a9-4870-b848-19cca6cc0084", + "metadata": {}, + "outputs": [], + "source": [ + "class_list = list(TorchSigNarrowband._idx_to_name_dict.values())\n", + "num_classes = len(class_list)\n", + "\n", + "# Specify Transforms\n", + "transform = Compose(\n", + " [\n", + " RandomPhaseShift(phase_offset=(-1, 1)),\n", + " Normalize(norm=np.inf),\n", + " ComplexTo2D(),\n", + " ]\n", + ")\n", + "target_transform = DescToClassIndex(class_list=class_list)\n", + "\n", + "datamodule = NarrowbandDataModule(\n", + " root='./datasets/narrowband_test_QA',\n", + " qa=True,\n", + " impaired=True,\n", + " transform=transform,\n", + " target_transform=target_transform,\n", + " batch_size=32,\n", + " num_workers=16,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "024c5f4a-7322-40cb-ba63-d17f672af032", + "metadata": {}, + "source": [ + "---\n", + "### Instantiate and Initialize the NarrowbandTrainer with specified parameters.\n", + "\n", + " Args:\n", + " model_name (str): Name of the model to use.\n", + " num_epochs (int): Number of training epochs.\n", + " batch_size (int): Batch size for training.\n", + " num_workers (int): Number of workers for data loading.\n", + " learning_rate (float): Learning rate for the optimizer.\n", + " input_channels (int): Number of input channels into model.\n", + " data_path (str): Path to the dataset.\n", + " impaired (bool): Whether to use the impaired dataset.\n", + " qa (bool): Whether to use QA configuration.\n", + " checkpoint_path (str): Path to a checkpoint file to load the model weights.\n", + " datamodule (LightningDataModule): Custom data module instance.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "54060a0e-4aa5-4a45-ba66-660191ceab36", + "metadata": {}, + "outputs": [], + "source": [ + "# Initialize the trainer with desired parameters\n", + "trainer = NarrowbandTrainer(\n", + " model_name = 'xcit',\n", + " num_epochs = 2,\n", + " # batch_size = 32, # Uncomment if not passing in datamodule\n", + " # num_workers = 16, # Uncomment if not passing in datamodule\n", + " learning_rate = 1e-3,\n", + " input_channels = 2,\n", + " # data_path = '../datasets/narrowband_test_QA', # Uncomment if not passing in datamodule\n", + " # impaired = True, # Uncomment if not passing in datamodule\n", + " # qa = False # Uncomment if not passing in datamodule\n", + " datamodule = datamodule,\n", + " checkpoint_path = None # If loading checkpoint, add path here\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c7fbc155-71a2-4221-bbe8-e0e1a900945d", + "metadata": {}, + "outputs": [], + "source": [ + "# View all available models\n", + "print(trainer.available_models)" + ] + }, + { + "cell_type": "markdown", + "id": "ab2e7d8b-b972-46cb-b08d-bc6bbba34d76", + "metadata": {}, + "source": [ + "---\n", + "### Train or Fine Tune your model.\n", + " Can load any pytorchlightning checkpoint by providing checkpoint path above, otherwise with train specified model." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1107bedd-f74c-439d-bfd1-607982fba8a2", + "metadata": {}, + "outputs": [], + "source": [ + "# Train the model\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "5761819e-bb6e-44f5-9760-59758655fec0", + "metadata": {}, + "source": [ + "---\n", + "### Validate model\n", + " You can validate a model by loading its checkpoint in the intialization stage or after training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "645aa759-fb57-4806-ab1d-53d4a2085e12", + "metadata": {}, + "outputs": [], + "source": [ + "trainer.validate()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8663744b", + "metadata": {}, + "outputs": [], + "source": [ + "# Train accuracy and loss plots\n", + "acc_plot = cv2.imread(trainer.acc_plot_path)\n", + "loss_plot = cv2.imread(trainer.loss_plot_path)\n", + "\n", + "plots = [acc_plot, loss_plot]\n", + "\n", + "fig = plt.figure(figsize=(21, 6))\n", + "r = 1\n", + "c = 3\n", + "\n", + "for i in range(2):\n", + " fig.add_subplot(r, c, i + 1)\n", + " plt.imshow(plots[i])\n", + " plt.axis('off') \n", + "\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45b6c815", + "metadata": {}, + "outputs": [], + "source": [ + "# confusion matrix\n", + "cm_plot = cv2.imread(trainer.cm_plot_path)\n", + "plt.imshow(cm_plot, aspect='auto')" + ] + }, + { + "cell_type": "markdown", + "id": "f9a6380b-ecfe-4cfb-9cf5-5528a5e3872e", + "metadata": {}, + "source": [ + "---\n", + "### Predict with model\n", + " You can make inferences/predictions with model by loading checkpoint in the intialization stage or after training." + ] + }, + { + "cell_type": "markdown", + "id": "1f907cf1-301b-4c4c-93d7-b1e079bf3210", + "metadata": {}, + "source": [ + "#### Load Data\n", + " You can load whatever data you wish, assuming it is a torch.Tensor.\n", + " In this example, we will load an example from our validation set\n", + "\n", + " Data needs to be shape (batch_size, input_channels, data_length). You can use tensor.unsqueeze(dim=0) to add a batch dimension." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f9baca30-3d19-4c84-b149-caa40ff8c867", + "metadata": {}, + "outputs": [], + "source": [ + "import torch\n", + "datamodule.prepare_data()\n", + "datamodule.setup(\"fit\")\n", + "\n", + "# Retrieve a sample and print out information to verify\n", + "idx = np.random.randint(len(datamodule.val))\n", + "data, label = datamodule.train[idx]\n", + "data = torch.tensor(data).float().unsqueeze(dim=0)\n", + "print(\"Dataset length: {}\".format(len(datamodule.val)))\n", + "print(\"Data shape: {}\".format(data.shape))\n", + "print(\"Label Index: {}\".format(label))\n", + "print(\"Label Class: {}\".format(TorchSigNarrowband.convert_idx_to_name(label)))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1acba2a5-a9d6-454c-8894-c6c669344be6", + "metadata": {}, + "outputs": [], + "source": [ + "# Predict on new data (assuming `new_data` is a torch.Tensor)\n", + "predictions = trainer.predict(data)[0]\n", + "print(TorchSigNarrowband._idx_to_name_dict[predictions])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/03_example_wideband_dataset.ipynb b/examples/03_example_wideband_dataset.ipynb new file mode 100644 index 0000000..645fe86 --- /dev/null +++ b/examples/03_example_wideband_dataset.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Example 03 - Official TorchSig Wideband Dataset\n", + "This notebook walks through how to use `torchsig` to generate the Official TorchSig Wideband Dataset.\n", + "\n", + "-------------------------------------------" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from torchsig.utils.visualize import MaskClassVisualizer, mask_class_to_outline, complex_spectrogram_to_magnitude\n", + "from torchsig.transforms.target_transforms import DescToMaskClass, DescToListTuple\n", + "from torchsig.transforms import Spectrogram, Normalize\n", + "from torchsig.datasets.torchsig_wideband import TorchSigWideband\n", + "from torchsig.transforms.transforms import Compose\n", + "from torchsig.datasets import conf\n", + "from torchsig.datasets.datamodules import WidebandDataModule\n", + "from torchsig.datasets.signal_classes import torchsig_signals\n", + "\n", + "from tqdm import tqdm\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "-----------------------------\n", + "## Generate the Wideband TorchSig Wideband Dataset\n", + "To generate the TorchSigWideband dataset, several parameters are given to the imported `WidebandDataModule` class. These paramters are:\n", + "- `root` ~ A string to specify the root directory of where to generate and/or read an existing TorchSigWideband dataset\n", + "- `train` ~ A boolean to specify if the TorchSigWideband dataset should be the training (True) or validation (False) sets\n", + "- `qa` - A boolean to specify whether to generate a small subset of TorchSigNWideband (True), or the full dataset (False), default is True\n", + "- `impaired` ~ A boolean to specify if the TorchSigWideband dataset should be the clean version or the impaired version\n", + "- `transform` ~ Optionally, pass in any data transforms here if the dataset will be used in an ML training pipeline. Note: these transforms are not called during the dataset generation. The static saved dataset will always be in IQ format. The transform is only called when retrieving data examples.\n", + "- `target_transform` ~ Optionally, pass in any target transforms here if the dataset will be used in an ML training pipeline. Note: these target transforms are not called during the dataset generation. The static saved dataset will always be saved as tuples in the LMDB dataset. The target transform is only called when retrieving data examples.\n", + "\n", + "A combination of the `train` and the `impaired` booleans determines which of the four (4) distinct TorchSigWideband datasets will be instantiated:\n", + "| `impaired` | `qa` | Result |\n", + "| ---------- | ---- | ------- |\n", + "| `False` | `False` | Clean datasets of train=250k examples and val=25k examples |\n", + "| `False` | `True` | Clean datasets of train=250 examples and val=250 examples |\n", + "| `True` | `False` | Impaired datasets of train=250k examples and val=25k examples |\n", + "| `True` | `True` | Impaired datasets of train=250 examples and val=250 examples |\n", + "\n", + "The final option of the impaired validation set is the dataset to be used when reporting any results with the official TorchSigWideband dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Generate TorchSigWideband DataModule\n", + "from torchsig.transforms import *\n", + "root = \"./datasets/wideband\"\n", + "impaired = True\n", + "qa = True\n", + "fft_size = 512\n", + "num_classes = len(torchsig_signals.class_list)\n", + "batch_size = 16\n", + "num_workers = 4\n", + "\n", + "transform = Compose([ \n", + " Spectrogram(nperseg=fft_size, noverlap=0, nfft=fft_size, mode='complex'),\n", + " Normalize(norm=np.inf, flatten=True),\n", + "])\n", + "\n", + "target_transform = Compose([\n", + " DescToMaskClass(num_classes=num_classes, width=fft_size, height=fft_size),\n", + "])\n", + "\n", + "datamodule = WidebandDataModule(\n", + " root=root,\n", + " impaired=impaired,\n", + " qa=qa,\n", + " fft_size=fft_size,\n", + " num_classes=num_classes,\n", + " transform=transform,\n", + " target_transform=target_transform,\n", + " batch_size=batch_size,\n", + " num_workers=num_workers\n", + ")\n", + "\n", + "datamodule.prepare_data()\n", + "datamodule.setup(\"fit\")\n", + "\n", + "wideband_train = datamodule.train\n", + "\n", + "# Retrieve a sample and print out information\n", + "idx = np.random.randint(len(wideband_train))\n", + "data, label = wideband_train[idx]\n", + "print(\"Dataset length: {}\".format(len(wideband_train)))\n", + "print(\"Data shape: {}\".format(data.shape))\n", + "print(\"Label shape: {}\".format(label.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot Subset to Verify\n", + "The `MaskClassVisualizer` can be passed a `Dataloader` and plot visualizations of the dataset. The `batch_size` of the `DataLoader` determines how many examples to plot for each iteration over the visualizer. Note that the dataset itself can be indexed and plotted sequentially using any familiar python plotting tools as an alternative plotting method to using the `spdata` `Visualizer` as shown below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data_loader = datamodule.train_dataloader()\n", + "\n", + "visualizer = MaskClassVisualizer(\n", + " data_loader=data_loader,\n", + " visualize_transform=complex_spectrogram_to_magnitude,\n", + " visualize_target_transform=mask_class_to_outline,\n", + " class_list=torchsig_signals.class_list\n", + ")\n", + "\n", + "for figure in iter(visualizer):\n", + " figure.set_size_inches(16, 9)\n", + " plt.show()\n", + " break" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "----\n", + "### Analyze Dataset\n", + "The dataset can also be analyzed at the macro level for details such as the distribution of classes and number of signals per sample. The below analysis reads information directly from the non-target transformed tuple annotations. Since this is different than the above dataset instantiation, the dataset is re-instantiated for analysis." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Re-instantiate the TorchSigWideband Dataset witbatch_size=1, num_workers=1, hout a target transform and without using the RFData objects\n", + "datamodule = WidebandDataModule(\n", + " root=root,\n", + " impaired=impaired,\n", + " qa=qa,\n", + " fft_size=fft_size,\n", + " num_classes=num_classes,\n", + " transform=transform,\n", + " target_transform=None,\n", + " batch_size=1,\n", + " num_workers=1\n", + ")\n", + "datamodule.prepare_data()\n", + "datamodule.setup(\"fit\")\n", + "\n", + "wideband_test = datamodule.train\n", + "\n", + "# Loop through the dataset recording classes and SNRs\n", + "class_counter_dict = {\n", + " class_name: 0 for class_name in list(wideband_test.class_list)\n", + "}\n", + "num_signals_per_sample = []\n", + "\n", + "for idx in tqdm(range(len(wideband_test))):\n", + " data, annotation = wideband_test[idx]\n", + " num_signals_per_sample.append(len(annotation))\n", + " for signal_annotation in annotation:\n", + " class_counter_dict[signal_annotation[\"class_name\"]] += 1" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the distribution of classes\n", + "class_names = list(class_counter_dict.keys())\n", + "num_classes = list(class_counter_dict.values())\n", + "\n", + "plt.figure(figsize=(9,9))\n", + "plt.pie(num_classes, labels=class_names)\n", + "plt.title(\"Class Distribution Pie Chart\")\n", + "plt.show()\n", + "\n", + "plt.figure(figsize=(11,4))\n", + "plt.bar(class_names, num_classes)\n", + "plt.xticks(rotation=90)\n", + "plt.title(\"Class Distribution Bar Chart\")\n", + "plt.xlabel(\"Modulation Class Name\")\n", + "plt.ylabel(\"Counts\")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The above distribution of classes shows all OFDM signals appearing less frequently than the remaining modulations. This makes sense because OFDM signals are drawn from a random distribution of bandwidths that are inherently larger than the remaining signals, meaning fewer OFDM signals can fit into a wideband spectrum without overlapping. Additionally, the random bursty probability and durations of OFDM signals makes it less likely to occupy a wideband capture with many short-time bursts, while the remaining modulations experience this behavior at a higher probility." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the distribution of number of signals per sample\n", + "plt.figure(figsize=(11,8))\n", + "plt.hist(x=num_signals_per_sample, bins=np.arange(1,max(num_signals_per_sample)+1)-0.5)\n", + "plt.title(\"Distribution of Number of Signals Per Sample\\nTotal Number: {} - Average: {} - Max: {}\".format(\n", + " sum(num_signals_per_sample),\n", + " np.mean(np.asarray(num_signals_per_sample)),\n", + " max(num_signals_per_sample),\n", + "))\n", + "plt.xlabel(\"Number of Signal Bins\")\n", + "plt.ylabel(\"Counts\")\n", + "plt.show()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "tags": [] + }, + "source": [ + "The above distribution of the number of signals per sample shows the most commonly seen sample has two signals present. The average is slightly around 4 signals per sample and the max is 26." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# For additional analysis, reinstantiate the dataset without a transform, such that the RFDescriptions can be read\n", + "datamodule = WidebandDataModule(\n", + " root=root,\n", + " impaired=impaired,\n", + " qa=qa,\n", + " fft_size=fft_size,\n", + " num_classes=num_classes,\n", + " transform=None,\n", + " target_transform=None,\n", + " batch_size=1,\n", + " num_workers=1\n", + ")\n", + "datamodule.prepare_data()\n", + "datamodule.setup(\"fit\")\n", + "\n", + "wideband_test2 =datamodule.train\n", + "\n", + "num_samples = len(wideband_test2)\n", + "snrs = []\n", + "bandwidths = []\n", + "durations = []\n", + "for idx in tqdm(range(num_samples)):\n", + " label = wideband_test2[idx][1]\n", + " for meta in label:\n", + " snrs.append(meta[\"snr\"])\n", + " bandwidths.append(meta[\"bandwidth\"])\n", + " durations.append(meta[\"duration\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the distribution of SNR values\n", + "plt.figure(figsize=(11,4))\n", + "plt.hist(x=snrs, bins=100)\n", + "plt.title(\"SNR Distribution\")\n", + "plt.xlabel(\"SNR Bins (dB)\")\n", + "plt.ylabel(\"Counts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the distribution of bandwidth values\n", + "plt.figure(figsize=(11,4))\n", + "plt.hist(x=bandwidths, bins=100)\n", + "plt.title(\"Bandwidth Distribution\")\n", + "plt.xlabel(\"BW Bins\")\n", + "plt.ylabel(\"Counts\")\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# Plot the distribution of bandwidth values\n", + "# plt.figure(figsize=(11,4))\n", + "# plt.hist(x=durations, bins=100)\n", + "# plt.title(\"Duration Distribution\")\n", + "# plt.xlabel(\"Duration Bins\")\n", + "# plt.ylabel(\"Counts\")\n", + "# plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/04_example_wideband_modulations_dataset.ipynb b/examples/04_example_wideband_modulations_dataset.ipynb index f8d062b..6df3f15 100644 --- a/examples/04_example_wideband_modulations_dataset.ipynb +++ b/examples/04_example_wideband_modulations_dataset.ipynb @@ -6,7 +6,7 @@ "metadata": {}, "source": [ "# Example 04 - Wideband Modulations Dataset\n", - "This notebook steps through an example of how to use `torchsig` to instantiate a custom, online `WidebandDataset` containing signals with up to 53 unique classes of modulations. The notebook then plots the signals using `Visualizers` for the Spectrogram representations of the dataset. \n", + "This notebook steps through an example of how to use `torchsig` to instantiate a custom, online `WidebandDataset` containing signals with up to 50+ unique classes of modulations. The notebook then plots the signals using `Visualizers` for the Spectrogram representations of the dataset. \n", "\n", "-------------------------------------------" ] @@ -28,6 +28,7 @@ "from torchsig.utils.visualize import MaskClassVisualizer, mask_class_to_outline, complex_spectrogram_to_magnitude\n", "from torchsig.transforms.target_transforms import DescToMaskClass\n", "from torchsig.datasets.wideband import WidebandModulationsDataset\n", + "from torchsig.datasets.signal_classes import torchsig_signals\n", "from torchsig.transforms.transforms import Spectrogram, Normalize, Compose\n", "from torch.utils.data import DataLoader\n", "import matplotlib.pyplot as plt\n", @@ -50,14 +51,7 @@ "metadata": {}, "outputs": [], "source": [ - "modulation_list = [\n", - " \"ook\",\"bpsk\",\"4pam\",\"4ask\",\"qpsk\",\"8pam\",\"8ask\",\"8psk\",\"16qam\",\"16pam\",\n", - " \"16ask\",\"16psk\",\"32qam\",\"32qam_cross\",\"32pam\",\"32ask\",\"32psk\",\"64qam\",\"64pam\",\"64ask\",\n", - " \"64psk\",\"128qam_cross\",\"256qam\",\"512qam_cross\",\"1024qam\",\"2fsk\",\"2gfsk\",\"2msk\",\"2gmsk\",\"4fsk\",\n", - " \"4gfsk\",\"4msk\",\"4gmsk\",\"8fsk\",\"8gfsk\",\"8msk\",\"8gmsk\",\"16fsk\",\"16gfsk\",\"16msk\",\"16gmsk\",\n", - " \"ofdm-64\",\"ofdm-72\",\"ofdm-128\",\"ofdm-180\",\"ofdm-256\",\"ofdm-300\",\"ofdm-512\",\"ofdm-600\",\n", - " \"ofdm-900\",\"ofdm-1024\",\"ofdm-1200\",\"ofdm-2048\",\n", - "] \n", + "modulation_list = torchsig_signals.class_list\n", "\n", "fft_size = 512\n", "num_classes = len(modulation_list)\n", diff --git a/examples/05_example_wideband_yolo_to_disk.ipynb b/examples/05_example_wideband_yolo_to_disk.ipynb index 85f575f..64eab82 100644 --- a/examples/05_example_wideband_yolo_to_disk.ipynb +++ b/examples/05_example_wideband_yolo_to_disk.ipynb @@ -5,8 +5,8 @@ "id": "2d579aba-7439-42a6-aae4-cf1983095dee", "metadata": {}, "source": [ - "# Example 05 - WidebandSig53 with YOLOv8 Detector (Creates and Populates Image/Label Directories)\n", - "This notebook showcases using the WBSig53 dataset to train a YOLOv8 model.\n", + "# Example 05 - TorchSigWideband with YOLOv8 Detector (Creates and Populates Image/Label Directories)\n", + "This notebook showcases using the Torchsig Wideband dataset to train a YOLOv8 model.\n", "\n", "---" ] @@ -16,11 +16,7 @@ "id": "40a026bd-f096-47f3-a262-48ab5defe23e", "metadata": {}, "source": [ - "## Import Libraries\n", - "We will import all the usual libraries, in addition to Ultralytics. You can install Ultralytics with:\n", - "```bash\n", - "pip install ultralytics\n", - "```" + "## Import Libraries" ] }, { @@ -32,11 +28,12 @@ }, "outputs": [], "source": [ - "from torchsig.datasets.datamodules import WidebandSig53DataModule\n", + "from torchsig.datasets.datamodules import WidebandDataModule\n", "from torch.utils.data import DataLoader\n", "from torchsig.utils.dataset import collate_fn\n", - "from torchsig.datasets.sig53 import sig53\n", - "from torchsig.datasets.wideband_sig53 import WidebandSig53\n", + "from torchsig.datasets.torchsig_narrowband import TorchSigNarrowband\n", + "from torchsig.datasets.torchsig_wideband import TorchSigWideband\n", + "from torchsig.datasets.signal_classes import torchsig_signals\n", "from torchsig.transforms.target_transforms import DescToListTuple, ListTupleToYOLO\n", "from torchsig.transforms.transforms import Spectrogram, SpectrogramImage, Normalize, Compose, Identity\n", "import pytorch_lightning as pl\n", @@ -47,7 +44,8 @@ "import yaml\n", "import matplotlib.pyplot as plt\n", "import os\n", - "from tqdm import tqdm" + "from tqdm import tqdm\n", + "import torch" ] }, { @@ -65,8 +63,8 @@ "id": "9a46c59d-e20c-435b-8a22-249a56bdd810", "metadata": {}, "source": [ - "## Instantiate WBSig53 Dataset\n", - "After generating the WBSig53 dataset (see `03_example_widebandsig53_dataset.ipynb`), we can instantiate it with the needed transforms. Change `root` to dataset path.\n", + "## Instantiate Wideband Dataset\n", + "After generating the Wideband dataset (see `03_example_wideband_dataset.ipynb`), we can instantiate it with the needed transforms. Change `root` to dataset path.\n", "\n", "---" ] @@ -80,9 +78,9 @@ }, "outputs": [], "source": [ - "root = './datasets/wideband_sig53'\n", + "root = './datasets/wideband'\n", "fft_size = 512\n", - "num_classes = 53\n", + "num_classes = len(torchsig_signals.class_list)\n", "impaired = True\n", "num_workers = 4\n", "batch_size = 1\n", @@ -100,8 +98,8 @@ " ListTupleToYOLO()\n", "])\n", "\n", - "# Instantiate the WidebandSig53 Dataset\n", - "datamodule = WidebandSig53DataModule(\n", + "# Instantiate the TorchSigWideband Dataset\n", + "datamodule = WidebandDataModule(\n", " root=root,\n", " impaired=impaired,\n", " qa=qa,\n", @@ -115,15 +113,15 @@ "datamodule.prepare_data()\n", "datamodule.setup(\"fit\")\n", "\n", - "wideband_sig53_train = datamodule.train\n", - "wideband_sig53_val = datamodule.val\n", + "wideband_train = datamodule.train\n", + "wideband_val = datamodule.val\n", "\n", "\n", "# Retrieve a sample and print out information\n", - "idx = np.random.randint(len(wideband_sig53_val))\n", - "data, label = wideband_sig53_val[idx]\n", - "print(\"Training Dataset length: {}\".format(len(wideband_sig53_train)))\n", - "print(\"Validation Dataset length: {}\".format(len(wideband_sig53_val)))\n", + "idx = np.random.randint(len(wideband_val))\n", + "data, label = wideband_val[idx]\n", + "print(\"Training Dataset length: {}\".format(len(wideband_train)))\n", + "print(\"Validation Dataset length: {}\".format(len(wideband_val)))\n", "print(\"Data shape: {}\\n\\t\".format(data.shape))\n", "print(f\"Label length: {len(label)}\", end=\"\\n\\t\")\n", "print(*label, sep=\"\\n\\t\")\n", @@ -152,9 +150,9 @@ }, "outputs": [], "source": [ - "# method to output .png images and .txt label files in YOLO structure from wbsig53\n", - "def prepare_data(dataset: WidebandSig53, output: str, train: bool, impaired: bool) -> None:\n", - " output_root = os.path.join(output, \"wideband_sig53_yolo\")\n", + "# method to output .png images and .txt label files in YOLO structure from wideband\n", + "def prepare_data(dataset: TorchSigWideband, output: str, train: bool, impaired: bool) -> None:\n", + " output_root = os.path.join(output, \"wideband_yolo\")\n", " os.makedirs(output_root, exist_ok=True)\n", " impaired = \"impaired\" if impaired else \"clean\"\n", " train = \"train\" if train else \"val\"\n", @@ -176,8 +174,8 @@ " \n", " cv2.imwrite(image_filename, image, [cv2.IMWRITE_PNG_COMPRESSION, 9])\n", " \n", - "prepare_data(wideband_sig53_train, \"./datasets/wideband_sig53\", True, True)\n", - "prepare_data(wideband_sig53_val, \"./datasets/wideband_sig53\", False, True)" + "prepare_data(wideband_train, \"./datasets/wideband\", True, True)\n", + "prepare_data(wideband_val, \"./datasets/wideband\", False, True)" ] }, { @@ -190,19 +188,20 @@ "outputs": [], "source": [ "# create dataset yaml file\n", - "classes = {v: k for v, k in enumerate(sig53.class_list)}\n", + "config_name = \"05_yolo.yaml\"\n", + "classes = {v: k for v, k in enumerate(torchsig_signals.class_list)}\n", "classes[0] = 'signal'\n", "\n", - "wbsig53_yaml_dict = dict(\n", - " path = \"./wideband_sig53/wideband_sig53_yolo\",\n", + "wideband_yaml_dict = dict(\n", + " path = \"./wideband/wideband_yolo\",\n", " train = \"impaired/images/train\",\n", " val = \"impaired/images/val\",\n", - " nc = 53,\n", + " nc = num_classes,\n", " names = classes\n", ")\n", "\n", - "with open('wbsig53.yaml', 'w') as f:\n", - " yaml.dump(wbsig53_yaml_dict, f, default_flow_style=False)" + "with open(config_name, 'w+') as f:\n", + " yaml.dump(wideband_yaml_dict, f, default_flow_style=False)" ] }, { @@ -263,11 +262,14 @@ "outputs": [], "source": [ "results = model.train(\n", - " data=\"wbsig53.yaml\", \n", + " data=config_name, \n", " epochs=5, \n", " batch=batch_size,\n", " imgsz=640,\n", - " workers=1\n", + " device=0 if torch.cuda.is_available() else \"cpu\"\n", + " workers=1,\n", + " project=\"yolo\",\n", + " name=\"05_example\"\n", ")" ] }, @@ -305,10 +307,10 @@ }, "outputs": [], "source": [ - "label = cv2.imread(os.path.join(results.save_dir, \"val_batch0_labels.jpg\"))\n", - "pred = cv2.imread(os.path.join(results.save_dir, \"val_batch0_pred.jpg\"))\n", + "label = cv2.imread(os.path.join(results.save_dir, \"val_batch2_labels.jpg\"))\n", + "pred = cv2.imread(os.path.join(results.save_dir, \"val_batch2_pred.jpg\"))\n", "\n", - "f, ax = plt.subplots(1, 2, figsize=(9, 6))\n", + "f, ax = plt.subplots(1, 2, figsize=(15, 9))\n", "ax[0].imshow(label)\n", "ax[0].set_title(\"Label\")\n", "ax[1].imshow(pred)\n", diff --git a/examples/05_yolo.yaml b/examples/05_yolo.yaml new file mode 100644 index 0000000..f722606 --- /dev/null +++ b/examples/05_yolo.yaml @@ -0,0 +1,66 @@ +names: + 0: signal + 1: bpsk + 2: 4pam + 3: 4ask + 4: qpsk + 5: 8pam + 6: 8ask + 7: 8psk + 8: 16qam + 9: 16pam + 10: 16ask + 11: 16psk + 12: 32qam + 13: 32qam_cross + 14: 32pam + 15: 32ask + 16: 32psk + 17: 64qam + 18: 64pam + 19: 64ask + 20: 64psk + 21: 128qam_cross + 22: 256qam + 23: 512qam_cross + 24: 1024qam + 25: 2fsk + 26: 2gfsk + 27: 2msk + 28: 2gmsk + 29: 4fsk + 30: 4gfsk + 31: 4msk + 32: 4gmsk + 33: 8fsk + 34: 8gfsk + 35: 8msk + 36: 8gmsk + 37: 16fsk + 38: 16gfsk + 39: 16msk + 40: 16gmsk + 41: ofdm-64 + 42: ofdm-72 + 43: ofdm-128 + 44: ofdm-180 + 45: ofdm-256 + 46: ofdm-300 + 47: ofdm-512 + 48: ofdm-600 + 49: ofdm-900 + 50: ofdm-1024 + 51: ofdm-1200 + 52: ofdm-2048 + 53: fm + 54: am-dsb-sc + 55: am-dsb + 56: am-lsb + 57: am-usb + 58: lfm_data + 59: lfm_radar + 60: chirpss +nc: 61 +path: ./wideband/wideband_yolo +train: impaired/images/train +val: impaired/images/val diff --git a/examples/06_example_wideband_yolo.ipynb b/examples/06_example_wideband_yolo.ipynb index 80a7912..7f7a401 100644 --- a/examples/06_example_wideband_yolo.ipynb +++ b/examples/06_example_wideband_yolo.ipynb @@ -5,8 +5,8 @@ "id": "2d579aba-7439-42a6-aae4-cf1983095dee", "metadata": {}, "source": [ - "# Example 06 - WidebandSig53 with YOLOv8 Detector\n", - "This notebook showcases using the WBSig53 dataset to train a YOLOv8 model.\n", + "# Example 06 - TorchSigWideband with YOLOv8 Detector\n", + "This notebook showcases using the Wideband dataset to train a YOLOv8 model.\n", "\n", "---" ] @@ -16,11 +16,7 @@ "id": "40a026bd-f096-47f3-a262-48ab5defe23e", "metadata": {}, "source": [ - "## Import Libraries\n", - "We will import all the usual libraries, in addition to Ultralytics. You can install Ultralytics with:\n", - "```bash\n", - "pip install ultralytics\n", - "```" + "## Import Libraries" ] }, { @@ -46,11 +42,16 @@ "outputs": [], "source": [ "# Package Imports for Testing/Inference\n", - "from torchsig.datasets.datamodules import WidebandSig53DataModule\n", + "from torchsig.datasets.datamodules import WidebandDataModule\n", + "from torchsig.datasets.signal_classes import torchsig_signals\n", "from torchsig.transforms.transforms import Spectrogram, SpectrogramImage, Normalize, Compose, Identity\n", "from torchsig.transforms.target_transforms import DescToBBoxFamilyDict\n", "from ultralytics import YOLO\n", - "from PIL import Image" + "import torch\n", + "from PIL import Image\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import os" ] }, { @@ -59,16 +60,16 @@ "metadata": {}, "source": [ "-----------------------------\n", - "## Check or Generate the Wideband Sig53 Dataset\n", - "To generate the WidebandSig53 dataset, several parameters are given to the imported `WidebandSig53DataModule` class. These paramters are:\n", - "- `root` ~ A string to specify the root directory of where to generate and/or read an existing WidebandSig53 dataset\n", - "- `train` ~ A boolean to specify if the WidebandSig53 dataset should be the training (True) or validation (False) sets\n", - "- `qa` - A boolean to specify whether to generate a small subset of Sig53 (True), or the full dataset (False), default is True\n", - "- `impaired` ~ A boolean to specify if the WidebandSig53 dataset should be the clean version or the impaired version\n", + "## Check or Generate the Wideband Dataset\n", + "To generate the TorchSigWideband dataset, several parameters are given to the imported `WidebandDataModule` class. These paramters are:\n", + "- `root` ~ A string to specify the root directory of where to generate and/or read an existing TorchSigWideband dataset\n", + "- `train` ~ A boolean to specify if the TorchSigWideband dataset should be the training (True) or validation (False) sets\n", + "- `qa` - A boolean to specify whether to generate a small subset of Wideband (True), or the full dataset (False), default is True\n", + "- `impaired` ~ A boolean to specify if the TorchSigWideband dataset should be the clean version or the impaired version\n", "- `transform` ~ Optionally, pass in any data transforms here if the dataset will be used in an ML training pipeline. Note: these transforms are not called during the dataset generation. The static saved dataset will always be in IQ format. The transform is only called when retrieving data examples.\n", "- `target_transform` ~ Optionally, pass in any target transforms here if the dataset will be used in an ML training pipeline. Note: these target transforms are not called during the dataset generation. The static saved dataset will always be saved as tuples in the LMDB dataset. The target transform is only called when retrieving data examples.\n", "\n", - "A combination of the `train` and the `impaired` booleans determines which of the four (4) distinct WidebandSig53 datasets will be instantiated:\n", + "A combination of the `train` and the `impaired` booleans determines which of the four (4) distinct TorchSigWideband datasets will be instantiated:\n", "| `impaired` | `qa` | Result |\n", "| ---------- | ---- | ------- |\n", "| `False` | `False` | Clean datasets of train=250k examples and val=25k examples |\n", @@ -76,7 +77,7 @@ "| `True` | `False` | Impaired datasets of train=250k examples and val=25k examples |\n", "| `True` | `True` | Impaired datasets of train=250 examples and val=250 examples |\n", "\n", - "The final option of the impaired validation set is the dataset to be used when reporting any results with the official WidebandSig53 dataset." + "The final option of the impaired validation set is the dataset to be used when reporting any results with the official TorchSigWideband dataset." ] }, { @@ -86,12 +87,12 @@ "metadata": {}, "outputs": [], "source": [ - "# Generate WidebandSig53 DataModule\n", - "root = \"../datasets/wideband_sig53\"\n", + "# Generate TorchSigWideband DataModule\n", + "root = \"./datasets/wideband\"\n", "impaired = True\n", "qa = True\n", "fft_size = 512\n", - "num_classes = 53\n", + "num_classes = len(torchsig_signals.class_list)\n", "batch_size = 1\n", "\n", "transform = Compose([ \n", @@ -101,7 +102,7 @@ " DescToBBoxFamilyDict()\n", "])\n", "\n", - "datamodule = WidebandSig53DataModule(\n", + "datamodule = WidebandDataModule(\n", " root=root,\n", " impaired=impaired,\n", " qa=qa,\n", @@ -123,12 +124,12 @@ "datamodule.prepare_data()\n", "datamodule.setup(\"fit\")\n", "\n", - "wideband_sig53 = datamodule.train\n", + "wideband_train = datamodule.train\n", "\n", "# Retrieve a sample and print out information\n", - "idx = np.random.randint(len(wideband_sig53))\n", - "data, label = wideband_sig53[idx]\n", - "print(\"Dataset length: {}\".format(len(wideband_sig53)))\n", + "idx = np.random.randint(len(wideband_train))\n", + "data, label = wideband_train[idx]\n", + "print(\"Dataset length: {}\".format(len(wideband_train)))\n", "print(\"Data shape: {}\".format(data.shape))\n", "print(\"Label: {}\".format(label))" ] @@ -155,36 +156,71 @@ "source": [ "### Explanation of the `overrides` Dictionary\n", "\n", - "The `overrides` dictionary is used to customize the settings for the Ultralytics YOLO trainer by specifying specific values that override the default configurations. The dictionary is imported from `wbdata.yaml`. However, you can customize in the notebook. \n", + "The `overrides` dictionary is used to customize the settings for the Ultralytics YOLO trainer by specifying specific values that override the default configurations. The dictionary is imported from `wbdata.yaml`. However, you can customize in the notebook. See [Ultralytics Train Settings](https://docs.ultralytics.com/modes/train/#train-settings) to learn more.\n", "\n", "Example:\n", "\n", "```python\n", "overrides = {'model': 'yolov8n.pt', 'epochs': 100, 'data': 'wbdata.yaml', 'device': 0, 'imgsz': 512, 'single_cls': True}\n", "```\n", - "A .yaml is necessary for training. Look at `wbdata.yaml` in the examples directory. It will contain the path to your torchsig data.\n", + "A .yaml is necessary for training. Look at `06_yolo.yaml` in the examples directory. It will contain the path to your torchsig data.\n", "\n", "\n", "### Dataset Location Warning\n", "\n", "There must exist a datasets directory at `/path/to/torchsig/datasets`.\n", "\n", - "This example assumes that you have generated `train` and `val` lmdb wideband datasets at `../datasets/wideband_sig53/`\n", + "This example assumes that you have generated `train` and `val` lmdb wideband datasets at `./datasets/wideband/`\n", "\n", - "You can also specify an absolute path to your dataset in `wbdata.yaml`." + "You can also specify an absolute path to your dataset in `06_yolo.yaml`." ] }, { "cell_type": "code", "execution_count": null, - "id": "cc6137ed-44f5-4dd3-a16c-0267a84bfe55", + "id": "783e7f33", "metadata": {}, "outputs": [], "source": [ - "with open('wbdata.yaml', 'r') as file:\n", - " config = yaml.safe_load(file)\n", - "overrides = config['overrides']\n", - "print(f\"Creating experiment -> {overrides['name']}\")" + "# define dataset variables for yaml file\n", + "config_name = \"06_yolo.yaml\"\n", + "class_list = [\"ask\", \"fsk\", \"ofdm\", \"pam\", \"psk\", \"qam\"]\n", + "classes = {v: k for v, k in enumerate(class_list)}\n", + "num_classes = len(classes)\n", + "yolo_root = \"./wideband/\" # train/val images (relative to './datasets``\n", + "\n", + "# define overrides\n", + "# Note: You can change use of GPU(s) or CPU by overriding the device\n", + "# GPU: device=0 or device=0,1\n", + "# CPU: device=\"cpu\"\n", + "overrides = dict(\n", + " model = \"yolov8n.pt\",\n", + " project = \"yolo\",\n", + " name = \"06_example\",\n", + " epochs = 10,\n", + " imgsz = 512,\n", + " data = config_name,\n", + " device = 0 if torch.cuda.is_available() else \"cpu\",\n", + " single_cls = True,\n", + " batch = 32,\n", + " workers = 8\n", + "\n", + ")\n", + "\n", + "# create yaml file for trainer\n", + "yolo_config = dict(\n", + " overrides = overrides,\n", + " train = yolo_root,\n", + " val = yolo_root,\n", + " nc = num_classes,\n", + " names = classes\n", + ")\n", + "\n", + "with open(config_name, 'w+') as file:\n", + " yaml.dump(yolo_config, file, default_flow_style=False)\n", + "\n", + "print(f\"Creating experiment -> {overrides['name']}\")\n", + " " ] }, { @@ -240,8 +276,8 @@ "id": "c45fa7bf-fa7d-4896-9022-4e608d93e5a4", "metadata": {}, "source": [ - "## Generate and Instantiate WBSig53 Test Dataset\n", - "After generating the WBSig53 dataset (see `03_example_widebandsig53_dataset.ipynb`), we can instantiate it with the needed transforms. Change `root` to test dataset path.\n", + "## Generate and Instantiate Wideband Test Dataset\n", + "After generating the Wideband dataset (see `03_example_widebandsig_dataset.ipynb`), we can instantiate it with the needed transforms. Change `root` to test dataset path.\n", "\n", "---" ] @@ -253,7 +289,7 @@ "metadata": {}, "outputs": [], "source": [ - "test_path = '../datasets/wideband_sig53_test' #Should differ from your training dataset\n", + "test_path = './datasets/wideband_test' #Should differ from your training dataset\n", "\n", "transform = Compose([\n", " Spectrogram(nperseg=512, noverlap=0, nfft=512, mode='psd'),\n", @@ -261,7 +297,7 @@ " SpectrogramImage(), \n", " ])\n", "\n", - "test_data = WidebandSig53DataModule(\n", + "test_data = WidebandDataModule(\n", " root=test_path,\n", " impaired=impaired,\n", " qa=qa,\n", @@ -275,19 +311,19 @@ "test_data.prepare_data()\n", "test_data.setup(\"fit\")\n", "\n", - "wideband_sig53_test = test_data.train\n", + "wideband_test = test_data.train\n", "\n", "# Retrieve a sample and print out information\n", - "idx = np.random.randint(len(wideband_sig53_test))\n", - "data, label = wideband_sig53_test[idx]\n", - "print(\"Dataset length: {}\".format(len(wideband_sig53_test)))\n", + "idx = np.random.randint(len(wideband_test))\n", + "data, label = wideband_test[idx]\n", + "print(\"Dataset length: {}\".format(len(wideband_test)))\n", "print(\"Data shape: {}\".format(data.shape))\n", "\n", "samples = []\n", "labels = []\n", "for i in range(10):\n", - " idx = np.random.randint(len(wideband_sig53_test))\n", - " sample, label = wideband_sig53_test[idx]\n", + " idx = np.random.randint(len(wideband_test))\n", + " sample, label = wideband_test[idx]\n", " lb = [l['class_name'] for l in label]\n", " samples.append(sample)\n", " labels.append(lb)" @@ -309,8 +345,7 @@ "metadata": {}, "outputs": [], "source": [ - "model_path = 'YOUR_PROJECT_NAME/YOUR_EXPERIMENT_NAME/weights/best.pt' #Double check this is correct path, printed after training.\n", - "model = YOLO(model_path)" + "model = YOLO(trainer.best)" ] }, { @@ -327,16 +362,32 @@ { "cell_type": "code", "execution_count": null, - "id": "107d38d4-36af-4d0b-8bd9-926228b3e4ee", + "id": "7a07a684", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ef3b004a", "metadata": {}, "outputs": [], "source": [ - "# Process results list\n", - "for y, result in enumerate(results):\n", - " boxes = result.boxes # Boxes object for bounding box outputs\n", - " probs = result.probs # Probs object for classification outputs\n", - " print(f'Actual Labels -> {labels[y]}')\n", - " result.show() # display to screen" + "# Plot prediction results\n", + "rows = 3\n", + "cols = 3\n", + "fig = plt.figure(figsize=(15, 15)) \n", + "results_dir = results[0].save_dir\n", + "\n", + "for y, result in enumerate(results[:9]):\n", + " imgpath = os.path.join(results_dir, \"image\" + str(y) + \".jpg\")\n", + " fig.add_subplot(rows, cols, y + 1) \n", + " img = cv2.imread(imgpath)\n", + " plt.imshow(img)\n", + " plt.title(str(labels[y]), fontsize='small', loc='left')" ] } ], diff --git a/examples/06_yolo.yaml b/examples/06_yolo.yaml new file mode 100644 index 0000000..37eb56f --- /dev/null +++ b/examples/06_yolo.yaml @@ -0,0 +1,21 @@ +names: + 0: ask + 1: fsk + 2: ofdm + 3: pam + 4: psk + 5: qam +nc: 6 +overrides: + batch: 32 + data: 06_yolo.yaml + device: 0 + epochs: 10 + imgsz: 512 + model: yolov8n.pt + name: 06_example + project: yolo + single_cls: true + workers: 8 +train: ./wideband/ +val: ./wideband/ diff --git a/examples/07_example_narrowband_yolo.ipynb b/examples/07_example_narrowband_yolo.ipynb new file mode 100644 index 0000000..83ff0da --- /dev/null +++ b/examples/07_example_narrowband_yolo.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "c2acbdea-bdce-43f8-86d4-fb602f67beba", + "metadata": {}, + "source": [ + "# Example 07 - TorchSig Narrowband with YOLOv8 Classifier\n", + "This notebook showcases using the TorchSig Narrowband dataset to train a YOLOv8 classification model.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "d2addcb1-6b3d-4484-a9b9-20d161840d95", + "metadata": {}, + "source": [ + "## Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bb874df6-3e3b-4fc0-a446-25f90923ec9e", + "metadata": {}, + "outputs": [], + "source": [ + "# Packages for Training\n", + "from torchsig.utils.yolo_classify import *\n", + "from torchsig.utils.classify_transforms import real_imag_vstacked_cwt_image, complex_iq_to_heatmap\n", + "import yaml" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "810529c8-adf4-40d1-b735-455a72df2902", + "metadata": {}, + "outputs": [], + "source": [ + "# Packages for testing/inference\n", + "from torchsig.datasets.modulations import ModulationsDataset\n", + "from torchsig.datasets.signal_classes import torchsig_signals\n", + "from torchsig.transforms.target_transforms import DescToFamilyName\n", + "from torchsig.transforms.transforms import Spectrogram, SpectrogramImage, Normalize, Compose, Identity\n", + "from ultralytics import YOLO\n", + "import torch\n", + "from PIL import Image\n", + "import cv2\n", + "import matplotlib.pyplot as plt\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "c63eff7e-5dba-459b-88ba-d2e855c8cd27", + "metadata": {}, + "source": [ + "## Prepare YOLO classificatoin trainer and Model\n", + "Datasets are generated on the fly in a way that is Ultralytics YOLO compatible. See [Ultralytics: Train Custom Data - Organize Directories](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/#23-organize-directories) to learn more. \n", + "\n", + "Additionally, we create a yaml file for dataset configuration. See \"classify.yaml\" in Torchsig Examples.\n", + "\n", + "Download desired YOLO model from [Ultralytics Models](https://docs.ultralytics.com/models/). We will use YOLOv8, specifically `yolov8n-cls.pt`\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1ddb6903-a7ec-4f3e-8555-8132cbfbc4fd", + "metadata": {}, + "outputs": [], + "source": [ + "config_path = '07_yolo.yaml'\n", + "with open(config_path, 'r') as file:\n", + " config = yaml.safe_load(file)\n", + "\n", + "overrides = config['overrides']" + ] + }, + { + "cell_type": "markdown", + "id": "3acf456a-4d02-4ff8-b44c-f0b0c3014723", + "metadata": {}, + "source": [ + "### Explanation of the `overrides` Dictionary\n", + "\n", + "The `overrides` dictionary is used to customize the settings for the Ultralytics YOLO trainer by specifying specific values that override the default configurations. The dictionary is imported from `classify.yaml`. However, you can customize in the notebook. \n", + "\n", + "Example:\n", + "\n", + "```python\n", + "overrides = {'model': 'yolov8n-cls.pt', 'epochs': 100, 'data': 'classify.yaml', 'device': 0, 'imgsz': 64}\n", + "```\n", + "A .yaml is necessary for training. Look at `classify.yaml` in the examples directory. It will contain the path to your torchsig data.\n", + "\n", + "### Explanation of `image_transform` function\n", + "`YoloClassifyTrainer` allows you to pass in any transform that takes in complex I/Q and outputs an image for training. Some example transforms can be found in torchsig.utils.classify_transforms. If nothing is passed, it will default to spectrogram images. It is important to update `overrides` so that your imgsz matches output." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc63726a", + "metadata": {}, + "outputs": [], + "source": [ + "# define dataset variables for yaml file\n", + "config_name = \"07_yolo.yaml\"\n", + "family_list = [\"ask\", \"fsk\", \"ofdm\", \"pam\", \"psk\", \"qam\"]\n", + "family_dict = {v: k for v, k in enumerate(family_list)}\n", + "classes = {v: k for v, k in enumerate(torchsig_signals.class_list)}\n", + "num_classes = len(classes)\n", + "yolo_root = \"./wideband/\" # train/val images (relative to './datasets``\n", + "\n", + "# define overrides\n", + "overrides = dict(\n", + " model = \"yolov8n-cls.pt\",\n", + " project = \"yolo\",\n", + " name = \"07_example\",\n", + " epochs = 5,\n", + " imgsz = 512,\n", + " data = config_name,\n", + " device = 0 if torch.cuda.is_available() else \"cpu\",\n", + " batch = 32,\n", + " workers = 8\n", + "\n", + ")\n", + "\n", + "# create yaml file for trainer\n", + "yolo_config = dict(\n", + " overrides = overrides,\n", + " train = yolo_root,\n", + " val = yolo_root,\n", + " level = 2,\n", + " include_snr = False,\n", + " num_samples = 530,\n", + " nc = num_classes,\n", + " names = classes,\n", + " family = False, # Determines if you are classify all 50+ classes or modulation family (see Classes below)\n", + " families = family_dict\n", + ")\n", + "\n", + "with open(config_name, 'w+') as file:\n", + " yaml.dump(yolo_config, file, default_flow_style=False)\n", + "\n", + "print(f\"Creating experiment -> {overrides['name']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "e9d94dba-1b87-4839-8c34-a2b449ede80d", + "metadata": {}, + "source": [ + "### Build YoloClassifyTrainer\n", + "This will instantiate the YOLO classification trainer with overrides specified above." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f134e461-456a-4be7-9c91-ce26c5c4f850", + "metadata": {}, + "outputs": [], + "source": [ + "trainer = YoloClassifyTrainer(overrides=overrides, image_transform=None)" + ] + }, + { + "cell_type": "markdown", + "id": "d1d1cb68-5452-498b-bf5b-a7b96478429c", + "metadata": {}, + "source": [ + "### Then begin training" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "51d670a0-54e3-4a37-9649-740412e302ef", + "metadata": {}, + "outputs": [], + "source": [ + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "454e71d2-fc9c-4f60-9545-bb0997fb5334", + "metadata": {}, + "source": [ + "### Instantiate Test Dataset\n", + "\n", + "Uses Torchsig's `ModulationsDataset` to generate a narrowband classification dataset. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ce307e25-99cb-4c72-9910-3d18d89d9c5c", + "metadata": {}, + "outputs": [], + "source": [ + "# Determine whether to map descriptions to family names\n", + "if config['family']:\n", + " target_transform = CP([DescToFamilyName()])\n", + "else:\n", + " target_transform = None\n", + "\n", + "transform = Compose([\n", + " Spectrogram(nperseg=overrides['imgsz'], noverlap=0, nfft=overrides['imgsz'], mode='psd'),\n", + " Normalize(norm=np.inf, flatten=True),\n", + " SpectrogramImage(), \n", + " ])\n", + "\n", + "class_list = [item[1] for item in config['names'].items()]\n", + "\n", + "dataset = ModulationsDataset(\n", + " classes=class_list,\n", + " use_class_idx=False,\n", + " level=config['level'],\n", + " num_iq_samples=overrides['imgsz']**2,\n", + " num_samples=int(config['nc'] * 10),\n", + " include_snr=config['include_snr'],\n", + " transform=transform,\n", + " target_transform=target_transform\n", + ")\n", + "\n", + "# Retrieve a sample and print out information\n", + "idx = np.random.randint(len(dataset))\n", + "data, label = dataset[idx]\n", + "print(\"Dataset length: {}\".format(len(dataset)))\n", + "print(\"Data shape: {}\".format(data.shape))\n", + "\n", + "samples = []\n", + "labels = []\n", + "for i in range(10):\n", + " idx = np.random.randint(len(dataset))\n", + " sample, label = dataset[idx]\n", + " samples.append(sample)\n", + " labels.append(label)" + ] + }, + { + "cell_type": "markdown", + "id": "caab72b6-df31-4a08-88c7-b37400aec5d2", + "metadata": {}, + "source": [ + "### Predictions / Inference\n", + "The following cells show you how to load the 'best.pt' weights from your training for prediction" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0b5fbfeb", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5644b3e9-a7c8-4865-8d16-f47b54cf7606", + "metadata": {}, + "outputs": [], + "source": [ + "model = YOLO(trainer.best) #The model will remember the configuration from training\n", + "results = model.predict(samples, save=True)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "135974bd-c0b2-478f-895b-96d487b9d33e", + "metadata": {}, + "outputs": [], + "source": [ + "# Plot prediction results\n", + "rows = 3\n", + "cols = 3\n", + "fig = plt.figure(figsize=(15, 15)) \n", + "results_dir = results[0].save_dir\n", + "\n", + "for y, result in enumerate(results[:9]):\n", + " imgpath = os.path.join(results_dir, \"image\" + str(y) + \".jpg\")\n", + " fig.add_subplot(rows, cols, y + 1) \n", + " img = cv2.imread(imgpath)\n", + " plt.imshow(img)\n", + " plt.title(\"Truth: \" + str(labels[y]), fontsize='large', loc='left')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/07_yolo.yaml b/examples/07_yolo.yaml new file mode 100644 index 0000000..1001e29 --- /dev/null +++ b/examples/07_yolo.yaml @@ -0,0 +1,86 @@ +families: + 0: ask + 1: fsk + 2: ofdm + 3: pam + 4: psk + 5: qam +family: false +include_snr: false +level: 2 +names: + 0: ook + 1: bpsk + 2: 4pam + 3: 4ask + 4: qpsk + 5: 8pam + 6: 8ask + 7: 8psk + 8: 16qam + 9: 16pam + 10: 16ask + 11: 16psk + 12: 32qam + 13: 32qam_cross + 14: 32pam + 15: 32ask + 16: 32psk + 17: 64qam + 18: 64pam + 19: 64ask + 20: 64psk + 21: 128qam_cross + 22: 256qam + 23: 512qam_cross + 24: 1024qam + 25: 2fsk + 26: 2gfsk + 27: 2msk + 28: 2gmsk + 29: 4fsk + 30: 4gfsk + 31: 4msk + 32: 4gmsk + 33: 8fsk + 34: 8gfsk + 35: 8msk + 36: 8gmsk + 37: 16fsk + 38: 16gfsk + 39: 16msk + 40: 16gmsk + 41: ofdm-64 + 42: ofdm-72 + 43: ofdm-128 + 44: ofdm-180 + 45: ofdm-256 + 46: ofdm-300 + 47: ofdm-512 + 48: ofdm-600 + 49: ofdm-900 + 50: ofdm-1024 + 51: ofdm-1200 + 52: ofdm-2048 + 53: fm + 54: am-dsb-sc + 55: am-dsb + 56: am-lsb + 57: am-usb + 58: lfm_data + 59: lfm_radar + 60: chirpss +nc: 61 +num_samples: 530 +overrides: + batch: 32 + data: 07_yolo.yaml + device: 0 + epochs: 5 + imgsz: 512 + model: yolov8n-cls.pt + name: 07_example + project: yolo + workers: 8 +train: ./wideband/ +val: ./wideband/ diff --git a/examples/08_example_optuna_yolo.ipynb b/examples/08_example_optuna_yolo.ipynb new file mode 100644 index 0000000..adbb173 --- /dev/null +++ b/examples/08_example_optuna_yolo.ipynb @@ -0,0 +1,407 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2d579aba-7439-42a6-aae4-cf1983095dee", + "metadata": {}, + "source": [ + "# Example 08 - Optuna with Wideband and YOLO\n", + "This notebook showcases Optuna hyperparameter tuning a YOLOv8 model with Wideband.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "40a026bd-f096-47f3-a262-48ab5defe23e", + "metadata": {}, + "source": [ + "## Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b4bff6d5-4b2d-4db2-97a0-f45843e7cc60", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "# Packages for Optuna\n", + "from torchsig.utils.optuna.tuner import YoloOptunaOptimizer\n", + "import copy" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ab949825-7c48-44f2-852d-56567f5952e5", + "metadata": {}, + "outputs": [], + "source": [ + "# Packages Imports for Training\n", + "from ultralytics import YOLO\n", + "from torchsig.utils.yolo_train import Yolo_Trainer\n", + "from torchsig.datasets.datamodules import WidebandDataModule\n", + "from torchsig.transforms.transforms import Compose, Spectrogram, Normalize, SpectrogramImage\n", + "from torchsig.transforms.target_transforms import DescToBBoxFamilyDict\n", + "from torchsig.datasets.signal_classes import torchsig_signals\n", + "import torch\n", + "import yaml\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "efea64b7-1d22-41bd-81cc-5c39a74c3bdd", + "metadata": {}, + "source": [ + "-----------------------------\n", + "## Check or Generate the Wideband Dataset\n", + "To generate the Wideband dataset, several parameters are given to the imported `WidebandDataModule` class. These paramters are:\n", + "- `root` ~ A string to specify the root directory of where to generate and/or read an existing Wideband dataset\n", + "- `train` ~ A boolean to specify if the Wideband dataset should be the training (True) or validation (False) sets\n", + "- `qa` - A boolean to specify whether to generate a small subset of Wideband (True), or the full dataset (False), default is True\n", + "- `impaired` ~ A boolean to specify if the Wideband dataset should be the clean version or the impaired version\n", + "- `transform` ~ Optionally, pass in any data transforms here if the dataset will be used in an ML training pipeline. Note: these transforms are not called during the dataset generation. The static saved dataset will always be in IQ format. The transform is only called when retrieving data examples.\n", + "- `target_transform` ~ Optionally, pass in any target transforms here if the dataset will be used in an ML training pipeline. Note: these target transforms are not called during the dataset generation. The static saved dataset will always be saved as tuples in the LMDB dataset. The target transform is only called when retrieving data examples.\n", + "\n", + "A combination of the `train` and the `impaired` booleans determines which of the four (4) distinct Wideband datasets will be instantiated:\n", + "| `impaired` | `qa` | Result |\n", + "| ---------- | ---- | ------- |\n", + "| `False` | `False` | Clean datasets of train=250k examples and val=25k examples |\n", + "| `False` | `True` | Clean datasets of train=250 examples and val=250 examples |\n", + "| `True` | `False` | Impaired datasets of train=250k examples and val=25k examples |\n", + "| `True` | `True` | Impaired datasets of train=250 examples and val=250 examples |\n", + "\n", + "The final option of the impaired validation set is the dataset to be used when reporting any results with the official Wideband dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca6e8bfb-92f3-4615-afac-7568350a2461", + "metadata": {}, + "outputs": [], + "source": [ + "# Generate Wideband DataModule\n", + "root = \"./datasets/wideband\"\n", + "impaired = True\n", + "qa = True\n", + "fft_size = 512\n", + "class_list = torchsig_signals.class_list\n", + "num_classes = len(class_list)\n", + "batch_size = 1\n", + "\n", + "transform = Compose([ \n", + "])\n", + "\n", + "target_transform = Compose([\n", + " DescToBBoxFamilyDict()\n", + "])\n", + "\n", + "datamodule = WidebandDataModule(\n", + " root=root,\n", + " impaired=impaired,\n", + " qa=qa,\n", + " fft_size=fft_size,\n", + " num_classes=num_classes,\n", + " transform=transform,\n", + " target_transform=target_transform,\n", + " batch_size=batch_size\n", + ")\n", + "\n", + "datamodule.prepare_data()\n", + "datamodule.setup(\"fit\")\n", + "\n", + "wideband_train = datamodule.train\n", + "\n", + "# Retrieve a sample and print out information\n", + "idx = np.random.randint(len(wideband_train))\n", + "data, label = wideband_train[idx]\n", + "print(\"Dataset length: {}\".format(len(wideband_train)))\n", + "print(\"Data shape: {}\".format(data.shape))\n", + "print(\"Label: {}\".format(label))" + ] + }, + { + "cell_type": "markdown", + "id": "2938efda-7b7f-42e9-8bcf-022ebcaf2d32", + "metadata": {}, + "source": [ + "## Prepare YOLO trainer and Model\n", + "Next, the datasets are rewritten to disk that is Ultralytics YOLO compatible. See [Ultralytics: Train Custom Data - Organize Directories](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/#23-organize-directories) to learn more. \n", + "\n", + "Additionally, create a yaml file for dataset configuration. See [Ultralytics: Train Custom Data - Create dataset.yaml](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/#21-create-datasetyaml)\n", + "\n", + "Download desired YOLO model from [Ultralytics Models](https://docs.ultralytics.com/models/). We will use YOLOv8, specifically `yolov8n.pt`\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "f8f5ad86-bb23-4396-8cd3-3b1c1c9a32b2", + "metadata": {}, + "source": [ + "### Explanation of the `overrides` Dictionary\n", + "\n", + "The `overrides` dictionary is used to customize the settings for the Ultralytics YOLO trainer by specifying specific values that override the default configurations. The dictionary is imported from `wbdata.yaml`. However, you can customize in the notebook. \n", + "\n", + "Example:\n", + "\n", + "```python\n", + "overrides = {'model': 'yolov8n.pt', 'epochs': 100, 'data': '08_example.yaml', 'device': 0, 'imgsz': 512, 'single_cls': True}\n", + "```\n", + "A .yaml is necessary for training. Look at `08_yolo_optuna.yaml` in the examples directory. It will contain the path to your torchsig data.\n", + "\n", + "\n", + "### Dataset Location Warning\n", + "\n", + "There must exist a datasets directory at `/path/to/torchsig/datasets`.\n", + "\n", + "This example assumes that you have generated `train` and `val` lmdb wideband datasets at `./datasets/wideband/`\n", + "\n", + "You can also specify an absolute path to your dataset in `08_yolo_optuna.yaml`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cc6137ed-44f5-4dd3-a16c-0267a84bfe55", + "metadata": {}, + "outputs": [], + "source": [ + "# define dataset variables for yaml file\n", + "config_name = \"08_yolo_optuna.yaml\"\n", + "classes = {v: k for v, k in enumerate(class_list)}\n", + "yolo_root = \"./wideband/\" # train/val images (relative to './datasets``\n", + "\n", + "# define overrides\n", + "overrides = dict(\n", + " model = \"yolov8n.pt\",\n", + " project = \"yolo\",\n", + " name = \"08_example\",\n", + " epochs = 10,\n", + " imgsz = 512,\n", + " data = config_name,\n", + " device = 0 if torch.cuda.is_available() else \"cpu\",\n", + " single_cls = True,\n", + " batch = 32,\n", + " workers = 8\n", + "\n", + ")\n", + "\n", + "# create yaml file for trainer\n", + "yolo_config = dict(\n", + " overrides = overrides,\n", + " train = yolo_root,\n", + " val = yolo_root,\n", + " nc = num_classes,\n", + " names = classes\n", + ")\n", + "\n", + "with open(config_name, 'w+') as file:\n", + " yaml.dump(yolo_config, file, default_flow_style=False)\n", + "print(f\"Creating experiment -> {overrides['name']}\")" + ] + }, + { + "cell_type": "markdown", + "id": "8f7ccf16", + "metadata": {}, + "source": [ + "## Run Optuna\n", + "Now we can start using optuna to tune the following hyperparameters in YOLO:\n", + "- `lr0` - Initial learning rate.\n", + "- `cos_lr` - Toggle cosine learning rate scheduler.\n", + "- `optimizer` - Choose optimizer (SGD, Adam, AdamW)\n", + "- `freeze` - Freeze the first N layers of the model.\n", + "- `imgsz` - Target image size for training.\n", + "\n", + "Within the optuna optimizer,`n_trials` determines how many trials to run, while `epochs` is how many epochs are run per trial.\n", + "\n", + "See [Ultralytics Train Settings](https://docs.ultralytics.com/usage/cfg/#train-settings) for more hyperparameters." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "0d7017ba", + "metadata": {}, + "outputs": [], + "source": [ + "opt = YoloOptunaOptimizer(overrides, n_trials=2, epochs=1)\n", + "study, best_params = opt.run_optimization()\n", + "\n", + "overrides_optimized = opt.get_optimized_overrides()" + ] + }, + { + "cell_type": "markdown", + "id": "22fa62fc-01d9-46b7-9cd2-38643f54d1de", + "metadata": {}, + "source": [ + "## Train YOLO with Optimized Hyperparameters\n", + "Train YOLO. See [Ultralytics Train](https://docs.ultralytics.com/modes/train/#train-settings) for training hyperparameter options.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad7e5f57-3b5b-4074-b87c-6e18c7b973af", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "trainer = Yolo_Trainer(overrides=overrides_optimized)\n", + "\n", + "trainer.train()" + ] + }, + { + "cell_type": "markdown", + "id": "7ad0c459-2367-4f7d-89ae-5f4cfd8f545f", + "metadata": {}, + "source": [ + "## Evaluation\n", + "Check model performance from training. From here, you can use the trained model to test on prepared data (numpy image arrays of spectrograms)\n", + "\n", + "Will load example from Torchsig\n", + "\n", + "model_path is path to best.pt from your training session. Path is printed at the end of training.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "c45fa7bf-fa7d-4896-9022-4e608d93e5a4", + "metadata": {}, + "source": [ + "## Generate and Instantiate WBSig53 Test Dataset\n", + "After generating the WBSig53 dataset (see `03_example_Wideband_dataset.ipynb`), we can instantiate it with the needed transforms. Change `root` to test dataset path.\n", + "\n", + "---" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "694c33f1-d718-40ae-861d-26c3431a6f41", + "metadata": {}, + "outputs": [], + "source": [ + "test_path = './datasets/wideband_test' #Should differ from your training dataset\n", + "\n", + "transform = Compose([\n", + " Spectrogram(nperseg=512, noverlap=0, nfft=512, mode='psd'),\n", + " Normalize(norm=np.inf, flatten=True),\n", + " SpectrogramImage(), \n", + " ])\n", + "\n", + "test_data = WidebandDataModule(\n", + " root=test_path,\n", + " impaired=impaired,\n", + " qa=qa,\n", + " fft_size=fft_size,\n", + " num_classes=num_classes,\n", + " transform=transform,\n", + " target_transform=None,\n", + " batch_size=batch_size\n", + ")\n", + "\n", + "test_data.prepare_data()\n", + "test_data.setup(\"fit\")\n", + "\n", + "wideband_test = test_data.train\n", + "\n", + "# Retrieve a sample and print out information\n", + "idx = np.random.randint(len(wideband_test))\n", + "data, label = wideband_test[idx]\n", + "print(\"Dataset length: {}\".format(len(wideband_test)))\n", + "print(\"Data shape: {}\".format(data.shape))\n", + "\n", + "samples = []\n", + "labels = []\n", + "for i in range(10):\n", + " idx = np.random.randint(len(wideband_test))\n", + " sample, label = wideband_test[idx]\n", + " lb = [l['class_name'] for l in label]\n", + " samples.append(sample)\n", + " labels.append(lb)" + ] + }, + { + "cell_type": "markdown", + "id": "de9947fc-9636-415c-af50-26d7e5d95953", + "metadata": {}, + "source": [ + "### Load model \n", + "The model path is printed after training. Use the best.pt weights" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "76e73a9c", + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e2bb8735-ce6c-482b-9675-302f6848b0ea", + "metadata": {}, + "outputs": [], + "source": [ + "model = YOLO(trainer.best)\n", + "# Inference will be saved to path printed after predict. \n", + "results = model.predict(samples, save=True, imgsz=512, conf=0.5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "107d38d4-36af-4d0b-8bd9-926228b3e4ee", + "metadata": {}, + "outputs": [], + "source": [ + "# Process results list\n", + "for y, result in enumerate(results):\n", + " boxes = result.boxes # Boxes object for bounding box outputs\n", + " probs = result.probs # Probs object for classification outputs\n", + " print(f'Actual Labels -> {labels[y]}')\n", + " result.show() # display to screen" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/08_yolo_optuna.yaml b/examples/08_yolo_optuna.yaml new file mode 100644 index 0000000..07d3286 --- /dev/null +++ b/examples/08_yolo_optuna.yaml @@ -0,0 +1,76 @@ +names: + 0: ook + 1: bpsk + 2: 4pam + 3: 4ask + 4: qpsk + 5: 8pam + 6: 8ask + 7: 8psk + 8: 16qam + 9: 16pam + 10: 16ask + 11: 16psk + 12: 32qam + 13: 32qam_cross + 14: 32pam + 15: 32ask + 16: 32psk + 17: 64qam + 18: 64pam + 19: 64ask + 20: 64psk + 21: 128qam_cross + 22: 256qam + 23: 512qam_cross + 24: 1024qam + 25: 2fsk + 26: 2gfsk + 27: 2msk + 28: 2gmsk + 29: 4fsk + 30: 4gfsk + 31: 4msk + 32: 4gmsk + 33: 8fsk + 34: 8gfsk + 35: 8msk + 36: 8gmsk + 37: 16fsk + 38: 16gfsk + 39: 16msk + 40: 16gmsk + 41: ofdm-64 + 42: ofdm-72 + 43: ofdm-128 + 44: ofdm-180 + 45: ofdm-256 + 46: ofdm-300 + 47: ofdm-512 + 48: ofdm-600 + 49: ofdm-900 + 50: ofdm-1024 + 51: ofdm-1200 + 52: ofdm-2048 + 53: fm + 54: am-dsb-sc + 55: am-dsb + 56: am-lsb + 57: am-usb + 58: lfm_data + 59: lfm_radar + 60: chirpss +nc: 61 +overrides: + batch: 32 + data: 08_yolo_optuna.yaml + device: 0 + epochs: 10 + imgsz: 512 + model: yolov8n.pt + name: 08_example + project: yolo + single_cls: true + workers: 8 +train: ./wideband/ +val: ./wideband/ diff --git a/examples/09_example_synthetic_spectrogram_dataset.ipynb b/examples/09_example_synthetic_spectrogram_dataset.ipynb new file mode 100644 index 0000000..aa55182 --- /dev/null +++ b/examples/09_example_synthetic_spectrogram_dataset.ipynb @@ -0,0 +1,465 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e2d63327-b7da-4060-8664-5cf1072d4cdb", + "metadata": {}, + "source": [ + "# Example 09 - Generating Synthetic Wideband Spectrogram-Like Images\n", + "In this notebook we will use TorchSig's `image_datasets` module to generate synthetic spectrograms of a few modeled signal types.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "d747c181-067c-46a7-9deb-650c9bfff3c2", + "metadata": {}, + "source": [ + "## Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "d0fa212e-6128-495f-8af6-4bba4629fc76", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "from torchsig.image_datasets.datasets.synthetic_signals import GeneratorFunctionDataset, rectangle_signal_generator_function, tone_generator_function, repeated_signal_generator_function, chirp_generator_function\n", + "from torchsig.image_datasets.datasets.file_loading_datasets import SOIExtractorDataset, LazyImageDirectoryDataset\n", + "from torchsig.image_datasets.datasets.composites import ConcatDataset\n", + "from torchsig.image_datasets.datasets.yolo_datasets import YOLOImageCompositeDataset, YOLODatasetAdapter, YOLODatum\n", + "from torchsig.image_datasets.transforms.impairments import BlurTransform, RandomGaussianNoiseTransform, RandomImageResizeTransform, RandomRippleNoiseTransform, ScaleTransform, scale_dynamic_range, normalize_image\n", + "from torchsig.image_datasets.plotting.plotting import plot_yolo_boxes_on_image, plot_yolo_datum\n", + "from torchsig.image_datasets.datasets.protocols import CFGSignalProtocolDataset, FrequencyHoppingDataset, random_hopping, YOLOFrequencyHoppingDataset, YOLOCFGSignalProtocolDataset\n", + "\n", + "from torchsig.datasets.modulations import ModulationsDataset\n", + "import torchsig.transforms as ST\n", + "import numpy as np\n", + "\n", + "from torchsig.image_datasets.dataset_generation import batched_write_yolo_synthetic_dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "9a375776-c294-4c66-bd54-9ee9911a29d2", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt\n", + "plt.rcParams[\"figure.figsize\"] = (10,10) #increase the pyplot figure display size in the notebook for better visibility" + ] + }, + { + "cell_type": "markdown", + "id": "2a3b4a57-0b99-4f63-a60a-2e9ba1eca588", + "metadata": {}, + "source": [ + "-----------------------------\n", + "## Modeling Signals Using CFGSignalProtocolDataset\n", + "\n", + "A `CFGSignalProtocolDataset` is a dataset meant for modeling complicated relationships between individual components of a larger signal\n", + "\n", + "Here we define two functions: rising_chirp and falling_chirp, which return slanted forward and backwards diagonal lines respectively.\n", + "We then combine those two basic components using a CFGSignalProtocolDataset to make a full signal shape.\n", + "\n", + "The CFGSignalProtocolDataset is an implementation of Context Free Grammar (CFG) logic for image datasets, such that each token of a string within the grammar is tied to either a dataset or a generator function which outputs image data. The result is an image composite containing all of the corresponding images side by side in order.\n", + "We pass in a single argument to the CFGSignalProtocolDataset which defines it's start token.\n", + "We then use the CFGSignalProtocolDataset.add_rule(token_in, token_out | list(token_out_0, token_out_1, ..... etc), relative_frequency) method to add in the logic o four CFG.\n", + "\n", + "Here, we are making a CFG starting on the token 'cfg_signal' and using the following rules:\n", + "- `cfg_signal` will always evaluate as one 'rising_or_falling_stream' followed by 12 `rising_falling_or_null'\n", + "- `rising_falling_or_null` will half the time evaluate as an empty string (ignored in the output image), and half the time as a 'rising_or_falling_stream'\n", + "- `rising_or_falling_stream` will evaluate half the time as a 'rising_stream' and half the time as a `falling_stream`\n", + "- `rising_stream` and `falling_stream` evaluate to between one and three `rising_segment` or `falling_segment' respectively\n", + "- a `rising_segment` is three `rising_chirp`s \n", + "- a `falling_segment` is three `falling_chirp`s\n", + "- 'rising_chirp' and `falling_chirp` are evaluated using the `rising_chirp` and `falling_chirp` functions\n", + "\n", + "The result is fed into a `YOLODatasetAdapter` to return objects of type `YOLODatum` instead of plain images (this will be useful later), and plotted below." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "78980771-86b3-44eb-bf27-e5c2715819ae", + "metadata": {}, + "outputs": [], + "source": [ + "def add_falling_edge(img_tnsr):\n", + " img_tnsr[:,:,-1] = 1\n", + " return img_tnsr\n", + "\n", + "rising_chirp = GeneratorFunctionDataset(chirp_generator_function(1, 20, 4, random_height_scale = [0.9,1.1], random_width_scale = [1,2]), \n", + " transforms=add_falling_edge)\n", + "falling_chirp = GeneratorFunctionDataset(chirp_generator_function(1, 20, 4, random_height_scale = [0.9,1.1], random_width_scale = [1,2]), \n", + " transforms=[add_falling_edge, lambda x: x.flip(-1)])\n", + "\n", + "chirp_stream_ds = CFGSignalProtocolDataset('cfg_signal')\n", + "chirp_stream_ds.add_rule('cfg_signal', ['rising_or_falling_stream'] + ['rising_falling_or_null']*12)\n", + "chirp_stream_ds.add_rule('rising_falling_or_null', 'rising_or_falling_stream', 1)\n", + "chirp_stream_ds.add_rule('rising_falling_or_null', 'null', 1)\n", + "chirp_stream_ds.add_rule('null', None)\n", + "chirp_stream_ds.add_rule('rising_or_falling_stream', 'rising_stream')\n", + "chirp_stream_ds.add_rule('rising_or_falling_stream', 'falling_stream')\n", + "chirp_stream_ds.add_rule('rising_stream', ['rising_segment'] + ['rising_segment_or_null']*2)\n", + "chirp_stream_ds.add_rule('rising_segment_or_null', 'rising_segment')\n", + "chirp_stream_ds.add_rule('rising_segment_or_null', 'null')\n", + "chirp_stream_ds.add_rule('rising_segment', ['rising_chirp']*3)\n", + "chirp_stream_ds.add_rule('rising_chirp', rising_chirp)\n", + "chirp_stream_ds.add_rule('falling_stream', ['falling_segment'] + ['falling_segment_or_null']*2)\n", + "chirp_stream_ds.add_rule('falling_segment_or_null', 'falling_segment')\n", + "chirp_stream_ds.add_rule('falling_segment_or_null', 'null')\n", + "chirp_stream_ds.add_rule('falling_segment', ['falling_chirp']*3)\n", + "chirp_stream_ds.add_rule('falling_chirp', falling_chirp)\n", + "\n", + "yolo_chirp_stream_ds = YOLODatasetAdapter(chirp_stream_ds, class_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "2455990a-d45c-4479-95b0-a4e5f5be35e9", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_yolo_datum(yolo_chirp_stream_ds[0])" + ] + }, + { + "cell_type": "markdown", + "id": "258873fa-bfe7-42d9-a463-727431b3e05b", + "metadata": {}, + "source": [ + "-----------------------------\n", + "Another kind of signal modeled using `CFGSignalProtocolDataset`, consisting of a fixed start and end sequence with between one and four 'bytes' of either large or small lines in the middle" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b794a276-c75d-4dfe-8d15-271d56b2eccc", + "metadata": {}, + "outputs": [], + "source": [ + "big_chirp_fn = chirp_generator_function(2, 8, 2, random_height_scale = [0.8,1.2], random_width_scale = [0.5,2])\n", + "small_chirp_fn = chirp_generator_function(2, 4, 2, random_height_scale = [0.8,1.2], random_width_scale = [0.5,2])\n", + "init_chirp_fn = chirp_generator_function(4, 2, 3, random_height_scale = [0.8,1.2], random_width_scale = [0.8,1.2])\n", + "exit_chirp_fn = chirp_generator_function(2, 2, 2, random_height_scale = [0.8,1.2], random_width_scale = [0.8,1.2])\n", + "\n", + "bytes_ds = CFGSignalProtocolDataset('cfg_signal')\n", + "bytes_ds.add_rule('cfg_signal', ['init_chirp', 'main_signal', 'exit_signal'])\n", + "bytes_ds.add_rule('main_signal_chirp', ['bit_chirp']*8)\n", + "bytes_ds.add_rule('bit_chirp', big_chirp_fn)\n", + "bytes_ds.add_rule('bit_chirp', small_chirp_fn)\n", + "bytes_ds.add_rule('main_signal', (['main_signal_chirp_or_null']*3)+['main_signal_chirp'])\n", + "bytes_ds.add_rule('main_signal_chirp_or_null', 'main_signal_chirp', 1)\n", + "bytes_ds.add_rule('main_signal_chirp_or_null', 'null', 1)\n", + "bytes_ds.add_rule('null', None)\n", + "bytes_ds.add_rule('init_chirp', init_chirp_fn)\n", + "bytes_ds.add_rule('exit_signal', [exit_chirp_fn, exit_chirp_fn, exit_chirp_fn])\n", + "\n", + "yolo_bytes_ds = YOLODatasetAdapter(bytes_ds, class_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "800d5324-83b1-474e-8239-45e159e7e8c8", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_yolo_datum(yolo_bytes_ds[0])" + ] + }, + { + "cell_type": "markdown", + "id": "d3c19679-7dea-4864-8a54-b2661c12299d", + "metadata": {}, + "source": [ + "-----------------------------\n", + "## Modeling Signal Hopping using YOLOFrequencyHoppingDataset\n", + "\n", + "A `YOLOFrequencyHoppingDataset` is a dataset meant for modeling channel hops. it is passed a nmber of channels and a channel size (in pixels), and a max and min number of bursts to send.\n", + "It will then generate images simulating a series of bursts along different channels.\n", + "A hopping_function can be passed in, which determines the order in which channels are selected. By default they are selected in either ascending or descending order (randomly with 50/50 odds).\n", + "\n", + "Here we define two `YOLOFrequencyHoppingDataset` objects, both using bursts from the bytes_ds we defined above, but one hopping in linear order, and the other hopping randomly.\n", + "\n", + "We then define a YOLOCFGSignalProtocolDataset which combines them and returns one, the other, or both in either order.\n", + "\n", + "`YOLOCFGSignalProtocolDataset` works just like `CFGSignalProtocolDataset` above, except that the data it handles are of type `YOLODatum` and not images alone. This is done to track internally where the bounding box around each signal burst should go." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "54ce6ba0-c325-4f2b-8729-20c4e512cab5", + "metadata": {}, + "outputs": [], + "source": [ + "hopper_ds = YOLOFrequencyHoppingDataset(yolo_bytes_ds, [20,80], 10, [100,100], [4,8])\n", + "random_hopper_ds = YOLOFrequencyHoppingDataset(yolo_bytes_ds, [20,80], 10, [100,100], [4,8], hopping_function=random_hopping)\n", + "\n", + "two_mode_hopper = YOLOCFGSignalProtocolDataset('two_mode_hopping')\n", + "two_mode_hopper.add_rule('two_mode_hopping',['12'])\n", + "two_mode_hopper.add_rule('two_mode_hopping',['21'])\n", + "two_mode_hopper.add_rule('two_mode_hopping',['1'])\n", + "two_mode_hopper.add_rule('two_mode_hopping',['2'])\n", + "two_mode_hopper.add_rule('12',['1','2'])\n", + "two_mode_hopper.add_rule('21',['2','1'])\n", + "two_mode_hopper.add_rule('1',hopper_ds)\n", + "two_mode_hopper.add_rule('2',random_hopper_ds)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c883df74-6816-4584-92b7-e2b86af562e1", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_yolo_datum(two_mode_hopper[0])" + ] + }, + { + "cell_type": "markdown", + "id": "701c114c-b7fe-404e-9c5f-1dc05722be3b", + "metadata": {}, + "source": [ + "-----------------------------\n", + "## Using Torchsig Narrrowband Datasets in Images\n", + "\n", + "Here we create a `ModulationsDataset` with a `SpectrogramImage` transform to make it return images of the generated data.\n", + "\n", + "This dataset is then passed into a `GeneratorFunctionDataset` with some filtering functions to clear the edges of the generated images.\n", + "We also add some transforms to randomly resize andslightly blur the result. These are useful for image compositing.\n", + "\n", + "`GeneratorFunctionDataset` can take any zero-argument function which returns images and treat it as an image dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "fa13bed4-8ae6-4c91-9546-ebc198d8cc62", + "metadata": {}, + "outputs": [], + "source": [ + "def threshold_mod_signal(signal):#quick function for cleaning up narrowband spectrogram to look neat in composites; not strictly necessary, but very useful\n", + " signal[signal**4<0.02] = 0\n", + " return signal[:,:,5:-5] # cuts off the ends to look more seemless in composites\n", + "\n", + "mod_ds_transform = ST.Compose([\n", + " ST.Normalize(norm=np.inf, flatten=True),\n", + " ST.Spectrogram(nperseg=128, noverlap=0, nfft=128, mode='psd',detrend=None,scaling='density'),\n", + " ST.Normalize(norm=np.inf, flatten=True),\n", + " ST.SpectrogramImage()\n", + "])\n", + "\n", + "mod_ds = ModulationsDataset(\n", + " level=0, \n", + " num_samples=53000,\n", + " num_iq_samples=2*128**2,\n", + " use_class_idx=True,\n", + " include_snr=False,\n", + " transform=mod_ds_transform\n", + " )\n", + "\n", + "signal_transforms = []\n", + "signal_transforms += [normalize_image]\n", + "signal_transforms += [RandomImageResizeTransform([0.02,1.5])]\n", + "signal_transforms += [BlurTransform(strength=0.5, blur_shape=(5,1))]\n", + "signal_transforms += [threshold_mod_signal]\n", + "signal_transforms += [BlurTransform(strength=1, blur_shape=(5,1))]\n", + "\n", + "mod_spec_dataset = GeneratorFunctionDataset(lambda : 1 - mod_ds[np.random.randint(len(mod_ds))][0].mean(axis=-1)[None,:,:], transforms=signal_transforms)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "2c2b1fdf-4c5a-44e7-a16d-49552601163b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(mod_spec_dataset[0][0])" + ] + }, + { + "cell_type": "markdown", + "id": "ed389479-d686-4a4d-a4a6-dfcbe5bd31e0", + "metadata": {}, + "source": [ + "-----------------------------\n", + "## Using YOLOImageCompositeDataset to put everything together\n", + "\n", + "Here we create a `YOLOImageCompositeDataset` which combines all of the datasets above into large composite simulated wideband spectrogram images.\n", + "\n", + "`YOLOImageCompositeDataset` takes as input a spectrogram_size, and has nothing in it at creation.\n", + "Components must be added using `YOLOImageCompositeDataset.add_component(dataset, min_to_add, max_to_add)`.\n", + "Every component will be added a uniform random number of times to the composites according to `min_to_add` and `max_to_add`.\n", + "Components can be assigned a `class_id`, which will be the class id of the resulting `YOLODatum`, or they can be set to `use_source_yolo_labels`, in which case the composite will add in any yolo data that is retreaved from the component dataset. Components with no class_id will be added to the image, but unlabeled, and treated as background.\n", + "\n", + "Here we are adding the `yolo_chirp_stream_ds`, `bytes_ds`, `two_mode_hopper`, and `mod_spec_dataset` defined above to our `YOLOImageCompositeDataset` to simulate wideband data from an RF environment with many different types of transmitter present." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "365478a4-b398-495e-b1ce-873924b2e81e", + "metadata": {}, + "outputs": [], + "source": [ + "def clamp_max_by_std(signal): # used to limit the dynamic range of the resulting image; stops overlapping image components from looking too dark or throwing off the rest of the image\n", + " signal[signal > signal.mean() + signal.std()*3] = signal.mean() + signal.std()*3\n", + " return signal\n", + "\n", + "spectrogram_size = (1,1024,1024)\n", + "\n", + "two_mode_hopper.transforms = [lambda x: YOLODatum(BlurTransform(strength=1, blur_shape=(5,1))(x.img), x.labels)] # add a little extra blur to help blend with the composite\n", + "composite_transforms = []\n", + "composite_transforms += [clamp_max_by_std] # limit dynamic range due to overlapping signals\n", + "composite_transforms += [normalize_image] # inf norm\n", + "composite_transforms += [RandomGaussianNoiseTransform(mean=0, range=(0.2,0.8))] # add background noise\n", + "composite_transforms += [scale_dynamic_range]\n", + "composite_transforms += [normalize_image] # inf norm\n", + "composite_spectrogram_dataset = YOLOImageCompositeDataset(spectrogram_size, transforms=composite_transforms, dataset_size=250000, max_add=True)\n", + "composite_spectrogram_dataset.add_component(yolo_chirp_stream_ds, min_to_add=0, max_to_add=3, use_source_yolo_labels=True)\n", + "composite_spectrogram_dataset.add_component(bytes_ds, min_to_add=0, max_to_add=3, class_id=0)\n", + "composite_spectrogram_dataset.add_component(two_mode_hopper, min_to_add=0, max_to_add=1, use_source_yolo_labels=True)\n", + "composite_spectrogram_dataset.add_component(mod_spec_dataset, min_to_add=1, max_to_add=3, class_id=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "eb39271b-4e4c-4a6c-b42d-256ddbcaf2fe", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sample_datum = composite_spectrogram_dataset[0]\n", + "plot_yolo_datum(sample_datum)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "f9344c4c-50bf-48b5-9c67-8766487fb131", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(sample_datum[0][0])" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/examples/10_example_yolo_annotation_tool.ipynb b/examples/10_example_yolo_annotation_tool.ipynb new file mode 100644 index 0000000..32d459f --- /dev/null +++ b/examples/10_example_yolo_annotation_tool.ipynb @@ -0,0 +1,132 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e217c4f2-4817-4ddf-a3c3-559ff22f632a", + "metadata": {}, + "source": [ + "# Example 10 - Using the YOLO Annotation Tool\n", + "This notebook showcases the YOLO annotation tool to draw boxes around Signals of Interest (SOIs) to create a image dataset compatible with YOLO.\n", + "\n", + "---" + ] + }, + { + "cell_type": "markdown", + "id": "53160dd6", + "metadata": {}, + "source": [ + "## Import Libraries" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "db8cb1a0-987c-448c-bbb3-2f7d3d908860", + "metadata": {}, + "outputs": [], + "source": [ + "from torchsig.image_datasets.annotation_tools.yolo_annotation_tool import yolo_annotator\n", + "from torchsig.image_datasets.datasets.yolo_datasets import YOLOFileDataset\n", + "from torchsig.image_datasets.plotting.plotting import plot_yolo_datum\n", + "import os" + ] + }, + { + "cell_type": "markdown", + "id": "35b25503", + "metadata": {}, + "source": [ + "## Set Directory Paths\n", + "`unlabeled_image_dir/` is where the unlabelled spectrogram images are stored. After annotation, the annotated images with be written to `new_yolo_dataset_dir/`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8a1d6ec5-31b0-4739-90ca-bcec3b5258c6", + "metadata": {}, + "outputs": [], + "source": [ + "unlabeled_image_dir = \"./datasets/10_example_data/sample_images/\" # directory of images to be annotated\n", + "new_yolo_dataset_dir = \"./datasets/10_example_data/annotated_yolo_sample_images/\" # directory to save annotated yolo data; be sure to end with a '/'" + ] + }, + { + "cell_type": "markdown", + "id": "123b9d5b", + "metadata": {}, + "source": [ + "## Create the YOLO Annotator and Label Signals\n", + "Run the below command, and manually label the SOIs by drawing bounding boxes. Select which signal class you are annotating at the bottom, and hit `Submit` when done annotating the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6f2ae761-b7f8-4f46-88c1-fb0c23c027cb", + "metadata": {}, + "outputs": [], + "source": [ + "yolo_annotator(unlabeled_image_dir,new_yolo_dataset_dir, class_names=['signal_type_A', 'signal_type_B', 'signal_type_C'])" + ] + }, + { + "cell_type": "markdown", + "id": "0cb224b3-45e3-4537-a248-43c5964441b6", + "metadata": {}, + "source": [ + "## Plotting the Resulting Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a3dcf767-0ffc-407a-bff2-a48e605f45c7", + "metadata": {}, + "outputs": [], + "source": [ + "yds = YOLOFileDataset(new_yolo_dataset_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fb54d59b-6358-4393-b15c-057810add6d7", + "metadata": {}, + "outputs": [], + "source": [ + "plot_yolo_datum(yds.next())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e353115c", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/gr-spectrumdetect/README.md b/gr-spectrumdetect/README.md new file mode 100755 index 0000000..b22a439 --- /dev/null +++ b/gr-spectrumdetect/README.md @@ -0,0 +1,69 @@ +## GR-Spectrumdetect Overview +--- +gr-spectrumdetect is an open-source example of using a trained model from TorchSig Wideband with GNU Radio for RF energy detection. + +`detect.pt` can be downloaded with the bash `trained_model_download.sh` script in `gr-spectrumdetect/examples/`. + +This is a YOLOv8x model trained for detection: +- single_cls=True +- 1024x1024 spectrograms +- gray scale black hot images +- Wideband with level 2 impairments with no signal overlap + - `torchsig/datasets/conf.py` -> `WidebandImpairedTrainConfig` -> `overlap_prob = 0.0` + +### Notes +- The first class of `wideband_yolo.yaml` has been modified to say __signal__ because this training method is detection only. + +### Training +Training YOLOv8x began with the pretrained YOLOv8x model on COCO and runs for one epoch. The first layer was frozen and the learning rate lr0 was set to 0.0033329 and optimizer set to SGD + +``` +yolo detect train data=wideband_yolo.yaml model=yolov8x pretrained=yolov8x.pt device=0 epochs=1 batch=32 save=True save_period=1 single_cls=True imgsz=1024 name=8x_freeze1 cos_lr=False cache=False workers=16 freeze=1 lr0=0.0033329 optimizer=SGD +``` + + +## Installation with Docker +--- +Clone the `torchsig` repository and install using the following commands: +``` +git clone https://github.com/TorchDSP/torchsig.git +cd torchsig +pip install . +cd gr-spectrumdetect +bash build_docker.sh +bash run_docker.sh +cd /build/gr-spectrumdetect/examples/ +source /opt/gnuradio/v3.10/setup_env.sh +bash trained_model_download.sh +gnuradio-companion example.grc & +``` + +## Installation without Docker +--- +Clone the `torchsig` repository and install using the following commands: +``` +git clone https://github.com/TorchDSP/torchsig.git +cd torchsig +pip install . +cd gr-spectrumdetect +mkdir build +cd build +cmake ../ +make install +cd ../examples/ +bash trained_model_download.sh +gnuradio-companion example.grc & +``` + +## Generating the Datasets and training with Command Line +``` +cd torchsig/gr-spectrumdetect/examples +bash generate.sh +bash make_yolo.sh +python3 verify_yolo_dataset_plot.py +bash train.sh +``` + +## License +--- +gr-spectrumdetect is released under the MIT License. The MIT license is a popular open-source software license enabling free use, redistribution, and modifications, even for commercial purposes, provided the license is included in all copies or substantial portions of the software. TorchSig has no connection to MIT, other than through the use of this license. \ No newline at end of file diff --git a/gr-spectrumdetect/examples/generate.sh b/gr-spectrumdetect/examples/generate.sh index 7fcee63..23af864 100755 --- a/gr-spectrumdetect/examples/generate.sh +++ b/gr-spectrumdetect/examples/generate.sh @@ -1,2 +1,2 @@ #!/bin/bash -python3 ../../scripts/generate_wideband_sig53.py --root=. --impaired --num-iq-samples=1048576 --num-workers=16 --batch-size=16 +python3 ../../scripts/generate_wideband.py --root=. --impaired --num-iq-samples=1048576 --num-workers=16 --batch-size=16 diff --git a/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train.py b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train.py new file mode 100755 index 0000000..182b676 --- /dev/null +++ b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train.py @@ -0,0 +1,161 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"]="0" +import torch +import numpy as np +from scipy import signal +from glob import glob +from tqdm import tqdm +import pytorch_lightning as pl +from torchsig.datasets.torchsig_wideband import TorchSigWideband +import torchsig.transforms.transforms as ST +import torchsig.transforms.target_transforms as TT +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import torchaudio + +root = '.' +train = True +impaired = True +fft_size = 1024 +num_classes = 53 + +transform = ST.Compose([ + ST.Normalize(norm=np.inf, flatten=True), +]) + +target_transform = ST.Compose([ + TT.DescToListTuple(), +]) + +wideband_dataset = TorchSigWideband( + root=root, + train=train, + impaired=impaired, + transform=transform, + target_transform=target_transform, +) + +output_dir_root = '.' +lbl_dir = output_dir_root+'/datasets/impaired/labels/train' +img_dir = output_dir_root+'/datasets/impaired/images/train' +os.makedirs(lbl_dir) +os.makedirs(img_dir) + +modulation_list = [ + "ook", + "bpsk", + "4pam", + "4ask", + "qpsk", + "8pam", + "8ask", + "8psk", + "16qam", + "16pam", + "16ask", + "16psk", + "32qam", + "32qam_cross", + "32pam", + "32ask", + "32psk", + "64qam", + "64pam", + "64ask", + "64psk", + "128qam_cross", + "256qam", + "512qam_cross", + "1024qam", + "2fsk", + "2gfsk", + "2msk", + "2gmsk", + "4fsk", + "4gfsk", + "4msk", + "4gmsk", + "8fsk", + "8gfsk", + "8msk", + "8gmsk", + "16fsk", + "16gfsk", + "16msk", + "16gmsk", + "ofdm-64", + "ofdm-72", + "ofdm-128", + "ofdm-180", + "ofdm-256", + "ofdm-300", + "ofdm-512", + "ofdm-600", + "ofdm-900", + "ofdm-1024", + "ofdm-1200", + "ofdm-2048", +] + + +for i in range(len(wideband_dataset)): + data, annotation = wideband_dataset[i] + X = str(i).zfill(10) + lbl_name = lbl_dir + '/' + X + '.txt' + img_name = img_dir + '/' + X + '.png' + valid = True + txt_box = [] + for lbl_obj_idx in range(len(annotation)): + sig_class = annotation[lbl_obj_idx][0] + start = annotation[lbl_obj_idx][1] + stop = annotation[lbl_obj_idx][2] + center_freq = annotation[lbl_obj_idx][3] + bandwidth = annotation[lbl_obj_idx][4] + lower_freq = center_freq - (bandwidth/2.0) + upper_freq = center_freq + (bandwidth/2.0) + center_time = (stop-start)/2.0 + duration = stop-start + if upper_freq+0.5 <= 0 or lower_freq+0.5 >= 1: + valid = False + print(valid, 'frequency out of bounds') + if duration <= 0: + print(duration,'duration') + valid = False + print(valid, 'duration out of bounds') + if valid == True: + txt_box.append(str(modulation_list.index(sig_class)) \ + +' '+str(start+0.5*duration)+' '+ \ + str(np.clip(lower_freq + 0.5 + 0.5*bandwidth,0,1)) \ + +' '+str(duration)+' '+str(bandwidth)+'\n') + spectrogram = torchaudio.transforms.Spectrogram( + n_fft=fft_size, + win_length=fft_size, + hop_length=fft_size, + window_fn=torch.blackman_window, + normalized=False, + center=False, + onesided=False, + power=2, + ) + norm = lambda x: torch.linalg.norm( + x, + ord=float("inf"), + keepdim=True, + ) + x = spectrogram(torch.from_numpy(data)) + x = x * (1 / norm(x.flatten())) + x = torch.fft.fftshift(x,dim=0) + x = 10*torch.log10(x+1e-12) + + with open(lbl_name, 'a') as lbl_file: + for line in txt_box: + lbl_file.write(line) + + + img_new = np.zeros((fft_size, fft_size, 3),dtype=np.float32) + img_new = cv2.normalize(x.numpy(), img_new, 0, 255, cv2.NORM_MINMAX) + img_new = img_new.astype(np.uint8) + img_new = cv2.bitwise_not(img_new) + cv2.imwrite(img_name, img_new, [cv2.IMWRITE_PNG_COMPRESSION, 9]) diff --git a/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train_scipy.py b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train_scipy.py new file mode 100755 index 0000000..cf0c06f --- /dev/null +++ b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_train_scipy.py @@ -0,0 +1,164 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"]="0" +import torch +import numpy as np +from scipy import signal +from glob import glob +from tqdm import tqdm +import pytorch_lightning as pl +from torchsig.datasets.torchsig_wideband import TorchSigWideband +import torchsig.transforms.transforms as ST +import torchsig.transforms.target_transforms as TT +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import torchaudio + +root = '.' +train = True +impaired = True +fft_size = 1024 +num_classes = 53 + +transform = ST.Compose([ + ST.Normalize(norm=np.inf, flatten=True), + ST.Spectrogram(nperseg=fft_size, noverlap=0, nfft=fft_size, mode='psd',detrend=None,scaling='density'), + ST.Normalize(norm=np.inf, flatten=True), + ST.SpectrogramImage(), +]) + +target_transform = ST.Compose([ + TT.DescToListTuple(), +]) + +wideband_dataset = TorchSigWideband( + root=root, + train=train, + impaired=impaired, + transform=transform, + target_transform=target_transform, +) + +output_dir_root = '.' +lbl_dir = output_dir_root+'/datasets/impaired/labels/train' +img_dir = output_dir_root+'/datasets/impaired/images/train' +os.makedirs(lbl_dir) +os.makedirs(img_dir) + +modulation_list = [ + "ook", + "bpsk", + "4pam", + "4ask", + "qpsk", + "8pam", + "8ask", + "8psk", + "16qam", + "16pam", + "16ask", + "16psk", + "32qam", + "32qam_cross", + "32pam", + "32ask", + "32psk", + "64qam", + "64pam", + "64ask", + "64psk", + "128qam_cross", + "256qam", + "512qam_cross", + "1024qam", + "2fsk", + "2gfsk", + "2msk", + "2gmsk", + "4fsk", + "4gfsk", + "4msk", + "4gmsk", + "8fsk", + "8gfsk", + "8msk", + "8gmsk", + "16fsk", + "16gfsk", + "16msk", + "16gmsk", + "ofdm-64", + "ofdm-72", + "ofdm-128", + "ofdm-180", + "ofdm-256", + "ofdm-300", + "ofdm-512", + "ofdm-600", + "ofdm-900", + "ofdm-1024", + "ofdm-1200", + "ofdm-2048", +] + + +for i in range(len(wideband_dataset)): + data, annotation = wideband_dataset[i] + X = str(i).zfill(10) + lbl_name = lbl_dir + '/' + X + '.txt' + img_name = img_dir + '/' + X + '.png' + valid = True + txt_box = [] + for lbl_obj_idx in range(len(annotation)): + sig_class = annotation[lbl_obj_idx][0] + start = annotation[lbl_obj_idx][1] + stop = annotation[lbl_obj_idx][2] + center_freq = annotation[lbl_obj_idx][3] + bandwidth = annotation[lbl_obj_idx][4] + lower_freq = center_freq - (bandwidth/2.0) + upper_freq = center_freq + (bandwidth/2.0) + center_time = (stop-start)/2.0 + duration = stop-start + if upper_freq+0.5 <= 0 or lower_freq+0.5 >= 1: + valid = False + print(valid, 'frequency out of bounds') + if duration <= 0: + print(duration,'duration') + valid = False + print(valid, 'duration out of bounds') + if valid == True: + txt_box.append(str(modulation_list.index(sig_class)) \ + +' '+str(start+0.5*duration)+' '+ \ + str(np.clip(lower_freq + 0.5 + 0.5*bandwidth,0,1)) \ + +' '+str(duration)+' '+str(bandwidth)+'\n') + #spectrogram = torchaudio.transforms.Spectrogram( + # n_fft=fft_size, + # win_length=fft_size, + # hop_length=fft_size, + # window_fn=torch.blackman_window, + # normalized=False, + # center=False, + # onesided=False, + # power=2, + # ) + #norm = lambda x: torch.linalg.norm( + # x, + # ord=float("inf"), + # keepdim=True, + # ) + #x = spectrogram(torch.from_numpy(data)) + #x = x * (1 / norm(x.flatten())) + #x = torch.fft.fftshift(x,dim=0) + #x = 10*torch.log10(x) + + with open(lbl_name, 'a') as lbl_file: + for line in txt_box: + lbl_file.write(line) + + + #img_new = np.zeros((fft_size, fft_size, 3),dtype=np.float32) + #img_new = cv2.normalize(x.numpy(), img_new, 0, 255, cv2.NORM_MINMAX) + #img_new = img_new.astype(np.uint8) + #img_new = cv2.bitwise_not(img_new) + cv2.imwrite(img_name, data, [cv2.IMWRITE_PNG_COMPRESSION, 9]) diff --git a/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val.py b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val.py new file mode 100755 index 0000000..e3860e7 --- /dev/null +++ b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val.py @@ -0,0 +1,161 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"]="0" +import torch +import numpy as np +from scipy import signal +from glob import glob +from tqdm import tqdm +import pytorch_lightning as pl +from torchsig.datasets.torchsig_wideband import TorchSigWideband +import torchsig.transforms.transforms as ST +import torchsig.transforms.target_transforms as TT +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import torchaudio + +root = '.' +train = False +impaired = True +fft_size = 1024 +num_classes = 53 + +transform = ST.Compose([ + ST.Normalize(norm=np.inf, flatten=True), +]) + +target_transform = ST.Compose([ + TT.DescToListTuple(), +]) + +wideband_dataset = TorchSigWideband( + root=root, + train=train, + impaired=impaired, + transform=transform, + target_transform=target_transform, +) + +output_dir_root = '.' +lbl_dir = output_dir_root+'/datasets/impaired/labels/val' +img_dir = output_dir_root+'/datasets/impaired/images/val' +os.makedirs(lbl_dir) +os.makedirs(img_dir) + +modulation_list = [ + "ook", + "bpsk", + "4pam", + "4ask", + "qpsk", + "8pam", + "8ask", + "8psk", + "16qam", + "16pam", + "16ask", + "16psk", + "32qam", + "32qam_cross", + "32pam", + "32ask", + "32psk", + "64qam", + "64pam", + "64ask", + "64psk", + "128qam_cross", + "256qam", + "512qam_cross", + "1024qam", + "2fsk", + "2gfsk", + "2msk", + "2gmsk", + "4fsk", + "4gfsk", + "4msk", + "4gmsk", + "8fsk", + "8gfsk", + "8msk", + "8gmsk", + "16fsk", + "16gfsk", + "16msk", + "16gmsk", + "ofdm-64", + "ofdm-72", + "ofdm-128", + "ofdm-180", + "ofdm-256", + "ofdm-300", + "ofdm-512", + "ofdm-600", + "ofdm-900", + "ofdm-1024", + "ofdm-1200", + "ofdm-2048", +] + + +for i in range(len(wideband_dataset)): + data, annotation = wideband_dataset[i] + X = str(i).zfill(10) + lbl_name = lbl_dir + '/' + X + '.txt' + img_name = img_dir + '/' + X + '.png' + valid = True + txt_box = [] + for lbl_obj_idx in range(len(annotation)): + sig_class = annotation[lbl_obj_idx][0] + start = annotation[lbl_obj_idx][1] + stop = annotation[lbl_obj_idx][2] + center_freq = annotation[lbl_obj_idx][3] + bandwidth = annotation[lbl_obj_idx][4] + lower_freq = center_freq - (bandwidth/2.0) + upper_freq = center_freq + (bandwidth/2.0) + center_time = (stop-start)/2.0 + duration = stop-start + if upper_freq+0.5 <= 0 or lower_freq+0.5 >= 1: + valid = False + print(valid, 'frequency out of bounds') + if duration <= 0: + print(duration,'duration') + valid = False + print(valid, 'duration out of bounds') + if valid == True: + txt_box.append(str(modulation_list.index(sig_class)) \ + +' '+str(start+0.5*duration)+' '+ \ + str(np.clip(lower_freq + 0.5 + 0.5*bandwidth,0,1)) \ + +' '+str(duration)+' '+str(bandwidth)+'\n') + spectrogram = torchaudio.transforms.Spectrogram( + n_fft=fft_size, + win_length=fft_size, + hop_length=fft_size, + window_fn=torch.blackman_window, + normalized=False, + center=False, + onesided=False, + power=2, + ) + norm = lambda x: torch.linalg.norm( + x, + ord=float("inf"), + keepdim=True, + ) + x = spectrogram(torch.from_numpy(data)) + x = x * (1 / norm(x.flatten())) + x = torch.fft.fftshift(x,dim=0) + x = 10*torch.log10(x+1e-12) + + with open(lbl_name, 'a') as lbl_file: + for line in txt_box: + lbl_file.write(line) + + + img_new = np.zeros((fft_size, fft_size, 3),dtype=np.float32) + img_new = cv2.normalize(x.numpy(), img_new, 0, 255, cv2.NORM_MINMAX) + img_new = img_new.astype(np.uint8) + img_new = cv2.bitwise_not(img_new) + cv2.imwrite(img_name, img_new, [cv2.IMWRITE_PNG_COMPRESSION, 9]) diff --git a/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val_scipy.py b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val_scipy.py new file mode 100755 index 0000000..3010461 --- /dev/null +++ b/gr-spectrumdetect/examples/make_wideband_yolo_dataset_impaired_val_scipy.py @@ -0,0 +1,164 @@ +import os +os.environ["CUDA_VISIBLE_DEVICES"]="0" +import torch +import numpy as np +from scipy import signal +from glob import glob +from tqdm import tqdm +import pytorch_lightning as pl +from torchsig.datasets.torchsig_wideband import TorchSigWideband +import torchsig.transforms.transforms as ST +import torchsig.transforms.target_transforms as TT +import pandas as pd +import matplotlib.pyplot as plt +import numpy as np +import cv2 +import torchaudio + +root = '.' +train = False +impaired = True +fft_size = 1024 +num_classes = 53 + +transform = ST.Compose([ + ST.Normalize(norm=np.inf, flatten=True), + ST.Spectrogram(nperseg=fft_size, noverlap=0, nfft=fft_size, mode='psd',detrend=None,scaling='density'), + ST.Normalize(norm=np.inf, flatten=True), + ST.SpectrogramImage(), +]) + +target_transform = ST.Compose([ + TT.DescToListTuple(), +]) + +wideband_dataset = TorchSigWideband( + root=root, + train=train, + impaired=impaired, + transform=transform, + target_transform=target_transform, +) + +output_dir_root = '.' +lbl_dir = output_dir_root+'/datasets/impaired/labels/val' +img_dir = output_dir_root+'/datasets/impaired/images/val' +os.makedirs(lbl_dir) +os.makedirs(img_dir) + +modulation_list = [ + "ook", + "bpsk", + "4pam", + "4ask", + "qpsk", + "8pam", + "8ask", + "8psk", + "16qam", + "16pam", + "16ask", + "16psk", + "32qam", + "32qam_cross", + "32pam", + "32ask", + "32psk", + "64qam", + "64pam", + "64ask", + "64psk", + "128qam_cross", + "256qam", + "512qam_cross", + "1024qam", + "2fsk", + "2gfsk", + "2msk", + "2gmsk", + "4fsk", + "4gfsk", + "4msk", + "4gmsk", + "8fsk", + "8gfsk", + "8msk", + "8gmsk", + "16fsk", + "16gfsk", + "16msk", + "16gmsk", + "ofdm-64", + "ofdm-72", + "ofdm-128", + "ofdm-180", + "ofdm-256", + "ofdm-300", + "ofdm-512", + "ofdm-600", + "ofdm-900", + "ofdm-1024", + "ofdm-1200", + "ofdm-2048", +] + + +for i in range(len(wideband_dataset)): + data, annotation = wideband_dataset[i] + X = str(i).zfill(10) + lbl_name = lbl_dir + '/' + X + '.txt' + img_name = img_dir + '/' + X + '.png' + valid = True + txt_box = [] + for lbl_obj_idx in range(len(annotation)): + sig_class = annotation[lbl_obj_idx][0] + start = annotation[lbl_obj_idx][1] + stop = annotation[lbl_obj_idx][2] + center_freq = annotation[lbl_obj_idx][3] + bandwidth = annotation[lbl_obj_idx][4] + lower_freq = center_freq - (bandwidth/2.0) + upper_freq = center_freq + (bandwidth/2.0) + center_time = (stop-start)/2.0 + duration = stop-start + if upper_freq+0.5 <= 0 or lower_freq+0.5 >= 1: + valid = False + print(valid, 'frequency out of bounds') + if duration <= 0: + print(duration,'duration') + valid = False + print(valid, 'duration out of bounds') + if valid == True: + txt_box.append(str(modulation_list.index(sig_class)) \ + +' '+str(start+0.5*duration)+' '+ \ + str(np.clip(lower_freq + 0.5 + 0.5*bandwidth,0,1)) \ + +' '+str(duration)+' '+str(bandwidth)+'\n') + #spectrogram = torchaudio.transforms.Spectrogram( + # n_fft=fft_size, + # win_length=fft_size, + # hop_length=fft_size, + # window_fn=torch.blackman_window, + # normalized=False, + # center=False, + # onesided=False, + # power=2, + # ) + #norm = lambda x: torch.linalg.norm( + # x, + # ord=float("inf"), + # keepdim=True, + # ) + #x = spectrogram(torch.from_numpy(data)) + #x = x * (1 / norm(x.flatten())) + #x = torch.fft.fftshift(x,dim=0) + #x = 10*torch.log10(x) + + with open(lbl_name, 'a') as lbl_file: + for line in txt_box: + lbl_file.write(line) + + + #img_new = np.zeros((fft_size, fft_size, 3),dtype=np.float32) + #img_new = cv2.normalize(x.numpy(), img_new, 0, 255, cv2.NORM_MINMAX) + #img_new = img_new.astype(np.uint8) + #img_new = cv2.bitwise_not(img_new) + cv2.imwrite(img_name, data, [cv2.IMWRITE_PNG_COMPRESSION, 9]) diff --git a/gr-spectrumdetect/examples/make_yolo.sh b/gr-spectrumdetect/examples/make_yolo.sh index 182d38b..65b42f4 100755 --- a/gr-spectrumdetect/examples/make_yolo.sh +++ b/gr-spectrumdetect/examples/make_yolo.sh @@ -1,3 +1,3 @@ #!/bin/bash -python3 make_wbsig53_yolo_dataset_impaired_train.py -python3 make_wbsig53_yolo_dataset_impaired_val.py +python3 make_wideband_yolo_dataset_impaired_train.py +python3 make_wideband_yolo_dataset_impaired_val.py diff --git a/gr-spectrumdetect/examples/train.sh b/gr-spectrumdetect/examples/train.sh index 62d46ae..ff9fdd5 100755 --- a/gr-spectrumdetect/examples/train.sh +++ b/gr-spectrumdetect/examples/train.sh @@ -1,2 +1,2 @@ #!/bin/bash -yolo detect train data=wbsig53.yaml model=yolov8x pretrained=yolov8x.pt device=0 epochs=1 batch=32 save=True save_period=1 single_cls=True imgsz=1024 name=8x_freeze1 cos_lr=False cache=False workers=16 freeze=1 lr0=0.0033329 \ No newline at end of file +yolo detect train data=wideband_yolo.yaml model=yolov8x pretrained=yolov8x.pt device=0 epochs=1 batch=32 save=True save_period=1 single_cls=True imgsz=1024 name=8x_freeze1 cos_lr=False cache=False workers=16 freeze=1 lr0=0.0033329 optimizer=SGD \ No newline at end of file diff --git a/gr-spectrumdetect/examples/trained_model_download.sh b/gr-spectrumdetect/examples/trained_model_download.sh index c7bb3a1..8b0c0d9 100644 --- a/gr-spectrumdetect/examples/trained_model_download.sh +++ b/gr-spectrumdetect/examples/trained_model_download.sh @@ -3,7 +3,7 @@ destination_path=detect.pt -file_id=1vdzNyXjnZ61D2vruFhslerNscL3rAV8c +file_id=12e9PPgKc_-s1bFmdc9ybETdoo2OL90v7 file_url="https://drive.usercontent.google.com/download?id=$file_id&export=download" confirmation_page=$(curl -s -L "$file_url") diff --git a/gr-spectrumdetect/examples/wideband_yolo.yaml b/gr-spectrumdetect/examples/wideband_yolo.yaml new file mode 100755 index 0000000..d4efcf0 --- /dev/null +++ b/gr-spectrumdetect/examples/wideband_yolo.yaml @@ -0,0 +1,59 @@ +train: impaired/images/train/ +val: impaired/images/val/ +nc: 53 + +# Classes for single_cls=True only first class matters +names: + 0: signal + 1: bpsk + 2: 4pam + 3: 4ask + 4: qpsk + 5: 8pam + 6: 8ask + 7: 8psk + 8: 16qam + 9: 16pam + 10: 16ask + 11: 16psk + 12: 32qam + 13: 32qam_cross + 14: 32pam + 15: 32ask + 16: 32psk + 17: 64qam + 18: 64pam + 19: 64ask + 20: 64psk + 21: 128qam_cross + 22: 256qam + 23: 512qam_cross + 24: 1024qam + 25: 2fsk + 26: 2gfsk + 27: 2msk + 28: 2gmsk + 29: 4fsk + 30: 4gfsk + 31: 4msk + 32: 4gmsk + 33: 8fsk + 34: 8gfsk + 35: 8msk + 36: 8gmsk + 37: 16fsk + 38: 16gfsk + 39: 16msk + 40: 16gmsk + 41: ofdm-64 + 42: ofdm-72 + 43: ofdm-128 + 44: ofdm-180 + 45: ofdm-256 + 46: ofdm-300 + 47: ofdm-512 + 48: ofdm-600 + 49: ofdm-900 + 50: ofdm-1024 + 51: ofdm-1200 + 52: ofdm-2048 diff --git a/pyproject.toml b/pyproject.toml index 97cead1..a8dde36 100755 --- a/pyproject.toml +++ b/pyproject.toml @@ -9,7 +9,7 @@ authors = [ {name = "TorchSig Team"}, ] readme = "README.md" -requires-python = ">=3.8" +requires-python = ">=3.9" license = {text = "MIT"} classifiers = [ "License :: OSI Approved :: MIT License", @@ -39,7 +39,10 @@ dependencies = [ "pytorch_lightning", "sympy", "torchmetrics", - "click" + "click", + "ultralytics", + "optuna", + "jupyter_bbox_widget" ] dynamic = ["version"] diff --git a/scripts/generate_sig53.py b/scripts/generate_narrowband.py similarity index 78% rename from scripts/generate_sig53.py rename to scripts/generate_narrowband.py index 822de97..c316ba8 100755 --- a/scripts/generate_sig53.py +++ b/scripts/generate_narrowband.py @@ -8,7 +8,7 @@ import numpy as np -def generate(path: str, configs: List[conf.Sig53Config], num_workers: int, num_samples_override: int, num_iq_samples_override: int = -1, batch_size: int = 32): +def generate(path: str, configs: List[conf.NarrowbandConfig], num_workers: int, num_samples_override: int, num_iq_samples_override: int = -1, batch_size: int = 32): for config in configs: num_samples = config.num_samples if num_samples_override <=0 else num_samples_override num_iq_samples = config.num_iq_samples if num_iq_samples_override <= 0 else num_iq_samples_override @@ -28,10 +28,10 @@ def generate(path: str, configs: List[conf.Sig53Config], num_workers: int, num_s @click.command() -@click.option("--root", default="sig53", help="Path to generate sig53 datasets") -@click.option("--all", is_flag=True, default=False, help="Generate all versions of sig53 dataset.") -@click.option("--qa", is_flag=True, default=False, help="Generate only QA versions of sig53 dataset.") -@click.option("--num-iq-samples", "num_iq_samples", default=-1, help="Override number of iq samples in wideband_sig53 dataset.") +@click.option("--root", default="narrowband", help="Path to generate narrowband datasets") +@click.option("--all", is_flag=True, default=False, help="Generate all versions of narrowband dataset.") +@click.option("--qa", is_flag=True, default=False, help="Generate only QA versions of TorchSigNarrowband dataset.") +@click.option("--num-iq-samples", "num_iq_samples", default=-1, help="Override number of iq samples in wideband dataset.") @click.option("--batch-size", "batch_size", default=32, help="Override batch size.") @click.option("--num-samples", default=-1, help="Override for number of dataset samples.") @click.option("--num-workers", "num_workers", default=os.cpu_count() // 2, help="Define number of workers for both DatasetLoader and DatasetCreator") @@ -40,14 +40,14 @@ def main(root: str, all: bool, qa: bool, impaired: bool, num_workers: int, num_s os.makedirs(root, exist_ok=True) configs = [ - conf.Sig53CleanTrainConfig, - conf.Sig53CleanValConfig, - conf.Sig53ImpairedTrainConfig, - conf.Sig53ImpairedValConfig, - conf.Sig53CleanTrainQAConfig, - conf.Sig53CleanValQAConfig, - conf.Sig53ImpairedTrainQAConfig, - conf.Sig53ImpairedValQAConfig, + conf.NarrowbandCleanTrainConfig, + conf.NarrowbandCleanValConfig, + conf.NarrowbandImpairedTrainConfig, + conf.NarrowbandImpairedValConfig, + conf.NarrowbandCleanTrainQAConfig, + conf.NarrowbandCleanValQAConfig, + conf.NarrowbandImpairedTrainQAConfig, + conf.NarrowbandImpairedValQAConfig, ] impaired_configs = [] diff --git a/scripts/generate_wideband_sig53.py b/scripts/generate_wideband.py similarity index 77% rename from scripts/generate_wideband_sig53.py rename to scripts/generate_wideband.py index 9830cf3..c83ca59 100755 --- a/scripts/generate_wideband_sig53.py +++ b/scripts/generate_wideband.py @@ -2,7 +2,7 @@ from torchsig.utils.writer import DatasetCreator, DatasetLoader from torchsig.datasets.wideband import WidebandModulationsDataset from torchsig.datasets import conf -from torchsig.datasets.signal_classes import sig53 +from torchsig.datasets.signal_classes import torchsig_signals from torchsig.transforms.transforms import * from torchsig.utils.dataset import collate_fn from typing import List @@ -11,10 +11,9 @@ import numpy as np +modulation_list = torchsig_signals.class_list -modulation_list = sig53.class_list - -def generate(root: str, configs: List[conf.WidebandSig53Config], num_workers: int, num_samples_override: int = -1, num_iq_samples_override: int = -1, batch_size: int = 32): +def generate(root: str, configs: List[conf.WidebandConfig], num_workers: int, num_samples_override: int = -1, num_iq_samples_override: int = -1, batch_size: int = 32): for config in configs: num_samples = config.num_samples if num_samples_override <=0 else num_samples_override num_iq_samples = config.num_iq_samples if num_iq_samples_override <= 0 else num_iq_samples_override @@ -38,10 +37,10 @@ def generate(root: str, configs: List[conf.WidebandSig53Config], num_workers: in @click.command() -@click.option("--root", default="wideband_sig53", help="Path to generate wideband_sig53 datasets") -@click.option("--all", is_flag=True, default=False, help="Generate all versions of wideband_sig53 dataset.") -@click.option("--qa", is_flag=True, default=False, help="Generate only QA versions of wideband_sig53 dataset.") -@click.option("--num-iq-samples", "num_iq_samples", default=-1, help="Override number of iq samples in wideband_sig53 dataset.") +@click.option("--root", default="wideband", help="Path to generate wideband datasets") +@click.option("--all", is_flag=True, default=False, help="Generate all versions of wideband_ dataset.") +@click.option("--qa", is_flag=True, default=False, help="Generate only QA versions of wideband dataset.") +@click.option("--num-iq-samples", "num_iq_samples", default=-1, help="Override number of iq samples in wideband dataset.") @click.option("--batch-size", "batch_size", default=32, help="Override batch size.") @click.option("--num-samples", default=-1, help="Override for number of dataset samples.") @click.option("--impaired", is_flag=True, default=False, help="Generate impaired dataset. Ignored if --all (default)",) @@ -50,14 +49,14 @@ def main(root: str, all: bool, qa: bool, impaired: bool, num_workers: int, num_s os.makedirs(root, exist_ok=True) configs = [ - conf.WidebandSig53CleanTrainConfig, - conf.WidebandSig53CleanValConfig, - conf.WidebandSig53ImpairedTrainConfig, - conf.WidebandSig53ImpairedValConfig, - conf.WidebandSig53CleanTrainQAConfig, - conf.WidebandSig53CleanValQAConfig, - conf.WidebandSig53ImpairedTrainQAConfig, - conf.WidebandSig53ImpairedValQAConfig, + conf.WidebandCleanTrainConfig, + conf.WidebandCleanValConfig, + conf.WidebandImpairedTrainConfig, + conf.WidebandImpairedValConfig, + conf.WidebandCleanTrainQAConfig, + conf.WidebandCleanValQAConfig, + conf.WidebandImpairedTrainQAConfig, + conf.WidebandImpairedValQAConfig, ] impaired_configs = [] diff --git a/scripts/test_generate_narrowband_scripts.py b/scripts/test_generate_narrowband_scripts.py new file mode 100644 index 0000000..23d1a26 --- /dev/null +++ b/scripts/test_generate_narrowband_scripts.py @@ -0,0 +1,58 @@ +""" Testing Generate Narrowband Scripts + +Examples: + >>> pytest test_generate_narrowband_scripts.py + >>> pytest test_generate_narrowband_scripts.py --pdb +""" +import generate_narrowband +from torchsig.datasets import conf +import pytest +import numpy as np +import torch +from typing import Any, Dict, List, Optional, Tuple, Union +import os, sys + +configs = [ + conf.NarrowbandCleanTrainConfig, + conf.NarrowbandCleanValConfig, + conf.NarrowbandImpairedTrainConfig, + conf.NarrowbandImpairedValConfig, + conf.NarrowbandCleanTrainQAConfig, + conf.NarrowbandCleanValQAConfig, + conf.NarrowbandImpairedTrainQAConfig, + conf.NarrowbandImpairedValQAConfig, +] +num_samples_small = 10 +num_workers = os.cpu_count() // 2 + +# @pytest.mark.skip(reason="works") +def test_generate_narrowband_clean_qa_train(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandCleanTrainQAConfig], num_workers, -1) + +# @pytest.mark.skip(reason="works") +def test_generate_narrowband_clean_qa_val(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandCleanValQAConfig], num_workers, -1) + +# @pytest.mark.skip(reason="works") +def test_generate_narrowband_impaired_qa_train(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandImpairedTrainQAConfig], num_workers, -1) + +# @pytest.mark.skip(reason="works") +def test_generate_narrowband_impaired_qa_val(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandImpairedValQAConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_narrowband_clean_train(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandCleanTrainConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_narrowband_clean_val(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandCleanValConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_narrowband_impaired_train(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandImpairedTrainConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_narrowband_impaired_val(tmp_path): + generate_narrowband.generate(tmp_path, [conf.NarrowbandImpairedValConfig], num_workers, -1) \ No newline at end of file diff --git a/scripts/test_generate_sig53_scripts.py b/scripts/test_generate_sig53_scripts.py deleted file mode 100644 index 38e34fc..0000000 --- a/scripts/test_generate_sig53_scripts.py +++ /dev/null @@ -1,58 +0,0 @@ -""" Testing Generate Sig53 Scripts - -Examples: - >>> pytest test_generate_sig53_scripts.py - >>> pytest test_generate_sig53_scripts.py --pdb -""" -import generate_sig53 -from torchsig.datasets import conf -import pytest -import numpy as np -import torch -from typing import Any, Dict, List, Optional, Tuple, Union -import os, sys - -configs = [ - conf.Sig53CleanTrainConfig, - conf.Sig53CleanValConfig, - conf.Sig53ImpairedTrainConfig, - conf.Sig53ImpairedValConfig, - conf.Sig53CleanTrainQAConfig, - conf.Sig53CleanValQAConfig, - conf.Sig53ImpairedTrainQAConfig, - conf.Sig53ImpairedValQAConfig, -] -num_samples_small = 10 -num_workers = os.cpu_count() // 2 - -# @pytest.mark.skip(reason="works") -def test_generate_sig53_clean_qa_train(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53CleanTrainQAConfig], num_workers, -1) - -# @pytest.mark.skip(reason="works") -def test_generate_sig53_clean_qa_val(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53CleanValQAConfig], num_workers, -1) - -# @pytest.mark.skip(reason="works") -def test_generate_sig53_impaired_qa_train(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53ImpairedTrainQAConfig], num_workers, -1) - -# @pytest.mark.skip(reason="works") -def test_generate_sig53_impaired_qa_val(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53ImpairedValQAConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_sig53_clean_train(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53CleanTrainConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_sig53_clean_val(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53CleanValConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_sig53_impaired_train(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53ImpairedTrainConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_sig53_impaired_val(tmp_path): - generate_sig53.generate(tmp_path, [conf.Sig53ImpairedValConfig], num_workers, -1) \ No newline at end of file diff --git a/scripts/test_generate_wideband_scripts.py b/scripts/test_generate_wideband_scripts.py new file mode 100644 index 0000000..47ed141 --- /dev/null +++ b/scripts/test_generate_wideband_scripts.py @@ -0,0 +1,57 @@ +""" Testing Generate Wideband Scripts + +Examples: + >>> pytest test_generate_wideband_scripts.py + >>> pytest test_generate_wideband_scripts.py --pdb +""" +import generate_wideband +from torchsig.datasets import conf +import pytest +import numpy as np +import torch +from typing import Any, Dict, List, Optional, Tuple, Union +import os, sys + +configs = [ + conf.WidebandCleanTrainConfig, + conf.WidebandCleanValConfig, + conf.WidebandImpairedTrainConfig, + conf.WidebandImpairedValConfig, + conf.WidebandCleanTrainQAConfig, + conf.WidebandCleanValQAConfig, + conf.WidebandImpairedTrainQAConfig, + conf.WidebandImpairedValQAConfig, +] +num_samples_small = 10 +num_workers = os.cpu_count() // 2 + +# @pytest.mark.skip(reason="works") +def test_generate_wideband_clean_qa_train(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandCleanTrainQAConfig], num_workers, -1) + +# @pytest.mark.skip(reason="works") +def test_generate_wideband_clean_qa_val(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandCleanValQAConfig], num_workers, -1) + +# @pytest.mark.parametrize('execution_number', range(10)) +def test_generate_wideband_impaired_qa_train(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandImpairedTrainQAConfig], num_workers, -1) + +def test_generate_wideband_impaired_qa_val(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandImpairedValQAConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_wideband_clean_train(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandCleanTrainConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_wideband_clean_val(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandCleanValConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_wideband_impaired_train(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandImpairedTrainConfig], num_workers, -1) + +# @pytest.mark.skip(reason="too big") +def test_generate_wideband_impaired_val(tmp_path): + generate_wideband.generate(tmp_path, [conf.WidebandImpairedValConfig], num_workers, -1) \ No newline at end of file diff --git a/scripts/test_generate_wideband_sig53_scripts.py b/scripts/test_generate_wideband_sig53_scripts.py deleted file mode 100644 index 4a1f91a..0000000 --- a/scripts/test_generate_wideband_sig53_scripts.py +++ /dev/null @@ -1,57 +0,0 @@ -""" Testing Generate Wideband Sig53 Scripts - -Examples: - >>> pytest test_generate_wideband_sig53_scripts.py - >>> pytest test_generate_wideband_sig53_scripts.py --pdb -""" -import generate_wideband_sig53 -from torchsig.datasets import conf -import pytest -import numpy as np -import torch -from typing import Any, Dict, List, Optional, Tuple, Union -import os, sys - -configs = [ - conf.WidebandSig53CleanTrainConfig, - conf.WidebandSig53CleanValConfig, - conf.WidebandSig53ImpairedTrainConfig, - conf.WidebandSig53ImpairedValConfig, - conf.WidebandSig53CleanTrainQAConfig, - conf.WidebandSig53CleanValQAConfig, - conf.WidebandSig53ImpairedTrainQAConfig, - conf.WidebandSig53ImpairedValQAConfig, -] -num_samples_small = 10 -num_workers = os.cpu_count() // 2 - -# @pytest.mark.skip(reason="works") -def test_generate_wideband_sig53_clean_qa_train(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53CleanTrainQAConfig], num_workers, -1) - -# @pytest.mark.skip(reason="works") -def test_generate_wideband_sig53_clean_qa_val(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53CleanValQAConfig], num_workers, -1) - -# @pytest.mark.parametrize('execution_number', range(10)) -def test_generate_wideband_sig53_impaired_qa_train(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53ImpairedTrainQAConfig], num_workers, -1) - -def test_generate_wideband_sig53_impaired_qa_val(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53ImpairedValQAConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_wideband_sig53_clean_train(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53CleanTrainConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_wideband_sig53_clean_val(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53CleanValConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_wideband_sig53_impaired_train(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53ImpairedTrainConfig], num_workers, -1) - -# @pytest.mark.skip(reason="too big") -def test_generate_wideband_sig53_impaired_val(tmp_path): - generate_wideband_sig53.generate(tmp_path, [conf.WidebandSig53ImpairedValConfig], num_workers, -1) \ No newline at end of file diff --git a/scripts/train_narrowband.py b/scripts/train_narrowband.py new file mode 100755 index 0000000..42cd2c3 --- /dev/null +++ b/scripts/train_narrowband.py @@ -0,0 +1,124 @@ +#!/usr/bin/env python + +""" +Script to train a model using the TorchSig Narrowband dataset. + +This script allows for training a neural network model on the TorchSig Narrowband dataset with configurable options +such as model name, number of epochs, batch size, learning rate, and more. + +Example usage: + python train_narrowband.py --model_name xcit --num_epochs 10 --batch_size 64 + +Command line arguments: + --data_path: Path to the dataset. Default is '../datasets/narrowband_test_QA'. + --model_name: Name of the model to use for training. Default is 'xcit'. + --num_epochs: Number of training epochs. Default is 2. + --batch_size: Batch size for data loaders. Default is 32. + --num_workers: Number of workers for data loading. Default is 16. + --learning_rate: Learning rate for optimizer. Default is 1e-3. + --input_channels: Number of input channels. Default is 2. + --impaired: Include impaired signals in the dataset. + --qa: Enable QA signals in the dataset. + --checkpoint_path: Path to checkpoint to resume training. + --use_datamodule: Use custom datamodule for data loading. +""" + +import argparse + +def main(): + """ + Main function to train a model using the TorchSig Narrowband dataset. + """ + # Parse command-line arguments + parser = argparse.ArgumentParser(description='Train a model using TorchSig Narrowband dataset.') + parser.add_argument('--data_path', type=str, default='../datasets/narrowband_test_QA', + help='Path to dataset') + parser.add_argument('--model_name', type=str, default='xcit', + help='Model name to use for training') + parser.add_argument('--num_epochs', type=int, default=2, + help='Number of training epochs') + parser.add_argument('--batch_size', type=int, default=32, + help='Batch size for data loaders') + parser.add_argument('--num_workers', type=int, default=16, + help='Number of workers for data loading') + parser.add_argument('--learning_rate', type=float, default=1e-3, + help='Learning rate for optimizer') + parser.add_argument('--input_channels', type=int, default=2, + help='Number of input channels') + parser.add_argument('--impaired', type=bool, default=True, + help='Include impaired signals') + parser.add_argument('--qa', type=bool, default=True, + help='Enable QA signals') + parser.add_argument('--checkpoint_path', type=str, default=None, + help='Path to checkpoint to resume training') + parser.add_argument('--use_datamodule', type=bool, default=True, + help='Use custom datamodule') + args = parser.parse_args() + + # TorchSig imports + from torchsig.transforms.target_transforms import DescToClassIndex + from torchsig.transforms.transforms import ( + RandomPhaseShift, + Normalize, + ComplexTo2D, + Compose, + ) + from torchsig.utils.narrowband_trainer import NarrowbandTrainer + from torchsig.datasets.torchsig_narrowband import TorchSigNarrowband + from torchsig.datasets.datamodules import NarrowbandDataModule + import numpy as np + import cv2 + import os + import matplotlib.pyplot as plt + + # Get the list of class names from TorchSigNarrowband dataset + class_list = list(TorchSigNarrowband._idx_to_name_dict.values()) + num_classes = len(class_list) + + # Specify data transformations to be applied to the dataset + transform = Compose( + [ + RandomPhaseShift(phase_offset=(-1, 1)), # Randomly shift the phase of the signal + Normalize(norm=np.inf), # Normalize the signal + ComplexTo2D(), # Convert complex signal to 2D representation + ] + ) + + # Specify target transformation (e.g., mapping description to class index) + target_transform = DescToClassIndex(class_list=class_list) + + if args.use_datamodule: + # Create the data module for the narrowband dataset + datamodule = NarrowbandDataModule( + root=args.data_path, # Path to the dataset + qa=args.qa, # Enable QA signals + impaired=args.impaired, # Include impaired signals + transform=transform, # Apply data transformations + target_transform=target_transform, # Apply target transformations + batch_size=args.batch_size, # Batch size for data loaders + num_workers=args.num_workers, # Number of workers for data loading + ) + datamodule_param = datamodule + else: + datamodule_param = None + + # Initialize the trainer with desired parameters + trainer = NarrowbandTrainer( + model_name = args.model_name, # Specify the model to use + num_epochs = args.num_epochs, # Number of training epochs + batch_size = args.batch_size if not args.use_datamodule else None, + num_workers = args.num_workers if not args.use_datamodule else None, + learning_rate = args.learning_rate, # Learning rate for optimizer + input_channels = args.input_channels, # Number of input channels + data_path = args.data_path if not args.use_datamodule else None, + impaired = args.impaired if not args.use_datamodule else None, + qa = args.qa if not args.use_datamodule else None, + datamodule = datamodule_param, # Pass the datamodule + checkpoint_path = args.checkpoint_path # Path to checkpoint to resume training (if any) + ) + + # Train the model + trainer.train() + +if __name__ == '__main__': + main() diff --git a/scripts/train_sig53.py b/scripts/train_sig53.py deleted file mode 100755 index 8888c7a..0000000 --- a/scripts/train_sig53.py +++ /dev/null @@ -1,188 +0,0 @@ -from torchsig.transforms.target_transforms import DescToClassIndex -from torchsig.models.iq_models.efficientnet.efficientnet import efficientnet_b4 -from torchsig.transforms.transforms import ( - RandomPhaseShift, - Normalize, - ComplexTo2D, - Compose, -) -from pytorch_lightning.callbacks import ModelCheckpoint -from pytorch_lightning import LightningModule, Trainer -from sklearn.metrics import classification_report -from torchsig.utils.cm_plotter import plot_confusion_matrix -from torchsig.datasets.sig53 import Sig53 -from torch.utils.data import DataLoader -from matplotlib import pyplot as plt -from torch import optim -from tqdm import tqdm -import torch.nn.functional as F -import numpy as np -import click -import torch -import os - - -class ExampleNetwork(LightningModule): - def __init__(self, model, data_loader, val_data_loader): - super(ExampleNetwork, self).__init__() - self.mdl: torch.nn.Module = model - self.data_loader: DataLoader = data_loader - self.val_data_loader: DataLoader = val_data_loader - - # Hyperparameters - self.lr = 0.001 - self.batch_size = data_loader.batch_size - - def forward(self, x: torch.Tensor): - return self.mdl(x.float()) - - def predict(self, x: torch.Tensor): - with torch.no_grad(): - out = self.forward(x.float()) - return out - - def configure_optimizers(self): - return optim.Adam(self.parameters(), lr=self.lr) - - def train_dataloader(self): - return self.data_loader - - def val_dataloader(self): - return self.val_data_loader - - def training_step(self, batch: torch.Tensor, batch_nb: int): - x, y = batch - y = torch.squeeze(y.to(torch.int64)) - loss = F.cross_entropy(self(x.float()), y) - self.log("loss", loss, on_step=True, prog_bar=True, logger=True) - return loss - - def validation_step(self, batch: torch.Tensor, batch_nb: int): - x, y = batch - y = torch.squeeze(y.to(torch.int64)) - loss = F.cross_entropy(self(x.float()), y) - self.log("val_loss", loss, on_epoch=True, prog_bar=True, logger=True) - return loss - - -@click.command() -@click.option("--root", default="data/sig53", help="Path to train/val datasets") -@click.option("--impaired", default=False, help="Impaired or clean datasets") -def main(root: str, impaired: bool): - class_list = list(Sig53._idx_to_name_dict.values()) - transform = Compose( - [ - RandomPhaseShift(phase_offset=(-1, 1)), - Normalize(norm=np.inf), - ComplexTo2D(), - ] - ) - target_transform = DescToClassIndex(class_list=class_list) - - sig53_train = Sig53( - root, - train=True, - impaired=impaired, - transform=transform, - target_transform=target_transform, - use_signal_data=True, - ) - - sig53_val = Sig53( - root, - train=False, - impaired=impaired, - transform=transform, - target_transform=target_transform, - use_signal_data=True, - ) - - # Create dataloaders"data - train_dataloader = DataLoader( - dataset=sig53_train, - batch_size=os.cpu_count(), - num_workers=os.cpu_count() // 2, - shuffle=True, - drop_last=True, - ) - val_dataloader = DataLoader( - dataset=sig53_val, - batch_size=os.cpu_count(), - num_workers=os.cpu_count() // 2, - shuffle=False, - drop_last=True, - ) - - model = efficientnet_b4(pretrained=False) - - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - model = model.to(device) - - example_model = ExampleNetwork(model, train_dataloader, val_dataloader) - example_model = example_model.to(device) - - # Setup checkpoint callbacks - checkpoint_filename = "{}/checkpoint".format(os.getcwd()) - checkpoint_callback = ModelCheckpoint( - filename=checkpoint_filename, - save_top_k=True, - monitor="val_loss", - mode="min", - ) - - # Create and fit trainer - epochs = 500 - trainer = Trainer( - max_epochs=epochs, callbacks=checkpoint_callback, devices=1, accelerator="gpu" - ) - trainer.fit(example_model) - - # Load best checkpoint - device = "cuda" if torch.cuda.is_available() else "cpu" - checkpoint = torch.load( - checkpoint_filename + ".ckpt", map_location=lambda storage, loc: storage - ) - example_model.load_state_dict(checkpoint["state_dict"]) - example_model = example_model.to(device=device).eval() - - # Infer results over validation set - num_test_examples = len(sig53_val) - num_classes = len(list(Sig53._idx_to_name_dict.values())) - y_raw_preds = np.empty((num_test_examples, num_classes)) - y_preds = np.zeros((num_test_examples,)) - y_true = np.zeros((num_test_examples,)) - - for i in tqdm(range(0, num_test_examples)): - # Retrieve data - idx = i # Use index if evaluating over full dataset - data, label = sig53_val[idx] - # Infer - data = torch.from_numpy(np.expand_dims(data, 0)).float().to(device) - pred_tmp = example_model.predict(data) - pred_tmp = pred_tmp.cpu().numpy() if torch.cuda.is_available() else pred_tmp - # Argmax - y_preds[i] = np.argmax(pred_tmp) - # Store label - y_true[i] = label - - acc = np.sum(np.asarray(y_preds) == np.asarray(y_true)) / len(y_true) - plot_confusion_matrix( - y_true, - y_preds, - classes=class_list, - normalize=True, - title="Example Modulations Confusion Matrix\nTotal Accuracy: {:.2f}%".format( - acc * 100 - ), - text=False, - rotate_x_text=90, - figsize=(16, 9), - ) - plt.savefig("{}/02_sig53_classifier.png".format(os.getcwd())) - - print("Classification Report:") - print(classification_report(y_true, y_preds)) - - -if __name__ == "__main__": - main() diff --git a/torchsig/__init__.py b/torchsig/__init__.py index 43a1e95..906d362 100755 --- a/torchsig/__init__.py +++ b/torchsig/__init__.py @@ -1 +1 @@ -__version__ = "0.5.3" +__version__ = "0.6.0" diff --git a/torchsig/datasets/conf.py b/torchsig/datasets/conf.py index 6921c3c..dfaf4ac 100755 --- a/torchsig/datasets/conf.py +++ b/torchsig/datasets/conf.py @@ -3,7 +3,7 @@ from dataclasses import dataclass @dataclass -class Sig53Config: +class NarrowbandConfig: name: str num_samples: int level: int @@ -15,49 +15,49 @@ class Sig53Config: @dataclass -class Sig53CleanTrainConfig(Sig53Config): - name: str = "sig53_clean_train" +class NarrowbandCleanTrainConfig(NarrowbandConfig): + name: str = "narrowband_clean_train" seed: int = 1234567890 eb_no: bool = False num_samples: int = 1_060_000 level: int = 0 @dataclass -class Sig53CleanEbNoTrainConfig(Sig53CleanTrainConfig): +class NarrowbandCleanEbNoTrainConfig(NarrowbandCleanTrainConfig): eb_no: bool = True @dataclass -class Sig53CleanTrainQAConfig(Sig53CleanTrainConfig): +class NarrowbandCleanTrainQAConfig(NarrowbandCleanTrainConfig): num_samples: int = 10_600 @dataclass -class Sig53CleanEbNoTrainQAConfig(Sig53CleanTrainQAConfig): +class NarrowbandCleanEbNoTrainQAConfig(NarrowbandCleanTrainQAConfig): eb_no: bool = True @dataclass -class Sig53CleanValConfig(Sig53CleanTrainConfig): - name: str = "sig53_clean_val" +class NarrowbandCleanValConfig(NarrowbandCleanTrainConfig): + name: str = "narrowband_clean_val" seed: int = 1234567891 eb_no: bool = False num_samples: int = 10_600 @dataclass -class Sig53CleanEbNoValConfig(Sig53CleanValConfig): +class NarrowbandCleanEbNoValConfig(NarrowbandCleanValConfig): eb_no: bool = True @dataclass -class Sig53CleanValQAConfig(Sig53CleanValConfig): +class NarrowbandCleanValQAConfig(NarrowbandCleanValConfig): num_samples: int = 1060 @dataclass -class Sig53CleanEbNoValQAConfig(Sig53CleanValQAConfig): +class NarrowbandCleanEbNoValQAConfig(NarrowbandCleanValQAConfig): eb_no: bool = True @dataclass -class Sig53ImpairedTrainConfig(Sig53Config): - name: str = "sig53_impaired_train" +class NarrowbandImpairedTrainConfig(NarrowbandConfig): + name: str = "narrowband_impaired_train" seed: int = 1234567892 eb_no: bool = False num_samples: int = 5_300_000 @@ -65,25 +65,25 @@ class Sig53ImpairedTrainConfig(Sig53Config): @dataclass -class Sig53ImpairedTrainQAConfig(Sig53ImpairedTrainConfig): +class NarrowbandImpairedTrainQAConfig(NarrowbandImpairedTrainConfig): num_samples: int = 10_600 @dataclass -class Sig53ImpairedValConfig(Sig53ImpairedTrainConfig): - name: str = "sig53_impaired_val" +class NarrowbandImpairedValConfig(NarrowbandImpairedTrainConfig): + name: str = "narrowband_impaired_val" seed: int = 1234567893 num_samples: int = 106_000 @dataclass -class Sig53ImpairedValQAConfig(Sig53ImpairedValConfig): +class NarrowbandImpairedValQAConfig(NarrowbandImpairedValConfig): num_samples: int = 1060 @dataclass -class Sig53ImpairedEbNoTrainConfig(Sig53Config): - name: str = "sig53_impaired_ebno_train" +class NarrowbandImpairedEbNoTrainConfig(NarrowbandConfig): + name: str = "narrowband_impaired_ebno_train" seed: int = 1234567892 eb_no: bool = True num_samples: int = 5_300_000 @@ -91,25 +91,25 @@ class Sig53ImpairedEbNoTrainConfig(Sig53Config): @dataclass -class Sig53ImpairedEbNoTrainQAConfig(Sig53ImpairedEbNoTrainConfig): +class NarrowbandImpairedEbNoTrainQAConfig(NarrowbandImpairedEbNoTrainConfig): num_samples: int = 10_600 @dataclass -class Sig53ImpairedEbNoValConfig(Sig53ImpairedTrainConfig): - name: str = "sig53_impaired_ebno_val" +class NarrowbandImpairedEbNoValConfig(NarrowbandImpairedTrainConfig): + name: str = "narrowband_impaired_ebno_val" seed: int = 1234567893 eb_no: bool = True num_samples: int = 106_000 @dataclass -class Sig53ImpairedEbNoValQAConfig(Sig53ImpairedEbNoValConfig): +class NarrowbandImpairedEbNoValQAConfig(NarrowbandImpairedEbNoValConfig): num_samples: int = 1060 @dataclass -class WidebandSig53Config: +class WidebandConfig: name: str num_samples: int level: int @@ -119,8 +119,8 @@ class WidebandSig53Config: @dataclass -class WidebandSig53CleanTrainConfig(WidebandSig53Config): - name: str = "wideband_sig53_clean_train" +class WidebandCleanTrainConfig(WidebandConfig): + name: str = "wideband_clean_train" seed: int = 1234567890 num_samples: int = 250_000 level: int = 1 @@ -128,43 +128,43 @@ class WidebandSig53CleanTrainConfig(WidebandSig53Config): @dataclass -class WidebandSig53CleanTrainQAConfig(WidebandSig53CleanTrainConfig): +class WidebandCleanTrainQAConfig(WidebandCleanTrainConfig): num_samples: int = 250 @dataclass -class WidebandSig53CleanValConfig(WidebandSig53CleanTrainConfig): - name: str = "wideband_sig53_clean_val" +class WidebandCleanValConfig(WidebandCleanTrainConfig): + name: str = "wideband_clean_val" seed: int = 1234567891 num_samples: int = 25_000 @dataclass -class WidebandSig53CleanValQAConfig(WidebandSig53CleanValConfig): +class WidebandCleanValQAConfig(WidebandCleanValConfig): num_samples: int = 250 @dataclass -class WidebandSig53ImpairedTrainConfig(WidebandSig53Config): - name: str = "wideband_sig53_impaired_train" +class WidebandImpairedTrainConfig(WidebandConfig): + name: str = "wideband_impaired_train" seed: int = 1234567892 num_samples: int = 250_000 level: int = 2 - overlap_prob: float = 0.1 #TODO + overlap_prob: float = 0.1 @dataclass -class WidebandSig53ImpairedTrainQAConfig(WidebandSig53ImpairedTrainConfig): +class WidebandImpairedTrainQAConfig(WidebandImpairedTrainConfig): num_samples: int = 250 @dataclass -class WidebandSig53ImpairedValConfig(WidebandSig53ImpairedTrainConfig): - name: str = "wideband_sig53_impaired_val" +class WidebandImpairedValConfig(WidebandImpairedTrainConfig): + name: str = "wideband_impaired_val" seed: int = 1234567893 num_samples: int = 25_000 @dataclass -class WidebandSig53ImpairedValQAConfig(WidebandSig53ImpairedValConfig): +class WidebandImpairedValQAConfig(WidebandImpairedValConfig): num_samples: int = 250 diff --git a/torchsig/datasets/datamodules.py b/torchsig/datasets/datamodules.py index c254c10..03deb73 100644 --- a/torchsig/datasets/datamodules.py +++ b/torchsig/datasets/datamodules.py @@ -1,4 +1,4 @@ -"""PyTorch Lightning DataModules for Sig53 and WidebandSig53 +"""PyTorch Lightning DataModules for TorchSigNarrowband and TorchSigWideband """ from torch.utils.data import DataLoader from torch.nn import Identity @@ -7,8 +7,8 @@ from typing import Callable, Union, Optional import os -from torchsig.datasets.sig53 import Sig53 -from torchsig.datasets.wideband_sig53 import WidebandSig53 +from torchsig.datasets.torchsig_narrowband import TorchSigNarrowband +from torchsig.datasets.torchsig_wideband import TorchSigWideband from torchsig.datasets import conf from torchsig.utils.dataset import collate_fn as collate_fn_default from torchsig.datasets.wideband import WidebandModulationsDataset @@ -47,7 +47,7 @@ def __init__(self, Args: root (str): Dataset root path. - dataset (str): Dataset name, either "Sig53" or "WidebandSig53". + dataset (str): Dataset name, either "Narrowband" or "Wideband". impaired (bool): Dataset impairment setting. qa (bool, optional): Generate small dataset sample. Defaults to True. eb_no (bool, optional): Use EbNo config. Defaults to False. @@ -91,7 +91,7 @@ def _set_config(self, dataset: str, impaired: bool, is_train: bool, qa: bool, eb """_summary_ Args: - dataset (str): Dataset name, either "Sig53" or "WidebandSig53". + dataset (str): Dataset name, either "Narrowband" or "Wideband". impaired (bool): Whether dataset is impaired or not (clean). is_train (bool): Whether dataset is train or not (val). qa (bool): Whether to create smaller dataset version. @@ -99,12 +99,12 @@ def _set_config(self, dataset: str, impaired: bool, is_train: bool, qa: bool, eb seed (int): Seed for dataset generation. Raises: - ValueError: Dataset name is not Sig53 or WidebandSig53. + ValueError: Dataset name is not Narrowband or Wideband. Returns: dict: TorchSig config file for dataset. """ - if not dataset in ["Sig53", "WidebandSig53"]: + if not dataset in ["Narrowband", "Wideband"]: raise ValueError(f"Invalid dataset type: {dataset}") i = "Impaired" if impaired else "Clean" @@ -113,7 +113,6 @@ def _set_config(self, dataset: str, impaired: bool, is_train: bool, qa: bool, eb e = "EbNo" if eb_no else "" config_name = f"{dataset}{i}{e}{t}{q}Config" - print(f"Using {config_name} for {t.lower()}.") return getattr(conf, config_name) @@ -166,14 +165,14 @@ def val_dataloader(self) -> DataLoader: collate_fn=self.collate_fn ) -class Sig53DataModule(TorchSigDataModule): - """Sig53 PyTorch Lightning DataModule +class NarrowbandDataModule(TorchSigDataModule): + """TorchSig Narrowband PyTorch Lightning DataModule Attributes: - class_list (list): Sig53 class list names. + class_list (list): TorchSigNarrowband class list names. """ - class_list = list(Sig53._idx_to_name_dict.values()) + class_list = list(TorchSigNarrowband._idx_to_name_dict.values()) def __init__(self, @@ -189,7 +188,7 @@ def __init__(self, num_workers: int = 1, collate_fn: Optional[Callable] = None ): - """Sig53 DataModule Init + """TorchSigNarrowband DataModule Init Args: root (str): Dataset root path. @@ -203,14 +202,14 @@ def __init__(self, num_workers (int, optional): Dataloader number of workers to use. Defaults to 1. collate_fn (Optional[Callable], optional): Dataloader custom collate function. Defaults to TorchSig collate_fn. """ - super().__init__(root, "Sig53", impaired, qa, eb_no, seed, overlap_prob, transform, target_transform, batch_size, num_workers, collate_fn) + super().__init__(root, "Narrowband", impaired, qa, eb_no, seed, overlap_prob, transform, target_transform, batch_size, num_workers, collate_fn) self.data_path = f"{self.root}/{self.dataset.lower()}_{self.clean}_" self.train_path = self.data_path + "train" self.val_path = self.data_path + "val" def prepare_data(self) -> None: - """Download Sig53 Dataset + """Download TorchSigNarrowband Dataset """ ds_train = ModulationsDataset( level=self.train_config.level, @@ -234,18 +233,20 @@ def prepare_data(self) -> None: os.makedirs(self.val_path, exist_ok=True) creator_train = DatasetCreator(ds_train, seed=self.seed, path=self.train_path, num_workers = self.num_workers) + print(f"Using {self.train_config.__name__} for train.") creator_train.create() creator_val = DatasetCreator(ds_val, seed=self.seed, path=self.val_path, num_workers = self.num_workers) + print(f"Using {self.val_config.__name__} for val.") creator_val.create() def setup(self, stage: str) -> None: - """Set up Sig53 train and validation datasets. + """Set up TorchSigNarrowband train and validation datasets. Args: stage (str): PyTorch Lightning trainer stage - fit, test, predict. """ - self.train = Sig53( + self.train = TorchSigNarrowband( self.root, train=True, impaired=self.impaired, @@ -254,7 +255,7 @@ def setup(self, stage: str) -> None: use_signal_data=True, ) - self.val = Sig53( + self.val = TorchSigNarrowband( self.root, train=False, impaired=self.impaired, @@ -263,8 +264,8 @@ def setup(self, stage: str) -> None: use_signal_data=True, ) -class WidebandSig53DataModule(TorchSigDataModule): - """WidebandSig53 PyTorch Lightning DataModule +class WidebandDataModule(TorchSigDataModule): + """TorchSigWideband PyTorch Lightning DataModule """ @@ -282,7 +283,7 @@ def __init__(self, num_workers: int = 1, collate_fn: Optional[Callable] = None ): - """WidebandSig53 DataModule Init + """TorchSigWideband DataModule Init Args: root (str): Dataset root path. @@ -299,18 +300,18 @@ def __init__(self, num_workers (int, optional): Dataloader number of workers to use. Defaults to 1. collate_fn (Optional[Callable], optional): Dataloader custom collate function. Defaults to TorchSig collate_fn. """ - super().__init__(root, "WidebandSig53", impaired, qa, False, seed, overlap_prob, transform, target_transform, batch_size, num_workers, collate_fn) + super().__init__(root, "Wideband", impaired, qa, False, seed, overlap_prob, transform, target_transform, batch_size, num_workers, collate_fn) self.overlap_prob = self.train_config.overlap_prob if overlap_prob is None else overlap_prob self.fft_size = fft_size self.num_classes = num_classes - self.data_path = f"{self.root}/wideband_sig53_{self.clean}_" + self.data_path = f"{self.root}/wideband_{self.clean}_" self.train_path = self.data_path + "train" self.val_path = self.data_path + "val" def prepare_data(self) -> None: - """Download WidebandSig53 + """Download TorchSigWideband """ ds_train = WidebandModulationsDataset( level=self.train_config.level, @@ -332,18 +333,20 @@ def prepare_data(self) -> None: os.makedirs(self.val_path, exist_ok=True) creator_train = DatasetCreator(ds_train, seed=self.seed, path=self.train_path, num_workers = self.num_workers) + print(f"Using {self.train_config.__name__} for train.") creator_train.create() creator_val = DatasetCreator(ds_val, seed=self.seed, path=self.val_path, num_workers = self.num_workers) + print(f"Using {self.val_config.__name__} for val.") creator_val.create() def setup(self, stage: str) -> None: - """Set up WidebandSig53 train and validation datasets. + """Set up TorchSigWideband train and validation datasets. Args: stage (str): PyTorch Lightning trainer stage - fit, test, predict. """ - self.train = WidebandSig53( + self.train = TorchSigWideband( self.root, train=True, impaired=self.impaired, @@ -351,7 +354,7 @@ def setup(self, stage: str) -> None: target_transform=self.target_transform ) - self.val = WidebandSig53( + self.val = TorchSigWideband( self.root, train=False, impaired=self.impaired, diff --git a/torchsig/datasets/file_datasets.py b/torchsig/datasets/file_datasets.py index de4bae6..f7207bc 100755 --- a/torchsig/datasets/file_datasets.py +++ b/torchsig/datasets/file_datasets.py @@ -1,3 +1,5 @@ +"""File Datasets +""" from torchsig.datasets.wideband import BurstSourceDataset, SignalBurst from typing import List, Optional import pandas as pd diff --git a/torchsig/datasets/modulations.py b/torchsig/datasets/modulations.py index e364d0c..37bf57e 100755 --- a/torchsig/datasets/modulations.py +++ b/torchsig/datasets/modulations.py @@ -1,10 +1,13 @@ +"""Modulations Dataset for Narrowband +""" + from typing import Callable, List, Optional import numpy as np from torch.utils.data import ConcatDataset -from torchsig.datasets.synthetic import DigitalModulationDataset, OFDMDataset -from torchsig.datasets.signal_classes import sig53 +from torchsig.datasets.synthetic import ModulateNarrowbandDataset, OFDMDataset +from torchsig.datasets.signal_classes import torchsig_signals from torchsig.transforms import ( Compose, IQImbalance, @@ -82,7 +85,7 @@ class ModulationsDataset(ConcatDataset): """ - default_classes: List[str] = sig53.class_list + default_classes: List[str] = torchsig_signals.class_list def __init__( self, @@ -179,7 +182,7 @@ def __init__( ) if num_digital > 0: - digital_dataset = DigitalModulationDataset( + digital_dataset = ModulateNarrowbandDataset( modulations=digital_classes, # effectively uses all modulations num_iq_samples=num_iq_samples, num_samples_per_class=num_samples_per_class, @@ -193,14 +196,7 @@ def __init__( if num_ofdm > 0: sidelobe_suppression_methods = ("lpf", "win_start") ofdm_dataset = OFDMDataset( - constellations=( - "bpsk", - "qpsk", - "16qam", - "64qam", - "256qam", - "1024qam", - ), # sub-carrier modulations + constellations=torchsig_signals.ofdm_subcarrier_modulations, # sub-carrier modulations num_subcarriers=num_subcarriers, # possible number of subcarriers num_iq_samples=num_iq_samples, num_samples_per_class=num_samples_per_class, diff --git a/torchsig/datasets/radioml.py b/torchsig/datasets/radioml.py index ec0696c..f1727cd 100755 --- a/torchsig/datasets/radioml.py +++ b/torchsig/datasets/radioml.py @@ -1,3 +1,6 @@ +"""RadioML Datasets +""" + from typing import Any, Callable, List, Optional, Tuple from torchsig.utils.dataset import SignalDataset from torchsig.datasets.signal_classes import radioml2018 diff --git a/torchsig/datasets/sig53.py b/torchsig/datasets/sig53.py old mode 100755 new mode 100644 index b96913e..ce55f67 --- a/torchsig/datasets/sig53.py +++ b/torchsig/datasets/sig53.py @@ -7,10 +7,11 @@ import numpy as np import pickle import lmdb +import warnings class Sig53: - """The Official Sig53 dataset + """Legacy Sig53 dataset Args: root (string): @@ -64,6 +65,7 @@ def __init__( target_transform: Optional[Callable] = None, use_signal_data: bool = False, ): + warnings.warn("Sig53 is depreciated. Use Narrowband instead.", DeprecationWarning, stacklevel=2) self.root = Path(root) self.train = train self.impaired = impaired @@ -74,7 +76,7 @@ def __init__( self.TT = target_transform if target_transform else Identity() cfg: conf.Sig53Config = ( - "Sig53" # type: ignore + "Narrowband" # type: ignore + ("Impaired" if impaired else "Clean") + ("EbNo" if (impaired and eb_no) else "") + ("Train" if train else "Val") diff --git a/torchsig/datasets/signal_classes.py b/torchsig/datasets/signal_classes.py index 3fedf56..d2abacf 100644 --- a/torchsig/datasets/signal_classes.py +++ b/torchsig/datasets/signal_classes.py @@ -57,9 +57,17 @@ "ofdm-1024": "ofdm", "ofdm-1200": "ofdm", "ofdm-2048": "ofdm", + "fm": "fm", + "am-dsb-sc": "am", + "am-dsb": "am", + "am-lsb": "am", + "am-usb": "am", + "lfm_data": "chirp", + "lfm_radar": "chirp", + "chirpss": "chirp", } -SIG53_SHARED_LIST: list = [ +SIGNALS_SHARED_LIST: list = [ "ook", "bpsk", "4pam", @@ -113,8 +121,17 @@ "ofdm-1024", "ofdm-1200", "ofdm-2048", + "fm", + "am-dsb-sc", + "am-dsb", + "am-lsb", + "am-usb", + "lfm_data", + "lfm_radar", + "chirpss", ] +# list for radio ML 2018 dataset FAMILY_SHARED_LIST: list = [ "OOK", "4ASK", @@ -143,19 +160,55 @@ ] @dataclass -class sig53(): - """Sig53 dataclass, containing class modulation names list `class_list` +class torchsig_signals(): + """TorchSigNarrowband dataclass, containing class modulation names list `class_list` Example: Access this list:: - >>> from torchsig.datasets.signal_classes import sig53 - >>> sig53.class_list - >>> sig53.family_dict + >>> from torchsig.datasets.signal_classes import torchsig_signals + >>> torchsig_signals.class_list + >>> torchsig_signals.family_dict """ - class_list: ClassVar[list[str]] = SIG53_SHARED_LIST + class_list: ClassVar[list[str]] = SIGNALS_SHARED_LIST family_dict: ClassVar[Dict[str, str]] = CLASS_FAMILY_DICT + fsk_signals = [] + ofdm_signals = [] + constellation_signals = [] + am_signals = [] + fm_signals = [] + lfm_signals = [] + chirpss_signals = [] + # automatic grouping of each signal into a specific class + for name in class_list: + if ('fsk' in name or 'msk' in name): + fsk_signals.append(name) + elif ('ofdm' in name): + ofdm_signals.append(name) + elif ('pam' in name or 'ask' in name or 'qam' in name or 'psk' in name or 'ook' == name): + constellation_signals.append(name) + elif ('am-dsb' in name or 'am-lsb' == name or 'am-usb' == name): + am_signals.append(name) + elif ('fm' == name): + fm_signals.append(name) + elif ('lfm_' in name): + lfm_signals.append(name) + elif ('chirpss' == name): + chirpss_signals.append(name) + + # specifically designed lists + ofdm_subcarrier_modulations = ["bpsk", "qpsk", "16qam", "64qam", "256qam", "1024qam"] + +@dataclass +class sig53(): + """Legacy Sig53 dataclass + """ + + class_list: ClassVar[list[str]] = SIGNALS_SHARED_LIST[:53] + family_dict: ClassVar[Dict[str, str]] = CLASS_FAMILY_DICT + + @dataclass class radioml2018(): diff --git a/torchsig/datasets/synthetic.py b/torchsig/datasets/synthetic.py index 4e49807..ecb268e 100755 --- a/torchsig/datasets/synthetic.py +++ b/torchsig/datasets/synthetic.py @@ -1,13 +1,17 @@ +"""Synthetic Dataset Generation Tools +""" + from torchsig.utils.types import SignalData, SignalMetadata, Signal, ModulatedRFMetadata from torchsig.utils.types import ( create_signal_metadata, create_rf_metadata, create_modulated_rf_metadata, ) -from torchsig.utils.dsp import convolve, gaussian_taps, low_pass, rrc_taps, irrational_rate_resampler +from torchsig.utils.dsp import convolve, gaussian_taps, low_pass, rrc_taps, rational_rate_resampler from torchsig.transforms.functional import FloatParameter, IntParameter from torchsig.utils.dataset import SignalDataset -from torchsig.utils.dsp import estimate_filter_length +from torchsig.datasets.signal_classes import torchsig_signals +from torchsig.utils.dsp import estimate_filter_length, MAX_SIGNAL_UPPER_EDGE_FREQ, MAX_SIGNAL_LOWER_EDGE_FREQ from typing import Any, Dict, List, Optional, Tuple, Union from torch.utils.data import ConcatDataset from scipy import signal as sp @@ -104,30 +108,8 @@ def remove_corners(const): } ) -# This is probably redundant. -freq_map = OrderedDict( - { - "2fsk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), - "2gfsk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), - "2msk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), - "2gmsk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), - "4fsk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), - "4gfsk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), - "4msk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), - "4gmsk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), - "8fsk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), - "8gfsk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), - "8msk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), - "8gmsk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), - "16fsk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), - "16gfsk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), - "16msk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), - "16gmsk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), - } -) - -class DigitalModulationDataset(ConcatDataset): +class ModulateNarrowbandDataset(ConcatDataset): """Digital Modulation Dataset Args: @@ -149,32 +131,33 @@ class DigitalModulationDataset(ConcatDataset): random_pulse_shaping (:obj:`bool`): boolean to enable/disable randomized pulse shaping - user_const_map (:obj:`Optional[OrderedDict]`): - optional user-defined constellation map, defaults to Sig53 modulations - """ def __init__( self, - modulations: Optional[Union[List, Tuple]] = ("bpsk", "2gfsk"), + modulations: Optional[Union[List, Tuple]] = torchsig_signals.class_list, num_iq_samples: int = 100, num_samples_per_class: int = 100, iq_samples_per_symbol: Optional[int] = None, random_data: bool = False, random_pulse_shaping: bool = False, - user_const_map: Optional[OrderedDict] = None, **kwargs, ) -> None: - const_map = user_const_map if user_const_map else default_const_map modulations = ( - list(const_map.keys()) + list(freq_map.keys()) + torchsig_signals.class_list if modulations is None else modulations ) - constellations = [m for m in map(str.lower, modulations) if m in const_map.keys()] - freqs = [m for m in map(str.lower, modulations) if m in freq_map.keys()] + + constellation_list = [m for m in map(str.lower, modulations) if m in torchsig_signals.constellation_signals] + fsk_list = [m for m in map(str.lower, modulations) if m in torchsig_signals.fsk_signals] + fm_list = [m for m in map(str.lower, modulations) if m in torchsig_signals.fm_signals] + am_list = [m for m in map(str.lower, modulations) if m in torchsig_signals.am_signals] + lfm_list = [m for m in map(str.lower, modulations) if m in torchsig_signals.lfm_signals] + chirpss_list = [m for m in map(str.lower, modulations) if m in torchsig_signals.chirpss_signals] + const_dataset = ConstellationDataset( - constellations=constellations, + constellations=constellation_list, num_iq_samples=num_iq_samples, num_samples_per_class=num_samples_per_class, iq_samples_per_symbol=2 @@ -185,31 +168,45 @@ def __init__( **kwargs, ) - # FSK signals with the Gaussian pulse shaping filter are handled differently than without - fsks = [] - gfsks = [] - for freq_mod in freqs: - if "g" in freq_mod: - gfsks.append(freq_mod) - else: - fsks.append(freq_mod) fsk_dataset = FSKDataset( - modulations=fsks, + modulations=fsk_list, num_iq_samples=num_iq_samples, num_samples_per_class=num_samples_per_class, iq_samples_per_symbol=8, + random_data=random_data, + random_pulse_shaping=random_pulse_shaping, **kwargs, ) - gfsks_dataset = FSKDataset( - modulations=gfsks, + + fm_dataset = FMDataset( + num_iq_samples=num_iq_samples, + num_samples_per_class=num_samples_per_class, + random_data=random_data, + **kwargs, + ) + + am_dataset = AMDataset( num_iq_samples=num_iq_samples, num_samples_per_class=num_samples_per_class, - iq_samples_per_symbol=8, random_data=random_data, - random_pulse_shaping=random_pulse_shaping, **kwargs, ) - super(DigitalModulationDataset, self).__init__([const_dataset, fsk_dataset, gfsks_dataset]) + + lfm_dataset = LFMDataset( + num_iq_samples=num_iq_samples, + num_samples_per_class=num_samples_per_class, + random_data=random_data, + **kwargs, + ) + + chirpss_dataset = ChirpSSDataset( + num_iq_samples=num_iq_samples, + num_samples_per_class=num_samples_per_class, + random_data=random_data, + **kwargs, + ) + + super(ModulateNarrowbandDataset, self).__init__([const_dataset, fsk_dataset, fm_dataset, am_dataset, lfm_dataset, chirpss_dataset]) class SyntheticDataset(SignalDataset): def __init__(self, **kwargs) -> None: @@ -237,6 +234,33 @@ def __len__(self) -> int: def _generate_samples(self, item: Tuple) -> np.ndarray: raise NotImplementedError +def ConstellationBasebandModulator ( class_name, excess_bandwidth, iq_samples_per_symbol, num_iq_samples ): + + # get the constellation maps + const_map = default_const_map + # normalize the constellation map to unit energy + const = const_map[class_name] / np.mean(np.abs(const_map[class_name])) + # compute the symbols to index into the symbol map + symbol_nums = np.random.randint(0, len(const), int(num_iq_samples / iq_samples_per_symbol)) + # compute symbols + symbols = const[symbol_nums] + # zero-pad the symbols + zero_padded = np.zeros((iq_samples_per_symbol * len(symbols),), dtype=np.complex64) + zero_padded[::iq_samples_per_symbol] = symbols + # design the pulse shaping filter: + # excess bandwidth is defined in porportion to signal bandwidth, not sampling rate, + # thus needs to be scaled by the samples per symbol + pulse_shape_filter_length = estimate_filter_length(excess_bandwidth / iq_samples_per_symbol) + pulse_shape_filter_span = int((pulse_shape_filter_length - 1) / (2*iq_samples_per_symbol)) # convert filter length into the span + pulse_shape_filter = rrc_taps(iq_samples_per_symbol, pulse_shape_filter_span, excess_bandwidth,) + # apply pulse shaping filter + filtered = sp.convolve(zero_padded, pulse_shape_filter, 'full') + # remove transition periods + lidx = (len(filtered) - num_iq_samples) // 2 + ridx = lidx + num_iq_samples + filtered = filtered[lidx:ridx] + return filtered + class ConstellationDataset(SyntheticDataset): """Constellation Dataset @@ -260,9 +284,6 @@ class ConstellationDataset(SyntheticDataset): random_data (:obj:`bool`): whether the modulated binary utils should be random each time, or seeded by index - user_const_map (:obj:`bool`): - user constellation dict - center_freq (:obj:`float`): center frequency of the signal, will be upconverted internally @@ -270,23 +291,20 @@ class ConstellationDataset(SyntheticDataset): def __init__( self, - constellations: Optional[Union[List, Tuple]] = ("bpsk", "qpsk"), + constellations: Optional[Union[List, Tuple]] = torchsig_signals.constellation_signals, num_iq_samples: int = 100, num_samples_per_class: int = 100, iq_samples_per_symbol: int = 2, pulse_shape_filter: Optional[Union[bool, np.ndarray]] = None, random_pulse_shaping: bool = False, random_data: bool = False, - user_const_map: Optional[Dict[str, np.ndarray]] = None, center_freq: float = 0, **kwargs, ): super(ConstellationDataset, self).__init__(**kwargs) - self.const_map: Dict[str, np.ndarray] = ( - default_const_map if user_const_map is None else user_const_map - ) + self.const_map: Dict[str, np.ndarray] = default_const_map self.constellations = ( - list(self.const_map.keys()) if constellations is None else constellations + list(torchsig_signals.constellation_signals) if constellations is None else constellations ) self.num_iq_samples = num_iq_samples self.iq_samples_per_symbol = iq_samples_per_symbol @@ -340,22 +358,8 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: if not self.random_data: np.random.seed(index) - const = self.const_map[class_name] / np.mean(np.abs(self.const_map[class_name])) - symbol_nums = np.random.randint(0, len(const), int(self.num_iq_samples / self.iq_samples_per_symbol)) - symbols = const[symbol_nums] - zero_padded = np.zeros((self.iq_samples_per_symbol * len(symbols),), dtype=np.complex64) - zero_padded[::self.iq_samples_per_symbol] = symbols - # excess bandwidth is defined in porportion to signal bandwidth, not sampling rate, - # thus needs to be scaled by the samples per symbol - pulse_shape_filter_length = estimate_filter_length(meta["excess_bandwidth"] / self.iq_samples_per_symbol) - pulse_shape_filter_span = int((pulse_shape_filter_length - 1) / (2*self.iq_samples_per_symbol)) # convert filter length into the span - self.pulse_shape_filter = rrc_taps(self.iq_samples_per_symbol, pulse_shape_filter_span, meta["excess_bandwidth"],) - - filtered = sp.convolve(zero_padded, self.pulse_shape_filter, 'full') - lidx = (len(filtered) - self.num_iq_samples) // 2 - ridx = lidx + self.num_iq_samples - - filtered = filtered[lidx:ridx] + # apply baseband signal modulator + filtered = ConstellationBasebandModulator ( class_name, meta["excess_bandwidth"], self.iq_samples_per_symbol, self.num_iq_samples ) # apply frequency shifting filtered *= np.exp(2j*np.pi*center_freq*np.arange(0,len(filtered))) @@ -377,7 +381,7 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: if not self.random_data: np.random.set_state(orig_state) # return numpy back to its previous state - return filtered[0:self.num_iq_samples] #[-self.num_iq_samples :] + return filtered[0:self.num_iq_samples] class OFDMDataset(SyntheticDataset): @@ -790,7 +794,7 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: resamplerRate = (1/2)/bandwidth # apply resampling - output = irrational_rate_resampler ( output, resamplerRate ) + output = rational_rate_resampler ( output, resamplerRate ) # apply frequency shifting output *= np.exp(2j*np.pi*center_freq*np.arange(0,len(output))) @@ -815,6 +819,93 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: return output[0:self.num_iq_samples] +def getFSKFreqMap ( ): + freq_map = OrderedDict( + { + "2fsk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), + "2gfsk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), + "2msk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), + "2gmsk": np.linspace(-1 + (1 / 2), 1 - (1 / 2), 2, endpoint=True), + "4fsk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), + "4gfsk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), + "4msk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), + "4gmsk": np.linspace(-1 + (1 / 4), 1 - (1 / 4), 4, endpoint=True), + "8fsk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), + "8gfsk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), + "8msk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), + "8gmsk": np.linspace(-1 + (1 / 8), 1 - (1 / 8), 8, endpoint=True), + "16fsk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), + "16gfsk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), + "16msk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), + "16gmsk": np.linspace(-1 + (1 / 16), 1 - (1 / 16), 16, endpoint=True), + } + ) + return freq_map + +def getFSKModIndex( const_name ): + # returns the modulation index based on the modulation + if "gfsk" in const_name: + # bluetooth + mod_idx = 0.32 + elif "msk" in const_name: + # MSK, GMSK + mod_idx = 0.5 + else: # FSK + # 50% chance to use mod index of 1 (orthogonal) ... + if (np.random.uniform(0,1) < 0.5): + mod_idx = 1 + else: # ... or something else (non-orthogonal) + mod_idx = np.random.uniform(0.7,1) + return mod_idx + + +def FSKBasebandModulator ( const_name, mod_idx, oversampling_rate, num_iq_samples ): + + # get the FSK frequency symbol map + freq_map = getFSKFreqMap() + + # get the constellation to modulate + const = freq_map[const_name] + + # calculate the modulation order, ex: the "4" in "4-FSK" + mod_order = len(const) + + # determine how many samples are in each symbol + samples_per_symbol_recalculated = int(mod_order * oversampling_rate) + + # scale the frequency map by the oversampling rate such that the tones + # are packed tighter around f=0 the larger the oversampling rate + const_oversampled = const / oversampling_rate + + # calculate the indexes into symbol table + symbol_nums = np.random.randint(0, len(const_oversampled), int(np.ceil((num_iq_samples / samples_per_symbol_recalculated) * oversampling_rate))) + + # produce data symbols + symbols = const_oversampled[symbol_nums] + + # rectangular pulse shape + pulse_shape = np.ones(samples_per_symbol_recalculated) + + if "g" in const_name: # GMSK, GFSK + # design the gaussian pulse shape with the bandwidth as dictated by the + # oversampling rate, which will then be fine-tuned into the proper 'bandwidth' + # by the resampling stage + preresample_bandwidth = 1/oversampling_rate + taps = gaussian_taps(samples_per_symbol_recalculated, preresample_bandwidth) + pulse_shape = np.convolve(taps,pulse_shape) + + # upsample symbols and apply pulse shaping + filtered = sp.upfirdn(pulse_shape,symbols,up=samples_per_symbol_recalculated,down=1) + + # insert a zero at first sample to start at zero phase + filtered = np.insert(filtered, 0, 0) + + phase = np.cumsum(np.array(filtered) * 1j * mod_idx * np.pi) + modulated = np.exp(phase) + + return modulated + + class FSKDataset(SyntheticDataset): """FSK Dataset @@ -848,7 +939,7 @@ class FSKDataset(SyntheticDataset): def __init__( self, - modulations: Optional[Union[List, Tuple]] = ("2fsk", "2gmsk"), + modulations: Optional[Union[List, Tuple]] = torchsig_signals.fsk_signals, num_iq_samples: int = 100, num_samples_per_class: int = 100, iq_samples_per_symbol: int = 2, @@ -859,13 +950,14 @@ def __init__( **kwargs, ): super(FSKDataset, self).__init__(**kwargs) - self.modulations = list(freq_map.keys()) if modulations is None else modulations + self.modulations = list(torchsig_signals.fsk_signals) if modulations is None else modulations self.num_iq_samples = num_iq_samples self.num_samples_per_class = num_samples_per_class self.iq_samples_per_symbol = iq_samples_per_symbol self.random_data = random_data self.random_pulse_shaping = random_pulse_shaping self.index = [] + self.freq_map = getFSKFreqMap() # TODO: this needs to be removed for freq_idx, freq_name in enumerate(map(str.lower, self.modulations)): for idx in range(self.num_samples_per_class): @@ -888,7 +980,7 @@ def __init__( stop=1.0, duration=1.0, snr=0.0, - bits_per_symbol=np.log2(len(freq_map[freq_name])), + bits_per_symbol=np.log2(len(self.freq_map[freq_name])), # TODO: this needs to be removed samples_per_symbol=float(iq_samples_per_symbol), class_name=freq_name, class_index=freq_idx, @@ -904,60 +996,34 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: center_freq = metadata["center_freq"] bandwidth = metadata["bandwidth"] - # calculate the modulation order, ex: the "4" in "4-FSK" - const = freq_map[const_name] - mod_order = len(const) - # samples per symbol presumably used as a bandwidth measure (ex: BW=1/SPS), # but does not work for FSK. samples per symbol is redefined into # the "oversampling rate", and samples per symbol is instead derived # from the modulation order oversampling_rate = np.copy(self.iq_samples_per_symbol) - samples_per_symbol_recalculated = int(mod_order * oversampling_rate) - - # scale the frequency map by the oversampling rate such that the tones - # are packed tighter around f=0 the larger the oversampling rate - const_oversampled = const / oversampling_rate + # control RNG orig_state = np.random.get_state() if not self.random_data: np.random.seed(index) - # get the modulation index - mod_idx = self._mod_index(const_name) - - # calculate the resampling rate to convert from the oversampling rate specified by - # self.iq_samples_per_symbol into the proper bandwidth - resampleRate = bandwidth*mod_idx/(1/oversampling_rate) + # determine modulation index + mod_idx = getFSKModIndex(const_name) - # calculate the indexes into symbol table - symbol_nums = np.random.randint(0, len(const_oversampled), int(np.ceil((self.num_iq_samples / samples_per_symbol_recalculated) * (1/resampleRate)) )) - # produce data symbols - symbols = const_oversampled[symbol_nums] - # rectangular pulse shape - pulse_shape = np.ones(samples_per_symbol_recalculated) - - if "g" in const_name: - # GMSK, GFSK - taps = gaussian_taps(samples_per_symbol_recalculated, bandwidth) - pulse_shape = np.convolve(taps,pulse_shape) - - # upsample symbols and apply pulse shaping - filtered = sp.upfirdn(pulse_shape,symbols,up=samples_per_symbol_recalculated,down=1) - - # insert a zero at first sample to start at zero phase - filtered = np.insert(filtered, 0, 0) - - phase = np.cumsum(np.array(filtered) * 1j * mod_idx * np.pi) - modulated = np.exp(phase) + # modulate the FSK signal at complex baseband + modulated = FSKBasebandModulator ( const_name, mod_idx, oversampling_rate, self.num_iq_samples ) if self.random_pulse_shaping: taps = low_pass(cutoff=bandwidth / 2, transition_bandwidth=(0.5 - bandwidth / 2) / 4) # apply the filter modulated = convolve(modulated, taps) + # calculate the resampling rate to convert from the oversampling rate specified by + # self.iq_samples_per_symbol into the proper bandwidth + resampleRate = bandwidth*mod_idx/(1/oversampling_rate) + # apply resampling - modulated = irrational_rate_resampler ( modulated, resampleRate ) + modulated = rational_rate_resampler ( modulated, resampleRate ) # apply center frequency shifting modulated *= np.exp(2j*np.pi*center_freq*np.arange(0,len(modulated))) @@ -979,20 +1045,9 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: if not self.random_data: np.random.set_state(orig_state) # return numpy back to its previous state - return modulated[:self.num_iq_samples] + return modulated[0:self.num_iq_samples] + - def _mod_index(self, const_name): - # returns the modulation index based on the modulation - if "gfsk" in const_name: - # bluetooth - mod_idx = 0.32 - elif "msk" in const_name: - # MSK, GMSK - mod_idx = 0.5 - else: - # FSK - mod_idx = 1.0 - return mod_idx class AMDataset(SyntheticDataset): @@ -1006,19 +1061,24 @@ class AMDataset(SyntheticDataset): def __init__( self, + modulations: Optional[Union[List, Tuple]] = torchsig_signals.am_signals, num_iq_samples: int = 100, num_samples_per_class: int = 100, random_data: bool = False, + center_freq: float = 0, + bandwidth: float = 0.5, **kwargs, ): super(AMDataset, self).__init__(**kwargs) self.num_iq_samples = num_iq_samples self.num_samples_per_class = num_samples_per_class - self.classes = ["am", "am-ssb", "am-dsb"] + self.modulations = modulations self.random_data = random_data + self.center_freq = center_freq + self.bandwidth = bandwidth self.index = [] - for class_idx, class_name in enumerate(self.classes): + for class_idx, class_name in enumerate(self.modulations): meta = ModulatedRFMetadata( sample_rate=0.0, num_samples=self.num_iq_samples, @@ -1056,25 +1116,87 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: if not self.random_data: np.random.seed(index) - source = np.random.randn(self.num_iq_samples) + 0j - taps = sp.firwin( - 100, # num taps - 0.5 if "ssb" not in const_name else 0.25, - 0.5 / 16 if "ssb" not in const_name else 0.25 / 4, - window="blackman", - ) - filtered = sp.convolve(source, taps, "full") - lidx = (len(filtered) - self.num_iq_samples) // 2 - ridx = lidx + self.num_iq_samples - filtered = filtered[lidx:ridx] - sinusoid = np.exp(2j * np.pi * 0.125 * np.arange(self.num_iq_samples)) - filtered *= np.ones_like(filtered) if "ssb" not in const_name else sinusoid - filtered += 5 if const_name == "am" else 0 + if ("lsb" in const_name or "usb" in const_name): + num_samples = 2*self.num_iq_samples + else: + num_samples = self.num_iq_samples + + # generate the random message + message = np.random.randn(num_samples) + 0j + # generate bandwidth-limiting LPF + LPF = low_pass(cutoff=self.bandwidth/2, transition_bandwidth=self.bandwidth/4) + # scale LPF in order to increase power due to balance reduction in bandwidth + LPF *= 1/self.bandwidth + # apply bandwidth-limiting filter + shapedMessage = sp.convolve(message, LPF, "full") + # remove transients + lidx = (len(shapedMessage) - num_samples) // 2 + ridx = lidx + num_samples + shapedMessage = shapedMessage[lidx:ridx] + if (const_name == "am-dsb-sc"): + basebandSignal = shapedMessage + elif (const_name == "am-dsb"): + # build carrier + carrier = np.ones(len(shapedMessage)) + # randomly determine modulation index + modulationIndex = np.random.uniform(0.1,1) + basebandSignal = (modulationIndex*shapedMessage) + carrier + elif (const_name == "am-lsb"): + # upconvert signal to bandwidth/2 + LSBMixer = np.exp(2j*np.pi*(self.bandwidth/2)*np.arange(0,len(shapedMessage))) + DSBUpconverted = LSBMixer*shapedMessage + # the existing BW limiting filter can be be repurposed to discard upper band + LSBSignalAtIF = np.convolve(DSBUpconverted,LPF) + # remove transients + lidx = (len(LSBSignalAtIF) - num_samples) // 2 + ridx = lidx + num_samples + LSBSignalAtIF = LSBSignalAtIF[lidx:ridx] + # mix LSB back down to baseband + basebandSignalOversampled = LSBSignalAtIF*np.exp(-2j*np.pi*(self.bandwidth/4)*np.arange(0,len(LSBSignalAtIF))) + # since threw away 1/2 the bandwidth to only retain LSB, then downsample by 2 in order to match + # the requested self.bandwidth + basebandSignal = rational_rate_resampler ( basebandSignalOversampled, resampler_rate=0.5 ) + basebandSignal = basebandSignal[0:self.num_iq_samples] + elif (const_name == "am-usb"): + # downconvert signal to -bandwidth/2 + USBMixer = np.exp(-2j*np.pi*(self.bandwidth/2)*np.arange(0,len(shapedMessage))) + DSBDownconverted = USBMixer*shapedMessage + # the existing BW limiting filter can be be repurposed to discard upper band + USBSignalAtIF = np.convolve(DSBDownconverted,LPF) + # remove transients + lidx = (len(USBSignalAtIF) - num_samples) // 2 + ridx = lidx + num_samples + USBSignalAtIF = USBSignalAtIF[lidx:ridx] + # mix USB back up to baseband + basebandSignalOversampled = USBSignalAtIF*np.exp(2j*np.pi*(self.bandwidth/4)*np.arange(0,len(USBSignalAtIF))) + # since threw away 1/2 the bandwidth to only retain USB, then downsample by 2 in order to match + # the requested self.bandwidth + basebandSignal = rational_rate_resampler ( basebandSignalOversampled, resampler_rate=0.5 ) + basebandSignal = basebandSignal[0:self.num_iq_samples] + + # generate mixer + mixer = np.exp(2j * np.pi * self.center_freq * np.arange(self.num_iq_samples)) + # apply upconversion to center frequency + modulated = mixer*basebandSignal + + # determine the boundaries for where the signal currently resides. + # these values are used to determine if aliasing has occured + upperSignalEdge = self.center_freq + (self.bandwidth/2) + lowerSignalEdge = self.center_freq - (self.bandwidth/2) + + # check to see if aliasing has occured due to upconversion. if so, then apply + # a filter to minimize it + if ( upperSignalEdge > 0.5 or lowerSignalEdge < -0.5): + + # the signal has overlaped either the -fs/2 or +fs/2 boundary and therefore + # a BPF filter will be applied to attenuate the portion of the signal that + # is overlapping the -fs/2 or +fs/2 boundary to minimize aliasing + modulated = upconversionAntiAliasingFilter ( modulated, self.center_freq, self.bandwidth ) if not self.random_data: np.random.set_state(orig_state) # return numpy back to its previous state - return filtered + return modulated[0:self.num_iq_samples] class FMDataset(SyntheticDataset): @@ -1091,24 +1213,132 @@ def __init__( num_iq_samples: int = 100, num_samples_per_class: int = 100, random_data: bool = False, + center_freq: float = 0, + bandwidth: float = 0.5, **kwargs, ): super(FMDataset, self).__init__(**kwargs) self.num_iq_samples = num_iq_samples self.num_samples_per_class = num_samples_per_class - self.classes = ["fm"] + self.classes = torchsig_signals.fm_signals self.random_data = random_data self.index = [] + self.center_freq = center_freq + self.bandwidth = bandwidth for class_idx, class_name in enumerate(self.classes): meta = ModulatedRFMetadata( sample_rate=0.0, num_samples=self.num_iq_samples, complex=True, - lower_freq=-0.25, - upper_freq=0.25, - center_freq=0.0, - bandwidth=0.5, + lower_freq=center_freq-(bandwidth/2), + upper_freq=center_freq+(bandwidth/2), + center_freq=center_freq, + bandwidth=bandwidth, + start=0.0, + stop=1.0, + duration=1.0, + snr=0.0, + bits_per_symbol=0.0, + samples_per_symbol=0.0, + class_name=class_name, + class_index=class_idx, + excess_bandwidth=0.0, + ) + for idx in range(self.num_samples_per_class): + self.index.append( + ( + class_name, + class_idx * self.num_samples_per_class + idx, + [meta], + ) + ) + + def __len__(self) -> int: + return len(self.index) + + def _generate_samples(self, item: Tuple) -> np.ndarray: + # class_name = item[0] + index = item[1] + meta = item[2] + orig_state = np.random.get_state() + if not self.random_data: + np.random.seed(index) + + # randomly determine modulation index + mod_index = np.random.uniform(1,10) + # calculate the frequency deviation using Carson's Rule + fdev = (self.bandwidth/2)/(1 + (1/mod_index)) + # calculate the maximum deviation + fmax = fdev/mod_index + # compute input message + message = np.random.normal(0,1,self.num_iq_samples) + # design LPF to limit frequencies based on fmax + LPF = low_pass(cutoff=fmax,transition_bandwidth=fmax) + # apply the LPF to noise to limit the bandwidth prior to modulation + source = np.convolve(message,LPF) + # normalize maximum amplitude to 1 + source = source/np.max(np.abs(source)) + # apply FM modulation + modulated = np.exp(2j * np.pi * np.cumsum(source) * fdev) + # frequency shift to center_freq + modulated *= np.exp(2j*np.pi*self.center_freq*np.arange(0,len(modulated))) + + # determine the boundaries for where the signal currently resides. + # these values are used to determine if aliasing has occured + upperSignalEdge = self.center_freq + (self.bandwidth/2) + lowerSignalEdge = self.center_freq - (self.bandwidth/2) + + # check to see if aliasing has occured due to upconversion. if so, then apply + # a filter to minimize it + if ( upperSignalEdge > 0.5 or lowerSignalEdge < -0.5): + + # the signal has overlaped either the -fs/2 or +fs/2 boundary and therefore + # a BPF filter will be applied to attenuate the portion of the signal that + # is overlapping the -fs/2 or +fs/2 boundary to minimize aliasing + modulated = upconversionAntiAliasingFilter ( modulated, self.center_freq, self.bandwidth ) + + if not self.random_data: + np.random.set_state(orig_state) # return numpy back to its previous state + + return modulated[0:self.num_iq_samples] + + +class ToneDataset(SyntheticDataset): + """Tone Dataset + + Args: + transform (:obj:`Callable`, optional): + A function/transform that takes in an IQ vector and returns a transformed version. + + """ + + def __init__( + self, + num_iq_samples: int = 100, + num_samples_per_class: int = 100, + random_data: bool = False, + center_freq: float = 0, + bandwidth: float = 0.5, + **kwargs, + ): + super(ToneDataset, self).__init__(**kwargs) + self.num_iq_samples = num_iq_samples + self.num_samples_per_class = num_samples_per_class + self.classes = ["tone"] + self.random_data = random_data + self.index = [] + self.center_freq = center_freq + + for class_idx, class_name in enumerate(self.classes): + meta = ModulatedRFMetadata( + sample_rate=0.0, + num_samples=self.num_iq_samples, + complex=True, + lower_freq=center_freq, + upper_freq=center_freq, + center_freq=center_freq, + bandwidth=0.0, start=0.0, stop=1.0, duration=1.0, @@ -1139,13 +1369,298 @@ def _generate_samples(self, item: Tuple) -> np.ndarray: if not self.random_data: np.random.seed(index) - source = np.random.randn(self.num_iq_samples) + 0j - modulated = np.exp(1j * np.pi / 2 * np.cumsum(source) / 2.0) + # compute a random phase offset + phaseOffset = np.random.uniform(0,2*np.pi) + # compute time indices + n = np.arange(0,self.num_iq_samples) + # create tone + modulated = np.exp(2j*np.pi*self.center_freq*n)*np.exp(1j*phaseOffset) + + if not self.random_data: + np.random.set_state(orig_state) # return numpy back to its previous state + + return modulated[-self.num_iq_samples :] + + +class ChirpSSDataset(SyntheticDataset): + """Frequency Shift Chirp Spread Spectrum Modulated Dataset + + Args: + num_iq_samples (:obj:`int`): + number of iq samples in record, pads record with trailing zeros + + num_samples_per_class (:obj:`int`): + number of samples of each class + + iq_samples_per_symbol (:obj:`Optional[int]`): + number of IQ samples per symbol + + random_data (:obj:`bool`): + uses numpy random values + + center_freq (:obj:`float`): + center frequency of the signal + + bandwidth (:obj:`float`): + bandwidth of the signal + + """ + + def __init__( + self, + constellations: Optional[Union[List, Tuple]] = torchsig_signals.chirpss_signals, + num_iq_samples : int = 10000, + num_samples_per_class: int = 20, + iq_samples_per_symbol: int = 1000, + random_data: bool = False, + center_freq: float = 0., + bandwidth: float = 0.5, + **kwargs, + ): + super(ChirpSSDataset, self).__init__(**kwargs) + self.symbol_map: Dict[str, np.ndarray] = self.get_symbol_map() + self.constellations = ( + list(torchsig_signals.constellation_signals) if constellations is None else constellations + ) + self.num_iq_samples = num_iq_samples + self.num_samples_per_class = num_samples_per_class + self.iq_samples_per_symbol = iq_samples_per_symbol + self.random_data = random_data + self.index = [] + + for const_idx, const_name in enumerate(map(str.lower, self.constellations)): + for idx in range(self.num_samples_per_class): + meta = create_modulated_rf_metadata( + num_samples=self.num_iq_samples, + bits_per_symbol=1, + samples_per_symbol=iq_samples_per_symbol, + class_name=const_name, + class_index=const_idx, + center_freq=center_freq, + bandwidth=bandwidth + ) + self.index.append( + ( + const_name, + const_idx * self.num_samples_per_class + idx, + [meta], + ) + ) + + # design filter + transitionBandwidth = bandwidth/8 + cutoff = bandwidth/2 + (transitionBandwidth/2) + self.LPFWeights = low_pass(cutoff=cutoff,transition_bandwidth=transitionBandwidth) + + def __len__(self) -> int: + return len(self.index) + + def chirp(self, t0, t1, f0, f1, phi=0) -> np.ndarray: + t = np.linspace(t0, t1, 2*self.iq_samples_per_symbol) + b = (f1 - f0) / (t1 - t0) + phase = 2 * np.pi * (f0 * t + 0.5 * b * t * t) # Linear FM + phi *= np.pi / 180 + return np.exp(1j*(phase+phi)) + + def _generate_samples(self, item: Tuple) -> np.ndarray: + class_name = item[0] + index = item[1] + metadata = item[2][0] + center_freq = metadata["center_freq"] + bandwidth = metadata["bandwidth"] + + orig_state = np.random.get_state() + if not self.random_data: + np.random.seed(index) + + # symbol mapping and padding + const = self.symbol_map[class_name] + symbol_nums = np.random.randint( + 0, len(const), int(self.num_iq_samples / self.iq_samples_per_symbol) + ) + symbols = const[symbol_nums] + modulated = np.zeros((self.num_iq_samples,), dtype=np.complex128) + + # construct template symbol + upchirp = self.chirp(0,self.iq_samples_per_symbol,-bandwidth,bandwidth) + double_upchirp = np.concatenate((upchirp, upchirp), axis=0) + + # modulate + sym_start_index = 0 + M = const.size + for s in symbols: + chirp_start_index = int((s/M)*self.iq_samples_per_symbol) + modulated[sym_start_index:(sym_start_index+self.iq_samples_per_symbol)] = \ + double_upchirp[chirp_start_index:(chirp_start_index+self.iq_samples_per_symbol)] + sym_start_index = sym_start_index + self.iq_samples_per_symbol # 100% duty cycle + + modulated = np.convolve(self.LPFWeights, modulated) + + # apply center frequency shifting + modulated *= np.exp(2j*np.pi*center_freq*np.arange(0,len(modulated))) + + # determine the boundaries for where the signal currently resides. + # these values are used to determine if aliasing has occured + upperSignalEdge = center_freq + (bandwidth/2) + lowerSignalEdge = center_freq - (bandwidth/2) + + # check to see if aliasing has occured due to upconversion. if so, then apply + # a filter to minimize it + if ( upperSignalEdge > 0.5 or lowerSignalEdge < -0.5): + + # the signal has overlaped either the -fs/2 or +fs/2 boundary and therefore + # a BPF filter will be applied to attenuate the portion of the signal that + # is overlapping the -fs/2 or +fs/2 boundary to minimize aliasing + modulated = upconversionAntiAliasingFilter ( modulated, center_freq, bandwidth ) + + if not self.random_data: + np.random.set_state(orig_state) # return numpy back to its previous state + + return modulated[:self.num_iq_samples] + + def get_symbol_map ( self ): + chirpss_symbol_map = OrderedDict( + { + 'chirpss': np.linspace(0,2**7-1,2**7), + }) + return chirpss_symbol_map + +class LFMDataset(SyntheticDataset): + """Linear Frequency Modulated (LFM) Dataset: + Calculates number of LFM chirp symbols that can fit in specified length, + then modulates random data upchirp/downchirp symbols based on a custom or + default provided constellation map + + Args: + num_iq_samples (:obj:`int`): + number of iq samples in record, pads record with trailing zeros + + num_samples_per_class (:obj:`int`): + number of samples of each class + + iq_samples_per_symbol (:obj:`Optional[int]`): + number of IQ samples per symbol + + random_data (:obj:`bool`): + uses numpy random values + + center_freq (:obj:`float`): + center frequency of the signal + + bandwidth (:obj:`float`): + bandwidth of the signal + + """ + + def __init__( + self, + constellations: Optional[Union[List, Tuple]] = torchsig_signals.lfm_signals, + num_iq_samples : int = 10000, + num_samples_per_class: int = 20, + iq_samples_per_symbol: int = 1000, + random_data: bool = False, + center_freq: float = 0., + bandwidth: float = 0.5, + **kwargs, + ): + + super(LFMDataset, self).__init__(**kwargs) + self.symbol_map: Dict[str, np.ndarray] = self.get_symbol_map() + self.constellations = ( + list(torchsig_signals.constellation_signals) if constellations is None else constellations + ) + self.num_iq_samples = num_iq_samples + self.num_samples_per_class = num_samples_per_class + self.iq_samples_per_symbol = iq_samples_per_symbol + self.random_data = random_data + self.index = [] + + for const_idx, const_name in enumerate(map(str.lower, self.constellations)): + for idx in range(self.num_samples_per_class): + meta = create_modulated_rf_metadata( + num_samples=self.num_iq_samples, + bits_per_symbol=1, + samples_per_symbol=iq_samples_per_symbol, + class_name=const_name, + class_index=const_idx, + center_freq=center_freq, + bandwidth=bandwidth + ) + self.index.append( + ( + const_name, + const_idx * self.num_samples_per_class + idx, + [meta], + ) + ) + + def __len__(self) -> int: + return len(self.index) + + def chirp(self, t0, t1, f0, f1, phi=0) -> np.ndarray: + t = np.linspace(t0, t1, self.iq_samples_per_symbol) + b = (f1 - f0) / (t1 - t0) + phase = 2 * np.pi * (f0 * t + 0.5 * b * t * t) # Linear FM + phi *= np.pi / 180 + return np.exp(1j*(phase+phi)) + + def _generate_samples(self, item: Tuple) -> np.ndarray: + class_name = item[0] + index = item[1] + metadata = item[2][0] + center_freq = metadata["center_freq"] + bandwidth = metadata["bandwidth"] + f0 = center_freq - bandwidth / 2 + f1 = center_freq + bandwidth / 2 + + orig_state = np.random.get_state() + if not self.random_data: + np.random.seed(index) + + # symbol mapping and padding + const = self.symbol_map[class_name] + symbol_nums = np.random.randint( + 0, len(const), int(self.num_iq_samples / self.iq_samples_per_symbol) + ) + symbols = const[symbol_nums] + modulated = np.zeros((self.num_iq_samples,), dtype=np.complex128) + upchirp = self.chirp(0,self.iq_samples_per_symbol,f0,f1) + downchirp = self.chirp(0,self.iq_samples_per_symbol,f1,f0) + + sym_start_index = 0 + for s in symbols: + if s > 0: + modulated[sym_start_index:(sym_start_index+self.iq_samples_per_symbol)] = upchirp + else: + modulated[sym_start_index:(sym_start_index+self.iq_samples_per_symbol)] = downchirp + sym_start_index = sym_start_index + self.iq_samples_per_symbol + + # determine the boundaries for where the signal currently resides. + # these values are used to determine if aliasing has occured + upperSignalEdge = center_freq + (bandwidth/2) + lowerSignalEdge = center_freq - (bandwidth/2) + + # check to see if aliasing has occured due to upconversion. if so, then apply + # a filter to minimize it + if ( upperSignalEdge > 0.5 or lowerSignalEdge < -0.5): + + # the signal has overlaped either the -fs/2 or +fs/2 boundary and therefore + # a BPF filter will be applied to attenuate the portion of the signal that + # is overlapping the -fs/2 or +fs/2 boundary to minimize aliasing + modulated = upconversionAntiAliasingFilter ( modulated, center_freq, bandwidth ) if not self.random_data: np.random.set_state(orig_state) # return numpy back to its previous state - return modulated[-self.num_iq_samples :], meta + return modulated[:self.num_iq_samples] + + def get_symbol_map ( self ): + lfm_symbol_map = OrderedDict( + { + 'lfm_data': np.array([-1.,1.]), + 'lfm_radar': np.array([1.]), + }) + return lfm_symbol_map # apply an anti-aliasing filter to a signal which has aliased and wrapped around the @@ -1159,8 +1674,8 @@ def upconversionAntiAliasingFilter ( input_signal, center_freq, bandwidth ): # define the boundary for the upper and lower frequencies # upon which a BPF will be designed to limit aliasing - upperBoundary = 0.48 - lowerBoundary = -upperBoundary + upperBoundary = MAX_SIGNAL_UPPER_EDGE_FREQ + lowerBoundary = MAX_SIGNAL_LOWER_EDGE_FREQ # determine if aliasing has occured, and if so, which direction, # either +fs/2 or -fs/2 @@ -1199,4 +1714,3 @@ def upconversionAntiAliasingFilter ( input_signal, center_freq, bandwidth ): # apply BPF output = np.convolve(BPFWeights,input_signal) return output - diff --git a/torchsig/datasets/torchsig_narrowband.py b/torchsig/datasets/torchsig_narrowband.py new file mode 100755 index 0000000..6a28191 --- /dev/null +++ b/torchsig/datasets/torchsig_narrowband.py @@ -0,0 +1,136 @@ +"""TorchSig Narrowband Dataset +""" + +from torchsig.utils.types import SignalData, ModulatedRFMetadata, Signal +from torchsig.datasets.signal_classes import torchsig_signals +from typing import Any, Callable, Optional, Tuple +from torchsig.transforms import Identity +from torchsig.datasets import conf +from pathlib import Path +import numpy as np +import pickle +import lmdb + + +class TorchSigNarrowband: + """The Official TorchSigNarrowband dataset + + Args: + root (string): + Root directory of dataset. A folder will be created for the + requested version of the dataset, an mdb file inside contains the + data and labels. + + train (bool, optional): + If True, constructs the corresponding training set, otherwise + constructs the corresponding val set + + impaired (bool, optional): + If True, will construct the impaired version of the dataset, with + data passed through a seeded channel model + + eb_no (bool, optional): + If True, will define SNR as Eb/No; If False, will define SNR as Es/No + + transform (callable, optional): + A function/transform that takes in a complex64 ndarray and returns + a transformed version + + target_transform (callable, optional): + A function/transform that takes in the target class (int) and + returns a transformed version + + use_signal_data (bool, optional): + If True, data will be converted to SignalData objects as read in. + Default: False. + + """ + + _idx_to_name_dict = dict(zip(range(len(torchsig_signals.class_list)), torchsig_signals.class_list)) + _name_to_idx_dict = dict(zip(torchsig_signals.class_list, range(len(torchsig_signals.class_list)))) + + @staticmethod + def convert_idx_to_name(idx: int) -> str: + return TorchSigNarrowband._idx_to_name_dict.get(idx, "unknown") + + @staticmethod + def convert_name_to_idx(name: str) -> int: + return TorchSigNarrowband._name_to_idx_dict.get(name, -1) + + def __init__( + self, + root: str, + train: bool = True, + impaired: bool = True, + eb_no: bool = False, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + use_signal_data: bool = False, + ): + self.root = Path(root) + self.train = train + self.impaired = impaired + self.eb_no = eb_no + self.use_signal_data = use_signal_data + + self.T = transform if transform else Identity() + self.TT = target_transform if target_transform else Identity() + + cfg: conf.NarrowbandConfig = ( + "Narrowband" # type: ignore + + ("Impaired" if impaired else "Clean") + + ("EbNo" if (impaired and eb_no) else "") + + ("Train" if train else "Val") + + "Config" + ) + + cfg = getattr(conf, cfg)() # type: ignore + + self.path = self.root / cfg.name + self.env = lmdb.Environment(str(self.path).encode(), map_size=int(1e12), max_dbs=2, lock=False) + self.data_db = self.env.open_db(b"data") + self.label_db = self.env.open_db(b"label") + with self.env.begin(db=self.data_db) as data_txn: + self.length = data_txn.stat()["entries"] + + def __len__(self) -> int: + return self.length + + def __getitem__(self, idx: int) -> Tuple[np.ndarray, Any]: + encoded_idx = pickle.dumps(idx) + with self.env.begin(db=self.data_db) as data_txn: + iq_data = pickle.loads(data_txn.get(encoded_idx)) + + with self.env.begin(db=self.label_db) as label_txn: + mod, snr = pickle.loads(label_txn.get(encoded_idx)) + + mod = int(mod) + signal_meta = ModulatedRFMetadata( + sample_rate=0.0, + num_samples=iq_data.shape[0], + complex=True, + lower_freq=-0.25, + upper_freq=0.25, + center_freq=0.0, + bandwidth=0.5, + start=0.0, + stop=1.0, + duration=1.0, + bits_per_symbol=0.0, + samples_per_symbol=0.0, + excess_bandwidth=0.0, + class_name=self._idx_to_name_dict[mod], + class_index=mod, + snr=snr, + ) + signal_data: SignalData = SignalData(samples=iq_data) + signal = Signal(data=signal_data, metadata=[signal_meta]) + if self.use_signal_data: + signal = self.T(signal) # type: ignore + target = self.TT(signal["metadata"]) # type: ignore + return signal["data"]["samples"], target + + signal = self.T(signal) # type: ignore + target = (self.TT(mod), snr) # type: ignore + + return signal["data"]["samples"], target diff --git a/torchsig/datasets/torchsig_wideband.py b/torchsig/datasets/torchsig_wideband.py new file mode 100755 index 0000000..a5b2c95 --- /dev/null +++ b/torchsig/datasets/torchsig_wideband.py @@ -0,0 +1,86 @@ +"""TorchSig Wideband Dataset +""" + +from torchsig.transforms.target_transforms import ListTupleToDesc +from torchsig.transforms.transforms import Identity +from torchsig.utils.types import Signal, create_signal_data +from torchsig.datasets import conf +from torchsig.datasets.signal_classes import torchsig_signals +from typing import Callable, List, Optional +from pathlib import Path +import numpy as np +import pickle +import lmdb +import os + + +class TorchSigWideband: + + """The Official TorchSigWideband dataset + + Args: + root (string): Root directory of dataset. A folder will be created for the requested version + of the dataset, an mdb file inside contains the data and labels. + train (bool, optional): If True, constructs the corresponding training set, + otherwise constructs the corresponding val set + impaired (bool, optional): If True, will construct the impaired version of the dataset, + with data passed through a seeded channel model + transform (callable, optional): A function/transform that takes in a complex64 ndarray + and returns a transformed version + target_transform (callable, optional): A function/transform that takes in the + target class (int) and returns a transformed version + + """ + + def __init__( + self, + root: str, + train: bool = True, + impaired: bool = True, + transform: Optional[Callable] = None, + target_transform: Optional[Callable] = None, + class_list: Optional[List] = None + ): + self.root = Path(root) + if not os.path.exists(self.root): + os.makedirs(self.root) + + self.train = train + self.impaired = impaired + self.class_list = torchsig_signals.class_list if class_list is None else class_list + + self.T = transform if transform else Identity() + self.TT = target_transform if target_transform else Identity() + + cfg = ("Wideband" + ("Impaired" if impaired else "Clean") + ("Train" if train else "Val") + "Config") + cfg = getattr(conf, cfg)() + + self.path = self.root / cfg.name # type: ignore + self.env = lmdb.open(str(self.path), map_size=int(1e12), max_dbs=2, readonly=True, lock=False, readahead=False) + self.data_db = self.env.open_db(b"data") + self.label_db = self.env.open_db(b"label") + + with self.env.begin(db=self.data_db, write=False) as data_txn: + self.length = data_txn.stat()["entries"] + + def __len__(self) -> int: + return self.length + + def _get_data_label(self, idx: int): + encoded_idx = pickle.dumps(idx) + with self.env.begin(db=self.data_db, write=False) as data_txn: + iq_data = pickle.loads(data_txn.get(encoded_idx)) + + with self.env.begin(db=self.label_db, write=False) as label_txn: + label = pickle.loads(label_txn.get(encoded_idx)) + + return iq_data, label + + def __getitem__(self, idx: int) -> tuple: + iq_data, label = self._get_data_label(idx) + + signal = Signal(data=create_signal_data(samples=iq_data), metadata=(label)) + signal = self.T(signal) # type: ignore + target = self.TT(signal["metadata"]) # type: ignore + + return signal["data"]["samples"], target diff --git a/torchsig/datasets/wideband.py b/torchsig/datasets/wideband.py index 14238d8..f5680d2 100755 --- a/torchsig/datasets/wideband.py +++ b/torchsig/datasets/wideband.py @@ -1,9 +1,11 @@ +"""Wideband Dataset Generation Tools +""" from torchsig.utils.types import ( create_signal_data, create_modulated_rf_metadata, is_signal_data, ) -from torchsig.datasets.synthetic import ConstellationDataset, FSKDataset, OFDMDataset +from torchsig.datasets.synthetic import ConstellationDataset, FSKDataset, OFDMDataset, AMDataset, FMDataset, LFMDataset, ChirpSSDataset from torchsig.transforms import * from torchsig.transforms.functional import ( FloatParameter, @@ -14,7 +16,7 @@ from torchsig.utils.types import SignalData, SignalMetadata, Signal from torchsig.utils.dataset import SignalDataset from torchsig.utils.dsp import low_pass -from torchsig.datasets.signal_classes import sig53 +from torchsig.datasets.signal_classes import torchsig_signals from typing import Any, Callable, List, Optional, Tuple, Union from ast import literal_eval from functools import partial @@ -146,7 +148,7 @@ def __init__( super(ModulatedSignalBurst, self).__init__(**kwargs) if modulation_list == "all" or modulation_list == None: - self.class_list = sig53.class_list + self.class_list = torchsig_signals.class_list else: self.class_list = modulation_list @@ -177,18 +179,11 @@ def generate_iq(self): num_iq_samples = int(np.ceil(self.meta["num_samples"] * self.meta["duration"]) ) # Create modulated burst - if "ofdm" in self.meta["class_name"]: + if self.meta["class_name"] in torchsig_signals.ofdm_signals: num_subcarriers = [int(self.meta["class_name"][5:])] sidelobe_suppression_methods = ("lpf", "win_start") modulated_burst = OFDMDataset( - constellations=( - "bpsk", - "qpsk", - "16qam", - "64qam", - "256qam", - "1024qam", - ), # sub-carrier modulations + constellations=torchsig_signals.ofdm_subcarrier_modulations, # sub-carrier modulations num_subcarriers=tuple(num_subcarriers), # possible number of subcarriers num_iq_samples=num_iq_samples, num_samples_per_class=1, @@ -199,7 +194,7 @@ def generate_iq(self): center_freq=self.meta["center_freq"], bandwidth=self.meta["bandwidth"] ) - elif "fsk" in self.meta["class_name"] or "msk" in self.meta["class_name"]: # FSK, GFSK, MSK, GMSK + elif self.meta["class_name"] in torchsig_signals.fsk_signals: # FSK, GFSK, MSK, GMSK modulated_burst = FSKDataset( modulations=[self.meta["class_name"]], num_iq_samples=num_iq_samples, @@ -210,7 +205,7 @@ def generate_iq(self): center_freq=self.meta["center_freq"], bandwidth=self.meta["bandwidth"] ) - else: # QAM/PSK and related + elif self.meta["class_name"] in torchsig_signals.constellation_signals: # QAM, PSK, OOK, PAM, ASK modulated_burst = ConstellationDataset( constellations=[self.meta["class_name"]], num_iq_samples=num_iq_samples, @@ -220,6 +215,39 @@ def generate_iq(self): random_pulse_shaping=False, #True, TODO fix pulse shaping code. center_freq=self.meta["center_freq"], ) + elif self.meta["class_name"] in torchsig_signals.am_signals: # AM-DSB, AM-DSB-SC, AM-USB, AM-LSB + modulated_burst = AMDataset( + modulations=[self.meta["class_name"]], + num_iq_samples=num_iq_samples, + num_samples_per_class=1, + random_data=True, + center_freq=self.meta["center_freq"], + bandwidth=self.meta["bandwidth"] + ) + elif self.meta["class_name"] in torchsig_signals.fm_signals: # FM + modulated_burst = FMDataset( + num_iq_samples=num_iq_samples, + num_samples_per_class=1, + random_data=True, + center_freq=self.meta["center_freq"], + bandwidth=self.meta["bandwidth"] + ) + elif self.meta["class_name"] in torchsig_signals.lfm_signals: # LFM data, LFM radar + modulated_burst = LFMDataset( + num_iq_samples=num_iq_samples, + num_samples_per_class=1, + random_data=True, + center_freq=self.meta["center_freq"], + bandwidth=self.meta["bandwidth"] + ) + elif self.meta["class_name"] in torchsig_signals.chirpss_signals: # chirp SS + modulated_burst = ChirpSSDataset( + num_iq_samples=num_iq_samples, + num_samples_per_class=1, + random_data=True, + center_freq=self.meta["center_freq"], + bandwidth=self.meta["bandwidth"] + ) # Extract IQ samples from dataset example iq_samples = modulated_burst[0][0] @@ -648,7 +676,7 @@ class WidebandModulationsDataset(SignalDataset): """ - default_modulations: List[str] = sig53.class_list + default_modulations: List[str] = torchsig_signals.class_list def __init__( self, @@ -1104,7 +1132,7 @@ class RandomSignalInsertion(SignalTransform): """ - default_modulation_list: List[str] = sig53.class_list + default_modulation_list: List[str] = torchsig_signals.class_list def __init__(self, modulation_list: Optional[List[str]] = None): super(RandomSignalInsertion, self).__init__() diff --git a/torchsig/datasets/wideband_sig53.py b/torchsig/datasets/wideband_sig53.py old mode 100755 new mode 100644 index 411b141..8ca6446 --- a/torchsig/datasets/wideband_sig53.py +++ b/torchsig/datasets/wideband_sig53.py @@ -9,10 +9,11 @@ import pickle import lmdb import os +import warnings class WidebandSig53: - """The Official WidebandSig53 dataset with optimized loading.""" + """Legacy WidebandSig53 dataset with optimized loading.""" def __init__( self, @@ -23,9 +24,9 @@ def __init__( target_transform: Optional[Callable] = None, class_list: Optional[List] = None ): + warnings.warn("WidebandSig53 is depreciated. Use Wideband instead.", DeprecationWarning, stacklevel=2) self.root = Path(root) - if not os.path.exists(self.root): - os.makedirs(self.root) + os.makedirs(self.root, exist_ok=True) self.train = train self.impaired = impaired @@ -34,7 +35,7 @@ def __init__( self.T = transform if transform else Identity() self.TT = target_transform if target_transform else Identity() - cfg = ("WidebandSig53" + ("Impaired" if impaired else "Clean") + ("Train" if train else "Val") + "Config") + cfg = ("Wideband" + ("Impaired" if impaired else "Clean") + ("Train" if train else "Val") + "Config") cfg = getattr(conf, cfg)() self.path = self.root / cfg.name # type: ignore diff --git a/torchsig/image_datasets/annotation_tools/yolo_annotation_example.ipynb b/torchsig/image_datasets/annotation_tools/yolo_annotation_example.ipynb new file mode 100644 index 0000000..e9810a1 --- /dev/null +++ b/torchsig/image_datasets/annotation_tools/yolo_annotation_example.ipynb @@ -0,0 +1,189 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "id": "db8cb1a0-987c-448c-bbb3-2f7d3d908860", + "metadata": {}, + "outputs": [], + "source": [ + "from torchsig.image_datasets.annotation_tools.yolo_annotation_tool import yolo_annotator" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "8a1d6ec5-31b0-4739-90ca-bcec3b5258c6", + "metadata": {}, + "outputs": [], + "source": [ + "unlabeled_image_dir = \"\" # directory of images to be annotated\n", + "new_yolo_dataset_dir = \"\" # directory to save annotated yolo data" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "6f2ae761-b7f8-4f46-88c1-fb0c23c027cb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "36c3e9bfe4274489bb6f3adb9b09b33d", + "version_major": 2, + "version_minor": 1 + }, + "text/plain": [ + "BBoxWidget(classes=['Signal'], colors=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd', '#8c564b', '#e37…" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "yolo_annotator(unlabeled_image_dir,new_yolo_dataset_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1581d833-c9f8-4a8e-9bba-24b2f6a680f7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 87, + "id": "eb02edce-68ae-44c6-adc4-8a9f7510b76b", + "metadata": {}, + "outputs": [], + "source": [ + "from torchsig.image_datasets.datasets.yolo_datasets import YOLOFileDataset, YOLOSOIExtractorDataset\n", + "from torchsig.image_datasets.plotting.plotting import plot_yolo_datum" + ] + }, + { + "cell_type": "code", + "execution_count": 168, + "id": "e78a20b7-9cac-4b01-b871-ced68c2a2771", + "metadata": {}, + "outputs": [], + "source": [ + "yds2 = YOLOSOIExtractorDataset(new_yolo_dataset_dir, read_black_hot=False, filter_strength=-40)" + ] + }, + { + "cell_type": "code", + "execution_count": 169, + "id": "f722ba5b-dd3a-43f8-a0f2-e4f7d8584e01", + "metadata": {}, + "outputs": [], + "source": [ + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 178, + "id": "54ff5ac8-a0fb-45b4-a2dc-81abde465121", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 178, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.imshow(yds2.next()[0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f6bfebf4-c510-44e0-86c6-297ab73e1714", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "a3dcf767-0ffc-407a-bff2-a48e605f45c7", + "metadata": {}, + "outputs": [], + "source": [ + "yds = YOLOFileDataset(new_yolo_dataset_dir)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "id": "fb54d59b-6358-4393-b15c-057810add6d7", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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d6elpQ7TmyqkmjcU5oIvpUwaYdA50bm9vrwVq9zDvqqqTk5NeM5I6KSdE58g7s6akRFHH6KEEEllLlHlnEBhmmll/E1w918/QbPTCz5JqtNY+R9fSAaIdM4Nlj3S1qnqyRb2fn5/36nhJXXoW3RiWFthRdiPKhAW0bFLJWiHANZlMal1Vo83NOnnc3DWbzer4+rquj47qmY2NWoStjTY3azSZ1Go8rulsVvPFohaP7Xb6uL6ddc6kVbOxLTsvAZRkEnKN2bPPC6aApZpy1g2Pj4+bDcjUADMgIJvqyNvYU7fQ1pmJAhWbm5uNcbjN684HOwbJMRM0Y7UQVdVrnKAothRkrYMjq6pe4TXbatPpcgYcSD6TE9BMko4z2/d97+TkpHXSZXaUDR5J/5kvis04BcTVatXQPqPh3MnJ+LNe6G9BP1G3eiMkmt9PuoTyD2sj5s+ZCETTabclIjML80ZHQ/3n5+c945cl+JysRkCQASZFW1WN2pZhGFPWXwTwpO/8O+eOfkbBJrXJYVV1jSQCEUdfdUMLCsIcbQYX36Xj9DoZgnSWOoUFm6zPZR3G77KrWZZF1p6TdqFu6veZ1eoO9jsNJACaLJoj5aSTWst1NH+t8LkeSZkmqMxsNmnY5XJZe3t77d/ZiKaUUFU9xqiVRFbd1p+XX365nnrqqZbtn5+fNzbkzW9+cz3zzDO9kslyuaxRVa1Xq7p6zIKwT+ts3Yw/7SppdnaRdWcBL8ESn5B0tvVNxktGvrW11WyIXx1mnf5P31M3+WQ1ZvVIeoW+vs3rzge74f6WRDGJVjn/pJtQaIqxVR3Vga4aj2/2OyU6GY1GPeSeNB2kLFhcXFz0MsJhlsGoobOqmwB2fHxce3t7vXpfGms616quUYfjtFcJTWkuHIoAkGg60WBVl4UyLEpsbPbD+V4WwpPylWWpB0HUyfFXVVsfdaSkoawPOibrAGTu/5z6xsZGeyZnwADRKhxc1nA5FmvhZxxRdm3Sj1aLiYyKXnGSScVljYr+CK5klkHNuN0Do2Degrnf05fM3jKLpUsZdNUoOUeBMOVGJhxuZoHZ/AKMZeNY1pWMNQMYh5n2nLXpzCKH9VdzNA6OW4anYUaml/qRWf3GxkajT90rKU+Beb1e1//9P/5HXT+m/kajUZ2dndXBwUGdnp7WeDyuR48etYzr8PCw1zW7MZ3WxnRayyhpyMwEtdyPKCMz7+3t7V6t3PrxQyjCnZ2durq6qv39/fY5MkzAl0A+G4QSHPOp7JYdG4vGkyyrZFDUROPn2aF+G9edD3ZpaLqBGDOEuFgsWpbDoDnNquotJGeZtFMi/izo50JmppXNKLju7IjzWQvvM0OFFVx8J517Ogm1KcEWLYi6yXoZhLy5udnawQV4l89zvgyfc+DM0/DRW+Y2pDY4sKRRBAvOGmpmkAwV5ZL0pn1BmSWfnZ3V3t5eyzLMf2Njo9GDAr61YqAZSFJnfBaVwylxauaYyJuDF1wys0mGgA4kLe5eAsL5+Xn7nPuoP9JbP8+uzdTJqs6Z0U1rlPfM4CaIZOefNZYFZjOIjFstPLNgQMrfaEYAIjPkHO9yuawHDx7Uw4cPe/qZtXRyYRdARFK1sku65fNka53SkW9sbLRsNilRoGS1WtXJyUmdXF7W1dVVvf766zWbzerRo0fNpo359PS0Na7Qk9VyWavlsjYe6xXbdxkzGQNOnj2fzxvI2traqp2dnV6913plh2yCZWxN6iD50MUM9GRET9JHkSP9klla02SKgPPpdNpkdVvXF+Q+u9u8cMqMiqOq6lqGGQAKK/lvAQOdQvHcu6rbEyLTuQzqQSNBBkBKpb7G0SVVVNVtjciMkbKgpjgVbcEZkBV8c1zql0kDJg1n7kCBOVR1DQJZH1PTIw91Rf9P6jc3rVL23KuGp0cdCzwCpuyR/NRCUSppPO5B9pyFMWeQpg8+Z6O32o21Mk6ZqjGa28XFRZ2entZisajXX3/9DfWVy8eOT6Age/K31ql75pB7EvMPB0Q/AS6OcHt7u1eHpvfW03fpA4qUg0uqznc5NHJJ3RCc1LASfD18+LABRVmDrHA8Htf29nbt7u42h520XDIDdEztzvfVMmXJgnQGdfNdr2+2zwiG1jAbhFrgeSwHwc5c2aAxJBCdzmb1zne+sx48eFB7e3s1Go3a2L7sy76sNjc3621ve1u95S1vqXe84x2tK7LV0apq+pjiBu6SKaLDMk9rNjwhxvpcX1/XyclJL4ihd5NBSPrbeDY3N2t3d7f5yaqOKUogtru729Y9GSryE8zcN9ee383snV+5revOZ3aypSFPz3DSYLMelxQAo7Ow2Z1GOQXLTOWzVrNer+uZZ56pk5OTtpAnJyetRsG5DbMKmdhisWjHkqEZ8tgpysPB+JNFd8bLmI0douaQGW5mhpTw8PCw0WPGmwDBZzka8+CoM3BcXFzUwcFBLwOQvfg79w9Bk8ZqvTwzawQMc7gBOenaNN48vcNaCgY+m5Q4I84sDD1W1WVh1opeWOfslMzgaZ0ySB4fH7faHl0FChIEmU92UVZ1NUZrX1U9sMFB0oOUgeyqqlqQAYSSls2tBXSB/ozHNw0/WcfLxgiyzxqvtcjN8RqszJUcMDbmlHUjAMB6qFmi8swhu6nJ6Emn5SRzk3rJhvNns8fU95/9s3+2Tk9P69Of/nRtb2/Xzs5Ovfvd7+7VOFN/eoBsZ6eNPWn0BMJ0mCyzBs4GksEBnvmQ3PcHKJGJ+1Z1p8mk7xsCRPfG0NCVoZ26B3vla82xqlrt8LauO5/ZJW1WVU0RGW2i0azFWIxEhhSIoXIsnD8lyZQ+N2dz3J7hPomEoByOMp21QjT0jgJkAHlqQ1U1SgLCywxXlgudDumJ5OjzGKxh1pNG5bnGQUaJmDlGxpTNQygiMszMajhmTTq+S36cFuqU8XEkHDGamiPMjjYykUFDu55HVzhU884Mze/MIQNsjlnGCQAMs2EUqzWC5rMDkuxluqj4zIarOibD3xkgZVucZzYZcbbsxTpad6Asx1RVrQ6Wn1cLzVoS/SQbcubwZTXkaz056LTbtE82R3a54dy46Bwa0jiBOzYz3CNmzQVq69eowMeB/s1vfnM99dRTTdc3NzfbySFJmdNXejkajWrj8TgEUbp0fn7eggSZ8z8JiujQaDRqJykpESS9jUlgB+R3fX3dA058UzacsRmHCbAdFC8QYx2zbJGllmxYSfbmNq87H+wYvGDnyk3TVR0dSBmSwkCvuB8jr7px7HnArM9wegwg9+dUdeddppELIhwOB0sZOQ3ZQXZZUZxsT6Y4ns0gE0Hj+tV5KPtkMmnbNdTSBGB7ebKLysWIkrLlRASGpFHPzs7e4JB0eJ2fn7fPcX5J2WR24fucJ4TMcQAIWRfIWkVV1ySRVFBVVx8SvOhHUllZL+E4ORPfFRASvABGSell27wAQ9fojcwcsh7qXGaAAnnWQ2STaFAsAXkIJnTDs4yfs7q+vq6jo6P2e2Oky+wHOEngIOvloMkxv29tZLzsSbaHWbCe9EMgSCoMcPS58Xhc+/v7ba1kP1lTAswODw+brGSAVV3zFJtumfljfchDjN/ylrfU888/3/SODbLfpA5ns1ldR4ek+3umYJMlFOvBD6xWq1625eg6383GlWyUM//MaJOlImtrRR6ZJVbdHAGH2XFP9rler5tepO2RayYFt3XdeRpTAwfnheqrqh5S51goHKXnoBhDNrlQrKRLkpqyeHmqCOqhqlpXJGTud+lQBCnKmYZWVe2eSb/KHPybkSR9JAhMJt1+OAHFGDjh5XLZ26OX2Uw21Qw3Sm9tbbVux3ze6elpmz8wIlP1+42NjRYUyVXXHIfiqCVjhzKrOsfHODkWczNOc2nUU9TOBGzrkBmsnzFYzgJAMBb6A4Ak2BCoBK4hq5B1qyzgJ03OoQtgAoR7Zq3PcWCCBMdKL5LqRiGbiznTQ4FeHTLpRU4z2Q+1Vxn7/v5+c6T0eX9/v05PT3tUGp2rqlaDtgfRerJdz8zacoI+dk8WyYT47snJSTsEPfUd7ZkljcycBKAWoNfrur66qvFjZke3ZepD1q5s/yCLzEyTjUJ1Hxwc9IIaEMle/ZnP50+s0cuaMhu3LnQV+5B+xbOyZEF+BwcHdXx83MBVvm3Gevms8Qh6jlt04AN53+b1/4nMDvrIFBqyFCyqqjkNPHf+HkpKBwAZW1yOItFJnsghwxyNumK4wi8ny3iSqtS4IhgZj2zCvNAgnIV7CpDeJIDGgvgcp5UNAYkc3ScbIJJCcop/osXJZNJqkun4MjhnPVFg0SmaXY7GASWTT9ZHOBDZBMSK8uSMUyabm5vtntaMnDhDch+27WcwBQwE9dzflM7AmvkuWVvf3PIxzM45A2ubzSr01PhGo5v9oSg26zadTtuG6ARUnDUZXF9f9+qBeQpMBnoAJptIspMyHV1mu/Zmkg3K8OjoqEf/A2JJfQrIZMDO2DV5csinp6e97Ng8fH61WrVtMut118WbWaTTbATbtFdAkO3RxdG46/rkU5LlELyS4XHQN/lal6y9s8Ozs7O2RoJ6nhpjbXd3d3tMBB+C0t/Z2Wn6CjRktpXsFD9pPDo5jVkwI3sB11r7vy5v9wJe3J9fut968DleiqpayyH8rCklEuU8ILWqjl6j7E5EEGSqqrdHRoAYfs/FqTDsqq5WxjApDKdEiSBw49JSz3lDaokSKWpmfIlUs67D+LI2IxMzTwpsrlVdzQHvryZqP5+gzXkmvZgBnXyADeuXp11w1vv7+716a8oJncLRpgNg5Aw/6V/0VTZScKDqHoydE0vaEF3JeNVXrDcnYK04Xc+xZ9G6JzjLepj9n4IFWWatJrMmv1M35qStOXl5hgAqcyfLqupl2Kk/GRgwKCjSzE4y8/J8dkD3PIccOWFZEBux2dz37fG05js7OzWZTNohwymLrHuz96wNAwlk5vAFzTh0KuuyjdJfLuu5V1+t+eMmi8V8XqPHGd9y1e2nXa9WNXkMDhaPdXB7Z6cePJb5IoJ02u5s1r1Djl0K2C5rwlfJaAXTzO68eodOZsnCnOh90q1DBsw4s9zCdvN7yQYkuLfe7Pk2rzsf7NJ5MF6OAIrjUGVHQ2oEwkK35XFGwwK84JkZUm6yrOoc4nQ6bS3ykM94PG7t4nnwNAdAQSlTcuRJeyW1yilSpOm0O7G/qn/yv/8DBRk0oPeqaodly06vrq7aIcMUX8v1cDNwOtbMzsbjcTvXM/dDCoyyUI5IlsBpWydgJTvpssFHYCGryWTSzvVE4dGd/H6uK1lBq56dWynIVKaQzRMK+AL46elp06GsK2ZdEruwubnZ5ECPqvpApap7XU9V9ZzYarWq/f393ulAxpwZZoKWBAmCOGclAMncjcXxZuyFvplTdvFmzZzzlCVmDRXwyOYHWTgZYzXUstRwdUAnbTnMmoY6kKd42BqT76YTtLPuu7GxUX/09rfXwelps+HFYwC6Xq9r/rg+KkAuHstw+/Cwrq6v63K9rt95xztqPh7X4jHoaN8NACIjw5awO/pBPqkTgrR5ZaZL/kD6aDRq2aq1SrmwZbaie1LApReaT8iPvMk6/SEKM0sQt3Xd+WBHCVAU0A4+mONKag9a9H3OLF8/IsNJx5JtzVlT8ByOqqoftDgNlIUGjaQd0nhRowJuZn3G4xlJh2VthkwYCkRufu4lQHKAHCxKUAs8tEnemgGS5vLdqmpGxOg5jwz81iWzx6pqDqeqeyde1hAysHleUoOcQm4PseaJMKu6rCcNNFuyOdjstBTYU2Z+piEoHYU6Wq6dMWY25x4Cg++6vzXMeVxfX7csNuumyQ4AFPQ5t9JkI1dzzo+dWG4PmM+7F3uSETCRDIo1zAabfH+cxqJszMhnZEexOeS+vAycu7u7dXJy0mtuSbDFsfpDH4ZNXj4rS0aPKk2Qh9rker2u1cFBvbq5WfPHL0cejUZ19FiGGw8eNHsGhtiTNcka6YMHDxpYycCVsmELbI/sUcpAg+fkm07G43Gbjy0e6N2Li4v2WTrOdt1f4pBvISGHpMlXq+5oQvPb3t5u2SZdoZMJNG7juvM1O8rE2ft3NjbIIJJasRiJPiAQCB1qTQpzeJ9sg+ZA82RyBjWsO+TY8qWzyW1DaZxn1ic5t+zmzJoPqiEzBmhXjSiDXDpyZx1mEwE5Q4AoP0cjyXo5TMqP908asap6ys95cnIpZ3WJHA+nRT65mR7g8WwGj7bhzDkO47VviBzdg055owAkixKyXu6RbeQyF84+naz5V3VBTgAx12xOcU+6JoCTed6PMxHw6VkGjNzLmPM9OjrqgagEdEnzkgl9Mle6nhQqO6L39EawtWZ+v7GxUfv7+7W/v99kDXySN5tE4/l7mIn5O+lemVvaZ3aMZldw7qPM+bGz1ap7AbKxo19RxQmohqAyt/GwafaUvo1t+J7Pj0ajOj4+bmtmbEDi2dlZ00VrrOzy6NGjple5HSl1JQMWm0y/ZPvCfD5vpyuRHaZmWKYBIG47s7vzwY4yc9hZU+GQBIEswmdHECTDYKuq/ZwzpKDDVm8URAbULKxnvaKqe61PVdc15RkURIYHJWlbz30x2VDC0XHOnlPVfyNBVTX0bw55Qoe5P4nCcW+OkTPLNnfPZGgCcVW/jonT59AFY9l57nVieJzAsOYoOAz3bnlrtaDg3kk/e3bWS5KuplcocMY5POKKQ0fnkgNHaVtL1uiseTpp652ZkjXnQK2zoGW8SROaz+bmZjsAOilAYCFZiwxGWTf1XGsosC0Wi0Z5CjzpgK09NiFr1AAP55plAONPx5kNOvkdchAsZMGAqvVJdkcNkk2zlaxXGWM2ixkDeedLijEeQ/mmrupCTD3gC7KzOjNksjBXVOdsNuudnpSHOmR9k+5lcM5arqP1Euzk+wWNVdYn2PJP5jBc39lsVgcHBw2o5baqi4uL1sByn9l9jhdDZmgWT9DTPp3URlXnfLNVHy3E4TBa980ide6dyhbaRPIZ5DxzWEdLROl7nLDf5VFZlNJ+MM5QsOLYc7+N+aSBcRCaE3TCZWBJOgwCzM3Z5KveKTuxtyqpMGtF7oJRdshmE0F2tFV1Dsl8JpObbtCsbSVStFXA8ViZxe08PrWiqtunWdV1hpKL4Cdwc+qoaOuZtaoh5eYekDEHnvWYjY2bfXUPHjxo4+VEIWEOkzNLvc86lLVDu3kWJyRj8mw6nPVBNsCGEnRUVa9xYij3BCdkSpeTOvYz9pfNDxw68CCoZudsOtfMXtVVdQBm0JYdnp2dNdCXcxBwyCCZFmNmc0kPa7gCjMw5M26fT7qZ3qT82FsG23xbh4agrLfTB4xBBnTjA0ouLi56x7Fl+QNolIkmtZvZuyCcvwfyvEXCwQhJn7MBunB/gsof46IM9hklLZf1K3/b88JZ5Llw2Yorgzk7O2v7gzKQZACy8IlWsl6XmUpml8O9LoyOYlOYLOrmK13UzhgONE/hqrqjyciK8fqT1Jj5cdAK/2jDpO9knwJ+Ov+kRXJeGZjUMQVSTgtggKRlSGRuc3x201V1TpSTSUqHYVujpOcgawDCczkkumENnnrqqeY8rHvW1jhw3yf/fIGmsfjManXzkl8OPp8pAxess1bHWXHSHFZV99by6+vr3jFVuiA1O2xsdKf/p5PPhgRBzDaBlGfWo7J8UNV1Xz4pQ6UDWRNFadIpAVDds6p7O8bx8XF7JpCRdensCDZecvV7mVQ2z6R+y0QygFRV22+pE9ypKTLErMnJPgG/rG3L5hKkqrtnrc7YsQSCIkaELgOtuf8ys0XBir3mYdjsCVOxsbHRth3luKfTaQMMScPyNXQCoFFLTP1I+7ut684HO2iTg6FYwyCRxWEcc6IYgY6CJsLc2dlpaTn0lwgsOWpIvOrmhAHoa3d3tyH4DMjGnShP8T2DWhoKh5kKKrBQMvcc1nNa63TdGIxOS7LjFHIvUjbTDB34kN8fjiP3RHHEubfu+vq6R5Ol40wwkNQfKsXacBxoZwV332OkAkVVtSYgc7KusirfTdozka3Pc1BJoXGmdNPGbA6SfIbPkXlw8pkxQtAcmOwDMBqPx3VyctLkNpl0J+Ubq8YEgSbb0ZOOI7OTk5MWoKFyOkh2iezpmxrqkC0wFoGBox5mdovFor1RIvdVZiZbVb0AY92zbMDGssaVwSGbpmRS9A0gBMjsa8zygSYU9pb7zTAG5pVHiQGN1p5tZW1raFM+rwM2QWMe58anZfNN3kvQNhbgAqAAwMwn560ux58ObQADAbzm2MnIPOjybV53PtglDQgpQBnp5KqqtzCJ9LIew6F5ZUYuEqO0lSHrPoIWA7q6umooMzlx382uRJ+RrUyn0+YY3UvghEIzsJhTVTUEXVVNmQUlnx3Wwjjv5PohzKRB0tFzRgL5sGbA2KBj/5bBcTY24jMcY9rb22sZRG7+TuflZ9bW/ZImNA6BLLP8rOsKHAlWbFgXOM7Pz9uakSnaXNs16ihrJ+TtjFVUVtaMgC6BLDO2ZAYSDecWBsEys9fMYNB/SVsZQ74s1niG57wKTrIQ2W9Sk2QN6Hi+9c3A5J7s17jomCwKdSszZYdDOSU7gG7OehTbEFCtXWaBgJM5JUuTRxICaPmsqu7sytxKk3OWZQN1xoi293l6pPt1f3+/518Ee/ITNACgDPLsLAGKrIzcrF87Haa67VP0A8Alm2x44R+SEcu6JL/jtUVZYrrN684HO0oCHWdBOutH6Vyk18lVD/fWcKZVnXJDNEmFeDYHy+l4C3M2yGSBGmVD6WWLk8mkOVUUZZ5Swhm4T9YlqrrDebOQn7w5x3N6etrbV+hzmSmhaCA/9b29vb1mZOv1ukdTkEVmr2pzfp+1JU0ZVR1wkU2aY+415Bise9YtBA0y9e90eMbhXD/BzPgV8OfzeXuDBao2kazsy89yX1M6/HTQq9WqZfacufnStwykeS4jelXtQ+bAIdK1rJMKHtnMASQZvyYSsqYL5sOOksZLuo8+5WtwZFhZIxdkkjbntK2JdRJEcx7GNwQA/r66umqdwehZNCV/oP6FfgYu6JvXN5l/2gKnTYerqp3eks1CeWqP9R9uOTCXXDP6wW4EjGRYyIcdsjOydf/UhzyYILt5ZadV1TshCoDkN7e2ttrexWGGxgZzu0iCyNTpXGvZPRr6tq47H+xkHpnBLZfL3qn3mgwYNUNAaabDTnSp0ynrIAwMsvPM+XzeUn4LS2EpqOCituDZAqLayWq1ag6WQgmgaAdjN3cXxCuYV/VbiW20F6zH45vDcjmzVFoyIVtOgrExToZhzhnQGBqHvru725xvHkeUtRKGyFkKGFAv41Z/tBY+q9VdwIfKGR7UnW9r2NvbazWwpFlkbRx9Nn4kUCGnpF3Jg/PlvBOJC3gAmHFm1upEk6wbVnWOIxuaOEGgLGuJuf0jnV1V/8ACtRoOlB0BY1nPBpLYEN2y/tn0YO2AgayzDalf+iLAyQYETzaHEQF0ssZHVpkFZsNFVXce59bWVu3t7TU5JBWa2ZT1wb6wC75hSGfTbzZirYeMjnVOVipriQAGfQYkZetJyfOJCbrQrq6knNk7sKJmDvQ5TSflAJDwF5nVLpfLHtXqLensj97f9nXng13SWAwqu8mqOnoBAkEJMh5GwdizXmchGUHSZZwtZ5ddWKnwmW3K4DgMCskgOQdO3/MVk4+Pj3scf27urbrJWNSuqrpjyqA3z+bQVqubvVWJ2rPeSTaTyaSda5lZgcwvnVRVlz1XdS3lVVUnJyctW81290SWQMmQ7hT83dP4oHWOySZ4+96GqNz6OHLNfBmqtee0OdukobNd3NzS8SXdmsxBvgHCszNTV7PLujDdMA/rlF3GOQZzzBpbdnYaB/ll9kcfBb1kCejSMIAkLZXZAnkDdkmdAgaYBfqQmZQ1TieNbXDv/I41w6pw2uSqluv5gJJs2VjoF9kNM2RykJ1l97I5ZmkEECbvtPcMfsaeDEfaYuoqvybAky87TCqdL/Asc/M89uvfbF6pBn1tjYeMTtaTzSMpciAoMzwA8DavOx/sIIXs/EN5MZjcn+SoLvteIOjr6+t2vl5Slcm9p0JaUAspyMkEGRkHnUgm61RZL2F4DCKRv2dzTtPptBm92oH5D52Q+6Ov1H9s6J3NZg0AqK9BdJl5kSXHTcYyEePNOiZnBMUn9eqznEgGWE7G/HNtk+rMoJF1uqruAGqfBYSSYsntDoxVMLE21kzGRK+sSWa//nAIfpcHRhtnOrrLy8sWgPIFsZPJpPb29to4yAS1TpeyqYBMkkIyd3Jxf2DP+si48kR7zVVV3eut1Jqsh99lDSybYzhY400b4MDJF+WW9cfMQDRJAEC5T9Y92Eg2XeUJIEkjy8YTfPh30oWZgdMVOuQZbIFcU5/4BPLRFQsQCSDmDvgmAM66NJ0RhNistU3gIoOlX+RkvZOextzwE4BP2mMeUs0WgAVsAHn4TOpQsmK3dd35YCdlZ8BV9YZNvK7F4ubEewFAVsQYZTqUFRobUmvqJ9nhlI6G4oxGo3aU03q9bqdTcBjoTQ5y2ESRnUwbG92Bx+YiUNtGUVU9w8w6WlWXDaVzz8Dk76wxGVtuos3syO+zmcX/PdvnM5NlaMCBukdSQoxJkF8sFi0oV1VrjpG1ckaZOWcdjNNOGcrwGGxuONZ2bgzmk92py+Wy0Z+61aq6DCszvFwjf0PFmeWqw2V90D0EIqCtqqu/JAU7Ho9bQMm1SPCQGS/qHJChOwCDzCebK6wPnTDe4Zw5SoGXXg3pNc84ODjodSnL2AWmBHHGa8zn5+dt72XSc+wdu0Of8hBrDjr3jprvcF6AiHplMjVZPzZuweby8bmZWJukrVHdwKB5G1sexHB6etqClmelnNkbW8REyN7m83mdnp72yjfZ2JRg1FpkM43Am/Rz6rcATb8Fa2vq37d53flgl11xgpSAxigtHofKAcoGLVQ6aUYJQTlUV1C1UKiTNKKjo6OesxVEIHSNBVXd5mrKljWfbPTI0zUSOSZdWVVtjp6Dwkw+PetEEBnHxMAODg4a6h46WRkaxM2ZJBLO2pjMLh2v5+c8MoODUhmiI4/yhZBVN9sN1Hxkr1m38m/PUtOr6ppR0vkZO2efqDa3bVj/pMCyDpzOMLv38vzCvIfPZ+05gZSsT5aOYiKDRObWeDabtYYTMqcrnk/3AAK6Ze2SQsw6Hj0ZZijkl+yI2p3GFU6TnPOUlXyjAuYkQRcdAnzcw9x0xXpTiIwWiGVT7CobYcbjcduKY38tm1KSSFm5h6yYrqvlkXnWxwWJqmovlgVm6eByuWyAXV3U2lq7pFcz0I5Gozo4OOiBUGt9fHzc7AUQQgV7/6CxAZ58CQZH1p17AAVt7wlMUCsw8rHsN8tMt3Xd+YOg0yFUdcVbCy84MET1uzyFfljcruqOUKrqb2kY0kmcMApPhsO4EvHaTnB8fNwUiCNzv6rOQARqDjz39yT/z7kKqhyeuTjrUvbAUWU9cjabtc3aZMm4qrrNq5m5Za2AnIAJckhkCrFyOAwL8hQss6mHLDiqHL+Ms6rfpcnxuwR3cqIfAIpx0AVyOTg4aO8V8ztUOAdtfaDYpJE426TcMrCmsU+n03ZPzjzHKoBxOAkyZDxJcyfNK+AlDZf1Q+NISjIbIqD6BFH0NANc1sTs+bNWnjfMKpMOp1P0cDweN2YkmzWs/Wq16mVxl5eXtb+/39af3qUN2nuYdbbr6+s29mzA8HuyzAwZCMpGNT/n5DMI092qagdU0B2HQAgIwwOX2QVZOrwZk+Li2zxf78GwDpg0O3vPQ/DZ/NnZWQvkefgF4Jg+OLufsQrq2wIr8IKKvQ92n+PFqWXdYLgxVHDIjcaUKxFG1hxcmWqPx+OGilKxGRUlYbSQMmfLKeXLIpOSm06nrQOKMZmb4Aup2ncks8h9ONBhvqvKnP0t2DAi4xI8M1isVqvehlEOwrjNq6payzaZelWMbPb6+rp1vfl8Bi9On4NNoxSw8r2D6Xg4Pc6B7LJWCNVnIMYEmPf+/n5bAwg2KaJkCwRHCNs6J9uQ8lXvkG0LTE7pSJoxkT550C/raH04NMHV5wES98vmLVld6lIG10T+MtvsGM3nTyY321mS0vV7ct7c7F6dJejKdjKI+T/gw27z9VKAjbkYFyYgSxW5RcCzybCqGrVHv8g2bZ2srq5u3iIOmHj7On8zDIyTyaTVIYfBK/Un9SbLA7PZrNdzYN58FxApe7ZmxoMtygYb/nDoG9LnsGP7hWXlbAHT4Hg01KmgJjPFFPgZ3cwa8m1ddz7YER40k91IFkXnpM9bqOS6sz5AGRgmJMtgkrbLuglD9LukqaqqpxBV3YHC2fxR1R0QnfWGqmovWR1SU9mkkFSbZ2WWSxmz/jY0/r29vfYZ9wEO8m8OFXoWXMmBsfk/A2T06RDJpqqjQbLDjJwYpiYF88usM7MNc1CLIPsEQMYisPksndDAY47unXPJF6AmS5DZn6BgjALzU0891euyzVpPBi0NReh0a3Z9fd17vxmHCAwk4jannGcet5ZBNddBMBRcyD1LBOaUNVT64zMC/OnpadN/epAdwTIRlJkMNZttULmyHfZI/ulknR4jeD5pHdJWcwN+2mOWBuyFzdp4Bmg/y5qezGYYcFJmmYUCaRmI0r7X65vN2t50nzY1rMFXdUf35Vr6Q0/5IAFXkKLzSgFZ79eUZ56ZnfI77FnQHSYWf9LrztfsGNve3l4r9CanLRhloTX5ZT+jpFmbkcILfIrjeTK4BctWbs+DqDxjPp/3iso+l/dzf4HXZ4aFes+SjTD2rOEl+lcAzywqa5WeIejmRlPBm2wyA8gjlPL7VdUDEwINwxHgbd8QHBmOTI7Tsu9wuVw2p59oV6DNJhdo3xyzXsLJZnPTMOsXNGW2Glmsi9rOZDLpvWWBvHVzom1046UOTCaTth2DLtMHzloA4YAFWJ+ZTrtTNuh71l6ScpcteDYHSieS/iRvY6MTKMOkpRLYJbhzb78HyLa3t3sNQxwtm0pgmLIBYOheBqEEPs50XCwW9ejRo6a/ZEkebEZDUHYUJ+VsLn6fp9dossqOQzqWQEDwyHtmTRnjkFmetaKLCbb5lpOTk1osFr33eZI5e0+A708CUmtnzFn3TIDne2wCeMKEpN0lJWwOZD4ajVoCclvXnQ92UF7Weyh/1nv8Sb7aGxGk6hwlFJ1OPanENAgKIItkvNlhxgA43KRZtre3W+MKh4LOy++Mx+Pe4dXpCIyN4+ck1XfcJzdecyo+JzM1V4FExxvFTUScyJ6iJ3VlI23Sf5xYUjOCXFJQCtlZp0HVCKzZiJLUifFoMkjQkFS2YJxZgHVNx8BZnZyctH2MVTcZcAYA8pJtO1KtqjsVXv20qqN4Na5k/ZgDTXScdcB0fuwg65B0XDDY2tpqTQgO+E5WgVP0/b29vTo4OKiq7hSeqmo1GPqtFule7EDgEADyHY+ce2bh9FRGRM/MPUEocEF36NJsNmuAwJ5U67S/v9/rDvRzwJju0oGkAtPOEnQlXUkHUs81laWchxR1VXcUW2Zbamh5MspoNGpNM3n0HDlngFYLzMwb0Eo/mXSrfzs5xjGACbqur69rd3e3ZfxAXD7X/ROU0wOAL7Pa27rufLCTBWT3V2Y5y+WyFbkpl6yAE2SEqZxooHSGVd35epwPY09En8FU4dvvoGvGrjbAUYxGo17Ni7JXVQvIWXdJI3IwrvELDMYvEFV1zrGqa1PXpJKZkP1pVV0WCkVyNOkYqjoaNuupaChO1f04+zQ+cuIQrK1gweF4VjoQ3XuAQwZdjiGN0jM5gpQZGQy74WRwDNf4qqr32iWyFXCqqjlJAUWmkU5V4Jc9QMn0bAioMnALDFVdYPLc8XjcdEQdK7eLCCjr9c3LfY+Pj9t9h+Aos94EXwBJVddVS2eGdV5OGCPAfoE2Dj73RtJFz0kAtV6ve+CGb6iqdroRoJQgNrcfcMpZAjB38jZePsX9yEFNd2h7GdDIQTAydz+XHZGhz6HM6VZmpcN9p+wYWGXzZK0b1PdllbqLAf5s5klK19yypGPOdEsQxgSQocz8Nq87H+zQJg435cSquqN9cq9VblZVn+MEk06oqnYY9Hq9bhldFlmzXkW5OV4OK+tQVV0wFIygaQo6ROUKytBdHrFV1b0DD2rlVD3XGNQI0IbGi/7KrsEMBlAmx6BeqKEiKSpGkE0ZSQfmlg8OdLVatSwDDWPTvu+aT9a8sgNSDUFDQgKT4YZZzx3Whjg7byMHOJLe5fDyVT2ZuQlAw+CltoNtkJGRedJISf3s7Ow0B5n1Jg4qm4iSysZMzOfd2wrIMpt5stElMy5HZwE01jg7OlNfZbj+kIX1lrnSCQ71+Pi458g5ToAnM5WdnZ1mf7KGDBKpK8mgaP4ZLxa1tVrV5Pq6ZotFba/XtTg5qfHVVe1OJjWPEkhm0fQk1zcz7/wZPdZdSbfypJDUQcBBYMjSxjADpp/uR3+ur6/r6Oio6Yz6bpZb3M8rn8hcIE0bJTe2lMGNH0tgUNXtF+Un6KgGPAFPsDSn236f3Z1vUMm61FBJKUwKO2nOREQCpOBm/xDFVYCt6owJyq7qt05XdbSV51XdKPHe3l6dn583I+SwBaKqG8elvT2pxaRTBcEMNH4vm4QU/e0z2XgAlflOBj5jGiKwIQrPLQIMi3wYhhpWOiWnLQiOidbTKQAqHKJu1qR9EgBk5kxu/gwbDIzXd3MjMRljCwR3SNmcjSObCIyHrmVwJUPrTg85W+CNjuaJHYDR0dFRy/CsLZln1pHBNDfDZ23z+Pi4gaB8lqyh6mZPmHUGBGT9ZODdaFnL2djYaOehmi8qLOu+SdUBnM5xxYaQIXkKegl48oR+67e7u1tf9X/9X3W5vV3LxaLGk+5dhKPRqA5ef71+/eu/vlb7+73Ak2zHarWq4+PjXlDb3d1tQCb32mXWa8x5AQHAjyCfNsDf0Hf3Vu80Z1Sj3wlMBwcHzbYSGLM9wCnLAJKBrEvnHNhy1k4BlaTb832HyWRoKvLvZJpu47rzwa6q/96q6+vrOjg46FEfqYRJUeG90+GmMsiksnsRKk3+ndNGa0FtWfQfZgfQcXLqxpUURCJNBsBBXF5e9upXWc/JP5yGuZJFVbXPQ3gUMLOJRMwpb/UnGQj5c8LkxgCT7jJPBpTj89zR6ObkiUePHrXnMqqdnZ1GO0O5KEHrAsmSTzZUQJ5ZY6IHxpvUTta2Muu1jhn4OJ3t7e1eQIVkoe507pkNJdVFJjLibLxKZiG75zKDSsQuw+eIrNXm5mbbDpH7n/J8y6puw7bAnvvq/ElqFfhJWh2Q4MRHo5v2fcAumzlch4eH7f6Z3SaoBWLYIB2TBW/s79f/+KqvqovHexTV61arVb39t3+7Nkajunx8TzJA06fs3Dv3hWaANYfcw5j6Rv/Sf2Rd2xYVz2P/dGRjY6P3NhRAIFmMzMLZBSDtOUlF0y1XgpcM3Moa6Xez5GHM6SMAbTqISt7b22t7MW/ruvM0JiHnPpY8qDgpBEbO0ClGtu6jELMOc3Bw0BwAI016g8PLRpikewQYz03nwCFl3U9ArapeoEiF3N/fb4XiYWZDDhxHbpSXgXKS0N7QiQy3UlBcik22HOywc9W4OddEtxpU0tjQIAwTJZ1bDLKeUtXfBM6Ysn5jPgy+qurBgwc9qlOATFpwd3e3BXEBKTfXkhWZQ9nWazabteYneigLoLM598zGBP/s7DQ2wRaD4LvZdGG9ss7p85wnConsUK/0Op0YIOLkjdVqVScnJ70s2lolq8JhA1FZa9awcn5+3qg2GZXjtEajUa9JI0+JMW+Bn4yyNksG9BHTcXJy0vTZPbA56fgxFukPMgvN7Ri+Y746dN276gY4nJ2d9eqxSV/TIYDIvBMcDjsnjQ3rkNl6Bsh8b5yxe4aASyfdd1hHzAPk6UqeGsV2NMUlgOMH1FATlN/mdeczu+vr63rw4EFboDQADs+/LXzW28bjro0YUuJAoNh0KJCeQJiLPTyhhOFnh5kxZ2D280RvVX1qUgDMIEwhM4gbWzpjiAotyfExeGPLGgM6g2xkKemYfR8ly2lkcwpHAS1C4FX97RFZUxxuBcg5uaeMOEGDe5I92TLgqmoOm8FlPTO7Pauq0XJ5TzqTnaYYAE4vKWeBGkWc68oRmOdwLbEDnNlQXoK6OSTYqer2buWh5J4HWOQYkgEZdgiinoyDLqQTzuYazS25Zy+ptqpq20VWq1Wr15KxZiFAJv/NJtMesSsCYjIVk8mkFtHIQv+txeXVVdWoa8Un9wRQMrrccsROdVwbUzaRZLPW5uZmO0xCxs9nZXMS3WWXCaqTZTFfNKfvZCe3NZXpu/9sNusdOJ10fmae7DmPCUz/igLlJ/zO35oByRCYsQ63ed35zM5C4eZHo1E7M08BlxNhXLmHpKp6RunfWd+ymduCZRef+oXgAbUyFs4utzRwEpx2oiDKmBmCceR71aDVpKlSQau6d5RlFsMRZRAQSBgFp2YM2TzA0PNA3iGIQA9mN9uQWiXPDB7Z0p51NA0OGeSrqn1XkM31NE5ZjDXKMyZtVSBfGZCAmIcMCywZVDOIcgYJhLAGvmM8ZMchy5xyj5U1GDZZZY1KTc34dnZ2elshrC2duRxQeBsbG+00GzQ8aj/p3qxhembKdzLp9uDJfNCWGajYgTGbL51PSju3GXDYZEt+AogaVtZNM0i0DOLx/Tlaz0iwSjeGnZeysaz1JaigTz3qdNC5mEdu5Tm5PpMUZNbbZZEA17DWRz7ZVZmAI5umzBWQ29jYaJkoGjvtnH15RvYX8Ct8nITBntMEg8kMrdfr3uH1t3Xd+cwuMyzGmBlWohX/z83GVR0VSjmhFIjWMziVRGR+zulBy1AMfp8SpnLn2Bghw6AoMiG0T843aScGkpmO+1JS45fpMTwOYb1e995XlTSa+6Efkn7Lrjn/z0DLYeW+OXPNLtLZbNacvD08WetMSjGPNwMWks7lzCHgbPdm4ByErIyxu3QyZp1rSGf6nPvLjgXq1DNBbBROl2MGdLx5ICkyjoMTI0N6/KRsPuttHJSmEKdtWIdkHlDIyVDIdDh9Y85MNW0wAVzS8Em7+p3vWEddtug58nQS0hA8sUX/zpqa8creXn3llfrsZz9bVf1tGVU31PXp4zGyXQFPZs0G6Hk2fwC65pS0fgYO49H1m1tgMmtT96OX7HR3d7d1tlrfrCVnNmbTPuACqKR+sT3MQwL9ZLaquiyafNTBrb3nunfS/dmQZX3I97auOx/sGJPOsHRMKeRENQwoMxqncqjnoPCq+u+Iw+snrVXVnVCSaDhrMEPKSABiAIn+PSML3dnR5H7mnwV5wUgDR26i9vvcZpFFdafEVFXvjebmI4CRIxnh4nNzPweoSaSqO5DZdXV11V5VY26Z6WWAIy9deoIn2UO4WXtTJ4Gg6QRD9TmoVvBK+pfMku4kSwHOZ7JZyJVzJzPPTEpOAMvMPjN2TiRrP/RXhowJkDGmXuWam19mqZkNZY1YZkiPPIsTNq9cj6r+NgrBlw7pNK7qDinn+J0uk9kQ+6P75CxgGDvdVl+SBS0Wi/rMZz5Tf7S7W8vVTePF+fl5vfDCC/Xss8+279Z02jJd9bKkmwEDgBOYS6oQEKMnnL61zZqb9aX7VdWT+fAs0SwdZLakrs3ujYs95XeTZuczUucvLy9rd3e3V4PV/JIy5ltl8vTOPOlZ2rtnHh0d9WRxW9edpzGrOmSZaDkdODSf3DqqhCPPRgooOjsbUVsyu6xl2P+zWCwaikqOnzEbH4TDSTDe1arbjJmbpxM5c5JJcyStkVRcnrdHQQWv3B9oTGTmHkkpMQTfy0OjgY3MsjhaQZrDyBqBuVxfX7fOL+vp3k6IzyAueDE4Tj+pWSfj+Flmuxm0yLCqe7sFB5OfIcuknE5OThr6zW63RLuoULqTb2kgX4Ga7Onyer1u7+9LOuzg4KA5wgyg7qf5hB5lLUwWxMnl85M+1MaPWUhQhOYDblD4QwYFCFJ3JOPxeFxPPfVUo60TxGW2lrou2LqwBOnAZfP+9r3ZbFZPP/VUfckLL1RV1Zd8yZfU137t19YLL7zQAvnysU3mPlxBZ7hPLP1BVfVsIPe62aKQoBvoSGqQHXqeksAQ7NC/yWTS1hjIzvVUxmHDbCrvNQTgmVXzQTkGNsjH+d70MUBgIwKp79nSkjVre2ITFN7GdeeDnWCRNF5SUnlqB2TCSXBkidoZ2HK5rMPDw/Z5zo6zzo3e7pE1GfVCQVTdj2L5XSpRVfUcu/vJfPxfXdDxT9BXfiYbQfzMuBMdujIDVCNK5MdYkmrd3Nxs2w7Icn9/vz2Tw6vq6iICkbXJuiq6VoaS777iNNWeAAaOjlOqunGEeeYmp+RZQ0SZ9E2uUdY/zs7O2vpnho7SEfjMFUWTNc2qjgof6h/ZV1UL/JyarCbpwdzbOOzCQ8tOJjdnkXq/maCf1GgCnLwPG7DuOzs7vboluyNXYEuGkgHQc9nd+fl5O88xtzpk9npyctKChDqfz2XWmYDGvY1B5rNYLOrpZ56pw4ODWq/X9aVf+qVVVS0jGo9GtQiglg0y1pRjFgjcl13RYfrBnyR4ToCblKWfAwJ0nI6Yv7JFUtqADNCTZ2ACMVlz5mvITa3YOp6fn/cOIkjWKBmXq6urZhN5bJr7CHz0NgE0wGCdbuu68zQmwWU7uYaFqo7mpGyCVdaUqjo6JYum+doVSiUL4GDRHen8OE3BDAXge5S1qutIYxw+k4GX41HDquqOSxrWA5Lyy2wh0VnVGw+pNW6OkHMyb4ajhT4dX9J+Wb9CL1b1a1fQqCDJMBhMdooZpzFnzcackjYmv+zGS3TNOaXzBT7IyroJgugyxk/GEDXHwrmORt0ZmJxg0n5knjSXzzlogCMTdNOhZ+3TzzKzpJdPaoqihwIUMJNgB2OR9bnUJ3LFCFhfcoD+MytIPWZvCbpkdGzX9g/ZKbDgs+pY5MLhkm9StNnkMtR5QWNjMqnVqL9pmzyT9cj9bEoFdIrOVXWgAxhMCnB7e7vVjIeUaPowm9m18p+cnLTN/eYlY/IcJ8zYg2r81tVYrAnfmHVg4N88htmzcWbAT/mTNxvgg09OTlrdOO31tq47H+wYYFX19iZVVY+7h4BtCE90n3s/LIbvOxQ1Ax16lAKl08rA6LOZDXHE/l9VPVSXzSxVXQMOg6aYaET/p8ieh57ihPLFmZl5cI6j0ahRhsvlsp0ckzSX/0N4VdXLmKs6ei4zXXPk/GWqZHxyctKjddA/SStXVQs8qDqOKuu0AoB1EZQePXrUO/kjg7vsOWuwWaO1zeD09LSXSVm/BD5kqjaoW1AwydqMoGvs0HxSYFkXcp2fn7cxJUr3uWwSImdOOtE2XbWOgoExbW1t1dHRUXt7tyCbYxFYPMf3beNRRjg8PGzHmZF3MiPYiqwpsjFjy3qml7Ym5Zddi4IqXZjP53Xy+DtZS7y6uqrr+bxG4+6AeOsvgKVO2yKRVF8G4sz69vb2Gvjz/9xfR65sKMGyNdIxbF58Uh4qgca3bvxGNkNlZ2iu9ZPAgMySHNhAZqTZQEenBTd0rftmPT2brO5pzD/GRdmSKqvqDi6mxFnMp1Ccd/LYZ2dnzUlnJyCjQrVQFLShZ1Z1B/4mvSogZXs+JaiqnhPJTkOOJhErRZI1uocT7QWwzH5kIUNaj5PQNowWZiiCpzkIdpxWVfV+nrWarCclqjTfDGzkDM2TzdbWVm9T9jPPPNPmxWCshwyZgVd1wSFRd9YVBGF/svaUgEQXm60QAmNVl7Gr0zkvEs2ZCBtYyaxGAM3tF+ovQBuwlS3m5soZkmuiZk6T/g+zO84vg1/SeZmZcWYJ0Kq6A9KTEhvqajaOea5nu9SFjZO8jD3fLGLesij3yhoYnzAa3Zxg88wzz7RMLG38IjaCAxCO9UOPkpE5qB8bbzrx9XpdDx8+bAzCYrFo7f6ZLSVDQm7sQ7ame1bGPzwkIlkAz6HbatT8Vtq/dUswIbPmf6q6w+hzLnlSTXa9+hy7SjbFmuehHLd53fnMTtCykGmESRehxQQYjpTz9Z3R6Ob4IpRAHgFmcdzP53HlSWkxjAykSb25ODH0VVIseW/ZWDqTrDUaj3skdZDNMQwgmy3ILuk0AU+QdV1fd29Jn8/ndXJy0qPxfN8zrYMAkhkeAxaIGA2aymcFZcgeghQgGGM2MnD+7pMGzjFkm70uQI4chWQ9ABx1pXSWEHcGa7qZhfi8v7EnnelPAhRZcAY2WUzWGjPz4fATpSfwyEYHz2E7Arq1NHY1O7+rqsYWZEaGJZGVKS/QAzp8cXHRTgASIFCb1lbgEYD9LSCkvGXluV6uyWRS0/G4ZotFfeVb31rjx2swfWynW49B29njWlPVDUAS6NNPJMDL8Sbl7jsOYmBztjAYE2o7Qaf7CQ5Z90vfwneQgTpvbv4mowRlWSukh7LR9Xrd9tzR96Ojo94+RkeeGQNbzgApobB1xDisqS7sZOFu47rzwS4dhaBE0VMxBBo/03gC5XJWnIvFzTpeGl9mPRY7EZpFpbB5OksaYwY3p4RnY8nZ2Vkbo3dZGZ9AmLw45Kd9OLdSnJ6e1v7+fs3n80ZTckSCDMMW7MgmA3i+lbuqy0iHjQiyGsEg6zwyNgEP6odaOU3rwPn6rqBj/TVcJIWbMueMPBfFJuPPdYd+GSpwoLZq3Ht7e3V6elrj8bhRVJkhCfKenwHcfIEadFDKMunnrAuRgXukzidTwB44J3qWVGfS6+Sv7nN8fNw7tzL1zT2GNGYCJzrIbtIxkjvdIycOPOvLCRbJ2ZiBRXNLR5yB//yZZ+or/p//p8nb3OfzeW1Op7V4zJDQbT6CHWikOTs7a+xJ6nxS4tnsRBYJkIwVoAIA8rsZXFNGBwcHzUYyO8yyxHQ67WV/9MAzsAobGxvtfEpzpDfGkF2f2esAPLEhz1ksFnXwuBGITupLqOr2pGJpbvO688HOa0jSqVgECpr1AbUSxiKTs+iUigIy0KQzq6p1DnLGlAdyT6THuDkl92ZUUFSifgYE9VIulFi+UBXNSemg3ES76EWOIk/oYHgcg9phNtWYT9a60ug1EdhXp2bFUBhS7t+rqhb8lstlW8usK2Qd0neMiWPMU0OqqslG233WQ7TA54b8zDgFgfw9JyFT0YiQ4EbgoRN0DaKt6jKqPKuQzgnSAE9Vd9RUVccm+L8Abe2y3pjP90ZwQWA2mzX0nnUYGTv527RPXlnnSzrW/JOKzMwi9SypzGGTWGYx5psdjQCP8dI7gRHgzVpSbkr/v//Mn6np297Wqzv11jyABpvxvKzxHxwcvKGjkW2mXZF56ha7yMBm7upwdCppUXKQ/Zo3SpFeykS9MYNt8X/8Cv2SfdJndrq9vd2YJmAu690HBwd1cXHRA7DT6bTJG8AV1DIRSOr6tgPena/ZUULC5FgsPKfL4Px/Mpm0lmyNGbPZrLV6V1WvUCvrq7rJOvb29nqNJomuOQJKOB6PeyeIC5BqeuYxzILSeCkqBeUck0JkAFXVQ5doUhu4NUzoNuWkk95kAFkHmc/n7c+QBiSjdMiJFNFYuScpxy/TzPpmNgaQK7lldmPOxmAdZbYy2aquESLrJRxSyhQNIxsDKATd6XTaWq/Nyzom2kf5uS86UNbIUXOw1i5faZTOdDqd9k7GoEv0FcWHil4ul40+M36yIK/d3d1Wq6UP7pmBgdPKU2Log/nnFhBz4XSzlgY4pqxyjxm6OoFGMivDWpd7CzTpVDOrIu8M9v7tOZ7l91nvzfXVuZsBDUBhw1XVC4ICcYJb5YDcN2kM5L+7u9uCHntUAuCXst6cDSGSgKR+6QXbSeYmtwJl2WcymbSza5Nmdj96ImhXVetNoF9sP+36tq47H+ygx+TNKQlqS4YAKXFgeYYmRXdQqXtZIAFriIQ8j3LIAHNsSR1kDQ9lqNYEPaFTMnNx6gMH4PeaILJhhdPiPNwnsz3ZpIwO8s9mB3x+GkpVNQOTFc9mNy+Bpbxp9LPZrKHwpLA4naT+oFYAJClFgSEzDDJ0P00tDO7w8LDphuDKaZEZx8kp0xdyPT097dF16O/MOM3B/ao6GpKzRTPZB5ZNJIJzvqdPlqbRiOPRkZoUEx3MN97LvLXGP4lyF4RsJRFA6EkGumw9t646l2Wr7IqeV1ULPmTCEbonQFhVDWxo/MjtOMCHtTMnzp8sV6tV02nBxL2zhurzsn2UYr4TL52+DCkZGjIkG5+xFsCfZ2Z26//2pWZtfBjgBRwvqE5AmmvFZ1mzZLN8BhiSdbEx+nl9ffPWgtwDB4AvFot2yMF6vW62obGKrionJAvFTgCjBCq3dd35YEdpqt54LqHAof4jQFESyiAYaP0+PT3tNYvkQcMUFrpzeC6kR5nRAavVqqdUglA6s0RzEFm26abj4NiguXy/XdY7vDolW+lle+lIBIKkCslzSNf5I2BXVesWpMBZp5pMJm8ABlnY3t3d7R0z5PcCgbXl5K3lMChbH5/n2ARf99zY2GhrwgizHslhkWdmvOTqsN9E/1mL5GTMLddA5i4zF9Sc9oLetd4CC+eY1KyfmaPsL8+FxQ5kMwTnvLu72w79NfY8cs+zjMMJQa0Gdn7eDlNPSq6q/8Z6Tpo+DBuqrCnAx17oWHbZJpClK4KOoAjcYFI8g91msxAHnuubmb7v5ZmpGUzoKbvNwIKBUD7QgEKeuXcuqVX3Ni76xB7pcFKsvp8BJhkW+mGM2bHMV/J91j8bWMhAQM6GFbojiFkztPh6vW4MCDmT/W1vKr/zwY7DphgcmRZmVGQGEid/yNIYUVUXIDlxKCeDZdI5kKCA4PkyR4EpDTbrfAKubITCMjYBldEOM0MBkMNgbFXVMpYMPOlEoH+OL7sDBRg0DbTLgWcjgvurncl0zJFhkaGgIPANN2eTGTosa3fqUJnBZaemQJ37EM2FnD0/T4pII6yqnoO0FlAvR5IAQDcnh351ddW6RtN5Zn3RPWTg6VwEQqCLU1E/EaiTkfD8qu41L856rOofROBzSbVnrZQs1QTJjW5Zd84/2+rZEns0lnTAMkHbONyTbGSA5k1/ZafJCFRVr74HYJlr1o3oIL3IRhA1Y59PNsFnsxYqyAyDdWa3QAP5AhLkkABdIJJN5sEK6uNAsMsz2FqC9wRZKVP6YF2wJYeHh+3N59ZPNuj/sryqrmEv/UHWFekkfQIs6Zls8rauOx/sKILsALJPY3TAMwUmeG3zuT8q27OrqtW3oJDcl5L8c6Jui2kDalWH1rIDjoFk3QvaZlwUiQFw2IJm0qhJuSYISMe5WnXvDktnwLCyLrder9vzM2vhCM2dgfgO2tLvjDHrHhx07nXM7EqtNMdu/aDXqu6lnUnbcIACSco9Dy+GvI2d/iQ9lgEUSveHvPNZxpC1vETVnkP2AAtq0pzIy5pPJpNGy3P2Vd0rYAQm988sJpsYkqrMOhydzVogOt9ZhsbhoAXAj5Pn1K0VfTMH+ki22IekNsnF2MmW48wam+CXc846W1V3zimHjVnJTHEIiskfgAI+2Yj1TH9RdXNUnsCXHZNkxMazsYTN08lhjY5t7+/v9wD9MFCbn2CGGs9zUjO4JOVovqlr1tHv+UgXn5j1zmQ81J3d1+cE3jxY4rauOx/snGrhyoYSynp6elpHR0dVVY2CoQzqGxaKQnAuVdVDvhBldt8JsFVdQV4WAJlBPUm9JEKSJWbB3d8CjayAsVV1p8vLcPzJoMX5QJRqfxxZZjwUPB03imyYPXHgjFTmK0ByYGSTmUNuO5AtorzIxDoKKOSbtRzzFjgySKcRk1G2pld1h4iv1+s2jtwyYq6MNfcOLRbdsVgafmT0Vf3XPXH0npmBOTcsz+fzevjwYa+e6NQYThKYGzYpaX7IZovMFugjAJe1PevpnoKZxhx/Z+aUjRlZKyQ/F/0WGNBg6NHcG8bRZvYz1Af3ziCaIDEdeVW15ohkcp7U+FJVvXNF6W+yMFXVglzqfMqN7yDPzKYEU7TgcB2H5Zasq6ZfkG3T25QPHQM0MoADRxpi+LzM3lK+utpPT097dpU+IH1jPtNmePqDAclzVm/zuvPBTssrx8twOL6Tk5NGw6ETXRZqsVi0jkxGx9FkcEFfcNgCjaCHzjw4OGiUH0TEeSQlJpAk5UeBBRedTBQNrZK0HHTOGKHLIWUC3Vd1FETWNtwPkuY4/Z8z8zyZAtTNeZFx1lKS1hEcBAPOXhDM2hvDQjHmEVLWiVNJB6sewqlz/LkBNuUtGOzt7TVKcVifXa/XrRFHrQ0VN9zKkXqU7xNzL0AnmYWsXdpfyfknVc6Zcxyc+nw+b04QADD+bNoSoNWWq7o3P6AXJ5NJ6/o8Pj5uay/w0qGs82Q2nOAqN5ZPJpPeaSfuQ5+GQEN9KoMB+/ASWs/BFNCtZECy8xEYJbOhbqY9ZeczQCUYZckiKb/MmrOzO4GW79IJIBMwGrI2eT9Zl3s5VBugSAZlseiOOLNOAEJSpgJ/0tX+eKXUarWqw8PDXtbnO0lXAzeZVZIxX5Qdm7d13fl9dtJlSsfIU7m1yyZPTmHzrEwGYOEpLid8dnbWK9rbZ6MNnbIxUossmF1fX/doFgGzqhqlRqGT0hCUc+yejTKArrIDzb6Z5bI7eT0zRxmKILe1tdWr/eDYcz9QBv7hAb1QvXGl85MNQN8Z0PPKWliexpFAJJtudLCSbVK4mdFmNyHHg27NegOjhfhRMBlYq6o3v8x4su6Uzze+quq9PBMQgowFN/KUOV1d3ZyJmDqcnXlJeTkz0UHEAqDx2IMGtWf37Hw+b0CAszTebGihRzJzjtwWl6TDsk5qfehzbiOxphkU82gp75wkY4ARoLBugOHh4WF7ewL7Q9Pu7u42GdNfQTk7b40NWAWiM6PJxh1yyX2w5K4eSK/9zHdlb7pc6cHZ2Vnt7+/Xcrl8w+HKmcmlHGXdOiPZD52znpmt5Zplt3Hu38s5A3fmAbhmXTqb4dhT1vJu87rzwQ4S5LgFH4s1Go3akVnQEkV+7bXXGn3BsXMu2bDCyWaLeaIZ3V/qYc8991xDS1tbW3V8fNwMxvN0K8lMNQIYQxbCBS1K6h6bm5u9eiXlTFqJIqbzRb0m+ne0USLP7GbkLATxdMJ7e3s92ic7BKu6RpkMHuaRvD8aREDnECFTzuDy8rIODg7avY1RzZRD5IDRexw4x0ZH8sraoftzEEk1Zm3XFg4Og7FzptbB/YcNDNZbFoGmlEkIhJyvGp/2cWuTNS+BgM4ngBIMNzc3G0ir6roo09lx2gKkQGNtktr2feMCOBLoCPr0WYDPDNR6CqIJ9LAM6/W61yQi86XX7AoAINdkRNhh1qvoKT3JYOGPYJz66bIeAJbPaE7KYAEIpc1PJt12l6pq2Zj1pM/W071zG5Q1EZiz3pr7IROU8SPJRJGBtZTdZfdyBrvRaNROaMr6N5vJ+rxn3uZ154MdoY9Go3ZwajZEWHgZDqVK58HxJI/MSaC9kgrk0CkeB+535+fn9dRTTzVlFhQEGoivqnpBw5YEAQ6iZqAyRY01UBlK4dGjR+04IQbrd1l/yaJ+BgCZTtbB3IcTGp6+AQ1no4Tv5X6yzEA8m5yrulfMGJ91sK65XYMRJ+WT2Vi+JicdKWPNdm3PAZAyC0kn7n6opqEzIw9dc/TMGkG9nAmZygYEa2PjeKwNwFFVb3DO5pQNIdPptIGXpIKH+kR+Ml6f85mkJAWOqmr1TR3H1jgPMOdo2RfKFDPhnglCczO27MO6Z6OQjD8blPL3CQySneHkgRDBLdmhYUaWtb3M8ufzm8MKZMTkkL4kAVJ2UVo31Pqw5GC+6W+yMYfuJ43MP1mvZIbcLzO8IY2f/QrmknbqM4AdGSaN69k5Vt+TDOTpVrd53flgl840nVNV9wLJXJAspFd1lKXvUgDGzPA4FMjI/4epeNKA7pvGB5EzQkFY5pF7oaq6F1UKioIdtHx8fFybm5u1u7tbL7/8cn3RF31RPf300z26Q4bjYNeshXB0glXWwxi34CKzyzn6PaMW7Bin71ZVL5MTZM0/C+lZAxpuzmVgjC6fydD29vZakIAsUdxkKrtNqtnn6UUGt2wu8Xc6eZkixOpYKeuXWUJSV+5lHjIenYrGmk7NcVB58jz5Zk1pd3e3zs7OGtUsI0udFQA5c5mJI6Nk2qenp60GDQwZ79CZVlV7vY3g5Xtswvxlnfkmc+tJTsafASQzZXqiRkSeCXZcCUI8g2zpRp5TmoE3s2MyZ9fkQJ/pHX+SAIlOoYjdw1isD33iH9xH8EuqEEBOBiuz8AS15Jsn8Mgkk4pNwJnZZAK9pFE9O6l4lLg6am7TcETjbV13Ptg5uiuzlewUohTL5bKdss5Qq7puR8bO+SealH2ozXHuAmwGL2MSKLNLiZNPI+BIXMbvuKuqrrj98ssv1yuvvNIytfl83oLXzs5OfeYzn6n9/f26urqqp59+uimcsaBH0iG4P/TJMXBCVdU6EB3CK+AZr6zFRd7j8c0ByeYiYCbHnzRPVbe1A4gRUDhxQXBYVM8CeGZyubGXYQomnDu0mU7ReqWBZy0s64NJSWUtJDesm6fsTA1ORkB/sibM2XEUWY/hWDOT8ywOO3UxAUnS3pxfnme4Xq9bRyn9BwZkve4ngJmftcpaeGYvAAC7Sf3L2lZ2f+Ya+/zQntT2ZM/GA/QltZv1KMHA2MikqnpgjJNOWjvXxJ7NBJB0PYOCzycYMwZ2Axwls2Q+xuj72dGZQTUp5wxUwHfW3JK5yUwug3cmCCmjrGNnExHb2t7ebjrNPgRUOnpb153vxtzY2KjT09PWVp21tQx2ir6JgpMioCiJStyjqpoDSOcMhckeE+UkQvf7dAgUQsfgeDxuNbysy/nu+fl5vfzyy22+ubXh6uqqnfry8OHDdtQWVMhJbG9vt3pJdgD6k5noZHJz6j2kJgsSUHD4ao0ZxN2DA5FRyW6sm8+Si/UR1KbTaaNWyVqjgoCX9UlrqXZ3fX3dNmC7J9m7V55RmM0JVdWK6YKDTlG1FzLjPPw7M1J65RnoZLVPQWMIAlLGWRPJJo+ktox3SA9nUMRu0F+Bsqq/vSaze+uzt7fX6jccoLNDIfhsWPBZ+mkM1lWARx0fHh62tUz6D53PYbMJzTK5/uTJroEOLfTDOSXLkBl7HuJAjkOactiqL6iQJX0hTz4FoEwq2br4OZC1Xnfn+LoE2ZSD+9BDIEvzULI2apvGM51Oe2xSgsys3QqG/JPtF5llp0/VIT/cl5xZ532w+xyv5NLTmBiMzAJyz86pVNTsBqMMWdeBuFLxsoMpaUbjqqoWDDjQYTcmZ0WhVqtVa28XvKuq3vSmN9Vf/It/sd71rnc1Wuvs7Kze/e5313Q6rf39/drb26vnn3++XnjhhYauyceWCUHGGY25Z0g2x8CyZfn8/LxXqM9j0vL1OOacdQFBIxFyvjcrMx8oErUzzCrMZzqdtkw9a4KMl4FdX1+35iEOKsGGz2XgSQqmqjvTkxGbM3Bizr6bshw2wGjJVpvNjCwzBnrMWWRzRCL+fAVQOnPjrurvyUr6Lh0ep5sdu55tHGjmpLHVuzyXkzQn/weUEoRNJjdnQ47H3cEGadNAqwwha9DsJveVJlWXjjXBK50fjUa9Lm2AM2vO7gMccuDWlryBED5HwGFHGcyqun2GCdjYfoIjupIZsefbnM2u+AvdrWxJkHb4t1JKlnzIgl4lu+NZqZtAvGdljU5GR4esQ1V33GE2d93mdevB7oMf/GB9wzd8Q+3v79ezzz5bf+Nv/I36xCc+0fvM5eVlvfTSS/XMM8/U3t5eve9976tXX32195lPfvKT9d73vrd2dnbq2Wefre///u9vQvlcLgrOqWT7dwYeCpn1kczeBLekaqr6dF7SLkkxVnWIK2tH2fiRm9EzuCQaZkwCQSJGjkqd7x3veEc9/fTTNZvN6h3veEd91Vd9Vf25P/fn6tlnn22FeMplPAI+g8paXNavhsVt8tva2uoda+W8xKQKNR0IlP7okLRXTgaUjhl1lciTzJJyYdzpTKuqBTpZij1/DJJRZr3Rz9QWOP9E9gACJ8sxGbv9XdCx+ydKT7Ah2PqsQEOvUE7Hx8dV1bEI2TQAENH/7Gocvr5oeL5n2kV2ipq3ecgwgQjfhci9XSDlNmw6yaw+aS73yzdv5Hp6pu0wHLTxsAP2mQ63qssyzD87CAW2rO2lT7DuybJoaEvAYcyem00nGmeysQO9mswTPyMDT3ocQPOZqu6tAj6bPsr9hgCevfis7tHc00jXgPDhK7+ytGC7j3XO/Y5+z19ah8wgMzjf5nXrwe4jH/lIvfTSS/XLv/zL9Yu/+Is1n8/rW77lWxrHX1X1fd/3ffXzP//z9XM/93P1kY98pD71qU/Vt37rt7bfL5fLeu9731vX19f1S7/0S/XTP/3T9VM/9VP1gz/4g5/zeCyG1N3f9g9R1lTGrLNUddlWZnNZzE/Ou6rfnp7Um++hDbIYni29Vf3Xfij4D+sFAhCnySC+8iu/sp577rl65zvfWRsbG3VwcNBqJEPn/qSxMmpKaN4QJweQ3WfqOtCq+yYwyIzAzzj6s7Oz9tncE+Ze1iEvNZaqaie+J+r3HG+CWC6XLYu5vLxs7+ISALPukNRhOtnhupiDsSdyzwaAyeSm6Wd/f7/JkdOErsksT2HhxAWkBGQHBwfNqaHkyMX9871u2QCSGRBw4/sCT1V/U7V5o7v9TcaAjm489CBdGwIP+o6OZa/GkZQrB59lAXqc3aRsbDQatYMg0I7W19w40+HJHihu48QUJK2fGUmuU1KeQ6AAwPh+NiZhKlImCUZTB40H0KKTbCep7mSVBBT2nqDOmJIiJa9kcTKr9ZxcB7Vrv3cQgPpmlnk8I7tnh+t+m9dobTU+T9dnPvOZevbZZ+sjH/lI/aW/9Jfq6Oio3vzmN9fP/MzP1N/8m3+zqqp++7d/u77qq76qPvrRj9Y3fdM31X/8j/+x/vpf/+v1qU99qp577rmqqvrn//yf19/7e3+vPvOZz7zB6T3pOj4+rsPDw/qRH/mRnsNeLG7elMtRZkpOaXTM/cEf/EFVdXx8tu8mwkO/5ctAsxFBcKu6WeS3vOUtbxiTrJASphJXdWdiJtWWTiTrSfP5vA4ODloAcS8Oy6ZeDjGRLONjIFXVmjU4jgcPHrRxVXVdfJyEdnDzY5xZ18hARRZV1SjF0WhUBwcHdXp6WoeHh40WGQZjMq3qF/XRLbu7u605JrdumAuHtFwuey+OzazI2DmgpGOtsy5IDiG79yD9dAbz+by9G9H90pEb/9XVVXtNkpNK0HPppNBvGoP8Lps6OGP6RQeShUiaFRA0Jp8DzJbL7i0PR0dHvczySeAwa6eZEWRZwN9JO1dVT4fpFKcouFvXrHmpjdqmkxny8FDjBGAAcFW3N44OZbYlkGZphHzzd9Y4WQhrbG6encGUfNiknyfrNJl076OUWVtrNdRsdGJn/p2ZtP+biwx5mN0DimSZh2QYo+BmrdRwUyesbdq26+zsrH7oh36ojo6O2r7ZP8n1ea/ZOXPy6aefrqqqj33sYzWfz+ubv/mb22e+8iu/sl544YX66Ec/WlVVH/3oR+urv/qrW6CrqnrPe95Tx8fH9Vu/9VtPfM7V1VUdHx/3/lR1i5PZC0SVdZCqakXjqq5zaJhN5Wnq7pEoj0ElhoCAPGs0GrXUvqrawbmupBJloIJRNhYw/jRgY06kJlMyf/Ph6CmtZg1G5kQYY/em8aw9JOLkcD3T+Ic1jxx30jXZ4YXW5OQZD/mppSTty7llVsNYsx5gXuSyt7fXWuEFmFz7dJ6pKxloUMzZPJQAxT3G467RaJj9Xl9f1+npacs+oeyLi4t69OhR0zNzzBqxOitaXDZobbMuAkVn4xHHI0gmk+D7Ozs7rZEr662yutxy4FkcGMoxqb10clXdKTuJ6FPPyZfuyMbYV74FgI7Qa8FMsEm6MOniHJsgkRSfbBW9in7OszcXi24LjXWln6hd90tWRoC0loANUEUW1kmDCT9njurnxi54ybDm83nb0pDUbZZhUu7syTh8ViBOejop/+GaDoGiwGddHYHHn992HvZ5DXar1aq+93u/tzVOVFVrjX/w4EHvs88991y98sor7TMZ6Pze7550ffCDH6zDw8P250u+5EuqqprCj8c37cf7+/ttAfwu619SaftShi3sMprklP3NUDhEypYLmr9LdE6xOGCOJJF2VafU2XmWqCsdrsyQsQ1rZRyGDEYXJ8euKI3+SKNJ5Zbx5L3JJI2BESmaQ32MG4KtugkmJycnzUDILTeikvlwX19mrbIgNa48UYecNNMkjcNJoDjNRXAQ1KtuMt9E/wzbOsnUHCLMQRtXnj8poKfTTMehZpO1Wm3kZJrULh2u6vZ4cmAccDpNQVawEKyzZptUIR2XqbpH0nB+JlvKWkzWjTLTJ0sBMp8DHCRIyrUXNPzs6uqqBQCy3NvbazZAdzyLEz85OWkUMRu1tll3TKCZp37Q/cxoUer0DfVILnSWn8pAaT7k7DVjSSmyqfR5gI019tzt7e0e+2TM1jcBWuptBiE+E3CyX3IIQDIxUMPOE2xms+6diFU3QBEFf1vX5zXYvfTSS/Wbv/mb9bM/+7Ofz8dUVdUHPvCBOjo6an9efvnlquqMdzKZNOFyEhSCYSc9xtFZ6KqOZkrqwz0salIZVd1hq1VdMwdl58STxqFQHORo1J0aMqwrJGWUQTVRVypxFrTzJZ/QY3L5WSj2/XSQnlnV1eAYXTow8iE3+9ZQmyhG2VlSN1mH5GDy2KLcLzWUSdZjc1xZT6zqH8/lTzYdcIicCHRr7uTJ+eUm86z10pGk2RLcmGPqAhnI7jncpLKh7AwQnCywkqwCeQExKT/yrqo3vI9NEweaObuTySUDMFDzpHpTnmdJzmxF7RcQBWwE5VzDqj74AVaygxL9rL5IL/kFmXkCqsz+UI30ExD0dvXhu/yMw5W1Y/cQ1NicE4+MKzOczDIBW8EChWz8fk+mslprxOf4nDXz7MyS2QTZpi7l63cy0NLf3A4ybNgBaOm+5iI2BEzzUbd5fd42lX/3d393/cIv/EL91//6X+uLv/iL28+ff/75ur6+rkePHvWyu1dffbWef/759plf+ZVf6d1Pt6bPDK9E4MOfEzrnw6hy/0h2eUHJ6XgyEFLEzNYolO/5W32jqnMgWfBN2ocSGi/nzplwahB30gnZZLBerxuHn3RFnriQwQkVqBMyi9fuB1GTU76ihAPmbJL6IRMycC9Oilz39vbq7Oysl1EI9k/KNJLCc2//VqdKmjpRqXvnxmzo3dpl/Sk7DYf1kvV63bY45LrZ10i+nAV6CkPgtT90Y1irHWYbqdfGyFlUdY5cO74xVnVHnq3X68ZcuJKaFGQyI1ej4aR9Pt8yMRqN2utZshbs3/Z+CvB0PuuC6/W60Y6o68zI/D0ejxt4VascHtTMRlNXjDMZHcAk9TP11p8MAAKgOiHd8znrqN7MHjIzz1o9u3O8mDnt7Oy0bDDHkr5BE0jWo4EG8gZurGceck43MvAZ187OzhMPeHYpF9D9fIMHP5Gg09rliSkCoi5s9G0yALdx3Xpmt16v67u/+7vr3/27f1f/5b/8l/rSL/3S3u+/7uu+rqbTaX34wx9uP/vEJz5Rn/zkJ+vFF1+sqqoXX3yxPv7xj9enP/3p9plf/MVfrIODg3rnO9/5xxoTI81OKMjTxVEkGk10nQFgaCxV1VAgh+r7rswqIF9KUnWjePa1JYr1O3vCsuYmm2D8AmnuAcOHQ7ucK6cka9BhJ+gnQOC4GAV5Jdo1r+zaSzDAkWX3YtWNQ1K8zkK8saSjEpjT+TDSzCiBDPMlV8+WvaobZTfbzs5Or6aazsozGDP6JWtFnCv9MCcOaTzuNj0nfYbikc1yIqkz5JAgxPgzc7P2/j+k3HPdyJouuneucVXHbAB9gE6yE1VdbUvAoG/AVzbqqM8CfihlGTDQsDmf187RUe0eHdXmZz9bW6+/Xs8ul7V/elrjV16pBxcXdXh+Xgenp7V7fFwPzs9r+9Gj2ntsU3SaUwUQVqvuPMas45oLO16tVq3WmlmYjIg+kXGWEbK5RuDP03OsJxpvtVq181tTxu5tXRPQ0W0Mk8ac9DnWTBkB2ElGIPdOstkE40N2SXY4ZMqGNb+h70jGx2f8Lud9m9etZ3YvvfRS/czP/Ez9+3//72t/f7/V2A4PD2t7e7sODw/rO7/zO+v9739/Pf3003VwcFDf8z3fUy+++GJ90zd9U1VVfcu3fEu9853vrG//9m+vH/uxH6tXXnmlfuAHfqBeeumlz3lXPaVCY6JuBIp0SlXdWZqcR/6c40/ly/oUZci6VzrnbMkWXLwjjVHINARMz8vMgTFx6Bk4cmOrrCTpCU6uqkNzaAn356DVFRPpOWMwM2JIktEn7ZNF+qqbTGZ3d7fnaAWf7ObKAObf/p7NZq3GB8miE3VwWltz9rc/nNloNGrIWOZT1Z0m77PkTm5p5MPuOTrmnpxF1no4Fs0k5s7xZeaYjUdkKaDIHjMgWa/MGlKOHFJmTF5rQ5b0PfdqDRkBn5dtZ1NL0o8cvL8BR6iejI1NluP82ePj4/qK3/iNWmxv1/Wo2xKzsbFRn3399dp5LPe9/f2a0KfxuN40ndbh//gf9d/+0l9q1K8aFv1OVmdoc35n7TLwZAARRJLqdB/X4eFhA1ds3HOsJ93zs8zeBYW0ZftzM8jI6HScs99kqBLADsF6lnlQimy4qut4pufJ6mQpyClAedE1Pif12JyANwDxNq9bD3b/7J/9s6qq+st/+S/3fv6TP/mT9bf/9t+uqqof//Efr/F4XO973/vq6uqq3vOe99RP/MRPtM9OJpP6hV/4hfqu7/quevHFF2t3d7e+4zu+o37kR37kcx6P7ErWZAFtrE5ev6oalSRIQJvpPBgn5c+6GKXPYJq0SVVX50h6JZWdo/NdDlORmcEYRzZHyFQ4C5kCKk4GmKd05NvckxpKeilrh56bjlvAROEl5Ui2mUUOAw5nRDZJW6LLBDpG6vnmLnsiq3yHmYYAYCnrC2pSAou3Y2hyMIesKSblbb0U+9NIs63fugmcHKyMUiAR7Hw/EbGMnWyzsSZ1PutggiS5JphzCXJ0jKNMQGUN6XpmRsbOGSZ1Zr3ZhvsYJz1LcJj1pNlsVhubm/Xyl395XcQzlstlvfJYRs8880w9ePCgR22vlst619FRj8ERYDMjxchkxox6FhxyDp7t39YSWBRQARmNLhpLEnT5Y30ALPLPOvD5+Xmjn2Vu1pTfokvZGWu+eSh3NtgYP/CS96Mb1id9YJ53m8CUzT6pEz6pzPS3VdUDsep2t3nderBL2u5/dW1tbdWHPvSh+tCHPvS//Mxb3/rW+g//4T/8icfDIfk34QsWnEzug8vsyGc4XA7APVFTFjMDS1JFnEVVV9TNoux63b1miFIJDhxCOgfzSeSXLb+ek80VFDvRLT4e5WjcWSfksLOuyZCG+2bUpIyHvKqq5yjIIDOopHTT0aWRXF1dtdcjoWSSRoOWhw09eV6l4JC6kRm59ffsBDeJcgVpDkltApAAOLKxJJtcODP1l6R9/cni/mLRP8rJlYfxkkluoQHeMnPKoJTBPLNbgMPz8hgrDhTVmGAy6XsOkQ5aj6S+rQOdGmZOV1dXVaNRLReL2ni8R/T6+uZcU3sPT05OGtB57rnnbu75eM0E3Kurqzo6OqqdnZ3ee+WAl2zfz3fl5en/ZJnMhsCMis+TR+bzee8oLnTo6elpj/pjIwnEMxCORqM3nLLDzlLXM2BnfT71iMwdApB+iC0k0Ab6jJd+ZF0t1xJIzUaunDu9GoLFtEfjuM3rzr/1gHKuVqs6OTmp3d3dho6S6kt6zEJxyJliJ0U4RCwUkUJxglUdCMjCd2adWZBPo8lMAhLz/TRICmou0KHv5TmYahhJ0eYRXeopGaSTystamaOJkuLJLHFvb68hW5QL57Ncdm33Q8TpPufn5729fbPZrBXsUxYMnKOs6k7pkFFlhuQZ6TA4LvcZ7idimGTiudYY0MlmHkCgqnt7vXmksxFkBE0ZOx2TceVaZxZUVa3O475kyPkAS7IuOqI+LLPzDE071iVPGtnf36/j4+NGVcm46Yr1rarGIMjqclxJsQEhnpF0ewKG1157rf7gD/6gnn766To7O6vDw8N6+PBh0+9Hjx7Vl33Zl7WMjX0Za76BICk7IIfesbusSa3X69YtKfCTbQJoY5VpnZycNPYjg1zS5OSaZ0fm31knpMOyIrpt/EBKMiUJstJ3ZfaXgHA0GtXx8XEba/Y8sI+qesOePfNhR8ae7Bm5CXz8bILO277ufLCzwGiFFC6hUv7lctlQnw20gpPiMUXL7jkIxuG/w5pHBhZt5MM6l8XOIMqxyqSm02nt7Oy0V9tXdUdoQcxZdKa4MizPq+pohzwlIk98gOAoJuQvQAsoiu1JW0Cj3qnGIBOZJsKs6gJFOpXsZiRDTjCNLQMc+sc4s/0/5SWgZ2cZJC7AJS2X9Qh/cxSoUFluOpesY5Fv0mFecQLU5NvoZeEbGxu9hhaf5+AAnmEgTCdl35YDgq0l4OfVN/v7+7Vc3mxMts/RRvLssANyMqhaT/emA4KGzef2oiULcn193V7FJfALVM0+NjbqarWq119/vVHN2tPZnHogar0ChGY2CsDkYc9sImvUHDbdTGcOLAnY9HUIwoyNbuiQpMNkQn7ZWAS45PaeBIzWBNgcj7uOXzRqlm6yd0HQM/7M5tgoP8I/0eH0MRlozSlrbulnfMa8gPD8N9/mObd1/X8i2CWdxGjRWoSfNRwB6sGDB20hk6aBEh0PlbQMqiapmKrqod48CYTz05GX9RWZ0GjUtSRz0IwkHSrnYguBDrjsJKSUeUq8caWSyoAE70TfyaVvb2+3gC8gyBAFDnRpblwGHrJ+R74Mn4Gn80YhoVAFdPLX7XdyctJrzkn60TNkLlk3FeBQu0BCtoyTAZkN668cChTOqbus/XQ6bS89TblCuNbZmLNGTD8EFo5hsVi0eg7QlllX1syS+soAo6Xe/cyBnurmM5eq7sQhDUNsQmBVJ7a/czQatXcrXl1dtfNb6c10Om3BNFmDvd3desc73lHT6bQ+/vGP13PPPVcPHz5sa7a3t1df8zVfcxPMVqvaegxAzANgsNYCnflfXl42kCurGVKuZGC9zRnozWBH73Z3d3sgluxWq1U71o99mLd7JtWcJZesreqezL1pV1dXrU6Y1P4ww85AlfOk3+4t22Xv1pl/Ycd7e3sNqGXTmuAKBNMNzFDqEZnf5nXng52Ot6SrIDAKRfktBocGVTPQ+bw7Sd2CJKqCiLKRwVaBPIWBceV3PcepBgJyUkGCY6LBbIjhvBx9xdlkZpR7DW1OZUR+7l4CSTrXNGKBXv2BHNF2SX2YB3kYk99vbGy0elRVF5ifRNcZb2ZXVX16LClEz0p0y1G4/B4lpT5W1W/1z4CWQThpmdwGkCh8mC34t/XN5oCqG6fnXYzuJ5hndsSRkL/5ZL3QOOy/pBfmls0WuT8ugYGAIItB+5k3e1qv1+2tCzI6tmhs+R7ADNyZLeWZplWPa9TVIf7lclnPPvtsHR8f19vf/vba39+v3d3drma57jZge7FwMhVV3YuHjcvn2Aq9omuoe3qQ9K4MJmu7Sc8CqurUvp+BP2vV1oivYd+5D9HP82/6l+vK/41Go95rqABMfoWO80/JrqBkrbHs1Dom+5L6RM+xYQKbbR6pUynn7D+4jevOv8+OcnH+DI+R+SMICT6QOeTOyTA09+Fgsw4F1aAxs/7DkZ6cnPSaBJL6qaresUOcpsCcgTtRuaDn3/h5zpjjg/plmGlgnJPmiawpMvAsmptrvk4FKCAL+/9OT09b9uW76RDIOE/4T1AgSOZ4EgwYM4OxPk9CrubBSQ/3e+VJKNkQwBnYp5Vnl3LgVd3RX2oxxuT//u15eR4nPSBHZ6cKyJ4DAKA5AaXcK8mpCVzGKlvlyFwpO06QvNI+sA501/YG8s0sLQFWVZdd0oWqjtIcgglZ+mQyqdVyWQ8fPmxOf2trq2VhL7zwQr3pTW9q916v1zUad8dnZe2bjquJZ92NPmm0SHrdepOf5jQAkeyAW3pNZtkMQpZV1TrCySQ7gbMcUVWtcYVe+1zKFsUrq/NdOpXrLJvP+qm50UnfFRjRo+ZHR/ydtWvBjy4ImHxzNjDlHI3hNq87H+w4R51YHAFDpigQarZ2V/UP/HUfDlVBH43h8OLF4mYTtwDLACFbCuu+mQkxKk6Eofl97snJri2BikOkYBz97u5uozySPuUIxuNxG7PDalOZGUJ2lw47qQSCrHdx2JPJpL3NOmmSBBBJM2ZnGQeYCNJ3OLDpdNrbJ1bVHQLu93nqC6eTzjkzHd17VdWCC10CJjgC6JVjBVR2dnaavlnPqq7ZIL+TY8kgkFll1mhQn+6HYhV4jJ2Oc97mkPRTUpHkRXf29vZarTVZkWRC6NFqdbMNQSaWMgP6MiPh4HzG2ue6Y2aur6+rHtP5nvcVX/EVreEMU5GMy2q1qums/45Btk5OmnPYpiAteLMnOsSGrq+vGzVpvuYgKx2eXJLrpLObnqZfoe/ZYMYvAJlklsfMCcZsJM+szN4Ac3ZZ92SCsuPcvauqgS1AxDqRAZ9Hr4blh7QX3zW/pFQBnNu87nywy+IopUhqTm2GEmRrNLor9xH547SRxWLR9qldXFw0ioBDkiEyYIbGECgJp+FPVecsUH2QUypBonJOiOMUwJNCcQ/BPPdbQW0MLOlRzjIdCtqDgq7X63Z2omDs9+aTNKOLg5V5kmEGetmN9fQdcs5mBp/LelmCGWuY1CGa8UnNLFkjzPVPGlxw4iQ5sMwm0glD1IAA+bsnB2vcAmdmzKhLmYBAbHz0IPdDpR6Srz9eugnxL5c35zT6Y+5AjDVkV+6riSGzYnop6HkOW8mMKanZHGtu8s+1futb39ruTd7z+bw2HmeDSgQ5fqUNQCDpPlQhUAQIpu0mRUdW2VGbDTuZ3QODgsVyuWxUqmxQRmWdk4ZM+00QaG4PHz5sP08QlhmXemg2nRmjnyWdzScaNz0VoDMzzCwzgz+5JYU/Ho9bIyBwZn2UkG7zuvM1O4LW9VbV7Y+ikDKBpIcSmVV179NiCJPJpL2QllL5N6PNbk8dbSgUXW/DojajSQoBJZC0SNJi2TQxGnWbsRNNM9qq7nQQjoFz2dnZ6TnHzCQpu1qNvWRJtxhTUl+cR2Y1DCozzaxNmGMWtHOrAQewWq3aafYyVGsOicq0dPolpZaZUZ7B6DO2gCQNTY7+VrNM+owTAzBSx7LRw5w5L5kGpyCwJUVIp7KjWEabmQI9EKgTTZPvsO6T9dvM3rI2KZACCn6eeikIWT/jHIK1pK4SPFhHddX1el2jenz0XXVvJLEmOk2tS1U1gJf0KYCRDVUOdM7MOAEZPR42o2iywBCwUfZFHhx2BlDdu7kunH3S6XQqmzyM0biyI5ke0XO+LwGDwJSdmv4A+Nabz8s9f8O6uTJGgkS+c5jh+T7fa23ppHllj8BtXnc+s0tHP5vNetRDdhlmBrG7u1t7e3vNuVkMTgDfPxqN6vDwsJfOUxIBMxs9OAXGmsGVU2EAe3t7PRolqYhs401ao+oGTfmucefmzyEl5plV1UOEjCm3TiRFyCnJajJYQHsZoN1PkXo0GrUzCRkJQ4S8zYuDkr0wlqRbkp5MZ5kZS1X1XmOUJ7kn1eg5NgOjqI07m0FWq1UPsXJugkjWKjhiYAkoyrVYr7t9XOZk7W0FgPLJhRPMuhfnaEzOpSSvbODxXNlX3s/nZa7Ajcwxg541kT1Ye29ol1VrRkk5Zj05sy3zmS8WtTmb1Xg0quurq1rM53Wwv1+jqlotl3V5cVGXFxc13dioyXhc48iGOPLRqNuYnXu51ObI1JUOW/Aiuzy5JoMyWVZ1dHXqTeoBwEJe2vGTVkzQQ+70POurVV2GKUvOmn6yQX6X/sA6s196JvBnQ5S1ch0dHfWaf55++une/mLz8xwAkm+jA7LnBNG3ed35zI6As+EjKQiLy0FUVS8TqaresVNV3Uspx+NxO++yqjtbjkHIJigmJfcMxuyemepnQwWFmc/ndX5+3oIHp0nBZXrZTp70CUeW3Dx6jpOGpnJvXlW1DjeZZxakncICvQpMwyaWq6ur5iSsiYCTaFuTDXkIeHnyRCJfwMO2i0TM2u7JyThlQsOaWKJjP8+tBdY9s3BBQRY2fG+YtR2+NVpAr+rQMEfCcQkW9rVxmu4vw1Dno5OpN7IgWQkAl/XT7PZLmp2TzbdvCFTm7OdZk8lAhQLNWmMyJRytuWs+kR1Pp9NaHBzUl3/847Wsqvn1dY0e32u5WBRLXT5es9HjILRerWrxuDknWYfMhuj27u5ub9uN/bbDumz6BnoCAJKX39OLZCLMV8Bk757ru9ZHFiYQJ3jNeqkA5nNJdVrPHL81p5sJPDTMZKcmeVhv8sMeuTefA2TQCcGT7eWBBckuJMWd/vI2rjsf7Dje3OS4tbXVOgPH43E7iBkyg/STR/ZzNMR4PG7F7KzFZcFVMHGPzJZSAbPBIymHpENlKNmkwMmjtfLgWPfM+kIqkXsLJBwx2sJ4BXAolzE6xSQ7TFEzghyHmUVvwc6pKpTcuIwhC/qZiSbdJbOBgtFUGdyzUSdl6eBj8895Zv2GY/Q580nj39raaqeXCFhZ66zqzgVNliDXe0gPWZ9hnSe3lJhHVbd1gvOzji0zmncnoZAB3VMjyeBCbrJuTjMp2cxGybuqy/qzfpqZebIQ1pfzNdbsal0sFvWpL//yevWxbml6kvkPa5j0Ra199Xh/mJ+zwWzWsu45HusCmDlLF+OTLAQ50rGsb7F7c7TmDoiwl5W9AgX2YMqu6Dt/kp2ZdHLY+CJIZ70yu7DpWdafd3d3e1mW3wuM9Hc6nTZ7BmKSvjX/7FQ3LvcZjmVIid/mdedpzERdlCwd8+XlZZ2enjYkkxx/Fkg5fiiEYh4cHLSglUiJMxMYBc7ZbNZoSeMaUlocehbIk5JMBKrAnbSCYvz5+XmdnZ31KFSOMg2EcnLcnkdexp6GVFWt887nONLMqvw7FT9lRdEFMp/htM2bjHzP+iXnb+zW7/j4uGU5Akd2mVnjRKT2ZamtCqJ0RmCWYZuPYJHGzphns1nb05TAg6zMn17QA44EoCG3qo4eIgu6JNAaRwaOrHmSadaUzMdnAKVh0E3UzRHSRfpI3zxfcE4dIxsyAKDcdzwet71zi8XNvkcB3WeHjR+yRrLe39/vndxjvO6DsdDBCmQOyxfGKWD6XYITwTppajpFHknTYivcTxNSVbUXuvqe37GLBBlpaxp5hk1q2SVs7svlsk5PTxtgSh8C1GTTSVKyVd1Zw8kW8HUaVvJ+2SA3n897m+OtW9LAt01j3vlgR7EYXFVHXQ4N3zFMDFbdJl8KmdmIgIRXt5BqdhkQM0Op6hD26elpbzM1GgGq9Bz0RDqvbB+GKKuqDg4OWi0MGhWwEnUtFovWLcrZ+5NBFopkqIvFojXYeLY65Hq97nUmkg9EbO7WxP3X63XvAFlNByiV9Xpde3t7zbgEoqTiZF8COgNS7LbW0+m0l5kBIBzi1tZW2yaBjoZiU96el7WIrOmkU01HztFAy9YpHbJuQW9yzrUnM0HCfVGZ9GAymbQMWkClZ+nIqroMz/jMQfAEDJ2ikffI5pvM6JL6HWZ5apOpZ2TqYGksivoafedwjZ89WrOktwVI4+CUs7ZZVS04ZgZvu875+XmvmYKdekbeH61PlhmU6FPadI4/2SK6nb4oDz33efKgG7LAqm4bRTIMAjE79Gx/AxXq1AIn1oiccouD4JWHTqQ9JgNQVc3vAoiAI+CZLMxtXneexkxFo0DZiZkGlBRU1Y1CHx8fN+WA7CkcJ8Kwq6plKJAyBdA1J8htbW31sgNKKjhlxpXUytBBDlP+5MlHo1GvWzLn7d6cCsVNrj0zrFQ8Tiidb46FkTFmc1K7SZklVZF70Ww/SLSISspuyVxD9Ba5ZlNJBmrZmvFmjSqpZ2uSczF/Y+M8BdTMxJK2ynouXUvaTHcmR5C0TlX19npZ2wxUGeTpqpooXcz6Ltp1vV73OvMEf1ScgAtkWLek0oEXPwMM2NpQ3tZA3Yb8BPgcv+wnM//8jvsvFotmq5yl7Jys6Fa+uJidCwgal5LR4PDT9qx31srJzmkoMvSkHv2drFEeApG1u1zPpPjYuTqvi7yBKwBDAMpyQ+qxNQW4dIuSGx8iiJFFljmAA3qb9WbNeCnr3M5FN5WWMvjf5nXnMzsGWNWdAuCEgWFW4TBfP4dgMkhlfYfBcCAoiEQ1FHx/f7+Nx+90MXLCWX+CyDh9yCzrEgLpsMajBpOZVNKAVV0NCS2alOFQYT2f4UH0Ouw4bgf9ur/5oAHJFW2BMhoGP8aUiHN7e7tRzrl3kbyTcnJ/NU2/ywaKBC3GZX3TSaaTSVlnvSGzFLphLhlojTf3ZdGxqmr3E3itcyJjziCDPqfCmdu3lPXUjY2b1+C4H3lnU0SyD2kzMnRUWuqTNbbvFH0mG0hqLLPSBF5pp2qGsu7JZNIyq8xMzUPgzvFnQNjc7N5VyL7TydMHARj1myyFoJVZmozT99iazEdJJGWSZRQydY9kitzHnFCh/MvOzk47f3IYRMhM920eGZf+zjj4rsx6jReIltUlDasm7OcCvb+xYMYF0NMHTIv9swIfkA8U3+Z154NdHhYMSaZiU7bJZFJHR0e9bqtE8BxkOgepPMPJOoXCdr7Gxb+rqnUmcuxVbzzhPOt9qRCUNGmxRK8MNjMuY3DfrLFUdcaimzB/V1U9p76/v98+X9Udtp2dkxyGgIEy8axcg+Tok0ZigL7PKNOpQfsMREZlzKNRt+9QwwZDzwBsPOSdP08ZVHVIn/w5E8ed5XfIOjtgOV9BJSlr+gbwoFyrqtfBCb1zisY7m928Tgblqg7FCQo0md2jsAGppKzoclU1/SN7+mWe7pHrQpeSoRBkrBfAZc9kNmdxvqmjPm+t7E0DAsiCzARnIE3Act9sk/ccunp5edmyNPqirptlBmt4fHzcAuFsNmsgSE3OHladsXnAQpYnEmgJnnTDlqXhXk32QW5ZuqEb9CCzR0En6+jkYUyYnbTVBN7JfBiLeSRtSi8SNACv9DXLQbd53flgh/qCcAUuQUYNDqqu6jZxy2g4DpRPUhkMdj6f1/HxcbsXh87JaotPYzcmToACDtF20nuMfDzuDvrlfJMC4jDm8267Qh6VxjhTWVE4nLesNnl3tIRmijQazxzWVhIhjkajOjk56TW2yP44U0aCuoL4yW3YYZpZgrVNg+cMHKrMCVtDDj4DUd7HlZQq6ikpKmuS2bNA5fvklc0cZ2dnbUN5fka2mQdBexVTol5yT8o+s3Vy4GzUYgTObLjILQ8AYjIEGaTV9ch3Op3WwcFBrdfr1vQlmGWgqupO7sm5eqZ9huzLH1lMgtRkXLIRy/2q+i+wpRdqcdYgAWOCILVYWVe+YJdtW7PMXkajUQOzwIZ5CbzWgq2w3fH4Zs8Zv8NuZZt5iLZ7+T/dzAYVZRWNbGkD2AE6kFuGBDBjTeCfGbbPPnz4sKq6F/ZiwVz8a+od/ZUc6NgGym/zuvM1O0onu8nurQwi2Y1JqbzGJx1etndrz00Eiudfr9et8SGpgURVuHHo8OzsrAWts7OzXltu0meymyxAU9SkiZKGFJigTGOs6h86DZmTnWCU4zRnztNz1bgEj8wAkiKEajPoJ30JkaoRGCvHRgbG49UxnFPWsnLdrJNAJljmv6HV2WxWx8fHvQ3DqFuyZKzun2ssUEHTWVv0PEDHZ6Fwn7cGVV02nPWSXBPBV7Ylu0/qjdOVnaVeq6tmA1bWKGXIxs8pCYToMK9WYktOoUkqNg8C8Ddg4/m5X1GwSqq/qtoB49aQU/csTjgz9NxPRv/JV8DiZLMOWdVRi5l5CejYBRlcjiHZE/XQZEb4gXx1kLUyxmFGmtlUZkF011yycYUObmxs1NHRUbtfNnPlZQx0O8sCAvrm5mZv+4vvsZEc6xDsCXJZXpjP5y0Y3nZmd+eDHSeRaXa+pwxVxmjQIn6eFJAFzABa1dFlFIHDs+gcFkOE3iD03DM05LhzT1RVdxwZ9JPOmNEIipzr6elpM0xOhaEtl8vG/6uVUPwM3ChgzyCvfHbOn1F7J5ggnll10q5Qo++hRKuqOVbBN+lRjkaN4rOf/Wyv48xanJ2dtWOYOO90jhAw3Rg2WLgPEIFSTJooa7XmkY58CEKg+mymyD2Y6Vg4/3RynjeZTFqtiE6RC93JDt90MPQymQr/T2CYQOX8/LzV1jJ4YkoAS9lI1iurqtWagajcQmDM7icT4aRT3klZJ2ihs+w/70uG6N2swSflat3t0SUPwFGDhbFYB1knu5Ypc/qZBWsScpRgNnCpydIJgT7XKwNpBiMHXaRNkbd78Q07Ozv18OHDXg3XlWMh3wSGCdhkvdkxmpQ5vynopQ+Q7fud79/2deeDHVpRh9hkMmlIJIvVySNXVXPgjI7Tubi4aEeEZZaha8lioRosdvL4Q8oqGzMgm3T0DCxrYtnEIqiiWBPJKWxnQ022p0PRFNezOS6G6Vl5An4iz0TLwwAIETOMo6OjVm+o6hxXAgCZgcyFM7duHD3nYW2qqjc3655NSToMFfyzJoWKJtekxTzPiSZOFVFvEkDpjOckXWcdq/rt/jKabJvnpNDExpjsQGaH5MQpW5sET9Y1QUfWCgW8dNLma14p8wzI6EAHVnPeCSCTvhJ0PDfrdFlbPTg4qNPT06Yrw8BpzLKQqq5Wl2WCrEVhHzj36XTaQCEQlU1EbDfpZYGEs+e4/d/BBdZdkDLOXLMh9Xh+ft7oZp8T8K1d0rNqp2lnnusZyQBVde8VBNrJLdeWDiaLwdZHo+70JHsV+THPsqZ8QcqSHzEP86Ij+czbuO58sKP0Ah2D5TATyVR1hVyBq6rbAE1JKTxjgro5Lk6yqn/ArBpQdrIJmrIGSsvQKQzElqd+JIWhtoM+rKpehxSnq5bj5AaXcXD2DDtfOMlxZVAW1MiZ8Qg43rAuWGi4yDUwn5Rr1gk5a0abLcrZSJMZEblzMObDmSWN5DnZMJMZgwwwAz/5plPIBogh5UxGwIRAnpRcUk45d2OR4aQDyo5W+l3VBcuse8kOyNp3BFLZ6Hp909ZuXyN5J10ogD1JF9P2kj6UKcmgBEZvDcmmIcBOYHZvQVFGZntJNpYJKAk6EoTu7+/3KHOyzTepk79sB6CUjboEbLLgQ+hmAo+kPwGklFfSfX5uPXw3tyfldgV1ec/jJ+iXZwks7CaflbQqn2Jcxs0XATkox+F2BYGWPKwfmpfuJ2DI48g2NjbaId23dd35YJdFTgifklhcqMRiJCWS9TGOQ13C76o62oQzFFyrqlfkFYB8Hk3CoIwp0VY6WPdNBM55QW8cHfQ6n3cbPtElHEui+tFo1DaLJ82TXZlovqRZHJrtO0Pqh9PObJZzM1+yz2cxhnw9EYoGSvfMlBWnkAg1KSiGZy3oSQYYdUXPhfY5tGxEyBqjzybSB2aMhx5UdXujNDu4vB5Kw5DPVFULRGTn3sbGmQoqib5zrXMjPV1cLpfNyQzrTuaVbfGyRUdVCVYpS1sh/Cy7+NDFCZr8X1ADloaNKFXVo2bn83l7AwRgJUO1zmSc9zBOYIIsk7qzvvbIWvukc8mSbWXtOOekti340PNhjTL1j/2i5Mk1A082VFmDzFwTRKo907+rq6sGQoFw9kW2GcSGJR76s7e31/oNzNfn6X/WI/3e1i1rQl9u87rzwS4pIEGFcUCHEPIQTVNaTixragIdhXIPBVvFZmgnXwrrFAoByRhloJmdpFFmxmB8VV1nm0wj0eLx8XEzhsxaErVmE0XKLAOzoFlV7d8oR1ljdqplXSPpw6wZmRsDHBomNEoWGTCquhefukeiTuMnK5lkZkMQ8f7+fo/CTRCDioJ0yc3+IHIXrMmvqmtmEmjSUVV19GLWOp4EuAQoQINDzbpY1kU1OWQDk6AkE8lGB/Pa29ur09PTHjDJJifjIePUQeCCrmXjRjbqJLXqs0DVsJyQwSobKOiqYJEyZ3vYGfo0mUwa1ZxMi8CWtdmcF/ZhNOq6iXOdE8DJ8N0ToJGBAlSAFz2kr/xC2h77SV9BHi66omHIZ/xJGwd0UMz0SXknSzICV/oq62zj/dAvCtR0JoFiMi+ph+ya32BPeWLRbVx3PtjlMUBDaiuL6+fn5z2Hnp9hVJo/1uub1mroP+k9ASMdgswxaQW0SGaOVdWOKEJbJt9d1WWJSZNmrSydOSVKGmW9Xre3PV9fXzcq0jxlThxxBt2k5LJOdnh4WKPRqHfqiXtyAgLY6elpU/ikmMk8gwoK1H3S2LO9PykraNtaVfUdq3lbH/NJmirrqLaMVFU7iFv26tmJ9MkKDZeOLwN9On01U9/3nQykdCz1LdmFBB6ADaeTn0M/qyEnxZT6JrChZ62PWnTSsKmTqWcZyLIBhePNl8rm3KuqZfPJeHDySgbmnGuprJAyYntoUVSqeWS2CzgkQKzqOlLz98PMpqprvkm9wHJkFp/ZOH0Q3NmFYDns4FytVi1DFSTpWAYvIDUbsvK81aGukwWaEQDD5GDJstaXLIvaY9aRc92zNEH/PTcPE0g9uc3rzge7P/iDP2h7hZ600NBHVbdxOpUqeXALi7I7Ojpq3+OU0il7DgSTAdBzkg4Yj8f12muvNeNJysz9KI3ncmrZJSVLymCcQcL3svuyqnuBJgcsU03kmpkvBJ5dm+fn583IZZV+B/FzVu5lnpn9ZTsytJpZ9TATHWa72d6ea5nInDFm9prncyalWtUBC85DgASSyIex2yBb1TUC0QPjNUZzzfXOYG38T6J9s9FEwLE2mcmb15A+yppqOmGUoGAnsJ2cnPSyi5SnP8addegM4mSVlCag4/tV1QMJCXrSZo+OjnpU29DuErR6ztHRUc++jCt13bx87/j4uH0uMyBZZ4Iads5fZH+A4O05i8WiJyNrl9TfxsZGvfbaa21u/MOTZJ+gWtBIKj19Av1L//L666/39C3BYPqxnHM2wLh/Are0xUwI0q9ml3XW4m/ruvPB7vd+7/d6/HdVVw9Lh5HIkMNIdJeokwJme3xV9ZSUY3iSoxVo/I4CVXX7gNIRZC0vkRKFY6BJHWRNKLMInxkqebaRZ3HafMgtHXSiVP/3OTJPx5WcfzrfYe0p5e+e6bTy89kUM8wcUk7DNTI/xp910cwucxyCnXVLWi8dpnEmRZtUYOpJ1v5yfeiF9U7KlD5wOpmZkI1ncDYJGtLB5jpC8BnIABvyIcusD2YjSuogB+bnSR9n+/zQbqxDyofNZMAnL/M1viGgcW+ZE9kOs8Kcb17GnEGD809/YLycPyef2V+CFUHEz8gVa5E0YVL4CYaNNcF1roN/0yUsVfoPsk5ATLddCVSyjJCUrjHJIocAlF9L2Se1nXqOvr7N684HOzw2Z0TInGRV9RxU1isgjWE9BoWVSuPiqCi8y4KjpJznx4goU1X1jABdk1nF0OmnwiatkO/xG9aXzG3Y8ZnUU6K6qi6zUYNL3j0Vf7FY9Ph/zxxuXk9qzTzSoIZgYlh8t3a5hu5NHu6H0vNzTpMzRKVq/899g+kUPYt+ZGDIGtsQIBk3R58yE0yT5kwGYljzotOaRTLj8Rxr6pkJWDg/Hbz0hj4nmMAc5OklSbHl+qUO+5010MGZshhSe5mRZBZERppZsobl2dk4kk1ImUnm4RJ+R2YJvFJ2Cb7QzRqSsAAJLt3D30Pg6X7ZbJQAapgJJ9BKH5bgNIF5+onM+s1BTSwz+9Go6/JEKRpzUtIZ8PU/ZI0+M7RkD/gA8jIPOjeky4cg+LauOx/sptNp60KrquYs8N6UJANCOsGqagbgSgUfOtHM3CjMsEisGcLPhwiKw0n+fZh1eS4H43O5Z8XvBCv3TgWsqt4809ENZZTOe5hZCoqZmebPkxZhPJxg0rruQQaJ2H3OZ7MBKANGVffW5OWye4t7zmsYEBhYOtTMRIyNQ0oqzbyyM8+6J5uQGcWQeiXXdJrWLNeRg7bmibKti2fluF1+l46IzNRIkhIf2kQeWkBuqfecYIJK987T8/Pew1q3e2bNmV6h51M/qrouS+PPLCUz3GFQyKwn554ZKpknqMxgn75GwPZvJYZkl3LsScfStwww7mFtgdHMvpJeTHtJej0BO1nnOaIyUVl2Ao8ER2xI9iZ4J3jM+dN5JQK6wu7JlTwE3Ox8vq3rzgc7bfGu9bo7yTyLpbmgw99lsElqzn0Tiafjzmwpg2VVv2ki6xJDqsh9qrpglSex5zMoSd47abF0TlkDQW0Yw/D5GRQTSXIgFD2djDkOKeSkoHx+Z2en5wC3t7d7ma25p3Ma0p9Jh/h8VbV3CSp8cwAZKNKZ+7/9Q1XV9iOaCydKNkmbmfuQDUiDzk42nxu2c3N4T6Knk5pLGnxIaeW48oxO86ZH1jprfBA8ecikkk5mGwLrk7KppGQ51MyEc92T7k2QRmYZxGUFww5RmXDSxcaQwCht0vxynaq605fyHFXrnxmfK1vq3SMzJLoqSzQfOmm8GTTN15qw3WxASmCTGSnbygwzA1j6rbT51WrV9MV9BKU8os5Yk1pOue7u7vZOBcq3wiTIGLIZdCKZoNu47nywe+tb39oUzGVhc49Wtj+nwaYDgqo4GUoFbXqh6JAW9PlURgueNRjICuKClCkghfc8FAhHZZ5ptLlReBgMq7oCdlK6nKnsJgN4Uk66E1Nu5JEIkyLLHIY8fQZnxpMofBhY1ZtyjYbOC6VifBydz+pKhOiHNF9myyjNzEoSBDH8dOx5v2xAspbGn5SpMWVQq+o3KtlDOXRydCN1mjxRWtZIBuozZJZAyFpnRszBG0dmfQlw3GsIIMxbNkInsryQa+R+ySJkhva/YmMyGGVGbW7DujdbS12r6jds5Ykf6QusG5uVqbOrJ9HjwzqZ5wkoWedMWj8BTTbLPamZyv2Na5hFJp2Z2WkC32HXd2ajq9Wqx5gNS0TJDCRAtDYARlX3yrDhYfls87auOx/snn766ZZZpCIz0FRaxWsBKZ25gJLpejo/5ypyLhzLbDZrx2MlLTl0EhyajIJxen52UVKMBw8evKEGpGWbYe/s7NTR0VGb/3BfUGYbiXi3trbq7Oys0UB5mkJVh2J17OnsdC8O3B42c+cMMvMdolIO3ZzTIaczzuCRNSc08Wg0anXL3BSbiDKdambBQyptGLRns1nbRuEZ6TgThFR1eyFlCLaJeLs2qo5+ZgMEujapOk4EE0G+h4eHvZpNzo0M2cRkMmkbgKu6PWWZ+QtumVGMRqM6ODjoOcM80aOq2xSfzRaXl5f14MGDtn2BbACRrA3P593xWtlYo6t1Y6M7Fclb52UXuWmfPidoOTg46AHLdNRqV7PZrMkms8iq6jnvDJDDw8Ynk0k9/fTTbR7a932H7bIXvmV3d7etYVUXgKy5YJ2HBCSLkyyUrVeZiSc4ZDtk5R5DNksgynIMf0IPEyhvbGz0anTZd5BB0TqOxzfds/n+v/vM7nO8ZCAcHsFyypReJxrj8qoMjtXLUDlujng2u3l/WHY6JdWDBlmv121z8mq1auk9A0rUKnBocIEu80WHUKNzCEejUduEnVnPxsZGPXjwoClqHmCdlBpFVesyDocnm19mYPk9e/Y+9alP1f7+fg89np+ft+9krZGcGX/SlIlms943RPDDemRmUBsb/XfoCeLGau3JdkhRpuPyt6yKDpBhvrXa+nOiGxvdIcOZNUyn05Z5+z45CJiO6sqOvARtZEY3ZFzZACNA22Zhnv6f9By55TsUM7Ony0NKzL/NMxstVqtVsydABFiiV+xiuVy2TfYaTtxbRkgP6KOGCd8hJ+efCkLeRmBd0JM+b1sMXQIwsiGL/R8eHjZbB4ZlrsaTWbGsOxsyrCEgmeUWn8mam/VUmtna2qrz8/MejWw9fN9RhmSYTUxkLlAZU95PkEuQLUuVRKTc+Co/A9jSl7HJJwVAe4AzEbit684HO84jTyeAULOgnsjc9/LwYQaWTvv6+rqhy0TOabhVXa0Hms9F5Hin02kLXo53oly+o5st6xbQ85PQ4Hq9rpOTk97cjJ2iCfRo0KSYMqPjNMjD5nPy++xnP1ubm5v16U9/up577rkWKLy2qKprS853B2YGIYiYt7FwHNYmkSlDkQ0O6Sv34tAEfVmAMfg/p5JUnbUgq6QZjS8D0LAZgJzNg95VdW+U4EzSAW5tbdVrr71WJycntb+/X1/8xV/cgkjWU9Op5DFteb+kkQROr+SBvo0nUXcCAM7XWLPex1nRi8ySx+NxnZycNHlxqFkvzzpfgkxjpa95eLtxAmRYAQEHs2IdUmeMUQD3BgL24V6a2dKGhse9+Tw2Id8JmQ1X2KLcOJ5NN+fn5y2AAgpkWFUNaPAvDx8+bOtNJskAVXXvx8t1ZAeZxT+peUe3burbYtGdNpPlGfqeGWNSmVizZGaycYxvSd267WB351/eqkXYgiTHPplMWvfdeDxu2wG03nK2glQacFWnSLIgBlx140AhMEqdx1UNu9Qc38PosgmDwcvoqvr7h3x/uVzWwcFBLZc35yGmgx1mm6i8pGOgZWjPmFNmVdXQIhrs5OSk/uiP/qiqqr166NOf/nS99tpr9T//5/9s74XzfZkMtM/IBHPyATiGxjqkuxiowJYdcJnFQqIcM1mmo2d81sN46IgMP+sndCRro8vlsh2knD83Jrom+GbH3Wq1qrOzs/qN3/iNBqiOj4/rv//3/16f+MQn6jd/8zcbbZf0nHUVkDNj5kRkUgK3zJJzFoB2d3cbONzf328yyYx62CRDR+g7Obnn6elprzs1O3HpeWZu5MbR5mHLVd1LTb38NutB0+m0R725d2bGbOz4+LhlL55Jvp6b89RokZQhXby+vq6zs7NevZvus9VkK4Z17iFTAYReXl42+dE1F/pP5mqsWU/LU1wyK6bjZJk1M78HGvybDRpj/p31Rs8mi42NjWYT9C/LSXmCFWBwm9edz+yyNmTRk4aB8GQG+VZoxXOLooZlUTkRyDqdAIPlSBkCo8ja3nC8qBzIytiTbk3aVTej4ChQVVVzfv5vThAi2QgaggQZGdOQJq26eU/eb//2b9f29nYdHR3VH/7hH9Z8Pq/PfOYz9Xu/93stmFxcXNT19XW94x3vaACiqn/ySdKT2bl2dnbWDiaGirNbzfzzqCtUXNbwyD0z9uyII5+ke66urlrGnBlpIs7MlgXrrLcmnSgD9Rl1qXR8nM0rr7zS6F/gYDS6qYHu7Ow0nRNchjRZ/izXmLwTzQMUAnNVdzA2ffEssjKndFDuwfFV3TAAR0dH7cSS+Xze9Gh3d7e9MsrYBcCsLct8rBF5AV3JeqjpGcvx8XFtb2/3smp6QD+yJEFfq7r9Xln7VArIQCCbXSxuDljGzKT9GXOC2NS9fGPJ6elpj8JNmWdWDIwul8t2pmnWs4HY9E3ZdOTvpPTNA/XLzv3bvPPzbI2OZL0UIyTLpZvklh3lbFs2fH825ud4WQj72lCFFjTbii0eZaEYWTdgkLkhs6o7HaCqT11xUtk2zKlwnJmpZAs4hOpzSZ85bzMRWc7ZWDSNJNIyZspO4cy5qjs0m8HLACDLzc3NevToUc1mN2/0Tuf9yU9+spbLZZ2cnFRV1TPPPNOjbxn8UF6c3mh0swGYYzcOBWtyzO5Odc5s1Ml14uDyfNOskWRdksPP1zaNx+N2KLR5qncN65nZHZtZHeeUa8zB0T9yf/vb317z+bx+4zd+o5577rl69OhRvetd73pDxyRnIkgaF33IgOUi62QpMrsYgquqLoio2QAnWUsEnhJE/O7v/m7LSBaLRf36r/96TSaTeve73/2G/X/uY01zG0AeXgBMbm1tteYvz6a/5A3YnJ2d9Wp05ERGnpHBhF/Ibtg8pOH8/Lx36oygRf6ZYSc9yKY92xzzPXPAG4BHL/J9j9k0hqkCUrLBJO095+8zmUEn2B3agrNvBSeZpGA+rLFnAKNX9MQzrSVb9H8sym1edz7YWUAKQZE1rihkUwiLylirqr1KQ50vqT4BEnJJjj+DhJbwpCZ1fVXdUKIOdIVWq7o9KRwTZ5n1qYuLi95JCBxT1j28v45TkGVkMwhnJwgaJ2CQDSbn5+d1eHhY73rXu+q3fuu3Wub6lre8pZ5//vmaz+f1R3/0R3V9fV1f8RVfUXt7ey1TySCeMslM0rvFkmLNQ5mNb3Nzs3X3VXXHimXRP7tg/Vt28eDBg5Z5JthIJ4Wm9j3jHjYVWQ+UK2AlCFfdoOThXj9OOoOhN0jv7e3V2972tjo5OanDw8MeFSbozufzpsvZUi5LTv0BADjePDBbZv3o0aMeVcV552uP6NqQIrcGVTeA6c/8mT9Ty+Wyfv/3f7+eeeaZetvb3tYCxqNHj1rGlo4SuEtd8fMEoeqa7FlwMgYMCtlmbTHtAN1obnQLC+H5mJ+kAGXW9AcY2d3d7en0ELR6NoBEz/mFrN2yS3p2eHjYzifNOnuWR9gueyJLdp8lmKxXkzv5OJieLRi/Lso8T9a86FVmamjiLA14drJhQG2yUbd13flgN51Oewi9qnuxpWOXGPT19XWdnJz09qagLbLpAHLd2tpqSke5OI6q6u0TYRAK/Vk/dG/InkOHAgU3z8mgZEwcNoPy98bGRo+m4IQFVYrscxk4UGVZd/JZTmRzc7O+6Iu+qKqqfvd3f7f29/frqaeeqqpqdYTs7oSeIeKcCyOCunH2Am7SXByt93B5s3NVtVqsZ2atKqlPhsXZyMYEjMwsUk6cBOBDPpwKXcr6EcPO+XLCnAwdnU6nbVvDW9/61trc3GwZXNJTgBIZJ2gb1gD9LBs4sjMz6y2CpPlVVc9RZ6NS0sH57j2ZyIMHD+rs7Kzm83m98MILrZkENSm729zcrKOjo6YHtrR4GwnwyL4EIOvu5+rWatbmjRpMIGeNExxmXS9p/aurq5ZVJZgAesmablxeXrbOU4CU47+8vGyvIqIzbJvfyDphBvLlclnHx8cNvMk2gQ2UqnljSej3UC/cc8hUkSXK1jOS/tbJnaCEbwUK05ey5az76iYFylDAmVHf1nXnG1SyxpK8d1X1Wv9Rbr5D0bNrrqpTIJ/L0w+ywK8mAblDO77PWClQFq4tfDobb7KuqobuOaR0pFroOSwGkDW5vb29N7R1M9BsNsiawd7eXnNg5uh7BwcH9cwzz9R4PK43v/nNzQAODw/bGDhT3XxDKk3wTBrLPDUM+W7SwgINCtRc9/b2WraTQCEdXs4dHaPBJpF0rlPWOQGIzAI1N8geso7L2eW9kv6xt4vBTyaTOjw8bHKjs1Vda7pxQM7uaa05btteOF91EbrOiXOegqlgxsllYwR74rTpj67EXGdBo6qj6wS+qpv3LgI4Tz/9dANah4eHb2h2EWAEWA5Ujefhw4dNzj6bTILuZeNn19ZV8MnaZuqLAOQ7gqZsx/eykWl3d7cn86wRWjP+KJtWqm5OgaqqBpSzu9haZ21+Npv1Wv2Tbvf8bDBJ+0vgnTJI2l8D3eXlZW9PLD+EBco+AnIXxLLu7r50nLwzWbiN685ndoyJs726uqqDg4P2e86IwxtukkyKx8JnsGPMAiqF86qbLNhWVe+9d5mlVHUOrKo7TT6NJ5+TgYGich65ZSIpAlfWsShiNiug+BgSg+b0yaqqc1zr9bre/va3NwSYHbAMCO1E4e2DShpGJpkNH/5vPLKsNDJztIldoBmNRu2kmcPDwwYaPIfhZSZvzEkfqZHSC4E0gx5ZZXZjzch3f3+/1TizTiwTzfZ3QVfQ4XiHWxXIm1OyNsPjmqw3mebLdt2XPqQsfCYpQLoguGfdLhE+mXzpl35pzyGTV1LTSZFn8EjAlFkKp5olBxkrO6uqxsCMx90pPnSGzK2HpjD6lA0qOXeO2M+SmUmdT7ulL/mdDIqCD7+UvgXoyvKG7mPj2d3d7TXgyCx9J7tfdRsnJc+us3mPrN0z2a1szBuyIbLLBGDARTIcxmCO/g+g3OZ15zO7qmpGnpTScnnTGk45ICGdhhsbGw1BUUoXBajqMqKNjY3e5k0Knc/k0HMDJgWZzWa1v7/fQ5s5do6F44dCoTaGQUmhuKquWJ8IHnITjClz0luCStZJOCzZS85zb2+vdnd3Gx0iEDJGWRXHvFqt2qZpzpJz4NTMc5htCebavNEhDOj09LROTk6aQxX0tG2b45MoRM6VzAUjqDqbmsh+a2urtaTv7++3AES2AIP9YoDQYrFodV7jsYaupCI5fPIwPpRgNpCoD1Z1b00nd5+BvDk765tdnGjCg4OD5uA4MIGQPQhWCYg2Njbq6aef7gWVpPuqqoEJa5wNFEnv0TVO03Pcx/xdKFtOfz6fN2CRHdjn5+e9OhjbUtsyhqy5VXXHqFknP5NRpU2xIXPXpWjs7pXZMN00V3Qun5CBOOucWQ/0PWtBttYdsPT59CWZ5SUwktklQwPopN0mHZn1VHKUefudZ+7s7NzX7D7XK/lvC5kZSlU11DWbzVrnWNanLBwaglJS1nyDQVW3Mdfz1KdsQOdwKYIie24z2Nra6gXmqu5Mu6RHIFwOR33CCQ+op/wOA3NvQT1b5RlrOv1E+cY8LOqjlzhFxpu1n5SjMaqLygzJ3DplEEcJcwCbm5vtlJyqLhNE+wioanTWBjpXOwAcEojQF2uVmZPAY96z2aw5NfcVNDi+qhujPz4+7jnmpIZHo1E9fPiwBbCkW+lzOrWsZQFeeRjBxsZGrxkrHXTuNaQrmVFcXFzUzs5O7e3t1enpaQuuGZAmk0kvWLEn4yBf46RfAq7vadLKDDUZE2v24MGDevToUVtrMrNm6cS1zfuTlCE/QMYYgCx1AIy5jQNoFXQykAFG2QHM7un9crls9U1zkgHRUQDNWrG7pGT5t7RrDXZsi9zoKRvzWaAWUHS/YRD2t38D0HSZ3eZamJOxZjOM9af/nu/77n+b153P7FKJGD1jkj3IOJIH51hzs2mm2lmLy71JWVfS/FB1s7ieI1iooXgWBM85OsHB/Sg9ekIGBH2bb6KupF4YxObmZu3v77faCuOlrIrnFBPNxMl6liK0bEsWKpAlOiRTYyB/TnhnZ6dRWhx21hSzlqI2g1JkoOSdNTkZH0RLblm/ylrQ+fl5u09St4L0/v5+c0ict2wWmva3YJBjB5729/ffQBWm45P9y4qBtXSC6NKqG6enEQQQ4+A4vWyI8UyATYCyZrlxmp4Ze66nz5ycnDRntru72/YnnpycNLBFr+nJcJtABiQOUzBK27S5nSwAHtmyNZbRpE3ShaquQ5K+ASRkiM5Nat26+L+gTxfy2Lik59gt8CQY0FU+IpvMyCiDd+oEn5RBKjNTNi07zcYV/QPWMilfTMHW1lavRgx4+nwyMWwm+weSRQCe1HXJ2ucExKzNO3jhtq47n9nt7u72nF/yxpASh6rOkty61tuqmwXPw20ZY7aLJ7edtbYsJGe9RX2PQ7XoEG7WjhJVGb/PnJ+fV1XXcm1sl5eXtbOzU6enp73Arv5EyRN1m18aOxmixXxOnSM7+SgwA9jb26uTk5PmVBLZJzUrS05En/UTc97f32/7mTiHqu6orKxDuoAAdRUBOWsnxpL7yayTMfmM+ijk/KTgRlYcVjr6RLHGT67+n/Wzqo6COjw8bA6fzvm97+Y6Gl+eFiR7AGoEBlmWeWmyMm+BlH5MJpP2xo8ERwIBmjAbPUajrksvmztkjjIOzhWoMnZBHp1sHkdHR+33PgtI5Wb+lLd5JAWYbILgnXvJsBq289Bf3x/WILMGyuaAowwGZOOy0T4Badb3suWfvtBrIN+VVLh7C2h+nzVQek+XACOAFKg8PT2tqm5Pqu/6fQa9tFE2JVkY1gYT6N7WdeeD3fn5eXunWXL8MquqbuM1o8t0n9GpvyTKRAdk4OTgqjqKKDvYLKLP+W6ebMIJQ93upQ0bHeLiMCCrzMrsQ2MwWfgWBFF+gm7SvsaW1B9j5rgz8/QzYx+Nbjb0+regk+sxBB3ZmACtGtNoNKqTk5NGEWZQg4YFQLIgD0a6tbVV+/v7dXp62qNhq7qaQaL9rHdWVa+pxab3qpuuucyoM3vJuWEK3IMzoF+5jSEBDgDA+atVOgzcOaTQdToyNK37AIH2lNKvzKwSFMqK8xgtQZhjy2CRdUGZYgZ5cyAXYxjqnXlw7JwnXSQfx+xlYE77RlEPX2VFxtaZQ0+AaaO29cqOxsy2fDaBjp9tbHRHBgLACVSSulWqqKpeZ3buPcPKjMfjnq0kq0IOw3VKKh/1OwxOw6067mNd+MSsjwI9ADV/4375gthMFM7OzlpDn3Gx59u87nywGzYbZDdjNiW4IDhpd9a8sn5DQfL/HEnWuKqqp1Ta4jnfqs65U96ktaC07JLKQr5xKsRzsNPptBl51riSKq26aabgeDgEWUg6fHLQbcaYKDhaRCedml9mHcZvXxznlgVrBpVUlnXhyIdNPkmfJIWT1EhmE1k/zYBEnlq9s4bK6aLMOFro1HoBHUmPQrtVXebGuRsfGVV1+9xkx2pZ5mCcPpdUHD1MMEC2nDTnQ16Jxl3mZqwCVwIDmaY1TAeYNWy2gyEAQoaZADuQfR8fH7caWFWXjWTQJE+BzmXN3SvLDuxYIJpMJnV0dNQ+k4xFVfX0ajwe9xrbBGE6nH/TLfU524UwC2QhWFb1M/20Wfss2Z71JlP3ytqwn+dBDOafHcnGY+50e71eN/CVjBW7S6rZuieoJbfM4BaLRQOa1jb7Isw7ffVtXXe+ZkdBLSRnOUSAPsNh5DFbnC+jyQ2iiVKqujdw+30a2dbWVgt0AhLkRqGXy2U7/gkn797QeBaM1Skoa9IgMifKzUlyeJmtkgcnknU8JxpkbUv3oVNREsFmNqMGwHGMRjedkigaz0ywwPA5wDx8OdeAA+Fon9T2TU4y8KpqGXJV13hgvIwYcMgDodVvZTdAyDAwe54sIY15tVq1Goo1lgULXlBxNvHs7++34JhvOqcbKQPbFzL7mc1m9dRTT70haJFVduPJXgAf4Ifjsm6y4mEjDefP9jh7AAh4sl6eL+v2HPagdgRUABzkuVwuW1Y6BE6Z9aM5R6NRAw/2papRJuPgXlUdpc+J07OhrdE7a0uPU//z4IasZ6UvSgCeFH/2F9Bv9zL+fI1Z6uBsNmsZYQZEQW8ymTR6PPdwZlDzXWuc/sT65WvEyCb137aFTAJ8fnt7u40pyxC3cd35zA4SycCjNpfOIFFG1kwsqHb5rNelo7WHzPfsIYNy1SGSRrU3Jrs0ZWFZT4OWOKlUnqQcknrJsXMUXrMCtRo3Y+Oc/B6C9Lk8Gujo6KiXQXEeKdOhEqfjSVkmAEGVOS7MfVarVe+cTA4kW+6h3WFmkXUYRilwcVrmn9koSq+qO7GDc7XunGM6AgHZvWTEm5ubbTsAGpOTcV8BZDab9c4izAPKE1RkBibwQuqZFaAps/nFemUmjzL0maw3Q/pV1au1JG0tQA1rRJl1eB7b1GE6zOj9XyDOrDY7h4Gv5XLZe+EwfQMSrOl6vW570ozHwQVZk2afCQDpusw55W/s1oy9Z+3dGtGx9E+2QgBCyUbxQWqE7lVVDUAnqEiZWnNjSt+Rep6Udeq05/FjCQrVLjX+5TYaNkeeZJTsEtsTJHMd7g+C/hyvpCIFEqghM7tUjrwYGgfG0aDDkt7jCIbbHTgD3aB5Thz6UrDNpg1BeVhoPjs7q/39/eZIBWloq6pDUxxlBlrOId8EnNy6PWXDbFGWmJSgOYzH43biewZZaNi9hsE06c7VatVqiMfHx1VVLZAZezaApBM7Pj5uRpjF9ayzceSc5DCj5RjIOWWgCUdjShbRPYMDE0gEKG9tELz9AR5yXMCCtRyPx00mibCHQCI7U40la8YyMkFN4LEmwFquTzrnHB+KnANO25H1WTMAIc+bZT/WN8FJ0u+e7T4JAHPsdIx+kqF1NP+s6QIVsvpkO7JbkZ4mS4K6H1Ki2dCU8jLffIsF4Ebuw7dy0/Xd3d26urpqb03XBbxer3svHPanqqMBzd/zs95uvLnOSVvS8fF43MZNF60bf5FbUTBFGVQBYkwUdofsyMQz8rzW27zuPI0JZXDQiRYyqCQaq+pa3R2zpB1X4BPE0vFpGHBBWFXdXpXsZBIcOft0nHlvDoXBQptQYFIhnHN2aXHglJNzy9pb7m/jUCjuaDRqdGRy9safXV2CXCJ+reAymkSF/6v60Wg06qFY9C4asao7bspYzDPb87OztKpaw4r1r+r25eVYyB3KXiwWrZbne7m+jJTDR40lCk86TqZFb8hKlqumktSfAKXuw5kAO9YIfZyBhWyHTh0gm0wmzXnKDsfjcTtWKx1SOld6Z7wZHGQMm5ubdXh42GMokg40BuMD6ICG1G/ySlpTsJNdJJjJoJ4NLlVdppaMCIrYWjiCzl5NzxyCy9zGkvs9UeJ0cDqdtqPDstyRFHbu2UzdQiUfHBy0LM74+Y38jkzeuBNo+U422Qhc7EBgTeBlntlVDBhUVTsYI8/29DeZAomZWQKTAvI9jfnHvKDEpHuqugAEhWaXYXLbUBalTWoC+su/s8aRrcNZJ8kGkKruDLvMYKq6V9frJBsWjjkczkBwMu7cp8PJZjcW5+rnVdUoEU43szv1EVlJUi3DrsyqakidXJO68F0BtKoz7Gz1NkbrBgmrF6nfyUzIxdpXdccbQbiZyWZjjg65BAGZWSRAEhiy5mFue3t7TT6cvLX0zDxXFcXDSchgBYJce/VkegvUkLnnkRM5mwuHrM6VNFk2NCQFr9MzUXrWlRKVZ2ZgHrlfK2s8go8MR8YELMkGTk5O6uDgoDVCZLBOWwGSHARNHzMTzNqun7PLrBe7L4o0a11J6Qn0Aqt1J9vMwgFWAUSG5/P+znptbl2YzWatu9k9gS/1TvOnX8abNmstycl8swZd1X9dGZ+pJME+ZfY+54B5cktmwiuLjCmbBH2fnMn/tq47n9lx6Jw/hdQJWFVtA242cUizIfTsGKMI+OnFYtHa9gWvs7OzXqeetz2jZRgt5J3nWlJKjrCqq2vlySqJsNKYBTnGkAZvzt6SIGCjKlCcnDsZptFC0AISx8qhms/QKMxf4EfBZpMLeXCkw6AseDAaa5KUyJCiA14E7WwGcDG8NPoslFtzQEXGSjaClbk+fPiwF+gyoAFC2bgypJUSMAjqsmYyllHRp0TS5Jt1ucziyMb9q7q9eWSXWSBHyrlZCwBLd6VsYXNzs7XZa8IhT9mL+QB4mTFaVzUhGVKOraoLyPk9Z4+6b2YKwEKCWLops3B/wcz3MlDnWu/t7fUoXWuZwTo7H83fmuXJM8bhuYvFzZvU+QFABEPBTpK29KykeVGhWc/MPgTPJwPgJTMzfkgQSt+Ioq+qJgN65YQjzJr6rns6gIAvMzZzuq3rzmd2jE2AQDVBUJAMZy1QWCy0Z6IfDpNBy4oSFbs/43CeoXtlJ2JV1wChSSApEwoneOSeKaeZuE+i+7wodFW3sdO4ZQ1Z76OQaaDpGMxR9sAIUunRGVVdKzjnRO6yTo4tGyvIQRAwDk5FrWs2m/U2R+cGd0bLGVkTsoJIBQggguPMoEnO2QJfVc2p534udSoZHSTtoG20ZjZgmLf7ZmaVwIfM6Jpmi2F3Y1KnWS9MUEBOwFoCjaxnAijGp4EI6Mj7Z+0mm2XYDdnlWmV9MJ0cveJ41ZNTX6u6A6DpJYfPptWxEtBUda/rylofnZDNWoPcv+nFpU78ybEAjGQC5GQTlOBCx+mF482SgpbR0j96wa4E1JQhuWadVPDMU6SMmz9JGyebqi7DsyZJodN/fokuZ5DEtgBC5nZ2dtYCW9aKM8u+revOBztOPg90zSwlGxkokiCYxpJInIIwfDTd9vZ2Q5VVHQXJkaLY0D1VXes3h0hJFaYFJohra2urKWtSLsMaSjZPMNCskahhqhdmrbKqoxOHhWIGSraUOB0ChPjgwYM2Zsh9+CZnRuf3SV/IXGQAQyTK+ZBZdsPlfDnVpLDRTcYssGVGmY0ZTtEw3qRaZA25z8hnhlloOlN1iqSCBQfySLox13tYH9SRd35+3vQg3yQt6AI4e3t7LatIZ0nnMiAKnhzSen1zPNjGxkZjMDh1IElwQYkDOEmXkh96jvwEmWz4Sf3OMkBV9cZINznWLCOwQ8/1HLYL8FZ1by/JAM5fGJv1sRUl9ceeusy06IPA5z7DE1d8jv3SL2wE2dF762o87CjBdQZZ+lTVbTEir7Ozs3rw4MEb2DB/JzuQzUMZfNfrdduWlD40gW1V9TalD2lLfvg2rztPYwpqWdSuqsYfJ4WSNScFXgpU1TUy+Bzngg7Mk/qr+of7VnVbGVBFsk3KSYmch8m5JMI+PT1t+3Q4PwEYBSrjqqpeYTupharucF33yIyIgietkcEE2l6vb5p4PGM2m9Xh4WHbEJvyqqoW2K1BBmTOLIOXcRiXbMhc9/b2Wn01KeusaXC2T1qX7EKTESSdmgAnKTtGnXphjf0uwVPWZq15NhgkC2B8nG8yAUlVpr4eHx+3uXiOoMPZk1PWatRmM1ibhwCZgCbfUmH9vQjX6T6pS9ZZUM/T7JMxSH3LrCNrtn7HZmVQSTUDCsaxs7PTmszyjEjMDOd7eXnZOg3JFNUmEHP2GnHoD70C5uh1ZsIYI3qemeewUYhOJSD3DPaY1DlAhE6kXzJW40gQ7L7Z+DSUs+YZP/e8IfNBt/3fRZ9ms1lbg6pqWwxkoVmeqKoGLPMdo7dx3fnMjgIN6SE1qWzRTodVVT2EmzRA0ggZADn3NNiq7hRvNYCqLmuhlBY+C+GoFI7F0ToCN3qWkmVhvKo7JYYjTWpDMMwtAZxY7hlkJIK52uLJyUlvryLEqN4oaFd1XaCcfzbxGDOaLGsNUC35VVXvVT4Cp8OkMxPY3d1t+6GMCa2Y2by1y4wN0pZ9c/qj0aid/KA5JAM2YGTvJHpriLCz9sP5kHM6vKrqrV9mDvTJ2MwlKbU8uizrgeqXHDBng0JDXWdARzNn8BEwctuD4GG8gg8KPunWrFvnIQ6yTPfjcB3HJmAIjlm3yk7WbJIAkHSu0vtsVloul6229PDhw2ZjmdHSlwzOdCHrZsaXBy9o1jIn8tvZ2amzs7OmJ3TCs/LZgkuyFLpoXXnijs8nsM36pzm6D72lz5nt5vYIIC4z1xyDgzHSPsyHDwLs2BG56YXIBqjbuO58sINQBBmKZCEgTf8XSCgvKidRXlX/5Y8oG6jRZuB0UGmgxiAAaD4wRoqDaqQEzv+rqvZd4xA0jbmq6/BkGMmDM8SqjrLJRgXGmZmK+3B2SQm6ht2bHIKsImlV9xfI0YYyAz/LgPGkQADIMB7fZagM28Vx2GzN4K+urnrdceahLuPIJnPY2trqvUKGjmhusj50yX64bPfOOi3dSAqJs7CWWcfIeWoVtyk/aSBZGedB3sM6IDvR6Sjwph5nLSYBRm41yfVK2/EZQIluZ6ZvvTw/9UkwSrvRmZm1SIFErTLpu6TmdAkO2RY073zevRA1MymBNGledL0gzcELYv6vqcl4Uf6y8Dz9JHUqm6Gyfkyv+Y+zs7MWrMwp/UEyFeQq8Odrtswhm6Q83zoms0EfjCWp9qxFey791KRDB42HH73N684HO46kqqvLJc3EcDmRbBjg/NQ2UuEglUR/glNy0mmQFjPRFAeUdcCqrqNJC3vWgaA+2QElNf4MXg5mreq/eHKxWDRjN1ZOgpGi/xi6/6OdZI6CnmwAus6sgFwzqzs9Pe1RybmPLNeJM0jgYs2yUSPBCXllqzWgwLjy2C50FACTWT46zHequtcL5Xpo+TbfrA/LbDj/3JbAAXnDQG69SOfv4qA9w2HQSV9lkEh5+p3GIvOju2dnZ63zMo+1SqDEudNB85ZtWpM8Id/cVqub7kUZX4KQpARlQ7ZisMNhQwU5DfVOFsepJh0poGTdSS315OSkpwO5Gd66C4L+n3vlkq7PzIhjT72hn6mvgMuw7pa1XefP8hvWKu0+dTlr4dkwlPega85i5avSP5jPxsZGO40pwZdn8isAB5/j+9bFOIDbpEWHNbzbuO58sEuEnSk1xRz+X4bmu4yVcXHsucfJwuWesqRWzs/PGwUD+bk/pMmQswPv+vq6vfakqgsmSX+5l+yBcQiW2Z5MwW2zyD1TmVlkE4paj7lnx5+xcAycKMXN2ttwXxRnqYiNyjHObIxRDxxmzF7YqlZS1b3CJVvbq/rZje+Qu/VK4KOID5wIYgKRi0wzKLmPLBOa1b2noYTjy+C3u7vbGg4cLcZRqtNmo0bWF+kDh8FRJz1MTuTLsS0Wi97vcl8d3c7Mje6ORqPe2xZ0Zgo8HF466GwWyrXITCRpZXqIJh+NRi1js+bpdDNbyQya7K25QwKszfACUrA9uUUgAy97yLWh9/RcZ6Z1Mk/zt0bGgUXIWhn7ZK8AeTYP+b1glbSu8bApP5MB0/XcVpK0I19pDvSSfzCePNSBbNhMnliTTTfWSOD2mdu87nyDStIvaaCyLegxhZ50RQYjRpKNARY0i9FV/UOYIeWkDxg+Y6I4vkPRh12UFFDwdS+BMjMezkq9ijNKKsKcnPzCEckCGAtHmccVVVULcIADpFpVLbCMxzdHiVHm4eZnMpWBqBkx/syOkjJBGQqSPp/Bhbzc3++sTzYBoJTMxRgh7qzZkQ250JPMvvO5WVvSwUd/dnd3GwVGvhw7J5hoPGkssqJ3mf0mDSsb80wBOampdJiylapq1KE1QMNay9Q3mWnSflnrEnDchzM01zzMQUAAVmXVxjXM6AVWdbpsSOPEM7MSKLLZJk+hyfVKijTrj1mrSh2QaWXNz8VG+Qv24B70MzN0Op9UPX+yt7fXtry4p2ezh+whSFB3dXVVp6entVgsekcQ5jiMPzt2vfnBWgOLgr0s1t/0gqyzzpuZbNpVyuU2rjsf7LI5guOjLAIaJ5eomEGgZiiT4MJJJrqu6t7LlVlOVbVuKQrJmWR9YmdnpzlJzjCVWAaVCE3w4vgolEaWrCdRZM/OOpngt7m52evQU7+8uLhob+Sez+etFsG55/Nlo4Iex0KmnAfnuLu72xp2GA9HktmvezGMdNT5uePj494mf8ZK3ihlhpk0Ve4Rcz8/01Bgrr5nvHlIbtZ0Etyk42LoAga9BKZk0RnMObiqLptPIIdyT6o2WQm0mudlMDFe9CWgRS84umEDiPtYF04r3zSd9ZrRqGv4SdbEz5POs+ZV/VM23DODiPUTBK2ptePkfU7QZ0O53oCduhHZ+5ObnumWf/MNMjD+J5mClHtSn8mYjEajZhu5ZuzGHKx30qV8WGZhQ9klMMaSAHU5NkBJLZru8hn+n9Rj0t7mJ/DleMgtyyXmwn/c1vV5D3b/8B/+wxqNRvW93/u97WeXl5f10ksv1TPPPFN7e3v1vve9r1599dXe9z75yU/We9/73trZ2alnn322vv/7v/+JVMP/20XQsrmqrksxlYUipOOr6va+cJIUUCND0mMWWxpu0c7OzppD4wSHm8wvLy9bIwRaL/eV5dmTAmJy6pSWM3YWYQZNBgSVZm2Fsiad53OTyaT36g1GIdDJXrJZYblc1snJSTPePDWBUQIGArBA549MN+kYATy7/IAS9SPo21pnnZKxZ+3vSWND9SZQSNlXVXP2Mg4bZLPFPB2y9eCIsvWdDJJZyLouuikzx6rurFXPeeqpp9o9s1Yt0A278gTwzKa2trYaxYfe1hxFH3d3d3sgRDaI3nf/pPc4MPNM2hX1mJSdDIreZl3JHLK2mUxBrjnnSnZJNSbrkvU/zUtZzwJaZrNZHRwctDH5Pf0aj8f14MGDFkwBuWRn1DFtnRF0+Z4MuslOpK0nqBKEptNpO9GFTNiR8UkAkuFZr9et2cq6ZQD3DMAx6XE+LH0lwGI7SlK+wLDv0IXJZNKSAuO6zevzGux+9Vd/tf7Fv/gX9TVf8zW9n3/f931f/fzP/3z93M/9XH3kIx+pT33qU/Wt3/qt7ffL5bLe+9731vX1df3SL/1S/fRP/3T91E/9VP3gD/7g5zyGFKrd/otF/xSB5Ko50jw9gCOCeigi5LFer5vDdL98ZQkDSTSPulE4T4qGQnq24JxdUmiKRJ72/FV1p/xDTyjFPBGdM84GCAElaUPGxsmYv7lnA0Ai6KRwEyEmirY+QAF0DwikYfq/vz0j63LkwWkJEAzRWgkS2d6sDVy2ntlSBht/sq6YmZ+1ZsSoyaRqUcruJfu1XmTGMXP8GQDJnkPPjPv6+rrXQr6x0X9LN/CVDs22EmNOB0zue3t7rWZmzekbvWZLeeCxg4tlXVXVxme9OGcZ5Ww2q52dnaZ3AI7PZxlhOp22bQPml+WG4XxTFwFMF93MzBDQTWo85cKmzP3o6KitO31IujeDYGZlAAObzuzImMnbvBJsC/CyO3bi3jKrzOKeVHrJLldjyqwrmRE2ngyX9SUvoGB7e7sODg4ayGGTgMD29navweY2r89bsDs9Pa1v+7Zvq3/5L/9lPfXUU+3nR0dH9a/+1b+qf/SP/lH9lb/yV+rrvu7r6id/8ifrl37pl+qXf/mXq6rqP//n/1z/7b/9t/rX//pf17vf/e76a3/tr9WP/uiP1oc+9KFec8D/P1cqIyPOmhiBC0zJvzMgKJuT4nSyhjKsAVEIY8g9Q6hGqDXpojxVXIaSwSnrIoJ0otmkttxXIBZoKVI2xmRwkBHkxk/KWdXfHA+5ZRYh2MvwyM7vGG1+D5Kfz+ft5HT34wiGjRjun5SU9RDgsz7ju+hiAcNFRvkak6Sqs3ZBp+hLVbffKakiupTH1EGtGfB0e1rHdCbuZ+xZHwGaMjDTNT8jO7pDnkmNabwRJOmC76O1ZV/km9l/Vfc6odS/lJP/JwCxbsaU9csEqH4HxAlQ6mxAjGxBQ1NScuzTvIGabKowX98VJAEGMjAvn8lmG9uJfE/myo7pBdkLpGw17Tl9Q5Y86BV/w0ZlpHTVswUT98zSAdqWjQONo9GoZVvsl+zoYDZmSQwAuK2trZYlo3Q1Ky2Xy9bFCyRgPhJs3Nb1eQt2L730Ur33ve+tb/7mb+79/GMf+1jN5/Pez7/yK7+yXnjhhfroRz9aVVUf/ehH66u/+qvrueeea595z3veU8fHx/Vbv/VbT3ze1dVVHR8f9/5UdbQfpAzVpNOC7i4vL1sbN+VBm2m5FSQsVm5yhrQYAWSa+8U46nRiVV1Lu4BkXFkb4LTTUI1lSDtmMwZHZs6CCqPhDLLWAEFnbTKdcLYiyzQpuwxIvYo8Ez3LVhhN1U3wYBiZFSa1ip5MKo8DypoE5yvYCfopU//O8zuNO9dGxnZxcdEO0OZMGHzOhcw5J0CKg7TxO5F4NhJkp2x+zxqRNb1FU1XdBFybg/PMQg7aM9iAjIw+aHZIoCB7S0CTACqzkkTmAlYGocz+s8ac9TTyya0x9Ha1WrWsks6cn5/X8fFx6z7lKPNAdXX3LGuks88mLME3ganAx5ZHo1EdHh72DoOXmdMjtg4kZAY03B6TY/dnyBBUdafOoNkT2I5GN52xTh65vLysk5OTZr9JVbO5g4OD5pv4l8x40xfxcQmYMttld4CVsoR7sD+6CqSkXmUwvu2a3edl68HP/uzP1q/92q/Vr/7qr77hd6+88krNZrN68OBB7+fPPfdcvfLKK+0zGej83u+edH3wgx+sH/7hH37Dzyk6Cidpo6wBWnDOJikrjhQNxUmgDwTIqv6Wg8y0MhBkLSMRIWcN+VMagTeL/FD/xsZGry0+UVvWZ2QO2UWWhWUBSxegeyci5kAmk5sOwtPT0zaupBST/jF+Y8vamwORzVUdME+CQb1UdSeZJO3Befnu6elpG4Pnc67WJ2sCUKjxq7kyOug5T3pJh8ngMQbWM+kmewk5Gs4za4AJLownnXDu/cxuwexYy27e1PP9/f1WD0adeza0rpWf886Gg6QQEwjlHFarm72ZaM7UX/ICzJL6zrXMOo01YItD2pBNA5XAnKCQbxhH3Wv/J88hVVxVDQSoqfsuQEm21pxdZaaZ3ZvJmGRbvlKJ51pLZ+KmvRsH/cvAsl7fHBQNAI3H495bBNi4dRuWMchEj4B7jEajRjfSdX6KP6Qf5Jlr+KQMcLlctoyUrg1tQJAfj8d1dHT0RF//x71uPbN7+eWX6+/+3b9b/+bf/JsWsf93XB/4wAfq6Oio/Xn55Zerqr9hsqo7pkvxVzYBMQo2FgfyFaAYfZ7xZ54UPmswlLaqq7MwXp9NpIse5eTz+UlVJT2Das3AyiFpQpGVcOiZnVTdGOj+/n5zhtnhmKi9qnpHgkGXxp1ZEYeQNQRZFaqKA7E2foY+4SA5g6yX+hnnnOvCQQAx+/v7LVupqravyPdyvj7nb/QeWgb4SLoSHZVAJDN48k6UnvRfUuGJoDMbSx2QWQE5soUMuPQKkMAy5CtnON+9vb2mGzoyLy4u6vd///ffQI0n/Wesw60VWI/MRjO79r2sCWe2pWmDXhsX3QFe6b56NCAlKJBJ1kc5dzpKpxJwsVPrlrLOgJOdzfQxm8+e1FSU8kw61zg3NjZahk13qzraPGltJRb6wsfkuK3V1tZWOxPVWtHTs7OzpjtJcwNoqZc5Fye2kD89HQY6DA9fdnR01J5lrBnUb5vCrPo8BLuPfexj9elPf7r+/J//8w3ZfeQjH6l/8k/+SW1sbNRzzz1X19fX9ejRo973Xn311Xr++eerqur5559/Q3em//vM8Nrc3KyDg4Pen7xyL1jSadkBSNmqqpddDV84SKk576T6UlkoJqNN2io78mSBFN19KWu+AyqzLErPGTBQnXJDfp9j8bthRsFAfQbySmckQFBGxi0QGlPStv4IbLmvUTdlVfWMM7NnQX02m/XoN8E7m4uyqzQDI4Sd1FwaLV0AatCV6Sxyw22+4cFzycjYOHlz4zDQWmqhAhiZ0C+BTtMJOWWNQ2ABrATorJVlEMu6G3oRqFosFvXaa6/VK6+8Ur/zO79Tr776av3hH/5he3WL+9CjBGuZTdLv1O2sL6UesjG6Yx0BkOl0WoeHh70aTtZo2ZMALIjkfjlBIAOmjAV1Rm5Obkn9SBqc7HWpAnJVXSMbnQIc3aOqes1X6HMy8N2sfblv+oAE1+SQL3Sl64CjICWY8BnsyJjM23zIVRBL3VN3Y8/JgGWgy74IcyTzPCaRPqSfyGaX27huPdj91b/6V+vjH/94/fqv/3r78/Vf//X1bd/2be3f0+m0PvzhD7fvfOITn6hPfvKT9eKLL1ZV1Ysvvlgf//jH69Of/nT7zC/+4i/WwcFBvfOd7/ycxpPdQJlZUX7cd3LXmaVwJD6fBsQgGUZmVslnS/OzaYATwKmrf6mvJQ3lxPBsTknqABrmPFFWHFs6Fo6NQ03jNPaqegPaQkMmbSmQJA16cnLSajXDg6HRmZog3A84QNWtVqtmTBloBMPMOAUyzjjlAvVmB6C5cwg5b5trBWFr6DPk576csd9nvVQmvLu724JVHjWWdUhHT2XXHoecAd3vBNVsG+fscyynp6e92ks6NDTo3t5eu8/v/M7v1G/8xm/UK6+8UsfHx/Xo0aOaz+f1a7/2a/XZz362Ob3MNAUc9pHUN72u6k4cEvRk9gkUq7oTcJI+k3X4XVKhSfGnrAQMMswtGmREhlU3mb5uYHpCr8gxM+IEI1Ud/awG6F2LmRllXfLw8LA9y3gA4KxhWvOkWgEp9VjBH0jIvgGUpkBjrMlwkWuC09zSIGhlZrqxsdEauQBccnc/8/Ncr4PKzenGkLXTBBa3ed16zW5/f7/e9a539X62u7tbzzzzTPv5d37nd9b73//+evrpp+vg4KC+53u+p1588cX6pm/6pqqq+pZv+ZZ65zvfWd/+7d9eP/ZjP1avvPJK/cAP/EC99NJLzWF8LpcgoZuKYhE8dObnVdWCSp6vlxt+ISzIOLsKGUPSE5ynhpa9vb2WdTGqzBQ41tVq1aiadHCcaqInykGxs2VYNuMtCvh7Y07qlnEwLo7SzwRV/5c163LM4DXMrLLOY13yuVX9+oB1S+SYYOH09LQdyJzdgxyh14VksElqG22lKSJpXZSY/+eBzVXd/kgOVk023zwg0+agrBX5VVV7bQ7doldABbBhrxuwNTw0OOl2tmiu5Jv1yARmAvxb3vKW+sxnPlPL5bKOj49rc3OzvuIrvqKeffbZmk6nbS8WfVHr8WwOPpkUgWPIRAxtSXC6urrqbaYmR5kUJwp0oDHNqap6HYIpR/cW7LJ1f5j9A21JuV9cXNT+/n77vszH+tFVdk7H+RQlk8zw6DxbI7Nkhdy3qhqL4hxTgdR88/iufDdg3oPdqvPSH7LIhiyyTn9qHbE+CYQyawaI3JcdZ72Z/uk/AGhu+/pTORvzx3/8x2s8Htf73ve+urq6qve85z31Ez/xE+33k8mkfuEXfqG+67u+q1588cXa3d2t7/iO76gf+ZEf+Zyfla25FBBFxzFQXIqpjVnRPpsSqrpzBSksw2fUiszmwjnJVtbrdY+mTEPz7zQUipz0RFUXFLJ2VtUd/ZXj3dy8ee2Ho5SSEhuOw/0ZXQbYNLjr6+t2fmPu1cugxmn4vue6t39zPAKGDA7KtD6aAjzDW5yzyWE+n7eORHJMOtH9sraZdJX5Z1BLAJH0tLqgf2dwz/qGV71kMFXrSjqKY/W9rH8Ze76fDwL3TkF6jbrOuqRaFbrQXM37TW960xuyrvV6Xc8991wDasYDbKiv0X2AzOuVBIutra0WPP3Od62H9R5S9UOZ+Fk2rWRARVVmDYgOyHTV86yv4OLzMqQEqejrbPKS+fAj9D4DVOqCYJ1t/snKsHNNTb6PQarq3kNJlxzEzQ7TTrNuSf9Tv2XlWV/GHpCDo8HoV2bWu7u7bdx0mW8y1rStrEcns8IW2EV2Sd/WNVqTyh27jo+P6/DwsH70R3+0VyvIzcRJDTAgyJQhJxLK5oBM+2UxaYSMdXt7uwVNSl9VvUwiEWiiSs+mCKnwQ4cDtVJWqDsbUYzfsznN3EbAEaFdBW6OA/VLqbNekxSWcUPslFemnAAjKbHr6+veXICNYQ3AZ3PPGwMUVKxF1kmzlZrRJn2EYs3mKlm2LDBrVYCGuTHQ8/PzFljomHugjxM1ky/nnbVgupodgePxuAVzYIae5p5Ca+H+7pNbXDRDcbCeV1X18OHD2t3dbbLBRJA3VkKTAn1038zirJ9MTKaKMTG+pBjzcAfONoOrtVBqEKD8PCnVpH+tUVU1wCKzTMqVjWdDGTmj8ocUHzBp7TOjSXrXWvARnu1e5s6uspZlDYFMGTZgDeRlDVsQM376OwyA9DHrZ9YnexeSbgVI2FOyQXxLVTW91SWfNVa1cmM6Ozurf/AP/kEdHR29oQfjj3Pd+bMxLXYGFI5BVsQoBJts3Mg6RFW3URjqrqqGeiDZ7BbMV9NXVRuL/Wk5Rk41L+Pb3t7uKVQGX+idIxFkKBJltR8NLauTKrNN519Cx9AwQ/GutESzHHLVzb6dzOjUwTiOLEpnU8l8Pq+Tk5Pm0N0DTZiNCwKxcafjzawlZZpFcAEza7Cy1KQ20ZQZ0Mlze3u7OX76AdBMJpPW2SoTsvZVXY0jT1vh6FGNSfXSAc5SDSozy0TyWTPxfwAiqUGOlY5bZ3p5fX1dDx48aA46nS5njl41v2Hd14Wy15CknptNSOwKOMjaINtSF8s6X+pfbu1hi5mlGgunmjaWwc3cklFJAMzGh01pfEoGdawOfeQDsgGlqppe8jVJgWZNE/AAptmJe2GNUl4AOnsWlMxV9m+NAZbsgE25p15nLZV86Zz1ZLcoe34zO57pYdr6bV53PtgRngWl4BlsIGOKkp1blNViJ72YJ9cnHcrBZru53yXyYxDDTtGsYaVyD5EhBeYQBYbMlpI2TOdAgckg620ZbDzX/Mgjuw9X/z/2/jbU13W977qP8TjXnONprZ2SvbMh1qCVJLXSQsomNu+6sbUiKagQ2D5QSyNqLO0LSwtNUJs2tFQJyYuGKsguJIpvLEUwpaQvRI27toqoLbbSSmPbvdO415pjjDnXmnM83S8mn+v6/q+9vO2mw/suI7lgMucc4/+/rvM8zuPhd/yO4zyv+3cv5Ly9vZ2XL1/udIcKyK27dc9j9wephcysL6ftxtQiZWshuPlbU04BC7nXUVmP0jFqIQ2Mxu13pXp6zuXh4eHOUVWdW9vP6YvGgoIJGVaBlEyt2aef01Py0fQjYAEogp55XV9fLx1+nIp1AmRk4KVYjd9n9vf3F2pTExV5lupttmDeMna2d35+vkNz3d3dLWfKkikasSUD2cN2w3uzJtsC6BWgxP56KgoZ6ajtsVtblgel1+BO79gQ2wHqrF9p2ZYStoChLAt7Vf9lX/RrC1wAnb4zkq1ss17r2z2tBRbk4zPsD13e9aie+DegXjlUPnxmG974pse6nnywQzu2nVaGQsk5cQvAUXGcPsfhFAFzohSn9ZE+z/copWBV5NluKf+mBGWbGWML2bIDwZSxdGsFymhLQwnsgq5xdu8MSpQzFhwp88XFxeJ8UZ1bpEzJIWKGtZ1HT1rwM07R2rjPzPrGblmrE9ybkQveBQPW8dWrV0vjBcTbBp3tgcSctrUl39YsoGsycl9z4yQAJ8Gq+ieTKEr3Ut1ttteMtlsX0FtlFjh5NUa1SBlAs1sNDH2noUBSUCOT6nvR6BBAVAqeY+uZk2Snk9WaAzHm6d4HB++aLOyP9fmu39YBz8ySZdGXMhsNztYf3Wm+9JUe+D49cNoS3bG1oQGzQa5ZZbcczMySRVm7t2/fzsnJyUItt2zAJzXTkqEBCfwAH9CGIrZTUEF2BYKtb/o9uVjfmfXA/QLDAsQG6cq7z33M68kHO8bWOpJF5oCgp2ZmVZq2l5eG6GcgFyiXs7WRtwoNFVvs7YkJRTyU3bumZtbXcxgDxwThG6vg6+iqOuyZtWPtk08+ma9//es7e8uKUGUclJQjtVG5tGppMN+HplvTqZGYEwTJYaFKIFXBoNszGBKnDTmiA9twwqn7v78Zl+Opmh1uaULOCRAQyMlV5yx9QN8UgHCoUHg7zwTHNmiYjy459FUpX7L3s2bg22yfXVReM7NQYvTT58jbxvPW1QAz8nLcG91FA9sC0IDRGhP9FVx9DhgppWd+Mj8UZwHSzHr0nmDc47pk4QUtxi04mRvZ16nTWzYxM0tgRtfRTbXVZshAAfuiIzJT9kYu7tXOZuAEcKjuNyA1cxcgyWBmFjk2q/N9WZzN6O7VoxZLD9d/tu+AbFrfb4MQGTcx8PvHup58sOt2AQpeZaXonIjF4khddXgz68kDlIJCuz9lZXClFny+7+6iCJSgjS6yUOOlGMYo+NT59uc9Xb7G4pnHx8eLk26XVJE8yqL1nJ552CwHEuQ8oUSAgqMmc8/f29ubz3zmM8salJIrwJiZnbV0wgR5d79P713E2Oy2DQbOcqwsACTjaev+3t7e8koVjorsZ2anWYL+lPoUPLr21lgQPD093XFwHF+3MaB96CVn0Zq0udIJWSO5yXjKPPRvBwLPrHVH2QZQ0boWGwMAPb+2U3kBNuwIGyNTIgNyQPFy3hy5WqaaKCfeUoLAsQWrBVqoVuuqMevq6mrnqCu2ZT/ltqbFmRfIbEsrAkDHzW7RlwK/+WBd2Ar5tnu54BfA6Hm4grDMvmUFmbO1ts5la8i8wAvoOD4+Xs7F7ZmyBfStaxbA8nWPeT35YKeZYGZ2lBl/LeuCxKAfzlRqX9qMgZcOa23OPSHtbd2ln4d+BciZWejC09PTne4yhksp6oSb+WwzzipgjdM4OKrSTpWBv21Wn5md+W25fmOscyZ/xtHMkXMEADhc3ac9FxJtTAZqGDOzNMPc3d0tG7Uh39Is1qA6IBuro6oRctCHh4eL0yf77jMzJ7Se37l36xkFHa0TdS+dewEyzR62dZH7+/WQ5NYoW9uyduTKoaAOG5A5MDJqnbDBjm5yrpwte1MK6FFQHDObouN02vcFTGP0TMHIPcootBWf3X788cfL6ShnZ2cLLese7BSIe/369Q7b03ljSsjp2bNnc3p6utOGz4b8W+AmA/LCQHQufg7kAOw+U/vpPlo2psua/BuUS3G7H1nTUWyTjNm6aDDyHJ3W9JeOdTwt48ysDTb0ZJvx8Uvk/pjX/1/22f3/+mLwUCc6CyqmBDY02vA9M4tTmVmbPFALkI5FZ2ylTqqUpRI4TMFkZvcdbZ6HhigVByWW12aIRawz63mTM2t3FyOYmWWTOUWFtKE7421Xli0a/jBSRt3W9QYLhnl8fLzTper/gox7MBxB+uTkZPm3YG6s28I/JGkO5NlOzmYRrXvJmkrPQLjuWUdM5tbe+EqZtpmIPhbF+vkWDFXvrJfaarcY+G4DFB0UuM2RY9rWWEqP0xvjrKwE6I4d2Dk4eNdxq7O2a4i661Yg+waBqM4bcCJ76835n5+fLyf2sG06LGOqExWYZUGcvvUiJ7LBZsys9bqWMYAFAd060YHWVMmI/ZKvOdKlOnhz6yZx/sSzbJOx1uRgKwzg1O+1+9s6ACMALVamGRvZm29ZptLHAqogZy+lbLX0PV0XAPkD+viY15MPdha+FOJ2Y2x/zvEdHh4uVEWRDuTPUUGbkOnt7e189NFHO4iLU+GY23Hn/tvsqvugXIyHc2AgpT20w3/88cdL8d7nWruReW0bFSggxa2sUIQ91suYZJClZhh4a4UFHqX+ULdkwxFyrIKU/7t3aREIX7bCSXDmDR7Ww6HCnIk5t54i62wW5PnbZg2o2vq1yUXdozWldvNVppwpPSS7bY0Q/dd9VJyE59BxmQ2baB2Mfbx48WKurq6WzNj3gRTrZf6Aj6vBUAArUGkdSJOF4GIuDdxkVKrPGrQWxQ4adEr3NehqBrKHVO2OPrbO3bdZNCtqja/PbHCwhsZdQFY9PD09XbZvVHeNwzNkVoBRu1pLC7J7zwc0q6cAAWaEve3v7y8ljfoEYLRNNHybcgyd5tuAB/bMn3U/ZQNxy0Qy7Me8njyNKTMpXVUlKWJrlxyDpOgW7eHhYWmfbvBSB3h4eJiLi4udzkf3QzUWVbcWw1j67Cpcg6u/ISIBo0oL9ZV+7Xl93efje0W9HGg3q0N+7bRqRxsFhgJL56A3zRXy47jajch5GGtRtYI5SlSAIE8OlLEAKwIsZyrTY4QCeWli9yiFRp9m1sN8CxTqYARcjgjNalyeY36e0frSpwUv8i4b4F12ajxtQqhzdFA451s74MQ5Ki+LLXPQTf+tRz1//nyp71Vn6SXqEcCqXj08PCx1PfMsG9B6H+ffZheyaiOQi7MGJJ0SA8DSmTZENJMkc7YEXLF96/rJJ5/M5eXlsq1DZtbAy/+UcWkNj59SvrD+nsFezQdIbvOaTdsYl4K1+/t324SsPflZL+vY8ghmo0Dr4eFdZ3APoAZ06AO7k7EdHh7uZPE3NzfLliWy6B7odqU+xvXkM7uZ3fdozaxtv46dohAzs5yCgQJoRlGKiQO/u7tbmkkgPz9vgRyyayt4gwVlo4gQLIqiDmKLDDmBFuIFEDTI1hh0zpU2kRlouCgl2nbs0hlHR0c7r0byDqzWh8iQjJ3UYuzGyIi7bu7BIAWt1qlm1vM7W4/b1gEgYI4RpeccRuPp1bUXmItSOWp60+5HcupacCh+Z+xqK62ven6DivkBazOzk6UdHKzvGqRjZSzUr9ynQQQYa+DqnI3l1atXiyOV6dAlDprMZeu+C1i1w5ee9R12Alx1k3PnoK1xqXNOvFlMa9rVS4EFuHE6C3sDTMmuLzMFTMmGHbc5CqAA6tooR4cwNdaoNLjAiFYns+pFyyozKxjrVis2QhetmxKJ37X+3kBcm+vbPqy5ubEdoGp7EpJ12sqiTWNAQxmPx7qefLBrgIKwOBx7VhhSEReF5OB1Dl5eXu6g7rbLzqwH1lqsFt3rYN++fTtnZ2cLFfdpBwqjVurwjK/ZqJ8zfgGK8/Ud/y7F6rzMvoCWvKBk6JzSe0azzC0KLn3Icfi3M0rJqnPgrOogXe1c4zhLHZE/pyk4MuzWF4yRjBi6QGIenAFDF9gdf8aZlCrjDDiwov/Wui4uLhYnSR+haCf9+x3nhZJUu2vAML7WXznQmVlqxT11XgAm09Lxrc18+OGHC7q/uLhYPt86kf83OxckykxA9G0IKmihj+ysNTf6ieasg0WVWsfav8BmDbZO/OHhYdGFdlLSUUxAGRl6CpCyVTbc5i8MDB/gPuRjPnxB7QoY46e2/geos+6yfDqzrU/T/cq3fsvxZUBQa9L0n39gk0oCyibWg23X7zpP1xz55fqUx+7EnPllQGNSvpnZWSQoQpBry+3MWj8puuKg/VwA4UwU4esk0TruyaGraXBUdWgcIwWm/BSH0pbuo3ToGN/bZoGaG2SjHHKzUoGqzyMbv+ecoTWOQldrZYFm7bw4YV2AjLbBhWP3/3YBynQFCCjXGG9vbxeHyYA4OF2f7tta4rZLkdy05ruP+c+sNVP36948c2TcnrcFFLIQAZrjFJTUbuhx61Mzs9QK0WFdP89sl2SdM2ftngJX9bHvRLS+nKP7kxvnbcxozJlZnJo3MniewOs+9FRAs0b0AcjwncpL5uf5xilouQ+dr29oBi5LbXesbIaubksIzTALTtsZ23U8Oztb5tiaW9kHetgDBbbNNPUbZFj9at2dTvq3uXimjfKVKx21JnR5Zg1YDw8Py4EOBwcHy0ENZM5Wfb9gwGfsS5QZPub1yyKzm5mdAjgF5FgoTrcAtP5igWQF5fndG40laPUQYs6YEaldQKjdy9IUH+qE+Hy+yjWz1hdKdRorZWsjQbNXjkv2Iwi1/sf4ZmZp8zdvDR7NBDzH1gnOp91o7VAtZWedGMHWkI1/Zr7BcXC29vUx2C2CNzd0j/XcZv57e3uL4TFQ60ke/Tm5CizNbq6urnZaucm8dFEPWWbo9MrnzK91PGvYul8Dt4yG3pVaevv27dIcQeaeVwpQcEM73d/fz9nZ2Y5jbE25TENpvlLS6FRO3TyAq9K6rcuh4egKpoHeceYdk6D29u3bOT8/X+5PzrKU7WZpulEKsYeby6A/7Sxaz+jWiC2LcHl5uaw9/dtS9KXbZd8FfQVD1UG+ovOxzj2QvXZWAN8OyZYOqiulaZtE8Jfsh13J+Jog8KMYGXr82Nndk8/s2t3XTIjzLfKABotWOQWZUpV4Zq2XUAbKfXl5udMtVipTAZlR4MgboNp6b58SR+h7pZJ8TvOMeoN7t914Zs1cbYFg5IzRixlLzzYrQGmVFm39h4MteiyFyziaZUHbL168mNPT02UNPbOy92fmXaA5Pz9fnLSgVyqqzlfdokZp7t1T2FqUdel9yLXBvYG0m345XyBGFsIBAVvmJwgV5BRh93QYY4HCfbZ0N3kUmfs+FN19XNbK+qvlmoOanrG27mI8M+tWAs1J9OD169eLA2xDxMHBu0O0q+MNfsZcRqT1NWtpbWbWrK1rzbn7fTtDPaNZY6lhYMF3Sk232We7T1NgBYR0VsqE6A1g0ewV8G7JhI84Pz9fQKp30LX8wP8Z1/aNHm0msj7NEkvrb8GJ3wus/JE+COPS6ESG1T1+w6EV5PQrwe6bvKTuM+txUYr4FrgpPM4ZHdEFlGEVxXPm6g+UdOvcWguy0L5HASFUJxxQvm3heJstenabKRog2rhShwQxF9VxpuZdeoExtgBeulRwkVkJWq0JkUspr8oYraVmNbNSVlsQMrNmWj6HPquMBPNmyj15o7R0jbUouRlXm1xm3gGCy8vL+aVf+qVlHKgthkt2Am0dJCfA8M1zu0bGKaPwFmr3w1yUivYdetYO4Toy+mhuBVayOM+SRVl3DRbmUcqu+k6Xu1b00lobs1OFfJ/T5LRtszFn69UMDvNgXNaBsy0bcH19vTAA9M2audR/jUe2VOqwQBhgogPbjmBBgHyAh1LhAAt6UNBrd6yzeyvLAh9jnZmdLNOlexTYA3xk8c3u2NDz58/n4uJiyXKt5d3d3UL/ClzN6AsoyKAZ/bYc8ZjXkw92LUZXIbzhmvGXcrm9vZ2rq6slQ5MVccB1sEVw9lExhlKnso7ScnUCt7e3y6b2voF6b29vp144s9Z9OAWdd+3um3mXdXKIRbU+w6igT44BfVEU2IyMAy1N22AOSb9+/XrJ2qA/wdj32hEqiAkqnIQmIs8jz1I6MlSytBlYra0ZBznNrIGpcwJoGiwLLlqQJ9etc5qZpa3aGDmNgqg2uWxBzdHR0ZydnS2O1Ng9UxYBaRt7g/7Dw8NCPUPf3UaD0irFz2F3ne7u7ubq6mrnDQGtxdDRsiOtFVm3ZjnNFIAo/xfY6A7nPTM7jh+9iCIHxJohmad7FnCw4YKNrj8wi94VdLdgR2AofWpOPdWmzAQmxpi3QMHz2OPMu8CvJio4qHs7h5c90X/rjSatD2qtc2Z2tjSgjAvcuy5k07H4e5sl828y8L4FvbVlDVHO63zM68kHO0KEeGfWvS6Xl5dzcHCw0AkWv0JGVbTmQCEFJkbsXXAUtBx3G1OMo+iecVCeGo0DeNW72r01s7tXZmaW/V0NTM0QuvnamN577725urpaFK9O2Dg5DGPliHsKP6dL6c2FTFEtsmyZZE9uIQcBGO1DTjPvjJKTtr5qhrJDV+ungi2nyyHPzJKtzMzicFuYr3Mv+jaX1i1kTRycuqfxATTW5c2bN8uBx82AUefQbp2+ubf7k8MVGOkCnfV8tuFiB3X27VxlH9agzk2gKyNCzvSl3+fIjId+1j4g/LINdLGZTZkaspD9skNNDzO7Z3n2oABzbGbmmd2TSy7WTYBvQ4ix0zt6UbageshO+QoUvq0QZRfch+xkhP4uu2OMzaoAl5YA2uQFgKDQBeJnz57trDOdo5tk7v6yWHuPASc+x6uXBE7rtC03POb15BtU7u7ullqDxeheFg0dpQhnVvqTAl5cXCxOqntFfJ9zu7q62lEaaKqpe7MMyKv3hHwYTmlHGVdb0Y2XQlEyAR71xsEKQJT25ubdBtbT09Mdeg86nlm7IYv0Z9bWa2MxB/Tp6enpTlDntNva7J49AcYYS3G0eN0MuzI2ds6UA4K8yaZ1vxo6IGScHHuRbjsfIWFGX5TuFBto9dmzZ8thy6WFZtbTXOqott253YtXdC8ItOmlzREYCXIVJMoAcEptgBFs1HCBILpIVrIQZ4GSxcnJybInDzUuAJRNEFDcjy7R6W3p4JNPPlkCAkeP2iTP0tZ7e3vLdg46we7oE+DaYN86tex9ux+tYJYdWs+ZlaJmD3S+lL+u7LOzs6VhZWZ2WvfLhGj84DMAmHa/lqEoM1RA2uwUaGupwqk/PldmqXpb8NQ9x81461u3frE+oE1t5PBY15PP7DgAQYOCQpEcn2BVB9pM0AsvOWD1gKLxHkTMgPxsZqUfHSzdrItDnFlrA5wDhSklZnw9/HlmpQSOj48Xh9DM9vz8/BvoUXKC5lBDpbGcHsEZbOmJNpRwALJcr1HhcL113WdmZscRm0MzaOeVckjX19c7tGVlZdylVd2Hs2iXWgv6EC99qaNrZnN7ezuXl5eLfsyszQloMhm0+7aGRuc8q4cHoEfLOHD29KC0aTMp8+5ramwbaK3QFgDOvI0MnEw399JbAW1mdeTedVhnS0bdMrGtkZtn69JqiwJfG7roBP2zvs2Gy9CUpvQM+tWmMjrQ2nFr3XSzTliQp1elLoFqPxNYP63JqX4HUDZONHg7LdXIZ1agC7SXXQBKBK9S+CjbZl2tpZunPcUNgC3hNHjRVTpI7yUR9RtkCzDWHls3brnhMa4nH+woaRe1hiCQcE6KswIQiqJ8tM3XNzc3c3Z2tpxZp+Hk7OxsWehmIBwdJ1Ln2ZpJsw41p9a2KJNisHv7Lufd8zNn3jnVjz76aAnWDH5mPfWewaJuuqm2fHprhi9evFja10tfMRIbld27r0OqQfh+MzRZYiknjkYQKg1UMNBuMoFRZjUzSxCemSWzaFZObzy7mYPPqQcKcNbBcxrcyJkT57xL17S2U4f8f1eDqv7IsOg9Hf7444+Xsy7V1OiktnA0mDFrsEFxASKtP1bfzKkdg13j0lwA6Js3b+bq6mqRo4zVXkhOvEHdG+W3tlBAW+pTNgec0m3t77L4Urgy821dDgvid4IrGlVmZMyexebpTWlq46aj3QdbQO531g4jISMz57JU5kO/BRdzakmltTjywmwBOjOzyAAr1ODV/YGt57X+zsbM0f1tYSHrsh6PdT35YFeKjWEwqDo2jsG+q24SpbCQOcfSbjCO3EIWbc6s7zDrAa0MlsLi4WU2Aocmkm0LsLrathHg7OxseS7Fm1npg2Y8kDwnVboLxdIgcXx8vNBtlL6UiJ97O8Inn3yyUH3vvffeIjsOsBmBACwrYcyloH2G8cn0iqIfHtYN0aWV1BFmZumEA3AEdsG39SAyMd7r6+vld5oCygi8efNmGZ/nWksIGABQMxFQ20xjDqXYZWsCYAFas8XtpnnOjFPcjsvVLNipLGTeLkXrQk7WdkuHtYHBH2MSEIBNb6qw9vf390sQrJ6W3m8mNDPL1oBmuWyUbdFJwdx6A1z0W6DgH4xDPdnffTWV77s3HXAf2Q0dFcDotADlb+Cmr9ppxlo6GkBW1wQojLnAyZgaMG03oh/tMNdcYj5lLMi/tCR78QxAqLZKP3q4Br3kFx/zevLBjuLLsspHHx8fL/QkA6JYFLsO/PT0dKnBzMxO92QbICAxBlaD6utKtsf5QHuyxC3HX+qD04IA3QfC5kjaBVoHjsYSmGZ2N94/PLzrcPR5jpRxaFAxhjbBtJbQGg/e31zJ0bz8e2aW446MvS9IbcZmjYsCtw06M+tZiByFz5fqaUcuZ1wajj54bhFsna9Xz0C7sghOs5m8sTsbVEC1vp4Nlbdrz738noNDY9Fn60ye9ImzoescKcTfBqJ23nJuaDCZFyffmq0Aydl2T+L5+fnyM06v59XKmFpjAg6aYQF+grA6YGnlZiTqWOzEWtY+6BUnLvh0j5zPATaCK8deWQoKGqPIiA4B4G/evFkYh4IEByhfX1/vULqaynpIxcHBwSKDbt8gx1KRzdoLYvxMhl8wX58wsxvEAbAmCKWx24BUGQDp7A/785jXkw92RY+oMTUfHVqET4FczdBa/GdYFKg0VBGwRRdMtny2monPCxYtJt/f3y/1qWaknm0TeWk7451ZC+cMu1Ts/f39UguTIVRerZ08PDwsVBiKTRu6cTVD7ktjZZOl89QfyUR24DuC8czsOFB7eMgKcJEx+JngUNrE+Np1aY05R/v+jJNzI49uyEUzFiCgZJppNYNm2C9evFj+LejTI46qtRRrXgdorq7WCGUanlUHBeBw/Giw09PTZWMvecvwOUj20drYzPqGd4i+6+5n/t7b21vAQB1j50L2at4dpyDc+pN1bbYnSLDRNvhgPyrfBiHyBBi3aw6Itu4I4LZbFGPS7KsNTm00Mx7rAgRt65AFTM3cS3HzU6U0fRbwb0YPrNCpBrxu7ZB5CWxkOrO+gsxc3759u5wMQ1+NUUNTx4ERmJmdd4o+1vXLohtTIIN8qpylBLbZXw1qS+OU1rJ4KBb30H1XtDkzOwGv1Jcic09f71i2lBpnSJE5D8GudUJOjjxKMbh/kZ+MTXYmM3AfwVkA6ZFnMmGIr+Oi2KVoGhDevn27OIOZlQJqEC4lLKu2pnWY2tE5L857ZnYOvSYnDqqOXoBr8Ds8PJzT09OFDts66Rqv7Ob09HQHzAiqZOH5fi9IoXjULcmbA6/OorRQ7HQG+rfmz549Wxoy1KwPD98d0N2tM9Zdpmj9ZS0cIDqu6ydLoO91tGhC6B2YqeOTQVefyQuwAsLoMDvD1ABqlS29tMYAZ7P2rR14tnta72Ye9Lk6IPjRTYGaPWwBKN0yT/olY2RzwK1Gt8vLyyVY0Qu26BkFZtgBduX5fBK/YBzWjq3Rs9Yt6QY9arZ9e3u7NDHVzwAa9Aa4EGyB3ce6nnywY7Boh5760EIrVNW0m7K3M9ExSW3uYDye1dPni944EEGTor1582ahsYx5G0DRA4K2sVI6yllHfXZ2tlCy6LK2fnMGbUSZmaWmpNOUAWgaIBfBvs0s19fXi3GRC8qL3MnNfNV7OPgGX+vQrEx2o6mmQIbBoYSMXUBxr1IrgoiL8etqLGr2HPv80Gx9geW2noQ221JhNvzPrJvbuydQ9kkPyKo1UrKQLXFiH3/88fJmBHXn1iLp26tXr3YyAPo8Mzv7q2wj8f4yz0XNdxwyZNlg17NsSOs3pW6bWcsmt4GD3NT1GqSqd3Si7fp0j70UgAFZzZgqk3Zture5AhEoXPIURK1r6dPSjN0uUX9iLKWbBc+3b9+d9amOrNZqvX1nZu107CZzYIBcG9gKQmWSZUoKCrrVp/Va87Pm7mXOWx9R/2ctHut68jQm5Dgzy4snIWVpufpZi/c4fZTfzLsF046LmqhTZxA3N+sZi80mFaBnZgm+sjlK0xMQminKdrpxtM67lFPrA+ZIwSjlzEpFXVxczMxKF0LdzX4hWohLGzsD46xRUnXw5k+OZKsrro0ar1+/XqjZOh/ZAEctgJtPz7BkbKVzOdnWHAGGtm1r9mhdbps5uhfnBlB0HRl2x8MJCj7kTB84+dJHKPdmrRyR55AxXbAGp6enS80WrVkkzqFWz635zO4bysnjgw8+WAL82dnZApYEJPZAp9gNB0t+1lpQ4Pw5Y59hP6Uv3bO0qaBb0Foqt7XVglT23zmyD3ZjPToHOuF+DfD2lrpn/YBM0f/JoUyGGhwAKfgYD/0DdmXL5mg8ZFGwQP/LNLlnA2tLDPSuHezNiIFWwLjlgwL+BtbS/mylWT3f9pjXkw92aLiZ2TEWjvf+/t25f+ick5OTBSUKBjI7jpFzeHh42NkDVFQI5ZTT//jjj5fXxEBdVTgGUBoJGi4irBH2e57tuZxrMwxOAmL+5JNP5urqaqE3KHS7Jredaa3TMaIqre+TeQPw3d3dUoeYmSWwCQIzu0iYM+9mW1lq2+wbAG5vb5dmCUEZPcJQW+Ns40BP3jA+gduczs/PF0dS50lOQAc9IOtub+DMZAZa8NtI0mxzZu1qc2q/7kX3mllfNsvpqWlZH46aU6JTx8fHy9yPjo4WYNb53d7e7mzMZgfm7nmABvpwZu2aLTgTXDhA7fsyaY602TiQZ91qb3SWvdrPyQ7amVlQ6f4Fj/TD4d4y8taGfY78ClatLfupfygIMn5j8X36aFwOxlBb5496iHZt3xF7220IntPAaa1rl763zWyBJ4191qdZZZuRCoT5ST6pnwHKtyWWx7yePI1Zeq7Gp1nl6OhoodjQj8+ePVu2FfTzlGPL2R8fHy8bssvjf1pdqPTF/f39DrqmGDPre78oaKnBmdlpDKDkrU8wJgrPmclI/K4b1ovGtjSNFmkOUHCC6syv9NbR0dFyxmiNjLGbl+5VAYGz7Msg1S0arI2vIAMld3V1tbw1nbw4XPPmJNSZ2uVGnhxyAUwbIZr1c6Q27KrnqWm2aaFbT2TtBRZ9ZczJycky9tYujQ/DIKBzomUu6G7b4aH/ghVzlaUBHLJBeuM77egtZWysHFazA3qsvDCzviaLfsjwjFUwsU7kps7lexwtINT6FFsFjgQOpQo1UnqwbaJowMVGmAd96B5a8iBX69x6Gr2uf+GbyAxI3W636bxmVoDKj9Bhcnv58uUSBI3Ti1qVU1Cbvuu+aolKG4BNGZPWdp2cQ1daQy4d3VoivUPD/kqw+yavbYDiJCmuugaU0bS8TR0cEAXoO61ubm4WOm5bo9K9VyXimFChEJXvMtarq6sdDp0jRbtyHO0y3e4V83/GyRkKqiiyNhO0HiLolOIgG8/wGc6ZnGVYDMn9eu/SV22UYFylUpu9tZ7gs31G6TggxFpwLJ7P2Xm+MZITWb333ntLfYhDNEZjMFdr2CzccwVajqgvO51511gD2fsM2hPwaCPDzBqgZAX0xNrTk1Ksftc9jPSFLnI6zX4KSCqnBl0ASu0I8pcVFDA2I2jTTmnwOmPPJJt2vxonaq/ZH0ZCIGkTl31+1QFyqx4Bis1Sjo/fHROonr+3924TdrckzczCYGxBSzM99ftSni1T+Buow6zUJrBZ3ZLjvmVN6DXQxPatK3aitt+tU/wqEAjMuy/58Vetq8v2+BF2XXbkV2jMb/ISeNS7+u60bcbXE0Ral6tBzqzGDXWjM0t3QsytMVhMii6Y2ZgqyBofeqT74ChDUXSNxjyNXTCeWbdFqOdwODJchlI0Xzrt8HB339nBwcHS1t5aZx0GuboftM4423zD2LUzv3z5csm8ZMAzs+MIrBdnKYhpXSYHfxu737UppzUJTslrimQUre35jmyt1GhliiY1f0GLYzEX1BFk29qrucqEAJxS05xYg02BnSCnVuY7HFeznKJwtaCCFdkpOXF0snU6I5Opzrb+K7DSX3PzPcG3bzowZ4ABkLQGAAZWoLItvUr2PgP40W1zKCtCR8oYoUzbEIL+5l88u5QrOQBi+/v7Czi05gXH1eeCxgJYzxA8uw4+20aVzsk9UPQ2mXeL1MxagmHjbZaT3bWu7nvd+oEVmFmDqpKDa0vj//1eTz6z0yzCYA4ODna6txx+3FRadnd4eLi8jXlrJDOrw5xZqYq24DKAGhUnw5AbOD1DLaV1iDaDGD+ajIKryUCqRUwctXvOrLUSxqX54/b2djnppQHbJnN1As6utFTpwRbNS62SRTNnWQXDvbl5t4nfHNyTgZG7sRkDA7u6uloMGkXF6QlmkPonn3wyr1692jn1BIip/LdFdDU4/9cxKRj7/LNnz5Zal+C3rW917Usjdq7dT+iZzVwECHudHKpN/gIf2vzw8HBevXq1rOH19fVOS3xZDbIUDJudlEYH9GbmGzJAMgPIqgs+b03dszW7sgA92LkOv/og4AmOPuNPm7fUd2uLzZTpb5srtmUC69DMx3oC2tsmmplZbI2/8gwBRrmg9DB7bnZacIpit53EPD3Tc0sjA39qgnTC/tquTQ/ecLhGKeaC8tpmg657lR6vnF++fPn37Of/Xq4nH+wU26+urmZmPXRXR6BGBnWHIqYiTI68HDtHyflQbEHT/7d0nPujFRlbayEcA9SH/nE/TkC2Y29LaYBuXaDUDITS+VmLygcHB0u9i7MvyjQO4+9G7NKrDK5t+W1f5ryNmYORZXACpba0ypMBwFAHxbgFCTVVRidoC6peN1JaR2YmQLQBY+vcW9tjrOp06o4NYG2yECDdo5kQx9Q3QWAXjE324vJd45QtCBwcoiy9NSS1pplZsku6WBq+2Qaqzvyt6cy6UVrzDfDX4CNwXV1d7Wz9aQZnDK0l00dzBiAaKMmZnvY7rRm7RwMLvWMXgoHssdSzgMc/YGrMh89w3d/fL6xOM9wCA7pQexDgBM1m7Le3t8uamTMg05one/O5mVn0Sm2NLbWhqTXKsgv9Od/q/nwJcNlGPvS9ztU2ZT08PCxbKR7zevI0JgHK5NCaRWaUHjVjkSggdOc7r169WlAQB+lZEGKpCM/nOCkRJy/VF1QFDPfX8SmrmpmFEoOuoEUG4/MvXrxYjmWaWbczlKrkgH1Xg0edWLNGTqpUESVV9NegoZjO0Xg/4HvvvbfQaIKyZ3v/GAq0zs5rT2ZmKXrPrK90KRU2syJRFMrh4eHyFgDybWdp19J3Z9YXVdIPMoGYXc1urKk1a91Oxtk6rv/TyWZxNzc3O5l8KXVZl+dZm1JUnLTvc0bbTIZzd5i5QFLwRT4QOfACeDXAVBbkDPDc37974bEmptKCXQfUXVvpvXKotbV+nkxLYdJbwUhgkcm07mmtG2QxIzPrmxTIiBzV/foeRr/r2tqa0NNyWs8ScMnDPG9vb3eCTUErmVrjHqF2f3+/MFTWsYBgG/g9D6jTg9AkANBhK+jblk2AENQ4P8J+6V1r3zOzAw4e63rywU42UIfU4GPhGIjF3N/f33mf1AcffLAsKmrt+vp65xBgSsO4evyO8+56f0ZVZOQNCpSM4jWbkHmhMts1WqpCsNVtxclsDds+H7/nFAUPDrx1INlBMwjBw4kdUC+kpxvNXI251BLj4LibkauNtrbYEzMqs5m17mmNzNu9WxfruGbm/9Zxly5rUwsdkM1xIpw7inRmFscP8TN02yK6pUF2ANzYytG3UpNbDwafmR0kTX7G3Nb4NgPc3d3Nq1evdrpFj46OdjqGAcfqGsBTelAdzD3Ir+elkn9pPVn1zDun57VUzQ5kq3SoWxv8vDWw7VhL5c7MsrWl8vS3wMSm6LbPAI0CQm2JXli/1upKpQMM7Iwuy4AE2v62NQABAABJREFUo9ZozcW8BJJ2SO/v7y9Ubkse3evKt3QMMlzy8JmZtfYPiNIPvpX9WgcgTULh38oA9b/8ANt5zOvJ05iyiLZcUxjOjmOyyK1LzMzSIbitLUC/FqgnVECPbdToQnIyMiyOpVy4xpTSOxDZ5eXlTkG6FCaDKWLyvZn1hIIi+rOzs50uLlTvzCwGB4mhD+sEzENnWjedVnHrcDi80pe+AxELmMbR4Op+smOGN/PO+cngZLq+0/UxX0GkjQzdWjHzbn9dx4Sudh0dHS0UKXSP/oPUOWqAgTy3tS9r2oafOouuL13um61bSz4+Pp4PP/xwJ7BYIxleMzTOamYWar80rrUENuih4IG2LW14cHCwnOjTOpnv1Zn2/7VBQQPFZZzWndP0PGtkvQDR0qMyOGvrWa0lN0N2dqi1lpHzA9YFKLCuAG5rc4CobM5Ym/13jd23utE9xNus8+Hh3Sk9smBMSX2KbJOvoZO2ZjRo9SXAfc1ZS0Oegwl59uzZnJ+fL68A82ygDLtjHMZwfHy8AxAf43rymV1R9Mx6agInyWnbgFoUz+lUaeu020rse9BVM6QG027CRC8w2DpASsYZos9QBZouOPrSaX05ag2p94baGvxRRDj0OmlOtHUb2YL7v337dskKZtbuUc/Y0nKcxMzsBDpIsdlYa03kL1NUwzPXyq6UFxmYO4RqDWQOMq5uTpbhVp6cIjDQupvDCVBGHEgDkXUHHoyhwR91p4aJkuUwzAm1bux0sQ6ooMNWGfLf1qqfP38+JycnC/jiaEu5OXvUetA3v2dv/blneA6dmpkdgFTK3Do0kAmw6szWs1miWhdb93P1K46ab7CWHRvQoi5N7tgLQasyl7H4XRu92PHh4e4pJu6N9naWahkV+gJktI7auZQR0Itgzg62YFvG1EMT2NI2SLcz/NWrVztA9c2bNwvAnJllw/nNzc1O7ZCPYT/kbtP8zMqqYHUe63rymR1nOLN2rM2s6T+lpWQc+IsXLxalgHwg7GY3/i3rgVgYKPR1fHy8oHRBtg5bN2S7/iDL1qLaAt1GA/PjVEvhNEA0uPm3TINsBIZmbcZSzr3cvn1FxsZhdS7GKJPicMx7ZpYMuZ18gqbvc0q6wMj67du3i4NuzeTt23fnOpYuLg3nWW33vry8nJubm6UrU4bDgdXp9xxUmZ91KrPQ7L4ZKoffGiCHDjgVaVc+2yzF/RxCztljD0pFNtucWSk6IMDn/JuDgsY5YusoGNFV49PYUdRPtzy7FB65GCN9E7y7/YU8y5C03uq+njszS5PT2dnZ4swFLE5ZNsieyNH6WL/W9UuJykxbQ7y9vZ2Tk5OdUkCDef1J5U92uptL/6sT0m3BhExL3VtbVwNjT5SZmSXb0hHdmqeg1TqmZ9EBNXL2pUZff9FMmv+rHj52g8qTD3bvvffe0hHIQItmtcvWwVA+lMLl5eXSnUjRoK02Y8zMjvLP7BbboSOKITiioVpPKq1ag+33KYnOL2Oukl1fXy9dcKXgKGg7pUqruo+5ttGhAdT8SvWQQx08o9vf31+yxG0tiCG1DVwGKIgx0D63QdHJDaWO3ZcMZ2ZZY/cAity/gEQ3ofvVSbXd2zxsDQF2BHZ1uaurq50ARS4FG/4W/IEaAdfv37x5MxcXFzuZu2DSuXYfGIrO9zEQpdDRpZwsu9G96t+e5R4zaxemrSmt9dDLgjnBvLbDaftMaXrrhkb8NDn1MGRASNZn3nSfLep4bEBw79Y4zaWB19qjDvu9UvJdY59pJtVtUQ0wAJZ5CtSez35kua25NjtjFwJkAzC7ZcvWvMAIANQHofQhoNPp+hiykfmVfubH+hYQ603PH+t68jQmZ+lqXQ5S7GcpUI/2cTJCKbe+sHRmdVCl4jh0zrA1BWhJwCp6gho5fcrMwZQSbTHffThe4/Wz0j/GjB7x754yQuGK9ltf9B3jNj6BAVp0aj4n2vqGDjHGXYdtnDLUonVOuR1opYcZ3hYYkFsL88+ePfuGI9vIe2YtyMsUnZdYR2vTM+c0s741QIBk1K2VoZfohjHPrHSdeTF+2ZwATb/QlN0y4vMN9Mbkd2hYcpL5GAM7sK5twPLCzYKn+/v7BfyVRWiNV+CnQw4eIFdzaoextSxzUjqwgNU9fE9zh/sCMmyNbzC20t2lJwV/z2pm0+DMT7i3e26Dic9Zf7rrvj7fl9jOvDv5qLVfr7uiW2WJCjaNid7TP4GyAKn7Rt2nYIutGydwjc1gY6VzjZWsAAwZsPkZ12NeTz7YWWROGrItz82h1CFoSIGIj46O5vT0dCeAfPzxxwuC0kEJPXLoGh+2mR5FrzPjxGdm50WmM7M449Jgr1692mmSwMkLgqU3ZnbrAhwq49iOm+JxbFvE6xmlQCtP87C14Ojo3XFfDVxQfumW3r/bGjgX3ymVaHwFJHU6lYHsGk2mpsYAOeG+a62UmvmpWaCBm2EKRJD9tnaEUpJZNKhyMM1CSg+pSwke9JbTVmM6ODhY6iA3NzeLrt7c3Mzl5eXSjXd7e7voOsdFz1vr4RBR757bzLC1zN6PbbT21Lb1gp2CoG7xKdgx31LPgjEZ1g4ACPoJONCdUsIFK5pW6FFBELCiJioIttbI59A9wa56QefKOPk5GymtLGi3p6DH5AnQBarNpNhhQYMszRoar21C1s1nLi8vF+bDtgQ218DY0gMbMk62Qm+3Xe31IY91/bIIdpS7x2gxjqLfomoKUucPrXJEFnxmlozQZwUBTrTn1DXYcHqoTPvMUF2vX79ejETWss3CKIUAbi5FfrIXz9c27I3tDVRQmHmanzG2zV3WXIcys+7nw9MXzZYOlG21vlKjQadaL+MTqBjK4eHhctIDR8IJcIhqaq3NlY5joD2uq/sS0Xk+6x6yIrqCqvWW5pn1XYDooAZO9+wWBXrgvm1GATLIsFkFveSk0EylGGVD1ZPSaqWEBUnf4yw9s7pXus3v1eu2AZqTdu/b29tlf6VMsk0eBQ/Wnpy7fjKLUsQFuycnJ3N4uPu6rWb0d3d3S73WiTIFE3QR7dbg1czEvUpFkhPZAA3GSPfZE13YMh0zs4BEQdWcrXvLAq0nlwYme6cmtWaMhaCbHb/Do7fnePp99ZXfEYhl/+YFkHfvXxmEx7yefM2Oo1LXapckp8m4Z9YGiZnZcdItMjNGdYl2AvpO+Xj35QTQKAKDRXYSedFYswqBGeJnWBxA23i3dE4dfOkRyqeBxrwFG2iyMoLe3WdmPQqq3+92DfMxF2vBmfsZYwVQ+kqZrhunWkTKWJshky8D1qKPXvX909PTRV+sFSfme63btEbSBhDfKaBq/YbD57iqW/RqZn01VT9vzlq5AQd0aam7bcbF4Qoysmx7LHsmo8AmIHB6dJHeocIKeoy3dd6tLOgqGdLJZhJ9SW0zeH8EEjru//SylOnMLHOwN5Yz1fRB91qTbXOO9Qfg2AX9b9DxjsItgC7Y6dVsrJk6mWu4Kqsh21Nnvb9f345hbMACPSiL1YalmZWqN09+gc7Tm+oG/TXedqwKdADn2dnZoh9lXXzX83rQwVZOf7/Xk8/sLDjDhQBnZkE1CuQC2cy6P6l1n22BXIak9mdLAGSFcmAknF0L4g5k7qtEatAUkkJQAN9vvWRmPVS1NQLUTrtNjcMfmWszHPN1j5ubm/nwww+/gVIxPki6m51Lsco8IbuZ3SOHZnYzBE5pb29vqUd2PJB6G13Mq80x9uzc36+vNiGDvnpHkKrBMWZB1xjVdMmgdVf/F4DLInDi5iF7A5Q0KPhegxid8AJhgUG20tqeMVgndBPn0202XatmedvuzI8//njZJO77PYB7Zhb5uSfbqXwABGCPDkD6GlvMt80v7YZuNl29Zh9lKYy5WTTaXwZccCfrBAA++uijBRzJcN1LrasgjE6VCaCn/EbHyHZKTftdKUHBqbQ+HZeRFuDOzA6oOzg4WAJVAQng0XpjqfHK3rq8fft22VfKVuiAufGjygKlV4Fr2TO59NVoj3k9+WBH2JRG2q2g271Vsg1/uwQECyEINZPpXjzGh89Xt6PIkPfMeryUmg4FgRKNg2Nwf46IExWABCjfbY1BkC3VNbNSLhcXF8tGbA6OIcu8nj17tnPEEYqUs37z5s2y164dgYyDA2AcBRP+Lv3i5/Z0tYbCYHTFeefe3d3dzlsdZmbZhO8emlQYZrPc1h1ac2vtsMV88p2ZJVCZu/cctrZpG0SBiTMCW5OamZ33q6mhCcw++/DwsGzD4KDpRgFHnSn5odJaG9wCFbrU2pKzHQVMwZl9AZAAGtqxjEKbMNoo4p4un6m8zad6Lnj5bDNFsqq9tBYpQAgA/EEBpu/5t3Gj9WVtnoEx6fFe5lcqnm9obcz4zNU6mXf/rv8CdtqkJUtUp90eVACEtmmuVLjv+7tgme5Yg5ZAyB14ITf3J5uCm+qDMs1jXU8+2EGZx8fHy3vCtoGPYVt0i9RmDgraTjWOBVI/OzvbQSntKKozmVkDaGku4/F/45hZTz2RcckQ3AulVDTJcIwTDdH6QOmN1k+gxZm1lXxL5coEDg8Pd9783qDP0XlW39xuHyPEaC519gKJTKZnaDJIP2/NTIBod1plrrsN8EFHt62foyAHwV4d1dr6nvqfubQzlU4IgBwrqhL67skUrQWhHukyXcUq9P/+zMzi/KFv9zBOAcCaFNmXuhYEHJrdOhIQU8ZETWq7H1BA6MkszVCMXfOH9W6QIfOyMQKhmjI9RsMBFG38AQCNjU6TY8sOOrQblMmYvqgZ0znBS4bSbm9rU18gixf82vF7fX29BGT20APNS1EKbNtx0kX26V7GV/8hk6+dsA1zsZ4CpTHTXXowM3NxcbHDjPljfvW/QPNjHxf25IOdDKs1NRdH3+4xCqEbrHWFmbX5gcOEdIuKW2OxwDU8xi3DaIbDsUGOnieIUA7jZrS+5+ecRx2BjAlibNATBPf29pb6lbHJnurkObvWiGZmya6KTt2zZwCSU4/rIl8GZz7Gvq07zMxO8IMoOVgygSoBH/ewZrI2n2tTjPUlD89pIwJ5WCffs06cnIycnumOJCOou4DBviRAAW3GQXmeLMIcrHufudVz8yIb+lmK99NqVNW9bZeu57RW1QYHjr7Zvc+4V4GYMbDlgq4GtFKZZEcu1dHagDHUVjzX9/y+Os5uAA+UoTlsa58FnjK4On6yEZz6yh8yOz4+XlgCa9xauroz0ExO7ovKL0XavaxkbK3Jb+sfBMctS8QWqufKAeylzwWm+ZY+b5sRP9b1y6JBBQLs4lBCKTOFPjo6WpB3uytbROckZEKcUFvW23lYnrtZpH0xW9rOnxcvXuzsd5lZqZDWcShYM1NOhVPbUm6UmOEXWXkGR9OGBHJxQcGaDgSR1n8of53r4eHhzll7FN1z0BzmKPBzIDUi92O4M7sH0s7sbg5u8ZvTbP2n8qJDHODh4eHyrjz37NihYd2Y19fXO/PjiICUvsZIENFM1XoXNG+eXQvBjy62NiJICXDGKxsuhUkGzfwEnJlZaixoy1KSdI0M+jNzZxMCgXsUHNEhtODMmsUVQKD7C9yMDbj1eiXjKjVYEKX+SPaae0rHATnNQI1PR6h7tPOaLtJp+tftSDLjZrA6v1uXt5Zo+9bQ6gesGxB4e3u7wxiwl7If/R7/CHh1LWsX5sOv8AvkLku3lvQGtdmM9r333lsOwCjgfczryQe7vb295cBm9F5rDU3RS7ddXFws79gq30+ZZ2ZxQrIL3YeUrkhSoEQ9SPcFB/eTmXiOoMEB9BSRnpxSNMYQGFGVrMhsZn29CeVqnc/hrTU2jslYGswZNUPX8NLGABSPTjjZBtm5TzO+ZtPQY+tqZN6xFmDI1KBispdBA0DQMCfarKU1oWbvdMec22WrecZcZ9YGDjUYzlfLvGc2+KpDChgnJydzdXW11Dtm1qxHhjEz30DnldkQRK15T/c4ODhYTh3iUGUh7GdmfVv66enpgs67doIDuQMHqLr9/f25vLxc9EmW54XK1ekGnlKmrW1WL9Hkt7e3c3FxsdBirS8DY2UQTk9PF3ltM9dm4c02AQc6360AxsjmjPvt27dLpmW92Aibotd0th2q7gWMGn+BQMsxbIN/sd2IfP1hm35XRsxn37x5swP63759u5SI9vb2dkBR67vNXP2b/m0zwJOTk/noo4/+Xlz83/P15IPdzCxOEzpucbo0kDPw1DYaDF1bJbdY0CqHVMpjS502GzG2+/v7pWOwNbfWqS4uLnaoNUrtOc1MICnj8syOz9ydAzqzexhuN4H6XefLAbV9vY0adQ6yDhneycnJUpeB7Di4GrDfbzPj7d9qb/b1dB5dMxkiJ9BgwZH2uC9ZXmsNKBioeWZtwZ5ZG344+tPT0+WkGHOpLskEWlujH3WagmHfx9gMQqa8BT/Wy/ml7U70rL4Edm9vb169erUTuMmm1Dx24cMPP9yZV+t8W5ahjhgQOj4+XhyigE23OFfgUXZMR1qrozPWkv56C32DOYCxpVs9n3yBLbISLF23t++OExTgemj4ixcvFp12H7XM7jH0N3DUF6l2vPS33Y/N6uluDzmwTtgV2V67fOm/+ZSmdm++jU4CAkCWckF/T8+MuwzS/v7+slVBFmmeh4eHyxaRx7x+WdTsnOVXZDMzC5og1DoaqNefmRV5lW6gEOotpfz6QlGcPQVodtL6kYuTuLu7W+pdmkwYjWzN57YKBWkJcBRRsC/t0SxlZi1WU3IZJlmZh67L8/PzpdGCXNt8cHNzs1B6kDOHjALWCcqBWYNPPvlkrq+vl7ltN7Iz/GYoKBSOd1u/3Dpia9bN9+3UFCSBC/vcOGUZqTX2fAG7DSJtXFCbQGdvM3UUvHWkb3t7e0vmTGeL0v2f7jYLBGSatVlPzzNP96vtkDG5NNDJhNy/eoBu4xhbJtD80ueSOQfNmbqHn3tuAQe9BnJ7aDj5mAN9BmAKMAQ6QMNzrT1QBKz1nFxnd7KLmdnZz+o5to5s7UIA8H/2QI4ubIkAS3bWWrNJKWrBtnU2wd1amb91KNgFuJqly87ZFZ8n2Atw9UdlRpqxN1t+rOvJZ3a4acKr8c3MTuFXgIBMWiepo2vxv7W+0gp4agGNMltkAbOce2k69QOnFaBlBO/SeJ7TyzwY/8uXL+czn/nMTrDg3FFQDZRa3jvndpZ9WpcdY/E5tN+LFy8WZ68+wHCadZFb5+SepXgYD2cgW+y/OQWfF4Qqt1LIjFK2oD4CwbpHAwv6xriN0XrXKXIKncfx8fHiENUqZmZByt0UXXbAz8kY3bilsltvbV2r7IDTM2QSdECQ6Vs5bP/o90r1sRlzdJErvfa5bcOIz1n/dj6adzukjcVljjI6dtqMr2xBGzzOz8933rINeHDyDfhHR+/eZadDUqAjI3IHVioH86E/MtIedsBWBHVB1Xyqj62zl6kCIOmmn7148WJZL8DKnEvH93g7Vxvg+IVuGbi9XV891G1XBWD1WewV7Y2mpQ/tdn2M68kHOwYEsZRaQ9fMrO25FE3Am1lPhagibX8/s7tJkzFSarUDFKPP955FVaUemp2gPUoNUGbOAYr1/yrmzIrWoEdjVdMwpqKtZouCmHExGMFVxvJpKJv8j46OFuUuupS1qAFwNKenpztOGjqucZRu4VAVvRkch9v9g9B517BOitPo2griggmZVi+qT60pyrBb5zRvtDYAwnEbB1TeumGPX2rwFrgrZ2OTKXU/JBq09L0sRVAHVOgJZmFbJyybUaBjbHTevAEMz0VXtu7ctZYNvX79eudsxpn1pJKt/Tdr4MiBvcvLy525o4sBOnpSYNogR5/9bpsdCmpAXOnWUpmlCmfW7U2Ah+Dg921S8TzBU1ZbZoG82evbt2+XPaktcfS8VPZfP9ITqfgIQLa0qrkba8Eo4An40WVgu0DmMa4nH+zu7++X1/NQ1Lb6Pzw87LwRvJ1u7WYrnWNBKDYniqZCGUF36gUUSIcSROZ+zaRm1voe1FdevXut2nSjJuCigM1ofYY8ZI+UjkEyKI7qzZs3i4NX1yqtab7G6X7uwWmRNSTKCRU0WAdOX5fj9kzC+/u1s60OXjbCiDiEUlWoRnM3RlRjna16Yym3zrtUnWeRR0+EYOzmykH6zt3d3dKle3Nzs8zb90qfc1rbPU7u0+AkyNN9dlDGoGsu8xHs1KVa6zY34IZuCqr9tyAI/JF/D1pvnbsBssCO/tKvk5OTZWwFWgAg/eghAMDQe++9twR4WUkZCvq6BVHWix43c2pNr+zRzOwEyO6TJZs2w6i/YTqaxXabTwMN/ZBdtimpAMy9NEqxLzRpSxgF4gWvXp2GDnXP6pBnAqzAPxnpEm1XaO2jgPMxridfs4NOKU0zhKIrR99Ags20ZFJ1ml2UNjwwXpvYteGqJ3GMKBNBamZtehF0KQ6l4FDbHABBcgozuwj34eFh51SNUjhQ/8zsoLZt4IA8W0OCrMkY1dtaZE9Mqfw4afI2l9IhjEgLP8qvIALAKArcbiCvM+IQOIvuaYM0W2Ng+M0UmtX7mXmhtDpnz7u/v1/qfOQo2zUX9yriLXBRH9NBqw7j/9YeENpu3WimYQ6Cns/Ktux/4mh7QEKv1ofIq0wAWotuYR0EGDrSY7taK2UvQKH7c+alUM2p+iBoklHrdF0P42mAAw58vl29JycnyyZ1Yylzwb7YRTd5CyDWpECaLIAT97+8vFxYDSCNrfJXBVj8AbtuCcIYjBe7M7P75g0BZ7um9ID9kWFBKEDCXtlbv8fezL1vwnjx4sWvnKDyzV4CEMfQDi7K1H0xFBzv7B4MpcpEOTnLIhXvcKM43Qjsd6WwoLPWu9CQPnN2draDvFBJAov7CybmzHFCkJQSjQnZy1ohTw7Q9wWd3qdBHw3X2glUTOYffPDB0vnK0NVMyX5Lm7Yx4OzsbIfeE3jJa2ZtPGKIZAvdy2rJCfKGOtVI1MNevny5zPP4+N0ZhDIAwQldJBNq0Hn79u1OGz/QZd0awEvryRQ5rOqKYG7M3VLS2h35c7qcPX30bOPltEqXt+5pzH0PocxaMOOwyBBwaDbpmYKC4OxZlW27BumXwNOmHcBF7VEmDoTJ3oyjTIaft55OjuZDfsCYTJIMBRCyM+aDg4PlZauyWvdrAwif0HrozHoqjEydfxKIGzj39/eX80s1pvBrdHRvb29pumEzQGHLAfQU21DGwtz5oYI/4J6uek6zU3LtnwKW/zdOUHnyNCaDpBAWl2FIybcZFgWrk/Pzmdl5/crMSmfh/uu4ZmZxjttsAzJtR5+AU4ewv7+/oGKvoPHdKue2SWNmpTLNH/JqbcHnUZWcB6WEmBkq6knwhQ4/rQGFEzg8PJzLy8vFOb/33nvLXkaODRjZZtSdGxTYoNea0czuaTFtPW9zjnur3ZTqatCWoWxpHEGVLLZbHuiYQGONyUc22M7W+/t3J8Q76Z5ecs6tb56dnS31lYeHh6VrV5bB0TX4CBTWpB2DM9+YCW2Dj6zGfGRR5k8+zTYqD7/3HU6enJrltzNRG3+pTAENGMWK+L45CFxt/AEOCjBb72QnbUYjd7ojADSDprfd5kQW5Nfg6hJ0ycZatz2/rAAwQzebmZKZe/iZmpz/Awky/TZnGTtdNR82sD0wnI5iCTTWFCjI4si59UD22jLLr2w9+CavLj5hUp4iNY7ewraBoLUKigFtFw3OzI7hyfAOD9dXYmwLuQyx3YeaB5rFtT23zyuFBTka4+np6ZIJtfhNaaHjGgPKYUsz9OgoaM5cOKtmIwyxFJYx2khsI7WMonNqXZIRCi7NfNs4BD3KFtFxkKmLIZbOahMTJ8hw1XZKQ7XTj474nXHO7DYDkXmfba04VPW5nl7DwXQriSyymSkaSCbMMVfXsAr39+vbxG0wLhU9s7u1o2BEMN3f31+y5Jm1MaTUsE3azaQa4BsAOXp2pJ7U4FcWQ4Av3fvxxx8vwb+t+uxo6+Dpcmv1zsC0PnQNTS1L2pYTyLe+p/Uyfgj9SRZATgONMVsTGW7vyRbKWJXiBGgKusiZDfBX9KV+pgGVnptvfYBuYrpvru6FEWI/tjABlgVjfFU7Yx/revKZXR1hlUUG4zOlLTnTtuJSlJn1QFcL48/MukGYojW74dQ4ynYWtpGCUjW916SiQYRhljOntLjuZq/QYLMQgWBmlj1urTehmIyjwdc4dXOVh2dMOviMk7PmXOpwGrDJuIHZXrQ6A7Jt0EW/tjkAiBEouqXEmm9plqJcIKHgx+foiKYSyJnTF2hafN8CMNkYGezt7e1sQq8MrSuHJsDINAS/Onn1rjpM92zm6XAA8wEKm3E3OKnjkoPPv379ekHmzfDVotvItM3ONXDQF/oxs2br3udXx+tEGRQdsNQ2+f6Nhuy6C0CtBTc7pHN0s5TyzNqu332anuX+7tuaHGDcZiW+gz637Z9eAQf8DblWx54/fz7X19c7zT/N2MpalGaWUZJjn9NGPj+TzcrUjZXN0++Dg4O5urraYUdaRqCPqPLHvJ58sOOcmh1BI0VIdd5+9vz5829weIyXsrfLcWZ20FkRO6VgxJBN0RanwHgo+8xuR1drczXIOqoaDkN69erV4kyKSGuAxn94+O4Ug6OjoyXbEOjqgLZIm2OF+C8uLpYaF6Pe1hyaRZKHoH5/f79QkORqrTiaAgTO3z5HGV3BAmP2LNSTgF/HBMQ0uLQGtbe3N+fn5wtw+OSTT5aTcMwBCLGurUs08JSy/LQuVlTe/f39QiO1tqQmQ1b01TqbR5+1tQF/y8SAowIMlHaDqDmSHZtogG72UWCl7qqGVboQ6LPvTqs/KlLwLPhTa1JqKLVML7sf0BrPrPXemRUAkQGbLFgpaNFEVRB3d7e+b1BAL73Hjit/gWZmPf3p2bNnO4cItM5ayrbyL0DvfsptfRuY5Ffco/VV8qvd6xSubdIRfkpt0FzLvtBHgbD+2dge83ryNOb9/f2CJGRT7bSkIJyG13T4/8wsBlYqztUaDyTJCQlejLWnDtTZ9JKl+MMZXV9fL2PyTN/neMv3C5LmMDPf0HnZfUGcZWt0ZGSMPZ2c3AoE+l4uTuv6+noH7XKclWHPjayRWi/z86dBD4VVempmljc0mB/jVfh2b+PhgNyj2ajA1CySrAAFMmrt5eFh3Yi8PWVERumoNo7G76BnYOX29naurq6WADMzSwMGlN79m7Lbo6OjbwBM1p9uOLmEk6mjblbV8xTVOdXsrN3+/v4CdoCDFy9eLFlX15OtCKpkbX3b0FO5t4bFljlzcrPu7mt8V1dXi02673YbzLaOZS4yjVKTh4fre/vqrOkhXQcIBWwZm3VXTwbC3Pvh4WHRD8813ouLix1f0U7k+h2ZEpDbwFSmxWXt6RPZswsAsvQmkEDfZPZ3d3fLXmaBkO4VPPlZM+jHfuvBkw92jKXKgtbSreja29tbzujzWXTOzCxH8VAUDtJ3ZRKeVcPkICwsg2ekFI8D40A54Kb3bTihNIyLk/OuuKLamVkcuDGSTQO+xgjKLGB5vvswFGi5GS6nN7O+WWBm3YfldzICzlNGhvITJKDKUlPm17cll1YDKHRQloYRCKyXhqAW/Cu3Z8+ezQcffLBk02jhNs1wMuRDrm/fvp3Ly8slCxDIu69LVsrhyvpcakXG2rZ5mQGw0T1iZNH6iAyKfQgGzQ5m1rM5Xe2ApD9kycGROz11GHPHQwbGQnatSTeA+m4pZz/v2m3tnS0VYFovLENLEs04yd76HR4eLnQ9mzSuvni3mat5CYa1OT5km2VjB2bW1+3I0NvQARCyB3psjcsOqVmTeWumvQRfGRk51rbUXVtjZDcff/zxAn4KxJrFAofAWZv1+IqyaY95Pflgx7E2mzo/P9+hGy0o6pJCMSCIs00jFHpmfWuxZ5VimVmbFCi6BZ1Z99a1Nmec7ZjsfCA9jg4S1WiB+sCXl9KcmZ39Sq19GY/ncBLdt8ZQSru2qcUc+vN2MpJPsx7yNs9mm6VnjcM6kJPxcmDbOiBESwd0xgkMR0dHc3FxsZP5+75gyzEL+gJ4X3/jdzOrQybHUlnkDMXf3d0ta9oaEmeBUqazvv/8+fOdfWx1nA0w5K3Ds7U6+lkWoZlTHbP7HR0dLU0WlSnnBQhwxkXwxsYBvvfee3N2drboSUsA5u8e5C4gcKytZwpEsrBmPMDEtka0/W6zTs8mk9YAAR+ArVlOs9jaiwYhMqA/BbQCst81+Ho2urPbo+iM//M7n/YSXbblWdUXz6WvbPfFixc7wdy4CxC2tlvf6Oo6kLP6rmSktdDHuv5fCXZ/62/9rfkX/8V/cb7lW75lnj9/Pr/u1/26+Yt/8S8uv394eJgf+ZEfmW/7tm+b58+fzxe/+MX5a3/tr+3c4+tf//p86UtfmvPz83n//ffnd/7O37nsH/lmLotDMfx/Zu2OgvBKg0F6dVhQoCBU/rvZFkQmM/N7C4kWLZKvbCgw5WlHHwS/rY/54/cNnhRWMXhvb2+ur6+XtnNnVwqCaApz8+f4+HipHaD1trRsKcBeRe3tKiSf7b5Egas0TOmlNoxUxgKrelmDHsM0Plm7NZMNtJ5r3JwvFM2JuKcM3VxbGxZU9vf3F8fT9veZbzzpphli6TXjt1UCQ9F6zMzqFBugATuOz/w46+0pI7Jn921dGjrXSFM2o9syZPj+3QzBXLwf0BoCaHRXxtDMTsDS6WecGIKTk5OlQQoQaU16y3Zsyw7mjxoVpK1BAU+BW+twqL/SpL7X5jjr73fvvffe8o7MBoVSt/7PPxSQF1C/fft2rq6uls8WcLAdulobA8QwMPabboFFwdfe3t5cXFzs/F/9HCiTpZa6rgwkEwVjj3U9erD78MMP5zf9pt80R0dH81/+l//l/OW//Jfn3//3//354IMPls/8sT/2x+YnfuIn5qd+6qfmK1/5ypycnMxv+S2/Zadt90tf+tL8r//r/zp/7s/9ufkv/ov/Yv6r/+q/mh/8wR/8psfz8PCuDZ3wb25uluOFGKBFaCY3s6KtZkbNyjgkgauBxaIJShxUKb2ZNW3nFOqMGPE2GxI0BEudgKVO3ffNmzcLjdKDVsvvN5i3JZmBCkIN7s0mOJE2b5T6qCPx7FI4M7NkWNqSORROmzOrU/dd82q9zefdo3WcGjFwwxEJ/NYI/cPBlJ7qPkDnilofAYijhcyhaOO23p4hOBVslVI9ODhYNtGX5pSFVs4cRlG3AFNmg3Nspg1d0z/3cD/yIpOe2+n+Nhf3dP066AIlwbNlATYpkyxl3+xbEKDTxoDhcKwVneghCq1T0mkNQpUXubIZAdhYmtHd398vzUSCtwYocu/7/2ZWJsV4moHRlWbejh7EPPBN3U5Ebj3tyBr3agcxv2aM7JRtkrMsHDhV91N+oYMNlmqSGsHoK2BrHepTHvN69G7MP/pH/+h8+7d/+/zH//F/vPzsO77jO5Z/Pzw8zI//+I/PH/yDf3C+//u/f2Zm/tSf+lPz2c9+dv70n/7T8wM/8APzV/7KX5mf/dmfnf/+v//v53u+53tmZuYnf/In57f9tt82f/yP//H5/Oc//w3P3e64v7y8nJm1CUMXIgWEZPy+beFOBod6tpQZpwq5+fnbt2+XwqwMUFCFuAQlAdKiamyYWQvTvkNujFomNLM2GVxfXy/GAAlyZq3PCL4zszhYiuwkE3LZ0mGceGs6DJZTQp9uM8vSacaCkmotpw5DRl5qxboZRx1mnYVnCx51XtafM9lu++DM67AFQPSh+bl0ezZI97mthQAUjktrZ6iMUbDsNgprU1pom62cnp4ur1IqeGyA5+zMExiovhSk1IFfXV3tbNtpPdzPZO7mRrdaZ6KH9GB/f2137942wA2YmFlpV92ndIddkG0z51KJMlONJ9XHsjKOS7PeAgsgi53gU8jRXMjZGgkc1stZr/S4BwnQu5Y4au90vhm1zwJIrfcDaK2510baJIbdMV6+yXzIx/q3bvfee+/trEkBF9211ao0avf7kaexPtb16Jndn/kzf2a+53u+Z/6Ff+FfmG/91m+d3/AbfsP8h//hf7j8/m/8jb8xX/3qV+eLX/zi8rOLi4v5whe+MD//8z8/MzM///M/P++///4S6GZmvvjFL87+/v585Stf+dTn/tiP/dhcXFwsf7792799ZtbWXQVc51UKDi2Cl1piPK4qXVFSM78XL17sNH/4DCdqoRlWkV3rBxSAM2pNDqLkSNWDbBHoczi91t7Uvpo9NIihKqE0qKsUUikwQKHokDH4mWf2/wUPpVfaXIGSbUNFz/m0Zs2K9vf3d87Vs24vXrxYMmRGVSDCKXAQ77333rx+/XpB461bcbbW7fT0dMl0GlRLa3E+HCiQ8eLFi2VtGHepKHpLB6xvKUn3vr6+Xtbc2pKbe9P31tW0tMu+6KKN9LKiq6urmZmdfZVlO2RdZEPv2EYZEWvRi6OH+MlOvZLzbUNDbYjcdFCaLx3d1pfoMzk0iFUX2Z112Orb2dnZ8lxgrI0WnrkFZQ8PD0vXcGVIb43D2GZ2M0BjoisCNR3nz8jcfcsKAFUobNl0SywFpnxF9ZEdzqwZHF+DWereQuAaaPMMMlKOKQP2GNejB7u//tf/+vyJP/En5tf8ml8zf/bP/tn51//1f31+9+/+3fPlL395Zma++tWvzszMZz/72Z3vffazn11+99WvfnW+9Vu/def3h4eH85nPfGb5zPb6A3/gD8zLly+XP7/wC78wM7vFUOfTtfNsZn2zco+4gZw53OPj42URoGFIxdaGIjcB8c2bdy90bLpf2sjPWwu8urpaApnxU/wGWgrU7LKoX0s+B9mOLE65qFA9qg0LFLfvNTs+Pl7OzBMgyLq0WR1oW6+32wLIow0CHDIjLP0zM3N2drY0ChXBtnnBvNUJyKZ/c2ju7V7ehNAso1Rcs0DzKdXczIr86rDbYGAere1yAvYptX7WWqmuTOsPjKAYT09PZ2al5EvDuwf9bh3KGIGUfk99d2Z2KOnqIHupw/RHdkuGAIogq3Gj9SlB7vj4eClLAIQFUG0M4zitH9DSOtXMLICDLdEjXZbkI9DoPKVHbKbBrttJPJ8++hx/wyZnVpp72wBXINlywrbG2ppaATf97zzZuTGRqzUWqJvNkptMl15ZS/LregBe2KICO4BOEJVBss/HvB6dxry/v5/v+Z7vmT/yR/7IzMz8ht/wG+Z/+V/+l/mpn/qp+Vf+lX/lsR+3XJorPu2qcrezSzrNqEplFVlBdxa5zQ+Hh4fzwQcfLM0O7Yqy6FBXa0qUiQKhEiiPzGBLxTFWSsgZQt+cMUUVYGQyjGZbT6wxCw7o3Jl171XrR80Qe0I+PfCZ09PTHQdVWq4ImXE8e/ZsoaEbRNA8nL1sq0hfQGptlLPoq4x8V+3DuIzBizmtV+XVWkovzqiZVceJhrYuZLSt4W3rYmqyrW/0MG30OUf74sWLhUGA9tuUdX9/v7AAqNSZ9dQaTlTjg6akLb03s57gX8rOOKD9169fL0jes9pAUcbDGNXVaguyYcxD6zoccm3cOJzm0yy0WXYze4H97u7d/jBr5NnAFJkKzKWjWyP0HWNk59taF90o/b3Nbtg0n+R31sU9m1k22yuY9H/0sIYna6DsAKiaKxlua6u+d3Z2tgR+4I4+AXYywplZanilLFt2eczr0TO7b/u2b5vv/u7v3vnZd33Xd83f/Jt/c2ZmPve5z83MzNe+9rWdz3zta19bfve5z31ufvEXf3Hn97e3t/P1r399+czf60UZGzTaVQmBlXOvAUFulKzHVskOZSA9PBgKFRhKSUKyftbjwlBKVdg2DpS/FyBaZyh1UcqvTReyjHbJoaiaUVJoCLRBm2Pf31/305WKQBc/PLzbEN8mn1LBnCNnIlOGnhXX9/b2dqhJzglF1eBjrv7UsBivIjsH5HsQpue2K7A1tNbj2oyjk6579WbWQCiYdE1ndl+tMrMidY6Qvlqj3kvQIBvr9/DwsLNNBkhAecr0jb31SuM1V4xFM2H3LBXfA467LoKPZ5HzzOy8T88z3a8Zv+69ZjSak+ih8cturXVrUAVJPudZGlzodteUntb2to1jzcjJ5vz8fGl64T+2DSn8FPugp2p3AK41bu3PuM2n1H7LBc3AzBlAbxd6Myv6LcsnO2CD7ZtPG8R68Wet0Ws483k67/f/wB8E/Zt+02+a/+1/+992fvZX/+pfnV/9q3/1zLxrVvnc5z43P/dzP7f8/vLycr7yla/M937v987MzPd+7/fORx99NH/pL/2l5TN//s//+bm/v58vfOEL39R4GGoX7/5+3R4AccysFCOnU2fEaRRBaf1+9uzZQsMURdlQKwPTDn1ycrLUVigqxTw5OdlpSmgQpagCt/ltMxif4Qxn1k3vpURtxhZ8GBlnSjFPTk4W3p1zKTpDvVBm3XdQHaQreHOI9/f3y8kaiv+cFkpkZs0C1AEBDOjcWDn8os6Z2TlvsY0gjkNjaHXadTayodZf6xT9DFK2HWBmPZuzcpIBNHgAMObTU2GsM7DWcw51r6KZ3GebOVqfNn0I0mQsW0TLm7fxNmBXZo5go1elUevk6tQeHta9h/S5md+W/qwD7WcdAdfaFfvpPlXHWjUwbDej8wvkTofZYmnmMhp0ofQ2mfID9/f3yzvp3r59u4BBAXxv7912IO9vtDbWm29xla70c8GUnrVM0y5dLFaTAHKgp8AY/aMLfCQfUT2Q9bYmTBcL5Mjv4GA9Xcf4rTc7eszr0YPd7/29v3f+u//uv5s/8kf+yPzv//v/Pj/zMz8zf/JP/sn5N//Nf3Nm3gnt9/ye3zM/+qM/On/mz/yZ+Z//5/95/uV/+V+ez3/+8/Pbf/tvn5l3meBv/a2/dX7X7/pd8xf+wl+Y/+a/+W/mh37oh+YHfuAHPrUT8//rBPfXLqTWB/D0M7OcZdhal3qAbilXaRXBgVEwEPRHaQaG2TpY+fuZ9Vw+p5/gszmntq23uaKBRSAqdcIR9nmMfmbtwNpSPM0SerxRO1GNhfKTUZ1fuw5Lm3JmWrhrjObTNShF2JoHw+dkzc+66QTkELVAc8Izazel7NgYm0U1S2v7u+8InDZ7W9NS6K2F9L4NRq15zqzvXTQelF6d/9HR0TJPcrEW3QxMH0tL9xL0Stc2i5ExsCdBQ8ZP59y72UqZjuppt2y8fv16hwK1Ns366KTspIwNRylbbLc1OyLzbqlx3+06sylBCuUnoHaT/v39/ZJBCpA+ZyzYEUdo8UczM+fn50tw3GbqdElG3MyY7V5fX+9Qr+zF+K39th7nAmQlAK1PW1fyQP32MwCcUgQfiM5tFsrOP/nkk3n16tXys9ZR/4EPdr/xN/7G+c//8/98/pP/5D+Zf/wf/8fnD/2hPzQ//uM/Pl/60peWz/y+3/f75t/6t/6t+cEf/MH5jb/xN8719fX87M/+7OIgZmZ++qd/er7zO79zfvNv/s3z237bb5vv+77vmz/5J//kNz0eWVOpJwpXxC0oUOBShX5XGk6WwenX4c2sDQEUTS3Gv41lZpYMhxMWGFEI7nd4eLhzKLLaS2tYJycny72cwXd/f7/UzMhBUf3h4WGnjdx4Dg9396LJCDgmz2csrW9tsywOk/zsx5pZm1r8TibAoBXrZU2+s7e3t+y9ajYpCFgfhqbBxlxkToIH+ZGl8ciainw1KnFwzUYYr7GjZvxeYDMOTk8mUiZCkOvaCthtRgDcOCBzOD5+d1yXtfb3zPoy4W0tsUyGutWWtixFjMoSXCF7c/F5604HNTipnQMoFxcXC5CxBj0lRi1sZrcTs3W6NpJx8hyrDLwy9TMBkIwwDACa+dM3GVdPcwFqzAdrxMdYdyCwvkGQaxmkAZ4uCJp0WmMHhom+sS3622db/+o1P4mtIDv3E2BbzpEUGGcbgVzN4rElsrlSwWWh0LePee09PHYV8B+Q6/Lyci4uLubHfuzHdqhFGRIFo5SymzrwZiely6rQlJWiaOzgpHtKvUIs4/b9mfUsR4qlUI1WnFmpLHtZTk9Pd9AdBa2TaQZmLgyPMimwoyFub9fWbYpsfrZxoHXbTar4XGRrfmoYsg7Zg//f3d0t4yCL1hy6h4kxkltrVIyGXGQ1guinZRkyFc5FVmdunDUapxQXGUK8xtQ5c4LWtJSjwORvz7Jezaya0VYG/Z5nkR0nBFBZAyCoR5Y9PDwslDP7aMZufmymY5elqncK9C9evFiagrYyK80mgO3v7y9bDThe++mAIIGtexa3tR/jMWcdztUZmTImhe5YP2d9kmvrlcAsUNfvz6w0o6wbOCkljy5sHez58+fLeMyhAKNAVyA2rzIvHWu/59nthqUb/B7/wlfQV4Dh/Px8GV9rmHzezLvtIsC/uRUcSkJubm52Oltvb28XUHd9fT3/zr/z78zLly/n/Pz8/9np/z9cT/5szGYI6AxGDPlbUIrHoelgLO1kMXvMTZs9GGCRuqCgTZzhG1dpP/tUGNnMLPUliiIbax1MTQ1XvkXinIaMkoFA1k7Ur3NBP8r8ODaOSS0CmjM2tYMGpiJI/y9AKIXXduXuZyvdC1HaBkHW5GFzf5G6TAmtbY6dg4BL1q1NNLi899578/777+8EITomaJRabR2CHpKZ56HZOga6VfoQsm8H38xKbe/v7+/UwwoGsAPGKQNCPxmHhowGJOtP7x8eHhbajO5smyzcm/O0FYLOGocDDYCm0oIvX75c5MdmS5tZF0GvtPHbt2/n1atXC6PTubIPgFBQsAZkyQYKYLfBxJzIBYXbfZqtR9F/gan2Q4fNiTz6f3rc021KG3a+1saadCzbzkhrzMZQ8mTMphzxxjath4a+giS2DCR1fcsSda8ehuAxrycf7DgOAoeuKaRg1v1aDpFtB2GDh//3eJ12dHXxKCa6zAL2FI7WEykXhFZnM7N7uoqLQnN2xtrgJJDOrNSiIDszy8ZYCJBTev369VxfX+8cHD0zO00WHC4E6Pd1xMZbSlMdxX0EbNSZLFLAIi9ylYne3Nws++5cpbeaKaFyOAefI1sBSVa3bVQQrIASzl+jAiBRPWnDTNGvJptmVNagQMd4rFk7JesgSy924y/GojTpzO4Bw8ZhHQGGbYMURyXr4FR7XFhtQaexgw9kaLYjkP/z588XZ0uu5ClLKuBAAdcJc/7msi0XHBwczNnZ2WL7bLjBnZNuE5Z6WG29ayszIhPzbqOWe3UuAqEAzQfVZ6AOt6xU2SWBhF3f3d3N5eXlomuCTKl1We/FxcXOZ9iZQE3f6YYAt7+/v7wuib8hF59pVgfQ+p0TbprRszlZOzt5rOvJBzvZSQujM6uha7boyfytq5QOqjM5ODhYDLgZmYaRdnq5LwPe29t9E7UMwnNllQzy7u5uqTG59vfX1nX/bubRelXbtduV2vmbbzsh1emgPD8vTfn27du5vr5e5FRkXQpuCwZkp4ybgy9yNwbGb+3QPzPrNgoOpcX4goyjo6O5vr5e1s7PT09PdyjFvb13HbEFAg2K9KgAiCOibxx29xt1czg9EFCsB8ewzSKPj4+Xbl+1vDID9KGBWVDqVgDrVkbh9PR0Z6500u8FCuMp4ibDZg6QfwPiq1evFtvYUsbu26zrgw8+WBiXg4ODOT8/X/TAi4QBw2aOZCiItg6nfocq9OyHh913RbYuzCYE6wLmUm5sr12NbEAQ1bFa3WaXBU0za5d3sz7PLYOAbu4WIw1tZacKZPxuZt2Taix8lLra1dXVTi+D3+/v7y+NOmxBjwC7MI9t9gycVv+tExtpFvuY15N/Uzljb7cYA+h+F464xWkO+NNqJIy6nLp7y1Yo/7Y+tLe3t6DmUpP+LStsxgSVUwCKqv6yzeiMs3WCnpUoQNoeAUVzcmgylI15tGZBTv6Ph299o7VRMke/mps5k5HncEDdeMu5CpIMrC+LdN9SmGiq/f39pfvLv0s/NUMWQDzXnAV+2W0DnjlzLHRKkCDXOizyI7cGE1TezDe+bZrTIHN/BI5m3u5Jpjaec/7dFvHw8DDn5+fLetG9buGwBjYKAxECkXnWWbIZjpUc1PpKL5faA8zIyPMEOOdclvEAYFpOwDyQM1lUZn1jCHl++OGHi2MnR5/vlpaZFRiVnq4PQUPTRfe8v7+f09PTefny5QKIrT15e07pcXbZ5wqoZY+MQxBnEzOzBMnT09Od80J7lmoze9l5GbPSqM1gyZzNFCDu7e19Q7e5l2dXTx7revKZXWsUHNyzZ88W49BVtK3rlCawOKWPZIylOigkh8l5Hh4eztXV1U6w8awqjfFwlOXAZSfGBgW2ptV2eM4Iauw+l5mVnpM5CD4tyMv0dHjVwUGEajHb8wyh19JJrYXd37/rEC2qNA8OuMGpe7wYAuOyTuqPPVG+lJw14YzbXNNmg9J9HIGfN3OUPTUrUreVNbS5QLdxM9xPqxV/Gi1Up9n1Q3uTVw8K6BrQnQYOv7+6utrRvZOTk6UW53xMzRrGX+fXQ8N7RJgxcN4N4mVI+jfdu729XcoJgjrAYf11HmtCcf+Hh/U1WfT/4GB9rQ5dtka6N9lQ7XxmPU1EnbhNUfRAYxpdcGjEzO57+gRpYKUHNphna10CIns0zh6lJ/hZlwY469JtFtv66vPnz+f9999ffo7WdfV7mo7omHUyZnIxdj6xvrJ+oYHYWgOmLTE8xvXkg13pMMpUw+jiz6wKVuN8eHiY09PTnfpPT1QopcXZQ2SUs00hlICRt4AtG3GiiWdD6JB3FdxV+oIjcqFSBFJjFuD29vbm1atXSybM2RuT8XE6KCz0zswstTTyY+RkgFIVmJpRcVqMngE1c5WJGkNrSWp75ihwta6xpdzOzs4WoybP1moAlW02ar0g+1LK7RCsfjFuGfa2tRpAETCtC10SdFtXRaFxOO5RGtlcb2/Xw7DpUmnj6lP3icrIXA3e/Y4s1/PNjU4008VasC/NDjc3N0sAkx0DjjIFNvrw8DAffvjhznaLsgVkKmi1MWabMagfC0ZkfHR0tNSyATP2z0asle+xi25rKrvAF/SQ9db8Xeg+wYJuYWL4kv7eM9gqsFAA3XLMzc3NYje+D0yU4ixTUFkJomVNfJ6Pq28yDoCoPoq9W+c25TzW9eSDnUVrpkXpOKwGvBaEq0T4a/f8+OOPl30yDV4MvQrcDczltGfmG16UygGhYTg9Dg4dIt1vLUuAqgM6OTlZFLQvK4U8OUTUkWdy4pBuaxGUn9ygVVRUUXOdpm0TPl9a8qOPPlqeYf7tJru7W/d8NXDMrN1eMyuS31KP19fXCy2HFtWJWYqvtV3j6fOa1QnYpZZRiqWDrTtnSub0EBVrznTPv9sIwlnKTNsEYb22gc88qvvNiLediaXoyH17VmX3khWZd3z0o47TGOk6J87B9Y3tzXJbAmiAQfEXUKjB396+OxYNUKgeC4JbFmfb/VhQyG7NzXwET9kTW6LrxuX/lRefMbOensMGPBPINrf9/f1l3VtKsT50jM15ZmnJznVmdrLEZmOe15OTWsvuPtWZ9cQgtg4skAfa2fObtbfOTd8e83rywa7dUzNr7QZKtgWhqFR6f3t7uxyd5F7bzZ4ykqbqlKUNHxRRhyGjMDa1MY6z7cUQ7sxat2mGx/hn3iny2dnZHBy86zxD37bLzNwarFC9pc4YF6XnJMmiFB45CpIQWkFFETMEvr+/vxydBAHLfh4eHpauLA60zokzhEZbn5xZjbFUHoqLbGWOjHZm7RxF3cksGWmzujoAjoOz0V1KXhx9M1tzJKuCJDUka1t6iXzR0GQPPZNlz3nkmKFoDtsfQaGBC+VvXq11tiHGXGQc7costdaAb07WCFAhD3pU+rSnmgB65kanOf79/f2F3q0+dj00SfkesIKif/Xq1Y7zB5DocLO20nwNGg0+5gFQGTe74VOMXYMP+brP2dnZQpn7HvaooJHeYmj4Pj6nW1MEu9PT04URk6nRmdYY6aGgte1d2DIOXppdG2F3BUHb5qXHup58g4oFVhiGUDi21m0oCiOuY2lLvcXxPYu/Nb6Zd5nb+fn5UufifNpWa1H9/ubmZs7Pzxcl4XQ0ZPTUed/vnBgsZX7x4sW8fv16J3iaNwMSMGZmCSZthZcBUUSnlwgyDEYgmJmdWh0H338bt8v4G2wZJRkYoyAB5UK3vtc1mJnFqXF2gnyDmcDVM1IZnc/JLDgBlJV6Ffmpc8nq2wQBNL148WI++uijZT7X19fL/kmgQHDt5u9SX+2mJT9UHYfv79J7df70vN9Rf4K2O3b30BhRAGAcPmeT+fHx8Xz00Uc7OtAzV9kXMNjmGmMlQ8+33upp1QF63efRN1m39VWDevny5aK7dAvLc3t7u7w5gU9wX4yJLJRNFBw1Y3a4BJ9BZ/zt2DcNOP2MYKdTtfVmm/nb3NbGkNoNWwCQsDul043zzZs3C/hmf5UxENCmIPrJx/o5WW5revbm0rEtDfoY15MPdufn5zuOkjJbQM5DAKN0FGhb46JwHIGFgXhkcqU2KRGDaOG7Nb/Sb6W6XLKEBpVtoJtZTzPhEFpLZIwuKLR1lgbHKurW0dS5NYvbcu3vvffekklXHjVwwbldfA2ap6enc3l5uXTTdSwaKoyra8UJ1jnd3Nws7zbkELrBWYCyFu5Hrg3WlWOziwYAa9HM6ObmZgfpdu8gfeMc9/f3lxemmnN1ZzsewGpmbcaYmSUjIXfr2w3Tr169WmyFYzo9PV1ehVRgU4dpPQQr9NTJyclCrZq/rNc4zBvAYkelEOls7cyY6Kr1oQMN/m08sSYCufVqoGz9VOCWjZFRmQqBoIGGvpsHP1M6tRkSipi9tMu0zJTxt7bcudEnAJbt8mU6g/vWDwCnAdpFt+7v73cAXIFcT1RqLbL+wDysUX2ZtQQM6h8e63ryNCaHLNVHATa49Civ1qY44dJk5eqhU04OxalGxlm2YaFoWxG69aVuSBZs0QkM4dMCmEConkeZdHxtqaOZ2UGYZDSz1ieh6/LrgqXmEMrPCBk1x6oJpMG5VFYDJiPpGpRu7GuR6twZkBpBN1EzRn/qFDhz6+aA8HZudk/XwcF6ZqdxPTysDUR+Zr9Qa2CyRvP3xzz6Jm+OrxTQtu7Hmdp/53uymRcvXiy6RL+M7+7ubnmV1PHx8bLP7OFhbdopfUh/2mBiHHXopa1KLZaCrszZE30GOKwpfaazAh15yrRbG6X3d3fvjggrNV5Kn27Iatko5gLIoIfqvIBza26fBij8mz631kX36Zjx8T90rG8DYDsyztK5t7e3S3bKXvkXz2LfBZ2tP5OprQX8JWAmAwNk+K32F/QACM8t0Gw5hI7Qu275KNX/mNeTD3YWmXJYaNQVQ2tNYWY9nLkIs9SNkyFmZimIQzN1CDOzU5NxMcLSP33dTWkjRkjZZtYtEv7dw5zNgwELmlq222LfbMh3GK8GiO5JEixcMtaLi4vl54wamm5Ab9H78PBwqUs06Gwv6yYbt14uTrIbaAXAGuPMLE4WhcXZdWwoSGMxD/ezNjPrlo9S1JAquWrt50Q5aGcQznzja1A42B520G0k6nnWqNsx6JHaj/s3cxEsBXPBqi3qHKgg025fYxU06IIA2yDMIRfs+D85slP633oinbu7W99C33UVKDhWdGxLDwCEczrpnnWStTWY1skrbQDK5m1d1X3V+PgWFK6158itaVkfNGnr3AIzUE6mfASwZM5kqYQBaJgr8G3Nq7vmSM/7uqmZ9UAFgas0pCBP5sbdWiRqlu8sG1DK2c9/hcb8Ji8LyhAohADgEkxm1qOTynM37YfIKZSARBkgIwZzenq6GJvsxQJDd/v7+8thtTVKFAsnib5ggAykc9zSMVDpditCg7dsiUOl4DNrUGyNiCNH6WiJZrgFEjJCVJZgJwsoVVgar1SG2kSpKs5D8Gst1XrUyQh0NVZBgKMFWByFNLPSV4JtM/eZ9WQcDoeuzOzuqSv1J8B7NkrIdzwbLfT69euFeqVbPUJsZvf9aUXXnKa1dAkSpdxndk9aUdtC31nb7sPilOgS/XSvbruhk1gRNerqOZkXWAriggAdto50u5kN+dIPsrLusrdt80cDmEOpWy7QtCZANHi2rnp4uG6WByz4h9bPy4SQkeaoN2/eLLU4227ohTb+9gGofxaU1s99/PHHCzD3HdkcMPvs2bPlfEuArHuO2YBxNHDZlH5wcLDcV7BvmeLh4V2NumCcn3rvvffm1atXj05jPvlg131FBM1R1xgsrs+VJtSR1owAsmbcHLPsYlsUb2PBluJyX1RNT9UQPDgehWCKbC9NHTpnKnvi0NRySin1vWuCdms/alGlOo3NHBgrKmJLAQn+baE2DhQGx9Hv+WyL1q3Xea7usbu79bgkTpQzY6ClTHXWCewCVmXkefQGCOGAZmYHLatlCNYNbM042gzjs8BQj6wiSy/6JG+y8HPZ9+Hh4QJA6I55mYtgDJzQybaBkxW5lBlokxeKuvvKDg8PlxeRosGAqW1GIItpN7Hg2m0SWAoArWByb+/d28B1LQKEfYFuAxlblTU2iFondnNxcbFT49aoMrPWoNQ6BRtr0YyI3QoO5ing+2436FtvnZDbJqzWusie3faNDQVVDUroUgcHFGwBtm1aainFfbeA3J5J91J/LKit72tAM/4yOY95/bKgMS3SzKr4DF2KP7MGDH9mZqEI+n45SiOwceJF/LKBNie0vsGRaW9GHWxTd/SfMZ2dne3UFATQvb29hToqmi2iff78+XKeZ2thRWwMuJ2QnuVz5nN2draMsc63CH9mPU2CA3E/YzNnjqDfNVcGxBAhwdYSGbVgow7WfT0nJycL8q3xq5UI+H3utukEjQdotDXcBVjQH/LU0t76ROt9dIa+uq+Aj7IstYZtaA1aIFF/RUV2Heiq55uf3xkDqgtwLK1INuqUx8fHy7FTfr/dxI12d9+Dg4NFdwFLAZUOlHUoTUwPbm7evSrGfspmES46IqjVwRdUoIHpEuaj9T/6RF8FME6cnMgAKCm4tUaeUyDZWhubA3LIrFkWvfCZNteVQSJz8xLMDg8Pd+h+Qdx6+v3M2rWrl6HjkmUDJerfbfShY/f390v5pWzGRx99tMOOPNb15DM7ThqSssgyDZRhszvOvEGsjgmq7QJSeA6JQ2awlKgUnu9J/SGeLbXHyO7u7paORMGlxgZtCWIUaEv7CZIcAUTfGp7C+8wKGDwLamxQNZdSZ0dHRzubQ92TTEvv9WelBcmy9Ul0Szvf0DfbIADcyK4EuGapnFnpaXNvdojeJcuZtebiua05GUODhOBiLsfHx0urfZmEOhQ6VtqusptZ6U51RxkL5Nx6G4CAdm89UiBTmzSGgpzWtVp/8+wevo4GK9UOIFa+sj9bPOgTWZsbCg6YLDUsYPhOT+roZu8yNzp5zZ2eeysDAFBGiEzdgzzJtnrKbvkgcxf4vMjUXK+urpYO6ZZN/Ex3NLsjY8CWPZSNmXkXoIDOlhv4l6Ojo4WydQ9+0X26VuRPh29vbxd7n1lfe2Zd2WJrhc+ePZuvf/3ry7ryQfxkKffHuJ58ZtdsgVOSBc28e8krQ6agW2TPiXNI3YflZ/4PDTUINlNqQwAU7rNN/ylylaroSjDlVNraDU25rw6sInzGNLO2c9cRQG0cXekk80YVmuf9/f2yL6mn4xsrpN2/odDeZ2YWp9OGFFkCR0Sm1q8G3CyVk/B5b6gobWkfYxuZBBBIv8bY7+h8bM2FM+tWA2sMcbelvk0CPUqsmezM+nYDz6MngFu3AbRpA73b4CfglPre1kzbwNB6qL+bnfg52dAVtuMZZCT4eDGnwO7Uk36HI0UPyozoJ10yd4BSACiVaS27nuzh8PBwZ09qKXj3pmf0VgBC+zV79PkGSeOuj/LZ6jvG4vj4eKkh7+/vL3sx1cTcu+tbECrLQp9vwXRtaX9/fzm4ojVw/pAM6Y8xtImG7XlfZHUcUBLMnj9/vpQhMF7NcB/zevLBDuponWPmnTNta3d5aQ7SYjUbYeCcT4vRfV0K2nPbGADhc6Az6z6z1hY5zBqSwNmmlplZiuOlXDyLobZrDv1HwcjBdxlrD8htZnJzc7NsfPUMFKmxc8Yya46sdayZ9ZSH1nIYeSnVFrg5+dYj1OkgS7KVqTQbMiZNIX0upEpuKGyyZrjmVLkJcGTfd97JRumYsTJsSNh4gJyZdfsMENWjm9BBsmcOHJDx8y1daVz0yjzpYF+/xIH525jocAGBLAvgKGjsHriyAIKLgNI6Yt/tuH3/XUFkwZUaEVl6jnv5uXWz1uzEM2Zmeb0QOdFD66cE0hoWp2+tdRljWar3grDAuc3eBO0yFoBgAROgY6zNsOgkur1A2DN8FqD0vYLGsg/bl2LLEGWrDV6e01q/NbC2AA6GQR3xMa8nH+yaQc3MktE1cDWzenhYj6hiJJwKapBR923QnKe6D0PiqBzhVYdvfNusZluj8jkZhGe4H/TdVukqStFxA6FxbjeWa7tXB1CQ1pjRcbXm8WkIV92MbDlHz3bocMfMAczMcg+ZLecNDbeGWWpvGzRlhpVP0S3Kxr3pB2Dx5s2bxYibOTYol/IWHDiTh4f17eWlt8xHLdU9vCyUY6s+aL4o9SPoq7epC9LXgpgG+YK26owAr1Z8f3+/1P/Mr3rexqTT09Ml+PgZnZCZ0GmBauZdYDGnUo2lmjnj2iKdaU0Sw9GMr2tk3ersdSTSi9aSUb0dC4pWsJKxdIuP/19eXs7Dw8POXsL6odYXW9unH+yY3G5vbxf69eBgPWCAngA1AhAGgiysvex1Zt0C0HII+WCLBCgAAdAsI1Z9w6I1uKKUOx8+AZgs4/VY15MPdkUUKMmm/ZAEpwFVQK4UiUHYMzWz7h/a1lag7/6sRusz/jRg+j3axviLdjsWyri3tzef+cxndrKk1tw4a89skGcoKBHjNCYK+PDwrl0YNdwgM7O+bqhokmMy5iI7dTMNDRBn6y4CdDNyhjazHt9mrWXEwE2duPk0aDH2LXp3/6Ojo51gr+7CiXMMDt1m+KgyMqJzzeLdSzDFQnj+0dG696qn01vf0u2eKYPhfGwCbnbJ4ZgHh1j74Oj9riCj1Kgj+GoPvtsgU5DXvaLAloPVX716tQT3ZkLm7D4aodivICfoaDgCXLqlBlg1VwGw9SXjfP369ZLxCgrbTseZWTo0gRMMTDtaNcBs/Uf3yDXbYycNINvaemvGDw/vOiutrUxT4BFkyBAg9HlBrbVscsdStLRyfHw8FxcXi85a1wZc9+0ewuPj4+UwDjqCbaKbBYKPdT35BhVOiZMV0Go0MisOGSJq9mIhGIk6AFRW50lROBhGSIlmZnFKnLfPcKpHR0dLQZnDaeBRu6B0r1+/XtqQOdDWJ4rQ0TXd3M3oOAaKOLMGw3Z21Vn4uwgTp399fb3ITXa23Wiqs64BrXTy3t7eQqm2rV9AkpUJ3pxN6SprQd6cBfqZwW6bkEp7NvOByBmzeXlOkaxg+mmUarN54AWdaqzb2rCgbj23nX5kc3+/HuPEMZZ6boZBJgASGtPzWofmRJsRqYECHm14mVnR+7aO2b2C9l1ZGxRh7cmRVl1/LIKTkDhJn+n/Of42QljHNl6RnUyU7EvxsXkt8vSbPaL+C/SAOtsO1BV9j0wEALpFr8yPjpbGdyJSa17NztpYwq91kzyA7J5sU21tm5F++OGHO+yHgOoestotpYnqtK5snc7Q+V8Jdt/k1dS8xmcjtKtdd3VepVTagdliuMDZPTQMplReqQnOSiBSbzPWHspKeZtRtAhu7FVszlsAMsbT09MFgTmkt3W0rVFwXhy1rOHNmzfLHhpXM2ZzZZyvXr1aHKg5eIbxy4g5V07Cy1ut5cxagO/3yLo1Np+zPuYwM0sHZ7MWwVzmUkcF9bc71LP9jgyACtnUzLpvTu1FZ+fMuiG7r0Bq0DX+8/PzHd2ih80sgLoem9bGG2tlHXQD+//+/v5OfWebnWo+qcNHNbcJgRPzPI0M5KPuZkwAku/RIY0sbMOcOWQZE9mad+2DTfpeM2T6KojTfzbq/u7NkdM9esAugBAMRKnz6+vrBXwU6G4bgDAUMytg5w/8Tgek7xd4+ze7BSCtXzNwMjT31tPocOlLGe727RSlf927tURywFrwSe0Cnpmlls3GHut68jRmU3QFd4sDqfUQYLWnLqLAcXd3t1MsViuZWRsAZtYXKqobFMF0rxD6S7ArCuZ0oddmmz0W6/T0dEHjnLQgVwfs93UidUjNRCjmzNplWTrDFo1maH5XakfwFRiLWjkbdRDIjqFcXl7O5eXlEoAYtM+U0kVNmv/JycniDAQGmVGdIPm7dFcKRta/tQVHyAmSMmEO8DOf+cxOUR7qLq1sPD22yT3Uekp/C0Q9IFg2YW3V8VqDsrZ0CVXU7S0zK5iwDqhFGQwZ00E6C7R5Ht2n71vKVcAqpdY1ImvADxW5bXSRZdSemm3qCN6Or0dZdX0uLy/n+vp6x3bo9bZm6PcFIgCu4Oa5dKj2Zi7k0HNlzYFvKRjy/TY8WSvfaYY2swYNMrAXkk6VTp5ZX4dGdvwjwEE3ZXiew5bUBMvsCJj0okGzPklQ5Af6mce6nnyw22ZfzZz29vbm7OxsWaRSTpwsZAPlF2FSNKi7WQAF9Pty05xbaxk9PJgD6N4gzgIyo9jqT0WZMyuNhD5rrbAGiB70Wah2Zg3gFxcXiwIyVBlN0WYbV16+fLnTMFAasHQIhwEgzLxz0qenp8uB1kdHR4th1sne398vXVu65qxFqbot7STLcZ5ngzJkq1FCRiBYGL/A0rM1qweoLzqo+I5aqzPdOpAifM69FHzRNmoX8CqFOjPLmAANHcacNycoE7QmreMW7ZOpdSXTbebw8LC+Kds61enL6vze2LsXURbQckHnCASRRTeSA1KtJwO7GklmZqHIZdSArHofKpX902W6rTzSLR8NmJqFyKR0OfCmN6DvVSz1Dzy2MafbV/zfNglyptfW0XrTLc/lc9rsVLmaJ73raVJscebdNq7W42dmh4rGFJQd813jJlcvfX3M68kHO2ipC++idAwP7z2ztuHKpuzt2SJFxtEiPkVtgGjxGw3W4NAsp8gSfaIepNYAEXFszdoYvgxAtlMF0wRwfX29BBIyaIb29u3bpWGHkxG4Wp9rkZlsNEdwZpyX+hFHIFMsiDBn40JvMqKZFcAw5jYLodY45tZOUJ4ff/zxnJ2dLY7P/TgVuiMrbJC1Xp+2XwtY4bwbbK25ZiF05sxaa3z9+vVSwKdbnKz1JHNo/ODgYAFt9Eng6UEJnNjWsb148WLZTmILDQdnPQpSABCBuk1JMitZ+OHh4c5JH8CVwE133XdbD9QNWfBZJ85WzK22Tf6C0scff7wDEJsZmyf7ZL+todKz1qJKTXs2uVlXWyeMSSNIT+0BTIxZoAMOBOpS0DOzNLL1EAEgBqMlkLInc262ZR5tQLGuZG5MBVwNSgX37RJm/2TS4/vqewBBMn7M68nX7Cg9QyjlQdAQqXScMxAEIDTOojUetaQuOA4agoZkipBm1s3W6iyl6FB/EK5xFEXWSRiH4jdF9Dlz7skNzUQpeukqzss+qAY0n7UBFY1prEWxCtXqpKXHGASDKg2C2rBupVN9xhFRnleqynmExmY+mnM4TGOzVqW9m30IemQowPVi0H7PYc6sB4yjuxr0yMJxVW0maWNL6zP029oLJnSrdd3SUw0InYfskxMky+pD56buwpkCQagoetA1PD8/X0CSJg3djgWfwIWxdKwNItalDQ/kDeCVQjOfZkCcd0sbbLp/ehB2a5zkWT1W8ywVaisCmt2cSrc2iLInhx0UfLmfDebq4Z5fkG2d+CBZbk9EwmwIzIK8MQKO9H97ogu9xWyUinz79u2ycXxvb28uLy8XW3H/AkO+teDqMa4nn9nh9v1d1FZ01MxM0NsqtkDHWblaHzo5OVkyqXanUXoOs1lK6ZAe4EuxaljGvqWQGEjbdj2T4tkXWOrt9PR0UXCfRdkJljO79cCCA46SQ+H0BF5FbM+ow2F4MtrWDQVBAb0osrTr7e3tkgWdn5/vnJepNtTT4jlRz5MZcDKl03oIdoMLHXL6vXmQA51pXVGQ8Dlre3h4OGdnZ4tjB5A4btc222/A4nA5u4IJh3mjrci7lJlnoww5P3KwXuQgiAMb3SqAwm3m5eR7oMvYZdhtAjMPuuBUk4eHhwWgNdtuDcvPUYMFRmzNODyHs97WqFvLbj2tGa97CBhYi8qyJRP3efXq1XKyCN0TOAr6ME6eyVeU6lWrlnH6jG0TzTBLFdLjskB+B6gAOPUBapSYkf39/aUcAChZY3O23ijx09PTRYZNHB4e3jWyedavnI35TV7oLErbOlmdtyOaekAt5eXoZA6MnrJaFLRgHUMRWw1Pw8HDw8Oy+BzulqKi/BBX9wS2JRyq14rOIThei0NQb2N427pVjaPotAhfoNs6c3Ns9x8n19plA5tAzZEKRJyXIO4zgIkskYxk2uTpPmdnZzvNNKhZGYhMzribAbbRgRP0JvcPP/xwqb08PDzMBx98sARCa8EZQ+d19Pv7+3N5eblkEoIwBycTaIDmSEpnldrlPAQeYINjbMek+WIyzNuc0dla8gEVOtLXt7SWByiUbSjTIEuynuYIqAFSbIqNCMpsiR4XfM28c6rvv//+EqA8RyCynhyujKTOnWzZGsdNjuTk5wXU7TYE5J49e7aUA9iOgFg9B+Sur6+X79KlmfmGMbiqA2Ta5g9jaydt2Rby6zm9/UwZgMrCfQU7euVqicdZmfUJZEDnyIPPfMzrl0Vmx2g5U1kUI2x9h2I1i2AQvt8aULOIInGBYWZ2DBdik/ZzeFBS6Y+Zb3w5ZtuEPbOIq0bvDdTk4FgwhqYOUG5+Zn0tkpqbzKG0H2RYJTf/NqS0vsRxUGKZFSde5+FnbdsHFIpOe0yU+ber6+joaJEph8ehaPyQ9XXMdfaeMzPzC7/wC4uzoltf+9rX5s2bN/N//B//x/zNv/k355d+6ZeWQAOUyMjacccRCB7mfXR0tATy1kkawBuIZW4CaWtRbX7xjNL4QMzZ2dmiyxgQTgkwU2P1fsbWZ1+9erVjV2TI4TZL2lJpXU82wimzAXVA+mTNS0mTgbGiiVtnstat0bUr1Nx7xmMbONwPCDB/umRt6U0BJubk2bNnc3Z2tpQcWrNrQxBau2ewWle2SGcaLIFca41mtKZsdGZ9vx2Zl1q09aoJAv3qtgP6xLcB4NadbW/nyTe27uvzpdsf63rymR3hUUSGMDOLk+ewWwD2B0qlgBRqZqVIi74h023drJRK6cyZ3Td7z8xOB2Y33XIeAhAax/vg/FvWadxqQKWE3MOJFcfHxzunvzMg4xPk0WA9rqk0KAfVfUq9B3THkNXSWriemZ2gxlmYjyzNHkHAhCNsBmidUHqQbtehGSq9MObb29v5v/6v/2v++l//63NxcTFf//rXZ2atkd7e3i7vD/vwww/n4eHdKTPf+q3fOt/2bd+2OMTWb+kb5yPIkg35Cx5ocyfkA2zNuGd2X8ZaahCNbE0FOjIDhEqJktuWxru/v1+2Awg4MpbtPkiBg7xk430Durm0A5CT/ZZv+ZYFDNEfz+lmcOMsoAGc3F9QJR/BVsBB2wMn5txgJcgeHKybsWungEeb0Upl0yuZkADsfsBQs2QyLPjqfd1HQGtQbTAqcOQf2E7rvJqz1OnoAdmaJwYF4LN2dG1mlrcotAQhCKqzop3t/22JoRntY1xPPrM7OTlZsqhy2wIF7nzbhjszi6Jp0OirSiCgHiVVJFpn25ocI5iZheOeeadoxkoxGKMxUTLPbo0DXaTjjOPEi3PMOHG8PrTa+tjMLI6M4R8cHMz5+fnioNqcwfG3btUaChpSUAEqtvKamR0qC0IEGARGNVLrYh2BCYYioDPorod14Bw5kDZlMMqXL18uWUIzsZcvX87Xv/71ub+/39ksXOqw1JlnlPZrFratY+zv7y81VetB1tA++dKpdhSbo85Sa2ccHEvrKgV6nufZxqRxiuxm1swbeCArTrvZA6cp6B4crO+UFJSsWbPQ6lep5WZRAklLEBxu51RK2z3ZkDqjGioAQP/29vbm9PR0kR2gIHCymdanbcNgi91C4rto61LWno2psE6yVPLymTb09EQfPqZ0p7GxPT7Kths61EanmfX0p9YZZcgtndirWR2kk3QB4AVKqoPG9VjXkw92KCcGUgqhdBuno4mg1J7f1YFvKbf7+/vlvu5Z2qmG6F51tDOz89YC9BxFmJmlqaCbNHsgrzk242PUrmaEHDsFRYGUJkKnoeTaSFPER4YMWcZD/qV/fMezBF6OuEChjqK1kY5PxrltspGFdA8kNNtx+nmfT6Zv376d7/zO75xf/+t//bz//vvzmc98Zv7Rf/QfndevX893fdd3zcwsaP5X/apfNcfHx/O5z31uPvjgg2WMnO7x8fGC3ovoza1NA9Zb6/0WQMmiMBf06+FhffO0lwIbHzlVJ8myVKq1IyN2VGbB5nYBpCAEnSVbat1JZi5w1V7U+VrzaaYJlG7ruNZvZq0nAYvHx8fLqTru3Tpxg47g0Oei8wGPUvX9XB00EKv+SycLTqt37eYGqmq3dJaONMCVDmeD28YbY3CO6Zb1UBOVlbmngFobpwvbbVi1wZubmzk5OVkYNffpPkh+wNiVU4xnZmVYHut68jQmx8yhl4dGJc3MUvDlwKuYnGYVss0jgtG2iaL8PRrEvzU6HB0dzde//vVl8Vt7aKYyM4vTV180dsEXQrYZFpoih5OTk53T8EujmkedvYCEVqsxtZal/bncextS2qAhGNdJqFH5eWna4+PjJfDLoslEsJxZqSFyNxcUr7UTBGdWB83wZScdrwzj6OhoPvvZzy4BCOj49m//9rm4uFju++3f/u07OiJDbfYxs9K07YhstnJ8fLyzz9CauG+zQgHq7u5uWfeZdS/Tmzdv5uLiYjkphRzqiNGIDQaCi+v+/n6pf1o/gMH36U/p7GYbz58/n6urq0W3vKzUPDjD1l7pEXDQsdr60nolnWEXpdlkMgLH3d3dAgw1oLkXgFCA+8EHHyzvlnvvvfeWNnpByNx7rF6bjYzBd4yPfPs2AM+XTRcMC4oNfn5HX4D70tFlFNQk9/b2FvtrRkX/21z18PCwHE23ZSb6NyD0/vvvz9XV1TccqUdnrAdmBDCrX32s68kHO06CQ24DR9EZY5PBlDtn3KWkdHg2y+M0ISOOsx1eM994SG63ArQz0LPbPIImouClAFGSlLP1roODg6UV2fc4T46n9InW7WfP1hdECiKlUMlXtqTLTn1EYGn9cmaWIONvNTiUbTNt4KHI9tPeWVZE6bntoOUE/ax7itzXM1Ckrc+20eQf+8f+sTk9Pd2pMer6LC0FVHAMW0Rd6qnUUWsb1kZHoiDgntZdxs+B0yeAZavPAhN50KHqzSeffDJnZ2fLCSjVx4ODg7m6ulqyAWBORtxuwDZpnZ2dLeCMTnWN27gkSHkGO2tLfXWC/W2zCtkTelWmrd7Klv1fdkxv3K/UHdkLnhp42I11NW/7I0sB217gOYLizO67CNutuF0z96J77mfdvXKpgIq98Ed8lhoaX9m5z6wHH7SZpzXN6vbh4eFcXl7O2dnZAj5d9WnmLkh6Ts8ufozrydOYjNvLASEV72XrXg5NDEX+W+fcTPDTMrym4RzNNkvyGcbL2CA4SiPDYFgCMWqzlJ7uSg68LzH1mTZ8cHY+g0bsu63MmzG3WF76hyPd1vLQWN3OwCHIjIrkZnZfE0RWHAKaTH3l7dv1fD9Op+/6c08BrKePcIjoxJm1Q49DLRouWle8b3ON7NQzIeUefowSc5J8G1Gst/XinH1OHcQ5rmRhf5Q9lLKdvuaHg95SZta0jQ7N4AWqy8vLBVCguwDCk5OTHefsc232kqnR+b6JHfCD9o2NU7dOPS+TrLYUOP33GW9BaA3eeqn5CqJ+tr+/bh7n7JsRtn2f3m7pXGDD/8nEszEc2yaR0p/slc2SlUAGdF9dXe3UhK25um7rnYK+MbRPoS/7JU+21kMsfLfgg02wq1LPaqB0jx9ojd8697gzz3/M65dFsCvtofjcTh8omoNXD2nmti0Eoz04PE6zdBWD8XN0CRTKMC2seoYAbc9LnUWpu9Ybi+zUaWbWTdCUSGZQGtDbD6ArQa/7XCi++QEPpY8YTbM6CL61GR2xr169WrrQGLDvdV9izyXspamnTkGwNc6uGTlwApC8+1pDGT7nB0X73ZY22majnuU+0K8xySrbVUjmKNPWRjlEOlUUfXp6uuhPmQDre3Z2tjQ+GUODARvhvEubt77JTnrcVzdLN9j6rIteu6ef+SMzo0vG5Lldy9ZTzceRb30dDXBpvD00wPisXQFL7QSt5vfWvhlJ9YkOWCN+ANAQCHyWb7Hm1sT6sbvqmvHR6dPT05lZ30rS7tTSo3SErW+pe3JDO7c80PnSZYGJrc7MwppYS3KgV+bSumibeQqI9vffbYd5zOvJB7tSLzOzgyJr2NuAx2FXUcs5e0/b2dnZjlJRNIYvI4OG3Bdi1hLcvSmQ4dXV1Q4aRBFCsDPrkUm+2z1DparMiwxa/9EY0Iy2Na86Gc7i7du3O63MLVbLmL1ZvS/3RBHW4JtdQM6CYuka6yIQqKVywpxOGxHayGG89oT1TQl+T5beLN3Ov9YgOT41L+NXbOecS12Sz8zsUJ30k5ycvkMn6mx6TmnriS4OzXescetqPm+PFYfX9eMcyY7jLjPhhBlBhdOsbHyvL7YlIwGp9FadO10r5UteMrNmSGrgMhHfa52TzQiwQGaZCwFctgXQtgbfPZ1lG+iLWmJP7wF0t01speoLaIGa1u7qt8wJwMOWAICCSOupZOxva4xCBcasa9eryUCzXnMqLS9Dnpmlc1Vm2bNpX79+vchK8GcXBUyPcT35mh0Bl5KyMJAExShaOTo62un8mpmFfmkWB9kJYu7Zw3y1LxfdQvdVSo60tYL33ntvef3IzLpfDSK296oZpvts978wMIpFkdtYgnbhaAUTre4+04zm7du3y5FX7dqSlULXKFaUI3qye5zIp7RQMzuZqnEIwrJSQYcBtQsRFdi2dIbZmus2mynV2NpbT/Jw8LXPthHFGNzbfbb1WWv66tWrefXq1VxcXCxBnINqPVbDDhrz1atXy9pq/VbbJBtXnZL/c7z9XRE3RwucoP1LR6mBlhL1/LIl5sxGi/ydHtIyAIpPJmwt3Ut2DjA1O2rtkOz70mbMgvEJWmzTPQUOAMnf2AvBsiCnYME4uw2HjjRYFCS0AaX7MNkQuZELqtt6GBsdaYBp5k7P2sBEjwGK+jA+QvMY/9H6ovG9fPlyWU9MReVYvba2x8fvXvz8mNeTD3ZtNGhmglqrUbosMORTZZOFtb0eMmxKjsu/vb3dyWyMwzP7M58pKudA1QlaKOY4Wtszx3acoUwhYEFDC3gzX0Y7szbwtHGmxfmiXOMUjDi+mVlkBUCoKUL6M7MExGbPzVBPT0+XjKwNOa1vuEepkzocBv/69es5Ozv7hoDDoDkC8rFOsnkyMa8i0mZEAgckC/GriQA8gvXM7DgC99YsILPiVMyRk3779u2cnJwsQaEBrC3l25NdKsuOvY0vzUBLXQMqwE+dunEZj2xpZhan2i5dY25W46W/jrUCMtqYVQfd7mCMAHk16NKbngPZ0kP3ZbartSUDzxQMtzRln0f/jFXTSE9p2WaPpfMxQWys25hkqIeHh0vjHF1C1fMnWCHzF7Bc9/f3Sz2aXaqr1xc4AxhY6t/VTTLzf8cpkuPR0dHysy27UCbkMa4nT2OWWsRLz7xD7JxnawOogCJ4B6Xe3Nwsjsv9WpxuQOUgi/CgMQvNIXDKjIWB9GrW00L1zHrItHman8BjjgKi75Quap0OsjLWZmqlTiDd7oeh6OonrXG1qA/JNlsr1Wsu1gOF68+bN2+Wg5jbpt+CuWBvPUrpvH79eifDdSwTZ7GtEXKYTm2ps+b0G3i6fvQDhWvMpcFk35wmPepeJ4FTACYHuuMzBR7bjFLmSHc81/q2VjUzy6kc3SJBR0oj+13rnvv77w6FdjA0IEHmgppxCOytxzlSr92Z5E22Z2dnC6Ak09YAC5DYHofdrARYbe0b7TYzS0MHXdjaXuuW9LwBgZwFsS1VC2zQH5QkWy+AKD3t6gkkstcyNhgnYyuTBOS+efNmZyP9+fn5TretQFybomv1JQBrgaD1Y5MFa/wkPyfIP+b15DO7BpS+wRoV1oKphSxfDP3JhDiXbqqElNCWrcNRHnSqe3P2bVFuIDo4OFi6+zjfmd29ToJyX0XE6aJJSuFxiEXcBwcHc3JystMxyegbhDgZhk2xzYk8yZCBUmJOgKLLVDmWOicOl6EYC8cigHM4HIR1gCQF4XZKMsBtFlz02YYghgiQlHImg86fzOucyEvDkTUABBrsSqmenp4uqHdmfXmnfWkPD+/2MclUzIGuQeddEzpiTRq4ZFlsRLCh+55LL2ZmqffKyrZOUGBtwNzS5+ptPdLOfOqIUfNdQzbRbKaNN623VQ/VlKwt+ylbAQCyVbIscyIwNOi1Ftd1LfDsBdzICNm7dWUr5qwzeEvB+7yxlqZu3c88S/VaVzqqz6AljjIQ5NTmn+o938KOBdUCZLZTPeOzyfExryef2ZVC6MbJmbW9uW3ZEIffC3SMn5MTVEr1NSNgXArzqIrSHBS76G9mdg43LuU5M0uxmKOliNDv9fX1zhl67s1Z+xzZkEufI8jMrLUl/+csNKBQzDZBUFT0JodtDhT94GB94WjpRPcSCK2J/6v/ybZbF5pZKSkZTjOp0icca5Fwn+U7n4bSOaiyAm03Pzx8t2Fa0d+4gBvft84yU0HcOlV3zs/PlyYMa2H7RbNBOrq/v79krIIHPaZnBUOo2daKXAWFuvkODg6WLQF02+eavdB5MgKs2I7sUcAACDEBaN/Wg9Qna+Olf63D2dnZDk3fgHBycrKsa2vlqF56THd7IoxaMl/R+vbWqZfxMb9uGQFcgYluwBZw7IVr12WbcKz5wcHBTvZMXtvgITiWATk8PFyOVtyeU1nGq/XP2sC2nm9du/WmQZx+Ki20Z8K9H/N68sGuwQpnzoihD85lZhalQ2epwVDIIvXuCZtZM4yZtYVYzQMVpSDcWiHlrtJAjxxQa4KcsfFDUP7dUxPKhZdOU0v0HQ6ibcOcTvfB1UkJSEW9qB9y5WzaXMDZMaTSWppRmjlVntbQ95q9+lvNTcArXd2aTdff/40d6uyJM9atGWkPBG6doZQNpKw5oHRqUTaquY0uHItMSMAAtMi+p41wwBxMP2/dGtw4nQKOdhne3d0t+9WsiXG0SULQ6fvs1IBKV9NjAMOY/DEWOtUAw5bs92sgsvbdJsJxqncDks3WPIdPcFoJedB7vsP4PIvel2ZsNlkdp7O6bUvP+l4BZKljoKdbU7pPuFnVw8PDDlgqACJHMi6gchUsz6zn+JYFIRv0KMYJ3atUUGqbPstMW6+0DqWfH/N68jSmZgQOibL1fVCMrA0GFLI0lsXS/cgwStmVrrSoPQkAouJgLi8vF+qgG5dbU3EqCsfe53Iq7ZCbmZ0OvmZ2nuE7Pk/5mgkzVtlXTweB3jl52cqrV6+WM/jaRcpZtAu1HXQcaQvUHUebLnpShEyJoQvWsoA211iPOoTSvXt7e8tzOo6i2x5D1SxWI1T1qI1RHJO1b/ZsPIKnOlWpMKBJQANCrKFnGWsdCP188+bNnJ2d7QCKmXXPW2u128OkP/roo8V+fJZubet5bAdb4qBxOlgWBbDwc/XUZoOVIUeorkgXZnbrPJxzgaTAuO22NC7NLe12lnWQQ+nX1gYBGpSvC1UqIPdIQTrTbJSOFahozmLXtowYU4EfgNRMDyixXrJMn2tTEl/FZwhudFrD0P7+/k69lk/w+9LdfE7rkYBwQRLwAVA+9vXkMzsBywKjAho8um8N9dGDcCFC/5YBtN7WZgaGwBn2pIKi057UMDPfkD3VoNA6aoZVjNY1isRkJ1tU7l4za3cVxMgJ1in4c3FxMTNr3cJpL9CofXXGhodHGTHwmdkZP2dHrgIFp14HzwnIiGUd1s/9UHQyQcZJLsb8/PnznT1g3dMnkJHddj+W7tJmyM220D51/L7fLKzvzbOv0f4532mjCDltM6UGAltBGlxRo6Uee45mz5A1Z/JvU5TAWmTfxhLApmvGeVofDliWXkqtwXJmrT012++xb61FNtBvaVj6QYdbayrIw+wIxGUEzJNMBTt6VjpaGcPzjR0IoR+t4+/vv+s87raG7ptsfRQw96e6Tg6Xl5c7fQulXfkkPqOyUqbgb4DZ8/Pz5VnNYK2TNe/JTwAPXQU466tawtAo1sz7Ma4nH+zKc9eQZtb9VNJlzuLq6mpmZql/QS8Wopke52xhIUUU5cHBwbx69WoODw+X4Ehx+h30o+yDMTfD4WAEI07W2NF3nDHnLbhTYg4MOvR9mR9DEYzJ6tWrV0s3KxSvbsHhcRDkjaZhIJpIamB1VhBynVqDXN8Lx3g4fvOHmI21AUNWb21LS86s4EH9wngEWL/vZ+sQt7SebE2Qaj2HXvqOP6XJ1WwcE/bRRx/t6BhHZN3p0vX19bImHKm10YUo+5lZa84za4eh+wE+Lj8ro2EdG1zpFXDUOhGQpTZIjmpfsruZWbqozc1Y6cj2FB1BvJ297MG6mYestPW+ZlmtiXc/aNetpQS20reEtPZFR1tjpaPWxFqrn7GL0sxd/65NbZiNqL+xE3Vp5ZBuf+jWhDbjlQWiu+5nPHoc2DfgSP6e4fkN9D6DCdvWGB/jevLBrk5cMKGsbRCAkmRkFtaCWhQLPbNSPIyF42BE6EyOY2btFmNkfgbN15kKGByI4EqJfZZy1wBko373+vXrpSPR/Gw1oLCeybAYAefd4rNx+F1pCBmpAN4ahA2t5Np7zsxCscpiGLtxqwmarwYG8mnb+JamNL8e4SUgOZpI0CpdKgPQkciRFamTIxqv7fbWEzDhPM21Qbh1X7ojgzg7O5vT09OlsYJTF1xKlRUk3d/fL1ltM4zWnQEKcnVv30UJWzNOWsBTJ2ZL9J0+0AOBzVhLIVuj09PTpbHG8wBQILHPa/CShQI4gqDTUJoxWYeCXuDNGsisZKHmQuZ0BXBwv2ZcAnVlYa4o/4IAerqtC3tuyyUYgAIln6V/7SYvs6VuSG7mP7PusRV06TFqmL607stO1BEFSf7S2skaew++d5vFPub15Gt2M7vvWqIQahIMADrb39//hlPPZRUz68HSfl7j7ecpIuSH9puZJcigWuqEKCUDgZRsqC06dV80ZZ3V/v7+QmXJOD755JOd2k/nRvG2NSsIsV2Fft6g0iyn8nDPmRWhQ93Wo7VIzyVrzrhzK50s8JpXka61qnNmlGQoi4QuizIFXI7e961rN/Izevc1xm1tzzwFOEX8UpLWGV1JHwQduqIOqMuTY0bnqa+1sYqzbK3NPRvYBWLyE8AEoeprm3SawVkDdRsBjxw9t6/PEvQBVOBEtkMnSm2Ts+ayBiGg7+LiYlmHBpU2nXj/X2v6Nzc3SzNMs1nP9fk2YbCD1tDJvNk9OzVfWa51wza4T2nDbiUw9wKZ1jgFQ3q2PX5PwGzQN0bPZw8tM7BH9mUMQL4xd29zj3Ir2AQe2eeWTXiM68lndpQKopNlcHgM0cJCgIeHh0sXX43ewlpczoGjLtKjWOXHiwgtrEV///33FyNsdx105SSJmd1DWUtVzey+8bqBqh2NM+urUQSIbROIjLTZovluA4hxCk7qQuTdZgFIcmY1ZIDEHGS/zXS7mdq6zMwynu3vGB5wI3NuLUADgTG0SUf2JKMRMJ49e7bU+UohcjAcCCfMyW2ZhL5DsZR0P9ushwPhDFFUZEAH6LYAygm2LtIMg44K0rLuBmx1lPv7d3vL6GdRO1BTfSFretUM3Hjef//9nfWy7gU0fl+A1eaJBuvtvi/bNczLH7K3RaZUItty0LKsA7gUWFvXbHMb9oVjb0PSFihbd88mmzYqkW0ZJkAVS2NLEf2Q1bFnYLt1zZZS2Ofbt+/O4QW29CuwITremrKAqnTBLviDvb29RW5Ahz/01ttfHh7Wc04f83rywY6DskAWtehnZt2YWprPfhVNKzPra2BmVoeu5dZnGXHrMuW2r6+vF5qrjS1bGgv9QNm9JYByMBoOWfejwjSEWgq0m4ihq9ZuGJ7P1qH2wGPzau3ERcnVXgQYgb3oXsBHY5RG5ejbdOKZBwcHS31BhoMaLMpmPM1MBcfWBdTVBOc6xc5z+31XO9I4Nw6hGa5xknsD3BYQtWu0DVPmyGGiooxTt1zpYRnAzLo3qs/k5HoAAX3ANjSbsx50sI1SkHpp7dYBrZ/5yWw4Qpk/h1egJthua4itXZflkH3pHDSuvb316Cy1W/rVe/h3tzQ8PLyrC7feVMqtAMpc2d977723c4CDuQPP2y5eek629L3lGVkzoPDw8DDn5+c7QNMY1cWsGbalQQnVWLqyHaoFhe4vsDbrNueCfYmH9d3b21tk2aRjS4U/xvXkg12FaxEtPASo86oc/szsIHrHVakndMMxJ9n6TB0u2qeBRACxl4tD09rOoLpxlSOBZCHknkYiazo8PJyPPvpoZtZ6DDqO0re+A5mhSDhSqP7gYH2tB8OUCXAE3ZxMsY+Pj+f09HQxaAa7pS06nlLDaD1O3Yk0d3d383f+zt9ZDKRbSV69erXIDdom/9In1Ydm/KU3m8lydMbbRgXgwBq7nj17ttA4PQeSU5MFvnr1aqHTWkc1NjI+Ojpa3uosWLUDtHRmAR3nCsTNrJuxyffs7GyRrVqL4MnJ0ifr0g5TuuP3ZCWbJPeCODbgu9iGBiZgx1idJtIs2XgE4O7/rA63XihA9bnWr2tT2xX4OXU+Q6dya26CPtBb3ZJtybxsQ+JDWntuByabqh62M5dNtcNXxzGbLZvkPgKfQLu3t7c0tpT659+sD2CPEWu3KrvcgifZIebsgw8+WBgrfqcM1GNdTz7YFYm2MN93XlFEC9TMj5E5Rb8bfgXR0peaH2ZWGoly9uWepZggQEGTEhtnO+lmVqrVxeicRoCuOD8/X/5dZ6i5BEJUd3l4eFiK8Q2eZEKpocCiYrKaWbtcFd6753CLXJsVG2MdWdHzzCy1zKOjo/m7f/fvLk5ZFiDQu5e5WvttY0rPNi21oyGCk6sTOj5+9+60rrXPcLQyZ7LhNOikeQpmh4fvTlzpG8Gta3X08vLyG2jhq6urhQLj+AU1TkRTggDe00lag6p8yK5r3Ay2NdctnSXYdCtK586G6FUDh+AOmHTjNKetw3lmfS+f2lOz/GY320BTit5RgqVT2ai5t9Zm/diW9TBvds6X2KZThsd8gTG/b4ZrTx3ZVcf5tS0979l8BttE6X4a8Pu0ewk6gnaPXtNg0gYZQJvdA7fPnj1bfoYOZ1d8DtBd6vWxrycf7GZWAytFwthaZ2FYKKPumbm9Xd+fRXHqJN27heEtQhe0OA0Brnuc+rLI1tsYJceCxuF4jo6Olhd51uAYD8fb2pOGAgraVnjOvXSHwG6+29bmmXV/X6mMUkoHBwdL9mDuDEcAZ/jmVwqttNmbN2/m5cuXi/MrlbqlFasHDHFmfftza081tJ6KD3i4p0DpuwIT+sfP/C3Qy/Yh477qiMxb/3z79u3y7B7mLdMWoMzZ2nNcpSv9zppA8OjtggR60kDd7MIhxcYuOJqXgOle1sX6cZZlUKyBwG387JcNGr8sBl3fMZtvnbp1MRZ2gaUQnPqC59Zkfc8aWMu7u7slqwSizKO0aPeiAcB3d3c7B08AL9ZWtsNnlOIrKCIjABDAaZeoMQlEfIC1aL2YLL0uqnJwzzZBdYz8GX1SA/S3+5O3tSAbwfExryffjWnBZtZGAE6TgKE9+8oIHDVHuXR0uWRgW8TOgFoPsXC+7/fqD5REUbo1JkHy+vp6CVwokAaWolL/r/NkhC2sM3And1B82QEkS46CrmDVrBlyMw4GUWqIU6HwzW6LlI2BQ2dgf+tv/a35P//P/3Pef//9efny5fzSL/3S/N2/+3fn1//6X7/I3nzRV9ZAEGJUgjd5mwPny3kK0ldXV0uDQhmDmfWcyXa6Qa03Nzdzfn7+DU1LpcgFT/ckbw6jHbuVLWd7cHCwOJKZWU7lQYFV5wAdKHp/f38++uijZR0dqkDuxsueqgvt3KWf5kbW5AeEYUTIiH2ZT0Gd5zw8PCzv7GO3pRJn1td2XV1dLc7SGK2n+dMx9sWOUMRAre9UD1CM9OTq6mqRSTNxoNmJSwW/tRNgj6/h/J89e7acnFLZAz/d3tPTlwTA1lILtHyuTA3dYCPkwWY92z1lm5235wq2grSMmQ9g29fX14tMS1m2/+Axryef2Vl0qffMephyHffDw8NOqz5FKn0xs24kbn2OYkBVqCGXeo7PC6LoGrUo1A3EU/rgzZs3c3p6ugQFFEODBQWbWbMX1GmfDclyRg5qriOAoCljjycr4mMk6FNjqQPjGBhRa4jOrmwnKKeLDrRuh4eH8y3f8i3z3d/93fO5z31u7u7u5v33359f/at/9c4+p9YxIOxSMxwyufZoIkZcykkWaW22R82VeoPU25hyenq6U+cqiOAIZEDG3izFes2sDVINtKWfm2EX8JTJODg4WLI5+iVgOvED0CiYMeaOQUbAQddpt1tvb29vPvjgg4VaQ083K/R3s1J6x2k+e/ZsCRylVzlb9slJk0eZBmtm3QqCfb4NGD6nflaWASBpXZNdPn/+fDnqC4VMp9RCb25u5uXLl8t6qLHxGfxUSxnWxOes18zapd2ssADDWtIFlDb7Ml/3cvxfx+DN482YZ2Zevny52BodtmUKkG3G2M5yvk5W3Uz6sa4nH+xm1k40TSal+vqZt2/fnUPH4dTADw7WN1O3SYNyQewUAKqZmZ2fcWIWl9M9Pn73Zt6PPvpoMYhthnF9fb3TuVUnRIHbnl7D3QZCz/B/Tt15dT1uytsUUC0CEANqJqkDsw0rDEAmXIoPhWtMft+uyNKEJycn85nPfGYZ43d/93fvNG+YS2kz2Ys3gJey4iSLyO/u7ub6+nqhaIEPz9C44P7NBE5PT3eozfv7++Ue1s56dO2smVMzfB5oktnSBxTwzNoVLHhwiKenp0vHm+BTlC9wbBtZPKtonS73nW3+lMLrQQRsyn3Q0e7NCVsrDVmthwsen3zyyXKyBqAHMMrMZtb3PbbruA6dbDjdmfUczTbVcM7W5u3bt0v9zDPpJJnPzM65kIAvfWnGDnA8f/58OaigZRXZ497e3vIaJaxN63bsqo0w2IQGdsxEaeXWZ+kZPydb7hm3/BAQcnx8PJeXl4ss6WsbU7onE2ju2he0twnI+j7m9eSDXTlg/DKjLvqWSchoIE2GJ+BRjLOzswWJc2ylFJvFcC4z6/lxDILDPz4+3kFq+PPSEBz27e27t2a3DuCzHPnDw8PieO0haq1gZt2o3QI/WqMZ7ba+KMhyJH2lkO7D0oZkJzAaS2uJ3U/l562ttohv3BcXF4sjNRb1CAYPZEDMjNUcOIw2YGxRfBuOOGzAoiCGHrUpyb0EOOvbjE89b2bdfkKnzLdHlrV2CcDILFCT9/f3c3l5udM1yaH0VBqvmKkd0GWAz/gFgvv7td3c8zmwrhtQ2RrPzHpmagM5OxOcmzmVhq/9AH3NWpu1CEptwiiwK1VcoAhEsOfKmg0ZU9980MyZDlh/NXVApbQgnSdD9xeMsRPkaSzdjL9tlMJKmQNfQ9fUCc1d7RNdbE1n1j2v5Kbkc3d3t3RaszWMgqwScyQgt+O1/pJ9oX3Z2GNeTz7YtTArM2DEkCLFsMgzs4PyKSDlm5mF/kHfoLoorkD64sWLpRZW9N2uRFsOOLjuR2ttoMbA+bXQzPFSOPTk+fn50m5OFhRJbUcwaB2yGePM7mtOmnGhGVtbevv27dJOry7Rri335cRKl3leT1tAjVmD58+fz7d8y7csQdj4ilg5aZkV9K7F3zq0ldv9OUX3UcNyQgeD5DDNzXe3AKj0jUzj7du3iyPRXCRLciRY66WeQyaCxcPDw84ZruSp+F8HJDDQ7VLuMjY1Rs6abAvY0H70ErNB1oKJrufS03SfvvqsNaiNqGuXui/Nvz1DlszJRUblKiVHFtbn4uJisftmqUCK5qTqSIN9basMkCBiDcigJYEC5Da1sJfq2czusXaehYrmVwAl2d7M+tqc09PTRa8FQbW/HtZOPi1ZfFpG37o8f8S+y4h4x6O1cgYqmbx582bZgtE63mNcTz7YMcI2hnRfDUQjQyHwpv9QCYXgrCjvzDvlOzk52eHmKZrnoblKOcgiZRAUTxZxdHS0FKl99uzsbPl8qc7Wciiaf3cDcL/T+hLUbLxF3Jwd5ws8+DkH0Q3OgrHaoUDG2Vif1qjIGGgQoLY1q1/1q37V8jYJ2RyjqvNsV1hrGLJX8+XM6rxm1iOO6vQLerq/qHIt1e05H3/88c7rmoyljr3nZHZe6C4In/yALQFbECyNxflaSw5P0G9tjS43+LemKTNpgKMDAMHMukWArNlCHWkpXrrfQGiLR+co46SbbJcO9nmCrfkDYK3Ts6n7+/t5+fLlPDysZ9cCD44rE1isn7er9w0bW7q6wYwsAeBmO44npKvN7PwtQAhS1UP1Mdt72PvM7PgJfwCInhOLOvWssjW+16YY83vz5s0CtoxRjbtlC+xF/UCZlfqZHlL/WNeTD3bbji0/o5AovnbmQeacBeFD3ijOZhqQK6VpMJR9NBP7tI2zGjUgJYtvDg2Svkvp68wFEI6TE/D8mZWSM0d/BLBuNr+/v18yjZn5hqD38uXLnTpnN7S2xlJwMLNum+gxR+obHHZpIY7Uz9F7nEnp4lJEpYbIwRscrPnr1693uuGs+8za7NFOOd9zD7+3nuhCc37+/Pm8//77O92h260t7uE7HLGMyNpxXDOzZJoCIF2zLYBzFhw++uijxek49qv7KemiIISOpq/0QtbkBJCyEdV/WQHZCDQCLHCHgm7gpyOYA3Nr6zyasFkv2QCtraN1nQQm9HizVLrPLxgPil52LAhUp0vL+07HJUB7lQ9HD3AC1L5fhofudssA/WUr+/vvzsUtI2Pe9Kg/AwTaeQnUAT100l5CtgZQ1q+StefMzA7LM7PuSa0vpRdsrnJ9jOvJbz3gxCAOzpcTkMHpiETRzKyddpzlzK4Ttam6bc6oqaJsRsmRFdF8/PHHSyCBoCy0111wnhSphWCos8YiULRO5r6carOp8vrG1kBeZyx4n52dLQGDo5Pd6CQ7OjpaMjqbWa2BwLulZWSz6KmTk5MdqtP90UEQKrn0wGyGWRq47e9kpU5gzci68rFeMpg6pXbI2UDbGiS5aUXfInbOxf0FI0BAViMrnJllzYEy9595twWAvOhA6UnUlfZvWyk49Dq/m5ubxYZqC8BUsy71OXMWGK2zTtjb29t5+fLlTiZ3c3OzUO0yvm0jTIEGsNauas6fvZX2a6YkIMvc1L6MB4grdQ7coKeNZ2aWg+NlkcbFh/j8zOxsJzAeWbc6ru8AKPTKPQCIbmLvdiI6AATQR/Kn962HPjw8LLVUvkNGaE22oLD1zeqsQEwnHXbBh7SWX0pXxtzeice8Hj2zu7u7mx/+4R+e7/iO75jnz5/PP/KP/CPzh/7QH9oZ+MPDw/zIj/zIfNu3fds8f/58vvjFL85f+2t/bec+X//61+dLX/rSnJ+fz/vvvz+/83f+zrm+vv6mx7O3t26aLb2kdmJBBTm0TovLPVVAbY5RqRs4BaDZxMy6KRnFQ8Fvbm6Wl0QKRFVC9ysabmbUjIWRMcTnz58vWUgzSIrofgypXW2MXpYgy5mZRW6crGxUEGuXFyek2xKaNobWp2wBgALbKED2bRIgd4CDk/Z/gbsH0qIbW0uYWU+ZMBcn3MugO/fKniPWpeleDw/vaqV1/DO7G4VndmtgQEuzuTZ6fPLJJ0tGZt1KDZIPpwyQAA2yJuu2pX7NvzXC/f31bRUzs4CGOss2zPgZu9g2eBXwyXrp6osXL+b999/fabaQ+ZLJzCxOG91mrTyXjdJFQUhAIDt2Isg0cyIjay6o0hn61L2H6th0ZLuuHR/9LcVcerHdm3SqtB87INOCtAKcgjTr2PqotaRfGBngic2UraBbBTjG3M5rVDF75Q8cdC+5UGtv09l2+8VjXo+e2f3RP/pH50/8iT8xX/7yl+fX/tpfO3/xL/7F+R2/43fMxcXF/O7f/btnZuaP/bE/Nj/xEz8xX/7yl+c7vuM75od/+Ifnt/yW3zJ/+S//5SWr+tKXvjR/5+/8nflzf+7Pzc3NzfyO3/E75gd/8AfnZ37mZ76p8RB4nX6dCoQEjXCc6JnuqRLsKF0DqGdxphabE2hWNDNLwClarIL2O3h+lxMN2gE1s/umBC2/pVS2xn1ycrJQUZ7ZQPjixYvld/7m/NuU0Qy2AZTTY1xtztgquuCCqimFQRZ1Gm0ekFnX6TLwZtTAhrGQN0Tf2oz5tHYhO5pZ3+yt61Nw97lS4rJM82zWU9qyeiZTbQA3t3ax0tfK7tMyZoAIDeieR0dHO926Ap2MsMHe72QA9MRYraVtNHWQBTLG14xGMMIAuP92nXyWLnh2gcTMLFku/TMX80D1s3PydD8AtPWjMg5kYS1a9zVumfzM7ASl6r1xm0+BIxBDN4xvO//S6gKqf7f1f2aWsZFZs9c2Pgn8ZaMaSI1HTa96ZF4FzQ5OwIbR8/oM9uSVZQVbj3E9emb33/63/+18//d///wz/8w/M//wP/wPzz//z//z80/9U//U/IW/8Bdm5t0Ef/zHf3z+4B/8g/P93//980/8E//E/Kk/9afmb//tvz1/+k//6ZmZ+St/5a/Mz/7sz85/9B/9R/OFL3xhvu/7vm9+8id/cv7T//Q/nb/9t//2NzfBUFFbKpDQZUEz66s8ylOry3jFTounHBxDlMGVHjAOgc6YesxWA5zP+be/KZogLWj7nvkJ3oynjQh3d2sHHkTXzsIGGQ7Zdzmu7dvD0SXmzBC7obvNLnWoHWcDY+s2zShmZvk3mXMCbTjxO1lX6xGcHkfXNTCuZs50w9YLmVcz+GZi/g8cASMcDRrY//2x5sbY46qAEw6hgKLUXqnZgoT+rHRf606CdhtZBMYGc+vTTlbra7ycfGXZGth2XOQoc+NsyYJMPdO60jk0mSDBdgW6sjPVU89Gjdemy4Koj2Nv2tBUZkHWSbfZrCwJeGm9S+bND21taev4BRg2XCAEUBZck5+1LOhlK12vll6sFd82Mwtwsz6e30zMz2SOaO/qV2VI5uyk56E+1vXowe6f/Cf/yfm5n/u5+at/9a/OzMz/9D/9T/Nf/9f/9fzT//Q/PTMzf+Nv/I356le/Ol/84heX71xcXMwXvvCF+fmf//mZmfn5n//5ef/99+d7vud7ls988YtfnP39/fnKV77yqc/Vsto/M6titKZmgQgbIi6i9h0KPLNuCVCDElwoHkWncIrMHFNPBuCMZ3ZfncNhFzkWyVNo6KeIrMGkdNj2oNh2ZlH4FskZS7PQvgvL97eBmHMvXduaFYOf2T12auv8+uJMBleEW+cuy2B8x8fHS0MCuQjWjN8zdWoeHh7OycnJsj7u5/QZciutCSwBH51DdalUUueobmYu/r+lhLs5uqCMbltfMua8t81L/t8s8ubmZkenW2oodff27dudjdo9e7XUHJai7Ilxkk9rxxzvy5cvF7n5/PZUD2NqRgnACMoXFxeL87YJ3boVlLp3ZVSKXUOJuc/MEqBspaAD28zW2vU4MPrORttDYOzWBQijh62ZNfsU8IEvLEdt0f2NaWbtQ1AbBMxbY9zb21v2xqmbW6ubm5sFbCk9kAM/wx4EVeMum0DfjBmNj95us9ljXY9OY/7+3//75/Lycr7zO79zEcIf/sN/eL70pS/NzMxXv/rVmZn57Gc/u/O9z372s8vvvvrVr863fuu37g708HA+85nPLJ/ZXj/2Yz82/+6/++9+6u9qtO1kE4AgbcVUxilLo1zNoBhbHZjAMzNL5tVzEM/Pz+fh4V0LutNQKCr6qZw/Z2TMisKyQtSI++vMKxLmsM3DvT/55JMlu2CAbaIoSlZwbyZTJ9/WfGi2dQLywsMLaAzO5wV977dqJoKqNF4HVwsYugc5AY0ddWQCH6dcZ9ZTS/xu26HXeknBS2sX1k0R3vPbPdfMUwAu+yB7mFlfCwSQ1R7aiNGsoHv2mulwhNYDaOo5qzKEZidswVwErQIJ+m692JhntoRQ2rmsAJ2YWRs7qv+ortKMxnp/f7+cevPw8LDU3+lsgzq5yPjYSu2+9XJrSh/JoXW1gjs0fRuYeqgFZgUrgbnQoWn+fIsO0NovXRNQ+BAycXam6/b2dqGyb2/fHUqhBlp7Rbl3K0GfdX5+PldXVzuAt7rQDJNdCv7dP0ivtv6ZTB67Xjfz/0Jm95/9Z//Z/PRP//T8zM/8zPwP/8P/MF/+8pfnj//xPz5f/vKXH/tRO9cf+AN/YF6+fLn8+YVf+IWZ2d3gS2E4p2447WkEM2tG0toQB1C6DDITTBgUo3N0VhsxIClOFwJF57W20ODQTEtQaw2iFCQnw5B8nqM6Pn531E+dinFDhltKc0uLUlJBkry6GbmZZ9Fglb/1qWazW5q0Bs/p9iry5JAY38x6UEApW87Lfi7z4AxLO6knydqbcRkb1DwzO1QNEHN4eDjvv//+0iUrqJJbs/PWmzAN5EO21qlZVNeKjDmnUoYzszhiIKV1XzT269evF91tVs1eZKXWoLRWg75A09oQECQI397eLgduHx0dLZ3KMgO6ymnT0a4XvZFlWnOydaB7uw7pmazq6Oho5+jA7fgFbifQOCavtdjS89VdTEkpc8+o/+mzyNm42b71L+XPXuvPWrf7tFp/KXbr1UyVvZQh2t/f33lbSo84Mw56XsDXzI9eAgCln433sa5HD3b/9r/9b8/v//2/f37gB35gft2v+3XzL/1L/9L83t/7e+fHfuzHZmbmc5/73MzMfO1rX9v53te+9rXld5/73OfmF3/xF3d+f3t7O1//+teXz2yvZ8+ezfn5+c6fmTUwVLGh1NIxrZ9UgSw6R82YGP3M2phiUSF0dRztuhxWAw9nq6Dfk1RQC1UARk7hBIYWsSF8351ZM65SuRyMDJEhFWGV7uVsBHtydA+0EGdU6uThYT2+rDJGb9aRcTwzu+3Zb968WcbKWMyJA2nm3tpEg0LXUIa1t7d2jjYAl44DiGQhdTIza11Q0C0Y4XgBJ5nkzHoyTTf1ttmj1B+kTI9bd5UdW196aY5HR0c7b9Gmw4K355UGcx5k65HWRXAiPzIDhqwPOZZhKHBrfbVr+vHHH+/UhwWQHlywpW6boRXcloEBMNr5aG5oQQGs+yLpnlM/3MO86QBfw24LRG5ubpY3nRg/fSogFCBvbm7m5ORkWVvrYI4tJ6hl81ee6TLP2o418H/gRJZ+dHS0dDJjIegaqvPZs2dzdnY2p6enC9Djp7qtZX9/f+fAbDrtMHy/b93xMa9HD3YUoRfHPzPzHd/xHfO5z31ufu7nfm75/eXl5XzlK1+Z7/3e752Zme/93u+djz76aP7SX/pLy2f+/J//83N/fz9f+MIXvunxzKydjgJWayb2CxmrQNjai2BVxMTYmxEIFgIe5WK4M2trvedT4i11JptrsGuThYzPM7tPUOcYxW69pgFiZqVEOC7BzXjqwNBIzXobeDmNHk9GBq25cEzallGCDRgNUmgStErfF8YwbKxtU8zMLDQdB+i7vThMholW8oxSgObHSFvIN391w26FsBZkTk+sZZt4OFmBS/DjyDg1Y6VLUPmnsQ0CRJ0pR1Yd6TYC1BedEGDpvme1nX/b4NVn0nu60noipkXDlM90P6zMwdysM9sCcFpDZqsuAVMderuvtpumycIpIeQg821djkx0SmtOEtQAuvfee29evXq1ABV2tQ2M3XdWpubZs2fLOZvGRobGoZEOAOweR0xJ69dAqnmXVcBItBv08PBwTk9Pdw4SLwi3TsoR7o8Z61q2Ianv9dvGkb/f69Frdv/sP/vPzh/+w394/qF/6B+aX/trf+38j//j/zj/wX/wH8y/+q/+qzPzTgi/5/f8nvnRH/3R+TW/5tcsWw8+//nPz2//7b99Zma+67u+a37rb/2t87t+1++an/qpn5qbm5v5oR/6ofmBH/iB+fznP/9NjYfylnqAEtXn1F3aKgyJMGjf4TRmdps1GJbvWdAWpYvI/I7ic2SM3/M4TIpa2oPDbqG5hi+waOeFHCk5Z0lBce7kxHgpd+lbBnR5ebmDBimoMXbMDMLPjVlm1/pa6Q1GPjM7wQNl6TDabmL2fGsoCJQWJPc3b94sAEFjgAAws77tvDUUAVNAEvz8nKytazPqUnl0y8858dKpkK7XxZRO6nvFZmbRHbUggckz2+HYYGydnj17NpeXl0sDFB0rJWdMbMaGd8+mkwBbmZPqraxScG939BZMWTuyub9/14Ep03U/dg7osUc6K3BjJYAfzrx0nePArJH1ai25jAO/0EMHjKcBn32wN3oliwK0gKs2c5Q5uri42Klpvn37dqc5iwzYTu3GuHrKj+e0ic6czPnu7m4JlsahPDAzS7bXZjD+tB289KCZtvUwlm77eozr0YPdT/7kT84P//APz7/xb/wb84u/+Ivz+c9/fv61f+1fmx/5kR9ZPvP7ft/vm1evXs0P/uAPzkcffTTf933fNz/7sz+702760z/90/NDP/RD85t/82+e/f39+ef+uX9ufuInfuKbHg/EQCE4Bc6XY5iZhX8WBMuJM2JBwwJB5xS6itC6EAVr91obC/xMbUKw4UxqSAcHB0v9iLFwWMaEFui9S83JVvb29pZTSjg3zqiGD+WWZoLIKLKgadxnZ2c76L1t7lBunb51sR4M9fz8fMnaSmuhPVyche+hqQRS68ipbsEJB4tSEpT39vZ2jjTzuTaaNGtt5m7uHNqWDnRPAdO8S1mrF7YBhtNUE7b2nB59p1MCh43z20zJ+nefU+lhdSMBjLw79tK8HKPMx9aAOj//b7ZRwNfaks+r75VNsSYFcgVen3zyyULvzswSYFtLfHhYTyGhx+wA+1OWhNxan8MglMokB/YkY8MgkIVxyRjZrhOKgBI0+8x6Uk6penogqDewla1y7+rTdqz1H+y5AdBnnFpEJ8qIzawAlU22cauMBFDWfz/mtffw2MToPyDX5eXlXFxczI/+6I9+QyARwFq7qpJw0OiHmbXAS0l9n7ObWZWmQVJXlHsy6HYLllevMhkfRyXwqGd1rM1qWovaZkkzaxZQLr20HXm5v/vUiUCzHLusVxBG4bivuc2sXbCQL8faeotAtM1gZQCytqJeByXPrC/tLbXYo8M4WQCHnIo4BeQG0JnZqZsxWNm7rINxyw7My5xbl2hXaRkHtLm1LqXebktI2GZuYIM8ZCNexOnfgiR9tkaco3Wt06MzAhWdAB694aOZMNkIBs12GmzMw+/LNNBXugpgzszO8VPAah0tUMlOfb975trqT5fJXyCovm2zIVk0oGD+dIOs+Bnr1vclXl5eLvW5Akc6aB1loW/evJnz8/Pl+DFj6CuU2NanlWA8Q93cHFuDbELgO62F8qk9fq2d6/QDgAXS2szSgO3/aO1/79/79+bly5dLD8bfz/XkD4KuknI8jARVQcAMglMTdNoBVZrJpaOynPrMLEpQg/CcdrVRumZ1M7O81NHnZt458aurq7m6ulqyJdQCp1WkVMSmu00w1ElmfM2s2/Ays/sm5C0dUkrH/ibGcXe3bhCVwck8BMRmcu7N2CDS/owcyPD29t2LXI2XQ+QsW+PyHPSNbIQs6iRlVoKUbLbgpAifY2xtSmDW+HB1dTWXl5ffUBdDgwESM7NkEKUT9/f3F+cmI4fUnYe4t/dum0uRemtQqF+O7M2bN0vdR/2NvAreBAR6fnh4uDQrdB8WeVtvcijF2AyqgdT3gCSgBeCT+dHPlhH29vaWrBV9X9lxvn62BYQcqrWj9w1wKPBSxaW9nTRUNqi2o4b26tWr5RVcbMGzzK0+qPVa9oXFIeO9vb25uLhY1pufK5W/lTlZ0negxTpa93YIu2f1lU4UvNXeja9lCvOjP7VvMn7M68kHO4uADhGIOFyL631rAoX/K5pbCEGnCAjVAyGVfmn9bGYWp1CaqU0oCrkNhgK0OkA77ARHjpnSQHLu3dZe9+oJHdq9m3VUftsaSDez+px9fqg64xRoyYTxbWsVfsbJbY98Ml6BfWalIs1HDebDDz9canKlxzgk30ODcoICLxm0Pks/1EVKr5WSs86MW0CHrOmA9eBcyLenatA1b7AwDnM3r4ODg4VFkF1xhuZRats4i9BlAcaJJixYLGUPHGjIaF1ZZoEyrFNslzGHLKO2abs1ZXM1trdv3+50QTaYdWtJu4cFny1jQZ/dmz2XhiODsgJtIvLv2iLK/e3bt0tG7dVcM+s+yQZ59tHmHrS4wGIMmnhkjNbEsVzoXrpsncmKHPgxGfW2e9c6s/nem5zIr3W6Pg8YK4tVlgwDQXf4MDTrY11P/q0HAtyWGrDI299DPJAHyoeBaEfmkCA5r69vNyEFOTk5mVevXi3OcWbtEESXMFzKMzM7tSGOhJLMrHsIoVr3a3bUjMBzWySXmZycnCyKCWFSTs/RmcdB4/0ZLtmq4zSYoWzMoxQGGWhz5yzssZIJmA80u30LwLNnz+bFixc7p/Q36y09aXzkXWO0Fg3QkLGMhpxkIjKcGjG5mx/nQtesiXGV4mzNjwM0Hs9Dyzaz1g6PBuz+PHptTi5ZX7NUYwC8rJsMVLCcWTtvBaxS16X8alel4A8PD+f6+np5Bjm7hzmyLcFVMN7WAV+8eLF0JwOAbIdzRR+yCfduNmp9qoPkBcw0WPhs6XLP6vF+vtfs1ncbaOkhmq/NRij9q6urHaqfbXZDeQOZe2KE6NGWRi5QMTeZKL9UQMxe6bLDLOozzPXm5mYHODVrbRbdzszHuJ58sINu8fjbmtR278nMO6XrSyM5cApWbrzGq8uoxfOiL5/lVCDb8vuyN/TX69evd05DgIzdd2Y9825rjNtGCZQYJ93uNFlgT2vg0Ck9eT579myRTzMcGWOpLqhP4C/3Dx02s/XMFsgrY4ZGdt1TVrDiYsiVmXkL4t1LBbj09Bwy9DwZQwPjRx99NCcnJwt12ky0TQ3bbIS8vdJEludCAwvEMrfW8gTW1kg5azKFslvb8vzSaEBYdcXPetoI+R0cHCyNEk4garYmuJt7qWrrYh4Nfq3TqWEWLLEja6B+1cwVUDRvduX/bHVmNyizIeNSmig4avBtsOW4BbvLy8sd/Szt3+yuAY7srdvMule1gQhL4GfGypeRTW3DWBvcPJNfM5eyDNgF+vbxxx/P2dnZ0jhWRqb+taAZWFK+mJklQDaQ84+lcx/jevI0ZheewjTbaS2s9QSorIVTCK7p/83Nu82VlNf72yiQ2hBUJWhwClXOKnOV9fr6enkm5FWDb+2KUWtRN28bg9XPGBGF399fN3S2joGKaRehDfDqXIInQzc+YELG0+v29nbpzOrzSulYo2aiAg7ESwYctHVigJwBmrbnXyrkd81RvqUKPdczfLZIVP3M2IrKm1GVTlTnQ5tDvkdH67viULMcgmyLwxbYZabNMiDn1kLo/JZ242i2+wIbkKzB0dHRAuw4RLRTAdLMu2CKgt0CsO6vLMVVm2UnlatxCNRoV5lcacmZ9Q0mgFGDhYwaA9H6riy+dPLMSqMLAOjJfq+BWEORYOBebBNIFBxq62/evJmrq6sliCiXNNvlA/b29hb7plfN+NrURQe6rnyH35OhLKv7Tn0ei8AW+KKCMGuCATB2/sFnyYyuG/tjXU8+2M2sBq6OBB3Wqcys7yyDsDj+Zgzlujl0++CaJT5//nzJGCymIFAn0me9evVqUWoBpM5hZj2ZgLOd2T2pnIPkiNQeKSCjqtOmVBxr6VbyoowUfmYt+LsnpMlx1AjruB8eHpY3SDSzK9XE0GTZfua+NrP2xA/faca7v7+/HHRQWnhmdjKQBsyZWTLzIkzUV4NS6xHbANe3SbSOSV4ylp5ZiLLkUBrU0ZKACx3kIDhH9VTf8RyNJ9gJa+OexiBoWKvuHfQsa196ij1gJeiGLmAAC8XVQw/I05xar+uGcYGh4MmpHJgDTMTx8frGdoDPfDSrlepmX9a1gAnIaKbVoCpTKlt0d3c3p6eni38QkKuf1rb7cCvr4+Pj5UxZdtQSQA+q0LxCB2Zmzs7OlvE0y5INy8jaNIIFMY76zwZN2XNrsq42mACLAncDXEsngjXbKUh7jOvJBztK16K0RWxR3u97tBCHWgdXvl03WukNSuF7AqumiDYsuJ8XWNYZdGtCKYfSdFC7e0CgqEIO/s2bN4vDoUBQqHuUtuyb011oS/fg2BgPY5XJ+X2zYECC4+WE/A4t1hqdOUHvpZo7bghccPJnZpbMggPzhuc68WYN22BuTqVorGVprzqzUlvk0kan7uFrEBVkBPFtG7f71PECXByMNfFsG339DhoXjGRXMntBVWCYWbMtoG1mdoAM6ly22CYUbwhxr3aelvZs09TV1dWi7+4lEAhk5Eyf6LiGDjajA5lD70kibfRAvTfraIA/Oztb7Klnw7Il8vaWAHMucOYfXA3eWx9j/dm07N/6t8GFrtHRZom+17UE5AR6a8J+6EH37JFFAVYbvcytvyNXoBvVjOkQ3PgK/m1bjniM65dFsJtZXxTYlmqOCmKEDGdmcZbN4CyK+6IvS5HIshiXrjHBo92UFNNbyZu96BLkKIqmOS78t2DKqXNAnKhgyJEYc4/Xmpll/hz3y5cvFwfQBgMy3QYqmSNUJngKChR4W+MwP89Cd3F+zd4KVGTA1sg6QJLNzAWC1il7FBLdeHh42AmO+/v7i/NimHTm8HD37e6cGRCAnpOpqH+ZW2lJ86EjsuMGDk6wx3rNrFs6NEFYvwISf8taZHdkaE3Ozs4W2s19W4vy/5n1yDPy4Ihvb2/n8vJy+U4D0cy6d8s9yPvw8N3Bys+fP5+Li4sFQAEFnKN5G4efuw89AGiqo8ZKf9lED3Emi75pQ4aKJWi9jX25L3BJhzRalPL3byxM54Rel5WyHxlYfdfMLLVS8/Q7diWrFbgELfrmGWUHMCJ3d3dLJv7ixYs5Pz9fGB4+CFBTF6an/G63SHTfIl3hm42l+vSY15NvUFF/sdilJzihNkHUIfh3a2hFlJwCVNOW4Zn1iByK3XohCq7B08ZoaJHiQVkWv7UjDrm1F2iqdRjOxLiKNFuLrJMno3aOQvjm7IQXe78YcGsrbezgtNqoQuErU4GEkaObOR7Oqyfu392tDTbewg6pAjHk0RpYg3hPt6hzbCBz6bozB0EJVcrA68y6T6njMQYOoNm5NfP8k5OTRZ6yYPpEz0oNOWXFz9BdPQ2oDmybIdMj46Unvk8WRfrmLID09S+9WnOiO0AHJ1ig49kFCmdnZzt0Oj1j56WWyVKgdr6j111hY7Y1Qzrqc91ne3R0tADfnvCyZZP4AvNpc8nZ2dlcXV3tZPQ9T5KPQMHe398vneACDh0EJti2tTWHmVn6Ea6vr5e9eXyNbJKebuvXZXK2zXF00HaL1vqMZ7ttBEAriCoofqzryWd2OO83b97M69evd2i7GjF0Lt2HxjizbsAuvUGZGfXM7DiANr3UUW5pqoODg+U9ZG00OT09XU75EJwpsIAkCzAmNRpbJvDyLfyXgmXg5d49S3diKQoKa86vX7+ek5OT5ZUo7skJ9x7GQv72/9XhNoNjDAzZvNsg0XqqIMMRcMCyGMF3b+/dBmSOo40gpafohK0FreFZZ9load1SZtaZXOmDNSlFaXyyzjpKjkrnpvE1WFaHmxGZE0fSPV8FPmRFB2UmpaTJ1Vr2OfRZkJ9Z2RX0aOvlDw8PSzOVJiJBsjVhAdNc2Wyp8ZmVeq48jK9dsjJmWysaUGTfM+trdTAsxlZWqEdg9flsmW2X8i1AkYWbn6C9lZPn0a3qIUalOkiGwBjAa62tF79IRrZJAZhlbpphu6qb9Z3WrfLQzV1AUFvyWUDtMa8nn9lRPi+pnJkF1XrVBqWp82GovQfUJOXWTt69bpyNUyogwO0Zhjo3Z9b9SQ0U29qKgLi/v7+DABV1W+idWamdGp+MjeIJOO1MEzhn1q6pBlmoriiRg9u2vVPWInPzVRNlvK17NqsyPuiPoXzyySdzcXGxU0/QPVrH50/ndXx8vARD2YHAVOf58PCw85JdhmstZC/u2+0ixuPnmgwg+v6+useJkRddaKbOqbY2Y77duD4zOx2K7lmWgv5wPNVPQVtwUc8mc59ttsk5cmbdj1WdM06BoIDEv9sJ2fE1W5INGtt2Y7vneX6zJkHAd40ZO+O+smNyIK8yP+Qjg8cS1Kk3wxVcfLbgonRwwZi3bPiMoOu5AJD62PX19XL/1s5ndrfN0He65si/UuCCMX+kBwGD0lpbQS76vzVvNHov61h691dozG/y6kHKLd5rVFDM9vuZdV+U/VZ7e+9OJqgRM8wWe/H07l3kX+ptZg2gnKAMUObg8wyVQ0XV1Qk23Wc0HKbajCzT/DicvkGZcqkF+v3WQbg/uZk/2tBzfMbVGk4zgNPT02UdGITmEc6wHYLupbtLZ2dpWEFgm6W0zkM3ZlZ0XEdZ9NzmiZlZ6ilkIZCTl8y3+/tsCWHQ5gukcFpbPe2pP+7FGRVUtGGJo/JzWXcPtL65ebfxHKjCgNRJ1/nUYXHWdFA2xIbqzEtJWmfB2z1cftdN6fSMw7WmsuOOQebQTIXOnZycLFkxObajtvXj1rPYXYFj6XhzNuYCBPc7Oztb1r6Z8NXV1bKFgiw8q3rVzu2yBqV23YNt0G32q47ZphAsAt9BZtaA/9B/IACXfmy22HGhttsd6vAHoFgCAHTM7HaFP+b15IMdh2UBKT+0glaAZgSvnkAPKfcUfb9rAwYFbRZTahEqKuWlTVqtgUKWzoBQZ9YuvlKxEFGdJMXnwFuLoGjdoMvxl+Yr5791SEXYnEW7EyFRz2utqtln91lxEDXa0h+ee3NzM2dnZzt1TwcbM85mM5DzNivTxEOuMhpZR4Mrh7JtmmiLdU8X4UiBEXL13OpIHQcw04aOBp6ZNYNoDdWpMYCWeQukfVXNzBqMSo/d39/P6enpTkYnwHiu5hpApfMHztqEQK7srbS54NEGMHTvll24ublZNiDTR3IomLEu1dk2jpQ23GZ9dfQff/zxznmpZXZ8H6CS4crMC/Z8XsMOncT69E0L7ts6NB9Bv2zDEaT5NLpNJoJYM0vroxxQ/SOjh4eHBQAJrvU7slyftbb0vIDIqUjVhe26bDNO/oUePub15Gt2FJIytiuPwVxcXCyCn5mdGo7F4Wyglb57qzRFA5SflRZCP3CcxlfE3poP50BBu09HvYCBqkO01tasrrXDg4ODJTuZmWU/kOBiXG/evNlB7QxoZj0zsJSqy70Fl5m1m7Bt6drVu1nW+GbW/TrNMjggn++8fba0LEMubQQUMFBrKtDVYdZB0yXjUnxvllaZ+j8HZCzNJAAVulHHNDM7KH1mdrIe820DSmlrAID+FDTIoNFrHQdZcraYi5l1X5s1B5hcnFprh9XtUmN0W1AkOw682Z+AbZ1kR62fkRnbkaGwHS9Nbe2MbGQl5INd4PjN8/Z2PcC8WbrvkaP6MR0FjGX47kM/BPnz8/NvaOUXtE5OThadIIMetO5v32tgsp2pW26audHzq6urHdqc33t4eJjz8/Od2unz588XCpttlHEQfBuUAd5PA171i49NYz75YNcsrIGFcGVwMiuG3uKvhocankWFXFozE0j923PRd/bEqXXYTK5GwaA5x8PDw6WRg3Fz0kWPHJ/5cA6CC8TP6eggm5mFijg6OprT09MdB9QMQNDsNgdjv7i42KmncGZF3jc3N0ubd6mh1hxm1ppjDeHu7m6hVsiomVcbOUqpWRdjQ2tBwicnJ8tn0S6+J0sUjDl9cwMeZmbnrFM1E/rWrka6wYEZ2/Hx8ULpCo6cjm5XFFQzt5m1U45jbqASbFBGwAWniOHwXP+3DtYHWMAcoEbNg7O3baaAodlYsyVr6nltjJBJWXvrKzspkAHofA6QKgC4uLiY8/Pzxamam4DTTLQsSGui6lUNcH2msQp6HDc/4+c9qYRMBEPyFiD4L9/3HevLvzT4VzfoQ0sSBfNqzfWDXbNSxW3cOzp6dw5rD7uv3FsWMEbypt981xYgmO9jXk8+2EG3aLrWg25u1qO+SvP5fQvl5+fnO5t/izp6EsLM+r4saFCwUojnLGbWQ3jbcShAC2RV6Jn1iLBSY5SOYnGGFMzzjB3Ka3bY45UuLy8XI1QDKLUnELtfKSrz2NZFoOsaUWlP82rw3jYCaSIR1LYBR4bVbKUBmFxlZwx+ZpaOS3IFPvy8esIRG2/pbMbs2SilgiKOFj3ULHFmdoz9/v5+ofD6xgZy9++jo6Od96HRUQ1MAiaaztUsmE5Uv8i5rMPt7e3S7dvuWjR816YUeylqoIojde/Ly8udPaTm0CxeDVeQAxQ8AyDsdoxSuYAo2dG17Vq09isgGHM3lremRobVAfSjeWp40f04sx7v1szo4GA9hs1n7R9s5tc6K0BD7n5m/eybYysF8OykNl0/2O+VZiwj07e0lHItQKPn1attjfCxA96TD3YUlmJx7JwYSoWTsrcFNWBRP/nkk8U4S0EJkJSzF9R+eHi4vOVXsb8vAJWJzKzoqHu2KHORLAfDmDmV7vVjLJRLkDCGdgA2yyplVwr45uZmzs/P5/T0dPle62qCPhmVXjw4eLcvCz1T2lHgYVxkd3d3t2OY1qpbKkqptKZFRu4lSLsH51wErJBuTax/qeruJ1Mfaw2kzpHsbbtoRsYxcLSMv/Xf8/PzRXZ0a0uXG0upH3SoOULu1o2j5mRkA22eMG9j83ygrJlc9b/7JK0tPW+NnE5ypkASedLjrsE2W+sroNxbMBHMS/O6F/mUBXEP9i94tfmCrEuVN3vjZ+hJu4TRh2xFmeH8/HwJzOR1f3+/vJ5KFub+wN7M2vxCt/iPXmWjgAKgkyzY3pbmrw/xu2bZ1r26ZsylycmXnxFYm02ycd3PMtrHvJ58sCslyQEzqJn1UFeO0QtT/d9Cc7yyOzQhZWEAAoqfCWRoIs0anltn0/06HL9mEk6zTqTUbJ3rzHqOo2yNQssuOWCo0pwEsVKqVXT1jpndl7FC+DOzbOAnOwGJUXAGaJFmV8Yr0+RAARNr0oxRkCqS5rD9mxGap5+VUkOLbh1WGyjcv6fRFAVzxO4pIMj2GDiDL6MguJgH3TIWP5/Z3YztuWRL3jOz6E/Hsm22cH86bH4FT9aGE3MEl+fSXQ4PcFQna922AK21ac5zZj0r0hmzwFFr8H2xcbMNNabWnumB7UZkDdR1K0R1hj0I0qX3BFu2jhpucLWeaMNS9T2Bhmy6PYnM1cVKDwt0PQGFzEvj17YwMp/WYSnQb/0IXyTg3t/fL5mmZ/Ql0y0VlYrutoJmybJDQKW1ytapH+N68t2YM7NzxA8DowBQXDf0ciKMluKXE6ewTfNvb2+Xmom9Va0RcnCcwqtXr3b20TB4FATDcZ+Z2XGERYStVXEMHDK6hZEbNwM1HhSY+xpXqcEt1ciJNyBeXl4u95+ZZU9bKb8CBXLxbi5zr8Mxv5ndtxzMrBQKZ19H6udbmrAdYr7X2pMsomjY3AVhzmRmffu53wm0PcbJWLqNouvc7F3A2W7Cntk9daU0poACZNHhZ8+ezcuXL5cA2dcNmfuWoej9GuytcR29TtBmygWBxkwmDaIalJotoDCxMNZaxgDckZ81r02UwkdDyxgAzTIdxuhgBxkLOk7wcn92s7e3t9TT6eangU7rYi2BKM8ix9YLyUmQ0g0O6NJpgaZ+xvpZQ/otePUq7Vn9b1ni9vZ2Li4u5uOPP17s2Zh7kHkBGP/i+ebbPoACEc8yj1/ZevBNXgTYza2Hh4fLOYJ9lQpEuC2At7lEYINSSklBtQyc8hRFNti0kCwIzuye8n52dracH3h9fT0nJyc7lJyMpucncp4cqfsZe2kzwaWUYRE7RW4nFfRWGtd4oWlZqO88f/58J3MS7HWXzcxC0bQe9X8HODjcUsGcAdnLahial3rWcQvUdULm0gAoG+7a1Wn4fwN6DZjT5Kg4YDpROt1Yv/71ry9r1Jom5ydg++7M6lSM4eDgYC4vLxdnzEE102pgqJwbfLYdoNYYU9HaXGs5Mn4BuEGizSoyk+573B4SvKXlrbOfk7PmGdtRuj+UvZiLNTPWOno2A0TadwusGaPAUjqu4NRnrCub1P1p7DPfeASdv9Vt3duf0pzVoZndY7f4MPb/8LC+YZ78+QtByLjPzs52DgxoQ5E9d1tKWRMYwNCMvpSrz3ULljWtHj3G9eRpzGZoM7snp1MEnZaQ/rZYenJyMmdnZ4uza9G+nDalt6jS8hZoZZgyor5MsjUwKLQK0SL+1hGrNfR3vg/FchBoQSgN+oQqKV6PBNrbe7f5WxCh7KVTfQ6d06zp5cuXi9wACm9X6D1rxMbIwQlwjKj0Vem7BpbDw8N58eLFDi3rFPi9vfWw3a18rIPP+Q4ZW2u0iyADaHBU1gB1SG7qN7K8OvGHh4d5+fLlTobouCqyAch8r1lwMybZveALxdP9dg9zxD0txN/vvffeQqc1iEPz9LqgT0D198z6KhrgiqNzH7I0J0eVyY6aYQu26Djj9XnBsrXz1tAENJmVrtzOHZVq3uzCPbs2Bc7mXb+CcqQL9NV6WTsBj/NvtkYHt+CvND3ARC7uDcgJzOzQ99XCjaPnrmKFSr+zfWvF99Bv4z0+Pt6hYNsYQzeBY8HdGB/zevKZXTnrmXXjK6QOqVWZKLzsr8GwiNjfPcdPJ1L5+RZs9/f3d97vVSTDITNYCtgagmPHZlYkihfnJDjuNn20FkGJBTZ1izoKwZ5zluF18/nd3buDZDkVGSZH3joONMzgZnb3izGKTz75ZCdLFUw0WrhXC9gNiBwZZ96anDXuOJ10IqOwNq1PoJIYn+zg+Ph4Li8vd84u3GZzrUGUUoKIAQJO2X3pjLkCLKV/mslaD06H4xWE6MfR0dFSQ8Ne/H/au/sYzc6yfuDXvO7M7rwtrd1lsYWKBBCQIJW6vBnDhlIIghITcMWqBIK2EcTwJoIYxTZgiEAQxASIsYKSQEWCaNMiFVO2L3aBFiwY0DaEbdV2Z+aZ3Z2d2Tm/Pyaf83yf07XSn9NWps+dTHZ2nvOcc79cL9/re133fXK8QJf1YgjNGYeeVKlIM3OmmQfXb/KZUSDjnYYzwWNGgOZIP+kTMMiQJgBViKbpBxk2tjTi7sNpMMB+X1lZafUB65H0IH1Ci2YqIG1Gl2LPY7X8CzRnmsOzU6ar+q+ZIqOetb7eP9YLkK/qszrk3vPYIA6I3ptPeTprzm7Nzs7+t8fr0VeV4+QgaXNMFQc7zNndx7a6utpWoTEeWRFW1VeAnTt3Vq/XG0AhaAhCgxrhMI8dO9YWvKCbEuEkfcPYW+CkwSiZBt0kD89IMCoUWs4geXt5FMa0qk8xyIEwrJTOiQeUu6r/Gh4Kma8cIuBV1Sp9VrJxnmngGK9UrI2NjbYqLfNt5iGdIeNMiSBm85yKTwlFZQwwVOse6CHOgxFPEJAGWtQCoGSOLR2s81c5fMUG8pP6n0bBuub4Rf9Z3m08QAtabGNj8xxYxrmbM+bwzIGXmM7NzbUVx4ytNc+ChJQzRklkxBBmJE6+yHg658ztJVOgoKeqWpo7ASQjCHih55LKzOelYU2asku50s+kSMla6rMxcbRra2utvikA04fMgdENzkABlOgrQU1S3jm+pJczwuvma5MeT8bANdbAc4wbE7K4uNgC66TZPTepVyfB2H+LtcqIXMt9ySnzedh9gtWtbNve2SVnXdV/sSPDxYCgk5QIU0KC5zu53ymFLPeUEAoGwP0JTKIjlBwnkoqYyXfUCLSZ5zQm+mbQGY2qvtJnpR20TdgZR8oCYVFySWnGlxKJEqEyNAwjACFX9Q/QlYgXlTKaeTaotevSZFom5bNKkVEz7qr+i1+7uQTPTmSfRRlV98zPGd/k5OZrjWZmZtr1zvUzz2jtdGSMl2jp2LFjNTs7W0tLSy3ISVnxXHODrsq8UhZ4ADHGIkdtDXOMomLvastqUbpDJsgaHWqazWOvMCD65PUunEI6OWcjdhkJkTbgksY/GQyy4nnWuLtuSYlZezJErjPaoNPk3Zozwpm2MBcbGxstuFWc1D3qy/2zQjtfjWS9VRs7uzPz0tZb7p6TzT2G5qybyhDFsoFpXyYnJ9siIJEUOTD/Sdln1G/OBALJtJDT1dXVWlhYqF6v19oY+iPyzK02CvGsgXneqrbtc3YiOJEElJcnpxBWSuQ4JA5QNASl5z6hqhpwaImmvLKkqlraaGRkpD1yJzearq+vV6/XG1BGQuVZWYGGOkvu24GyIs2pqamBV5MwgJnn8R3zkkaAQbGVgLOhfNB/okdzkwUy7pv0hfUwHnkiAp90y65duwYMHeSMgtQHecGkTjKZnxGaqBeKrOqfj5nFAvZEUrykvhhRzikrRNPpJghhPER+mcMVCSvZR2cyhGRLMYBoIStoM4dlHbrbTziO3PupPyhABg51LjI112QGMFxeXm7XnvF1XeatzXeuM8q2qqrX67XOjbzRQ8wBECCirdqMLnJeyGIyIbt27RqovMw8KGCiuKVqMFWRubykWulIskaen/KfuTlgyzae3GbAGZBT8zQ2NtbmzDPyNp+5Fca/bAadAy7If7Is1ss9M/+aue9Mc9CvzBXmoRizs7Otk8+tB57jcIZMNXGw5nUr27Z3diI3xoIz4KhMbIbdmTCF2FFQBFAy22s3qgYpvx07drR7hJLTh6K7CqCJbiYmBo+bUixCCBjWrK5KRAil5v6uNNgELKNWFBgFyy0Fmcj2HagutxUkTWqMVf3cgTWQE6JciloAhqQKbYDPqlJrx1Akcu5Gd4wn58SgJDXGwGSEbtzmwP0BCUYD0EErZTSY0YDnMCxZaLO2ttZSnJmf0ncUj3VlzFG0aei1zMOaj6mpqXaukxL2r8+tszWG4pPqzTny3kVzZHwjI5tnLa6urg5sGE+w0N3KkcZcH1CzSceaH/NLxrOAQsTAUVpH65O5dDLbjfDIvrkx92mYM2IyFrKeRSrun/lNupDgLiMrc2l9/c24kykAkACIzDeiURVlqX42ZxgZUVxGsJ6Z9oyj8vwEJOhOwLlLZwOrmXvNY/joxla2be/sUC2ELSucMinerRKzCLk5l0Bn9EMgCblnKlQRQWVFJqPGqTJIiSSr+vRHRk/pfDmtLKX2d04x6dPMByQnTjghv+wHJ+Xeudk+kW5GxZm3ci0DllWl5t48ZaSUNChn7V6Z17Jmxmw8xjQ9Pd3mYNNZGg/lYhwoLKoXKBgdHW0jrtywvLKy0hpofeGMM0JPx560kXUFghJ15zzIO2eOD2KuqoGoRf6YzJKfpNytW9WmIZJTZmTIq7HnsV6Zr4L2RYwOWu7mpURh/u5aOkYWPYND7kbD9DiLI1SK+ryqf8Zpyo+19rk1SCqQXtCdzLPn+bSYAZRuRoOiNfLsvXJJKwLO5BIwzKIhupHHr7ElQK5Iig0h3+bROuV+xl6vN6DbGtuYIMJcZQTrWUnv0lvjFPFr+aw80CDvxxbq91bn7La9s2OQKQOjTNkIyK5du9pX0mQEBBGNjGyeNiIZTaCV1Sa3zakkdUMxRW7QeRaXuEc6P0awqn+4cyJKBj6NJiPLMECsGcmczrky+olyOcauQ/d7Go3cB5SVmAwyJ0+gIVuGK405Z0UpM9/gd/QqRZL8h16r+hSYOWYklJLnXLuXSMJcJJr3fU5MTif7mMfQpTN3TdJfZMxz86i1BEcpk9bemokeyJTr8m+chj5wJNYzI046QPZOnjzZ6krulUKpkrkcg/9nRJtonV6IFMiKe2WBDXDB4ZI336+qNk9tfZKVaZr+BndAI6NmDsWz6CBnQj6z/L4b7aPh6Z5xjI9vHhVIfsw/CjMBo/lJ3aQPwJhD1DmTjLrcy1pjH4AYUbK+sD3mEFgi89YQFaxPmTfOaI2NEvVZa3YL4HCYtO/J19MvkR3nuVVt2zs7zorCcliMZVX/GB0OqqpfNQSNOBezaZq2SIWgLS4utshGZECxCDGnID+T9E0iQYaUQ67qv5OPEaJUUKVrODMRTRry3NNGiLqJYPfrXudaRgY6V4ig7xByVT/S8L2MwKoGjwXjaPw/qw+Ne319vR1D5gbt1UKbWEuKnLy/ueYcUK+Mbb765MSJE60BY1iyQCbXgdxk8UfmbRhdfeZU8/AAY8+1SPbAhv+qfoSQFHdSg5xu5l5EhuQ85ckh4t5i4PvZOLCu81IFSpa6oI7DMheZz00dRW+SGXJh3qwRec6XuCa1q4++C/C5J0eaDktkK7LIKI/ccLT+xmAneEzdEfFbO8+lI8BSRt7WKmlYfSSLCn8ARfKXjgdQAvRyq0IWhCTrROYy90uO0b7pYM0reST/yWBxWhktktkEqLkW5inTLlvVtr2zSwoNzSYC8KMYIGmQRKcojCweQdeJEnI7goWl0NAZAU0jWVXtXq7c5KlxYqurq220gvLoHkXFkTIoCg44EI4/Ua8xpBLbaH7q1KkWoaGL9Gl5ebl9jpav3KnqV8JS0nSK/paOB5oDJqanpwf2i62srLRvj8hiCvPLKGZRTlW145AnqepTmXKrlNWZj5nHSWoLquVw9DkLTERF8mn+n5FcHqPkeSKHzPlRfhEfA5cRgWggZd3vmY+Rk7MG1oO8Z97RGBNsjI6OtjRutxoSFclA+U46XI4qc0U+S0pRn40h55zeWevMH4tgutS+aKh7v5R9P9bH9zmqlZWVVrdQmeSPXLAxol/zS67QywC3ucaaWKM8lCJ1yZqZZ/JpDlRSWo/x8fG28M66kzn3FPnR68x9JgjLOfU390pGi5Onv9gveciMHn2Wzp6M6/tWtm2/9YCRsqlyfHy8FSqLbW+MhcvELyMIvSVVgBKh8NBoVi1mKXJV/xDjpCApuo2leYIFCnNsbKwtDReFuIbguBdhzcR1Vb/aUKVeRiLQMoeVOYWsvBNBAQ7pQBj9qj4t5rlyN5kHhXKzLJyR5JzyyCNI15xQyFwzhjPpTd/PEyyyKi/HRLG7kUJSgklNJ61qDhJ8WAcNks7quYwYMupOAMDAZpSSoClBEsPkb65N6tm/XSfgWd7UjT7M6j7OIMfiOUmv0r3c0oAtYejRWxmtAp45b5xQUuk5n8BoUu5JO7uWnpGHNNYps+wE3crCCWswOTlZvV5vACxl/pa8cazmLPe/JluDrrRpPZmULoBJRocD9BJV46Gz6Rgzj5xbSDJvVlVt3wEafddfY5K3xYpk9ag1zkiSbtBz10xMTLRbWGxBkevcqrbtIzvVZEkZVlWbsM19NFlZeezYsfZQWFVCSUt18wFVfcHLZG4m9lPRc6GhG7kZ/arq5zaymKaqr+SUJtHa8vLyQGUTw59oNzd2Ux4vkRVhoIiq+ls0xsbG2pJ1/2f05cuSajFfcjkZ7WSkk/mLpIZ8X+RsrpI2MTfWL6mqpF20NPzmn6M35vHxzZfRZgSgLxnRMKoaZ+vfLJLIfVDZryzwyAo6hRrJDIhwM++XdCc5M4/mkA7IwZnrrJbVJ2AhnTnwwbiZA7JEzsh2UtqeldEsJ83x5hFZOcf5JuxuZEsuqqqNEkRPefqHV0sZW5f24/S7+kvG89Si7t5Bxl20lpQzXU16M3OvxsJZpS5yUvKVVf333ekfe5LRMFkR5eqnsbBz6YBEgWQrKyrX1tZqaWmplW/PT8BtuwjnTB4yCNCfPIIsI2q6o69dqnsr2raP7Cgo4c7EKifH8It4CBK0Au3mRkgLkbTc+Ph4m+sjSBkdMMaiCEiQ0PjJzx3B0y0WSfo10X5V/zgrgkOpsrIrE84EN9+mrOiGk+AU9J+wUji/J6+fDpyA63NSbqI764NWsS5VmxFuvkVifHy8rZpNaifnG6WkMXRpbDNSceCtZ1PQubm5dn4zErUWciLWPNeI45iZmakTJ060Zd/QL/owDe/oaP84JrKQa2weRB7mMakqlKAINIse/GstRCy52bk7j6I38m3OOe+qwUPFM5qv6m/AJo9Ziq4f5BdQzP13ZBm4shUogQuQksU4yTZkdGPOkroU6ezatauOHz/eArMEVxkB23+4vr7eHthu3c07+6AvwAwAmNWr5AIoQG1mbg5AyhQBOTbv5PPkyZNtv7IGICN/ttDcspUiL3ZPv8hgroux0SXAl4x7DvCf0XlGgMm6ZBpoq9q2d3Y5YQxIGnHCk9QAR9I0Tbu/h2IzPFnVyahQlqycSsqC0CV9Ab2hVDiFqk2llxurqgGjwqBC74mGKUBVDWxQTTrPd5MO8UxjJXgZETKw8mqQcxZIeD6aKqMH/RaZKOdPx6SvieQhR2PyDH+DBNOJKiyQd+GMKVxVHwwlJbh79+4BesXYzdXpkvz6aNxJ4TI85E3jUBl+fYfMrRvj4qADIIARSeqcgQQoyGDSc0l1cTzmPelgc5J5WetiThg1a+iavAcqNuWODGBGfEdO0PyIiozf/+35O12ukLEka2SYnLqWLgMV5p+DcfIHEJtrrdLR3KNq05AnG2PtkkbWF7LE4dM3zly/JiY2j5njSD2b7KBz6QN5B1bIa8p9pj3MV/YlC1McqI5iTjkE2FJGuzaq61yBDHNatZkesoc5qza3om17GrOqfzIBRfFDSSlPN++FNqvqo02Or6pajts+rqo+omaMPSMpw6o+IkIXUCTKSvg8m4KhIIyFcc3IhtHN148kneFZBBFdBu2lUVIS3KVCsuilqp/DhLr1h0FLpayq1vl38yipKJxXt1Amcy3dCM3frUVGoRkBnDx5sjVgp06dal+dlBtZu07QvDMeHEqCk1zfXIvMfUDkVf0cLuNsPbLQQRVqGsekVLs5R/9n0LIKLufCc0ZHB98uzQmYO+sg0klajuzZ75XPynxnFt7QJRHiyZMnW+rdnIhSPS9fbZU5w9QrsmUuEgyMjo62+xizMIeBTgptYqJ/3mruPU3GghxU9YEifUA9Zu6Wnptzdin1IoEn/RRNdcG2cYu6sFDGm1E+WU1wyK6J7IF26y1K9Tx1DUAgfeSYE2hmYUr+WBvrnzQ2W8u5igK3sm37yI5QiugohIkV3XBIGrTi1AHOxmJQDtdyAOi/LDGm7EkXZOQgv0ZBoSfOg1BwHs4jpFgEXIRobBQsi1SSpjXeTNhTqsyzLC8vD4yZQ0g6LF8HlGivqp/sZ7DT+AMY0Jw+UdB8Ductmc1oJ12XG6JVuOZ8OE1DpMeI5du0k1bTn9wOYW6y/FxfE3CY29xES+bQo9Yt6SNFG2SmW8zgeobaZyLCzC8yznnkGdo1nQO5zKpLURP5g8IZ5izcyDyLcZoz98kDCebm5qqq/1JR8p2OiByl3KFbc12BpsyzpUNPqk2BCd00tqS7gRJRSoJLYMT4V1ZWam5uro0Ac43JWRfgilKBjJxr89SNEq0j/Z2dnW2fkQBHNJd2zLqguoEgzopj5CQ5OMfxVdVAPj/lLe0BOSLDySglGLGm+kRH2AjzSw62qm37yI5w5LFHJtFiQHYqkBgeqIcxTY48w25Oris0yT/n/TwTUpJAh4AYSwhKBEfIlPcq+BCFqpqD3jjRpN26uTv3Tro2o79MaDMInmlrAMOctGjSdfqTSDgpDTlDz8/5zorYRPVQN6WluJTLOYKcKceX2yMAC5EV58ZJGJvEeq6D3JX19nzzxsB4aW3KnOjd9YmuGcHc8J20L4PKyZizLlXcRfx5cDbwkvS9wgogK2mvNNqZd5U71CcReDIJvltVrZ6RzbW1tbYvohTVn2SaQyE35t4cJBOgr8CVCD6LUtgEOsQBM+bAg7GyG5kOyfm1vulAcg+p6Nz2oqz2pS8bGxu1tLQ0kFvEnACZSVeTjzx20OfkIgEXShDIoROTk5tvaUi2K08Hcg07kNewbcmusJ8ZvWa0ieqkw3K4Gd16zlbn66oeAs6uS/dxMgSSYNmASZAgzTyOp2rQcCfyzVwXpSMIhLuqf7xYCkXSrPJLIhPCyfAyEml4GOgUkjzmiIPOc+2M3Rzh9kUmDFSXGhQ9iNIIMEOcFDHlY5Byn5L7ZmFKKk7mOsw5BYYA9bULMNw/jQ4Qky/M9JkIoZtMZ5zGx8frjDPOGGAIsioUSGGAM3pNQ1XVP4LN2LNqLsdivar6G3s5paSf9bWqanl5eWCeMhpwH/JAhjNiYdg4B5uJ3QP16PvkIClr/beWWY1svXxvcnKyFhYWWgdJTnu9Xvt2dWCH7HA+npv6bT1F8VlwQZcy7515ZUY/85nG0i2WEM3Qc+tAF8gBXTl16lQtLy/fA1TTr9xiQNcSXLoX4A3sKXgzzkxZdPNjmIqsenYqjkiKw2avrPX4+HgtLCy0c4ZW59ita1X/zSZZ0eveQDK5zBQA6tq9MANb2ba9s0OvqRKi8BYk0U/yz90cE6EmRFX9XCBFys8gbsJvobNgJQU7Q33GN69lsH0OnRGezAsyhK5NROskjaREMjGf32UMPauqT1WkYmXUYc4zn9Q0TetMUXpJrZpjRpJidAs3OKvR0dGBzea5LrY4uN5m1qS4GHZrZ/6Mk0Oxhk5TgZozwsvCnDS8qlDTsZELEVA6PiAkQRLnlaXpCiMSGDHMDBMayRr4yY3pST/llpvM22XEyJlbs3xu5kyBh/yXXO3YsaM9yk1RB0Nu7ujowsJC209RIz31d9GfOZWnTppPnzmEqampAYCYTIZqZNHN2lp/nydqDzi17oy+uXDwQ+Zq6X03lwlA+Nfco/b0gUNOubFW1iPrBLqRHf1TyUvXOD1O0PdEw6urq7WysjJgKz2PM2MnE0ShJckNeXavjKDTKSsISjp+K9u2d3YEDEJMZ8IwpbJRLNVGiVwoWFI6iaqqBo+Jck+CK8dDEBlfNFkm8IX/BDE/Y4w5EsaGkYfMUT6QVeaDKHtGmXlqjD5ROJ/5Tha/QHg+X1lZqV6v1yo0gc59hRkd+ZwSVNVAtJNOBa3T3b9mLUUDos6lpaUBqlrkwoj4PyOgb5n8T6eRc4AKJF9pOPPsw/ybe+ScQdD6RS6wCuvr6y3NRs4YUetvvXyW8u+ZGXnnGwrMOwOtv+gvfbdeDFzmcTK3R87zgGNjyopDst+NEvLNCL6fe/QUbcjB+df96XGCxmRd0tmkPipgsd4cCzYhwYz7+RtAk3mpqn71q+g1WQy6IE+WRTHWPiNj/acTHA9HQw4zTZCyb86Trs+cGtthH6UCogRoZB/gzrekZJqAbuYRbI5ky/niWJO2Vqg0LFC5j+3kyZMD9FZGWxBT0iA+y+9Y9KRVVlZWBvjsbgI2hZHTIdRV1SIXFA2Dl3uuEv0L6wkoeobiZsnyyEj/lBFCiTLLPTz5Ykj9zIjx+PHjNTs7246NoZCboIBovIyevast85X651mZoxH5MZqMSPd0+KRZ3IMyJkXFuXZRtPEyYCJE/cx1zQgwgUXmFPOQ5lwP+R5ONSMS63f06NGBHJHCC3OQ9/WS2KpNJL579+4W7YvQ7JNLKjcRt/v6XuavyUQCAAApq+Q4AVSySCeNflW1h2FXVVvZx6E6JYgxs19NVJrGOIEeIET2MjeU+WpjT6qarKV8JOB0fRZPkPeM0hL4ciRJW1f1aUdzMTLSP0WGXqVjdr1tIuySzfTGmLk2gIz8ZIRs3ZIpQFFygiLtLArJopUEgUkBc5psCh0SYesb2aNX9KfX67XXiiatE/tHrtiwrWrbPrIjgOgDv6dCVw0K0uTkZBudVA1usKVMs7OzrYIQagvobyItpbi2KGR+xOeMJ8PO0eWBuhlVQr76RmAJletEIBC06AiCzOICc0GBzjjjjNZYUhARHWOByshoqUsd2S9EQRKhihC0pN/MfZbAo5AYPuMRAWfkkMbI2nGSDH8WeHCuDLv7iB4zp5KUDRCUxln/rbFou2rTuOe+rHTg1qvrUMmA+RVVAR8ZLSQ17d5ra5snYTh9xQHa5JFD0WfrznGkAU9w+N/dgzyIGjP6sk+SQSRD5DrzjGSm+w4485QObnx888xWc6BfZCjnhJ6tr6+3esA2kLukfzkBNiQZIXOcVHXmm9xLf+iyzzLlQL4A1oy8OdisfkydJR/dYhxzTL+slb8bZ4LlBBL6lTLK5uUYq/r7egESNiHzzPlGhQTC1jaBxVa2be/soIisZMvz27LKkQNQyqzCMg0Xw0vIk+ev6leMdRdqYmKifXM5AeAMGACcts2bBIGAMrzoHsqOAsl8VnL7orvM51X1FS/RqjFKLh87dqzW1tZqZWWljWoIvXtkTjENre8Q9DT4jAZD0zXSSXFloUU38rPG1ifRtIjE/GbOLvchodFEigBGUseLi4sDji4T/b5n/KJnThYt5fOsgGMY0zjJPSULoAQ+kXiiZ3Ob+UtjEJ1x5F4qzJFA9Ix1RiqZT86qzKS5MvJK2Ul6jNNIx8SZohnTEXZpXfIC1KReWePJycmanZ1t59Vam5eM+pLhyRREVb/aMtfbmuhLgmfApEuFpvNjGzL6EUnmXlD6yDm5r/GQ42QbMp+Z4NcaAbu+AzCmXSMraSeMzb2tY9oNzIixmKu5ubkBW5POfXV1ta1AR79jIzI3vNU05rZ3dhl5WECnoGdkxAFBlpnPSpqDEcgkdnL3WTlVVQOIiHNNXjpRkD16EFEeI6SvWaXHSBDepmnagoxerzcwjsyfuFeWzjM+EL/5gIIJLcPJ4GZOLykhxQCiGt/N4hYtDRqEaR45NqfOq6bMNz4oeqjqbzWBgFFkfmzXIBvpTMmJ4hhGICMFY5FTsW+PcZfnSyCV0R6jga52ne+lQUEXadmPBAQih9xmkRQRWbU3kbwacx5WDiRkbkc+OXPdeX/fJVNVNRCpcIA7d+6smZmZmp2dbWUfe2Fe88g28k6H03BmTiojRUY85aCqWkefc+lftgEAoFPWWP8yQmScgSQFTuRM0c/4+OaRf10mBzADAjhPtiWdOF1POcE2kK8Em8aWc0bvra8TkNiATKXQGY6HbJHdpCdzC5Nrk3GQziA/ZJvDnJqaql27drVOH8tD57eybfucXfedYZSccdYyYkCRZXQmAqPMrnNkUXLxEDODZOGr+tFHRkfC+lOnTrV5G4Z8dHRzz1hy2RQ6HYQfEebCwsIAYjIXXn9jDIwVlGtcFJvBhnQphfnpVmzaf0YxnV8JeRPwzJclTcKYoiqTKu0CEEYCFZUnpWfxgnky3ww4FGv8Wf2W9I65dh8OQv+7OZ6kyDJfJD/rNVO5xgwmufR/Y1A0UNWvvMu8TMoVg1LVZxo4DUg/++OQAvk04CvHB+QkZZtFGeQSg6IvHKr5oC/G6p2IngVwJENiLCICepnzjvajY2mcx8bG2ujYWDJXyXFsbGwM0OhkMGlbvwOMaT/SMdEnUYo1GRkZad/QnqeeyO/meyjpJ3vEMefYR0c3D2LuRp3mN2nhZAQcXUgvU1YwE/RhZmamBSA7d+5so1ey6zNbjPL9illFnuyK7wE7GAJ6Yc23sm37yI4xzuQ+dEKgU5gJE9SdNAFBGRvrvxbItfj1pEt8h0GFwqDiVEhOOcvxNzY2y8vvuuuuAaolCwmSV08enhJRHtQmI5a0YM5JVuWZj6RaKJxok6NX/cgoi8Rcrw9Vgy9uzcgjAUFSYRlNp0FMZJkRjvlE5epjOgSOF4oGCjKvqN9kQLEMgyjiEd0z/qdb+4x65Z+yIlF/UYpJgSYA4LiyAIOh9ntGXOkIjcu1DGk3h+T1MuQsnbd5dC8RFjmxboxsyj5GhXFkWEUF6+vrbfl51eDZkiKzXJuk9AGfjN7oVFLmmXNKEGOOGP7M7Sb4cJ1rgTqVk8lkoPKzX9aMfaGjilNEhT4zR8l6eA5b4RjBpJmzcIh8pJPGkJw4caLNc5pTb22gh/bjqYLNKC6pYPObW5hEsdaX3rIxriNLWJ3cDrNV7SER2TH0GamkEjG+wmm0DuoCKoOE8v8MQHLgqLAu5VS1GWE6raSqX7lJcdyLwaBIiXIpVOZdKKKfLPPP4ha5H4hTv0R8DsumGNn3mZmZgY2oic7kbBiJpPdEQl68mgqf1J8KvVQoxqyqWsVLQ0CRrRUFzryaCFe/RZeiFKXyckq5FzCjF84m6R39su5pkBIwKcPOiCujc2uauZJ0wj6HgBPxQ9TmwJwzVhsbG9Xr9Vr6MAsmknZPUIMyTvo885VovzwFI4ElOTZ2kTdnSPbNWRZNdU/DQcEBMXmOrfujhdGC/jVvHLYIxxi6FH0CKvNmbM7ntH6+K0JjzMnEyMjIwJmrmdsEsrApo6Oj7UuTAdRMFdDDBI8AhO9aH2tkXbpMABklQ+bV76J7OkYecw4503weeQPgpqena3l5eQA80Vf6oCX9KmjY6uhu20d2qBuLn1FJ5mlGRjYr1LzPieHQkpawn8T9M/fi36pqr0tHlclifaGAmRiuGjwvMp1s8vMQPMXgxKr6xpsDymjIPaDItbW1tjCnatMIzszMtP2hIObC/j3zyzmJYNKgKAhhkFGXkD8DY5zQYXffkzFaP2vIGPssQUJV/3T7jCKSFvY3awwkcDJJEVH+9fX11vFnRD02tvmm9YwIbKsgT55n7c0TJ1VVrWwyLJ6rz2i4dBaehxasGix4YYiTKvPvsWPHBuhVVJRrusUrCrPIHockosu8Ze7D0h9yZo0yXynHxbErV6dv9GB9vV/1mMadTtNLa5T0vznMUvvUdWtATuWE6anrcl9b02zmzLOPqfsiL32jLwBo7quTcjCvCeDobspSRpMAW+q8PHUCJeArI08RpDHIc6cjBU6z6Crl0boCSvZQJngEyJM1k+MDmtP+bkXb9pFd5l6qqs0jMbJpdBhFVBdB9A6yVBYGKp1Q5sBESWnEc58cR7O8vNxGgRyhbQ0Ug9KdPLn5+gsVg56nr5nT0gc5EjkLm0CzwCCT3hAiai35fvm4zJvg27NCkjHQ76p+dIu2cW8KkTmIjOQygnad8SaSzrngqDhcVV/GrG/mOJ0oB07RUuGSBk3wk2NHAVH2brFKOnfP8nyImuHg1M0LeRA1Z/+Mh3HOYo18Ya6+Hz9+vI0+FdrkxnWUVR7/xKEm5cxgrq6utnuyzIFTTETSGbGLqjMHbv9dygwjCzB4flUNVLUmLQagZQ7Tm9eramCvrHxZ5vQ5zHx+6j9ZSiaDbLp/giCOOyNIz8t9wPRWlJrUdFKnGJXR0dF2zkWJKbcpNxgROiXVkHbS+gDE1kReF6Ag07ZpccAiWc54ZWVloKqUYwVAqmoghweAYngSgGxF2/bOTriMbpG4TcNDUKoGDzCmCJQ8k+FZOp3Ixt8sPgGGpBgzwuZ+nBmknALFSVVVq6BaCo3neYaEPeGT12AwMlnfdSSEOA8UNgeEHlqjqAwmhbGNQz85HUJvbBmRcGrmN6lBqDILSRheipJbNTi9PLkGurQuuY45p0kXmq/MT2TBDGWfm5trjVFGtCKAjY3+JnRAiBEzVvJg3RjITOpnXiplO418VZ8qzejDfM7MzAzkf1BYVf33FiaFzXGLYNfW1tpNz0nDcdocpPFxDsaTtGuyFPoP/HEU+kXfrD3HkqfBiGg4gi7YQHsCmaIPek0XOBEypH9sg7HJXeffq/o5wqTlkhlIWrC7D7CqWgqVbtDNpJU5JYUjaGBA0pyhM1OW6AfHknQ5QMVh+47+Nk1Ty8vLLfuU2wjYURSu+RDh01l6MT8/3/aTPQYAt7Jtexoz6bSq/mbtPFtR4UZSWrlIFJGi5UZayFUhQZcOQIkm5UfgCYoqztHR0bbyqYtSRWZd2mFiYqLdtJ6REwMiikA7qbpLKijnh0LlHBE+eU/XoYcZCG9ZcJIHI2zcmcPi8CcmJlr6hnFHpynacI88ud9aUR7Oz7qZf6XPVYNvOUjw4p4Z2SXF7RryY+sHp0Qx82g2ziGT+1ldmn1mBDjR01E6nIhohhxk36r6BU5kzFgBgmQvqvpHZaWTZRi7TtV2jzSAxp8l/11DzkD7IWscCXCgTL8LpNzf+iQw4Uj0vUv5qxIlF3QtnWLmG7EUimlUrqYNAGjISAIg1bbO/zRGoIDDz4Ipspl9SkDlvidPnmwPyLa+5k7u7tSpUy1zYk2lI/K55JMsqxPIQyPIk+fpq2I0VL69uNbZ+vguKps8sRfeBcq2mYdjx461W6e2sm17Z5doNivRsvRWdZyFYtBENZlHYQQIMSOGHvI5JSQAqTh+z31jyetDbAzM6Qx8FsRkVZqKNkiSU0EborQoT6JViNE8MVRVVfPz862jS8U0Z0kFMy65z45j9Z319fV2k31GUlmxaTuHXGIaniylz0gUYs1Scd9JOiyj6F6v18qFiEfuNnM+fmfsOGL9yXwl46l1qzurqj2KjXy6lyjWGnHiKTtV1coQI2R+zXWCAUUUolzPAqDItwgFKge8rHOXKquqtngjwZH+ksOUbX1mmKv6NPSxY8daupxckP0EKiJSkZA5BS6z/D/3MJKVfGaCUeuUeS1j5awyx5dO0D3pDGPPYSvUAoAyIpWzM27FPKmj+pP2CFhyfdM0dfTo0da5oAWNFSVpDvVDgZrrkkpNx5tVzhmNc370ylwkc5IymZGkObbW9mMOI7v/j8bQJQ/cRQ0WAr+fxSycVpYPZ5FAKrLnVfX37+RbAFBteW6i5/tO5hk4YsiH42NwU2hFX3Iwud8towCUpjE5fZ4xU+2WfHvSjRldzs7OtkpGsRkMh0FnVWF+Vy60qn8+IodbNbh5mANTZp0Grrt9JJ1wFockQBD9mj/Py8KjLAhghBhHzZxYG0qaBtT3NOuQqBnYIjMZIQFmSaty9mTRT85hRj3Qde5prOozHaLHBFs7duxo34GWxl5EngVVafAZqQSaZDIdM4Dofn7PdEHmWjnPpum/TzC3WhiDwqyq/rmcud3lxIkTbZGOMZm/iYmJVh7MN701PvKERqVn+peVoQy++eHAu2yRdUQtsylAXDJUmaPT9IduV/XfyqIPCrSWl5fb3KotUxxi6h8HmrQ8e7d79+57OKq1tbVaWFhonap5yh9rYO5yXkWI5ApLsVVt2zu7PGme0I2NjbWctN8zuuoKZreYYmNjo92bkoY2EZfoxfVyABQI0skchGcm0m6afsVk0gOMdhYEQOqETSSxttbfrJoRkP6jyKD5rNpE5SXN4BoIfWNjo0WSmV/hNDlFR5oRdgbVfZ2gkHtu0JeUZWlpqX31DSPRBRLoS/edmJhot5VQXsYjKyEz92V8QAH5SadJGfUjcw2Me1X/lUeMmXtzZnmqizxT7rv0fHMNBCTlqu8o9ar+vjiG0PP8m8gdBW29VaCSw+np6bb4wBxnXg8gyoiqafon+tjik441nSs9M6a1tbWan58fcPL5PeMT/WJUGGXjlmPMakTg0HyLbvNUJI6B05QKSH3lQJJ6TDocwE4aNXPrIlBsUm5h6LIkSTWPjo4OvC7HOMleVksavzVm04AGc+YZ5D1tWEZl9Nv7+TI1gZIEQqr67/1ka+RhyQVACuxm5DekMe9jYwCz2MHC+3smxEVCjEJuOuYUGRMJ5Ko+LSQ3kkqBf2ZIKFFuDejmkjJ3mEakql+dpSIrEe/6+vrAlgVGgKD3er22si5pTiCAcOsDRXK8kH55LmrRGDJHl8UaaA7KxwB5BY8qUXNC6FMRGA9UUve9eeY898ll+XM3P1TVj0g4fHPMEKB3zDe0m3kfv6fxP3HixMBhwNYiv9M1KFWDVXRoLUa9qv/KmDyNIo0Wo85Icmy5sdgcKNBIehtNK4KAwHO7CwCZudSksjnRpK5FLEnFkyPjIxv6llt0unnmNNAZCVp7jsT7G42J4QdW8thA/QQM8jrjorvdgpPM/7pXyiOb4nvmQjQDQBkPJ9tlYewZdgZosirsnTe0W9ukoDls/ze/ExMTA2/WSLuUb7DwvASAWfyE6kQD04WM2PUzUw4ZoZPfZEO2oj0knJ0cViKgjK4gy6mpqTZXg9pLB6Us3329sZchFHGJaAiaIoksDhBhUSi/Z4SU+aisqCPkHEMKeyLERHpJ8ylycD+oHDXE+DAGi4uLbeFJVbXGOClhCJuRlJ/gAFBqDEUqB6SaykMBMxrtFjhkvkkEIDIUqeQrjRiZU6dOtYdUp2NdXl5uDVV3f1Mi7TxLcm1t8zglUV+eWgH1K/tOxywKhJRPntw8moqMKXBgfJL+NZfG5F7JXHg+RF1VrRxlkQJZJuueYxxJJ/Z6vTYysHaZE+QskuLkmJumaeUS0DHPVf1CFs918kii+6QPuzmzfBVUGncntljXTD1w1lklipHgbBh8807fOaXMJaJpATLzlGzRsWPH2nUy7+5pjECKKNrzfEchlDUAIHzGqfksI10Faums6EhWUpIf0Rp5YSPc+8SJE9Xr9Qb2K3b/NV5glZw4c5RDxwJlMdJWtm3v7EZHRwdOjkgDnNELJJRhvH1CqVhV1YbiuRgUv2rwJI2kFxN9WdDMi3HMBJTCQ5bT09NtvgoaTMNMwav6Z/VRHD9N07RFCtB/fk8kmEUe+uhajoSxEBVllEOhGDNoWd8Y4OyvsSYStz0hq0CTBsocUxqtfLks52IfF+WHJq0LkMIh7t69e2Ddq6qlhsgNA+f5SYFDsvrFoaSxIU/yJt2IIaOAkZHBYgNGjRMhD7lfjtxYg1wjc50MQlW1peu5Md0YrDl9ApjMncgPmEiH5ftJX/Z6vQFDx0lwFJ5t/Nayy7JgGDgT1b2cmryndWaA9dk6owCBRECHPCkcI2fmHLBICtBnnEiXPUC3p8NJVgT9q6/GiPHQN2uZ88MZHj16dCBdwdagHelzpmLouWIo+pB5VTJsLeiaCD7tQLIubDDZyeBB+sW4trpt+312kCAnliXSjlDK8/g4JwoqemKg5S0s3sbGRpuHImCJuEZHR1u0RVAYoKxuTAOHYiVEjJKjkqBHEcOuXbvazbg290K1nJvcZCb8URJZgAF5ZRTlmZwEw2N+k45j/Kr6L83M3ETSK3I4jEsaN3/PHBHkyqAAK+Y881Pz8/MD+cc8yaaqWiVnVH3PxmPzm9SW9eTQ3CNRehoahofhS6orHTaaKCMWRRRd+oj8iYatq/xnRvrmkGz5DCNhXLlVJfNFDJg5m5ycHHBiGUnlnHASGQHljyg+6dWxsf6ZoylTXcPKeVov1YUpRyJWkQvHmvk/ji6LJMiVcQMzGZGxIdZVP5I6rdp0qL1eb+CddMZDNzAo9JacaJlXZZ8AQGty4sSJAZuUusNeVfWLojhg4CqpTXOib/qdVey5hxJATaZKy7yme4mEOTrzlRXFrpucnGwPdd+qtu0jOwIjZ2QhoScTTtFFfhl5MS5Vm4lZe3AIrmuTlkrnxdFqmdegLNC0fUYZhSbaquq/JJYByMT37OxsW5Ke+QXRX9IrFBc6TqQFwUJlmdes6hs4xkfxiWdRepRfVsNV9d8ZljSNdTp+/Hi7idfcj4z0S6zTeFf1ETJUqNDGdVl8YpzGQvGAE3Ts4uJiO99ot6yM69KyAEbm1jiTdGoZ6XDsjCnDZy0TGFgDY0QVdyM0eVIMQMpIOmtRCGNjHuW4RHqcB2Yijbx5FBH4W1X/SDLvhOR8UWL0D4VO7gAExjvzOfqQbzKxqT4jU6/KStkh89ITmXvO/JgxGrNTRgCEzI0bu7ElC+NtDiJQTpstAGbpqNqAZG/YqyyaAYzZB87o1KlTbXQJLCT4y5wgm0W2c64wB641PnLhWvdkCxLMAQ4AVQIfp6pk/YRInAwADklhb0Xb9pFdCjqDjdZJpco8mN9FFowew0SwLQ7n5N+kYKDdmZmZWlpaaq93Twtq4ZeXl9vvcwQKUBhr36FcaXjQaQxklj0nBZnUA8GDWNPhZbVYCnFGdpQOcqM0Ig2VY5RdLg3q5Ww3NjZPXUA5eW7SG1X9yrhEg5mLMg/WolsCzWklhaywiPFljB3Me+zYsbaCt0vLpZx0IzQoO6MI66ZwyDrpd96fMWaQrQ9DndFVRgWAQ+ZmyDXDlHQbg2NeFDmceeaZA3lBEbnjo0Rj5Mxce6b+mxvrCixl8Q0nhWLTMp8oejG3KYupR+jA1C/fzShG39fW1tpTSMia/i4tLbUO28k3SatnsZPxumemSpJuxwigNsmNfomAkx5lvzL6TlZF9MnZ5ZrIwZvPpDuzQCerUxMgYxHIUx45SC48L/XS3JBJlDWK1JrKuZr7BI5b1ba9s8u8msVSHltVbZIVapIXYRgY46x8y02phCXzQ5lYTgVPFJOv/rC48nRJA3kLAaFKI8GpZTk/Y0RZ/U2fUA/QPUVK4+wzdFw6OX3PE06g9ar+xuaqGnBAmURP9J+bV5PWEXH6/+Tk5MA5nOkA0FIMqnUwB0mhZXGFo5Uy78f4kJs88kk/5WgSCGWk5xQc/zd/6TRElVX948mShszIhmz5frcwh7Go6lPH3YIMTinpXzqRLxM1xrm5uVpdXa277757IKIBFtLAZWRgLjAf1ixZibxfVmkuLy8PVNJmbg/o4SwYd44hK2V9hwzSE47C/VH/nH2v1xugCBOgpVMcGxur2dnZWl5eHogOfTejv9Sp7FvmwxMcOl5PlM4x5nqmk5+amqrl5eUBWcsqSI4u85WAOcAF7Hk2vci8s2i0u30EC+aAhLx/jg9okNbJlAgw6xm5XWwr232OE6+55pp64QtfWPv27auRkZG64oorBj5vmqbe9ra31cMf/vCanp6uAwcO1De/+c2Ba+666646ePBgzc3N1cLCQr3iFa+4Bz/7la98pZ71rGfV1NRUnX322fXOd77zvo8uGsokDQdhZoD8LXMleUpFRnO5r0eiHJrOTd4EPHM+lCwTz+gz99IHlYV5AksaGdWjDCdHxfhXDb4bj3IaS5ZEQ5ES+JSAoqytrdXi4mL7XYqoZDlpWX1ZX9/cCrFz586Bsm+N0kF5HFFSGQx/5hNEfUnJZsSdZx9mmT6HWFUDRy8x4r6vyIIiptwkiDB31qqbwzAXnFv2IXOPWdXn3uYKFeZvmXtkjNxfH7tVsCIdjkW/rDUZynxi0pn2QHadlnkRCWcFMUo5qU7XKwAZGemfC5tHVZFfcobS1x/nQGIcsoiqqk/JWxfzkyDPfLpnOhNjcJ/V1dW26nB9fb2VHcY/c5HZB7LbXa9kY44fP97SsSImAAm9XVWtrNNHYDujR7JFt7PYBSgwXrqfqR3/J4cZbcndGUtVtQU2ALnIeHV1tZ0bdsM8uQewwwYD4tIVbNpWtfvs7FZWVurJT35yvf/97z/t5+985zvrve99b33wgx+sQ4cO1a5du+qCCy4Y2HN08ODBuuWWW+rKK6+sz3zmM3XNNdfUq171qvbzpaWleu5zn1uPfOQj68Ybb6x3vetd9fa3v70+9KEP3ecBEi7cP2FOx5U8NDQNEXUplOSlbW7Onf+5B6qqWrqCY81kbdM0LZVX1X9Dw/Hjx1vnT+AYiap+pSV6NItOCE5VtUoBAXNeOYY0oASU0u/atWug6ktf0gjqL8fC4GWFXa4FxWC8GN0uTZZrItoW9aj887nxZKSqGg+IgZTTKIimRazmG4DJnK7IgiEhN+YuI+SxsbG2kEZUQMmTVkzDLxelX/6eQMm6ci5oUIbMeKwLlJ3yyHBldWdGfZnL5mhsnZmdnW1lRF41C0zW1zePgMu8rCjU3Li/nJi5k7/FVIhUk54lh5kj5Fwyj7S6utqyM4wp/a2qARCCvsxN8+Qz1zgdOPAjGmGUyTDKMIFzVnZmHtXa+rtcdTpclL2qyXx3HfsBUGQ0nMVywGG3mnhlZaV1WFoXNOYWigQMGdFaB9Gt8etrt2/AVzo/MmKtsk9b0e7z3S688MK68MILT/tZ0zT1R3/0R/Xbv/3b9aIXvaiqqv7sz/6s9uzZU1dccUW99KUvra9//ev1uc99rq6//vo677zzqqrqfe97Xz3/+c+vP/zDP6x9+/bV5ZdfXidPnqwPf/jDNTk5WU94whPq8OHD9e53v3vAKX4vDcpaWVlplSxphZGRkfaFopCZRU2nUNWvzOQA0Wxor8yFQfeelw7OT3fzc1V/LxRjlIom51NVA5WejCDFERmgGQmbnJ0y5zS8jFsqYW7RYCQzx5fOl+IzbO6fFGEeUJtRCGOwY8eO1vhC7FV1j2vzSC9UqWclvcbhQI0nT54cKCShgN38a1JymTflyH2fgUm6UH9FW1XVRmeZp7BOMzMzLa0jJwQE5Nu5q/q5SbR7MhLWCEgDPjjNrApMx24tRVUchqgmo/R8a0eyBRgBMmsd1tbW2nf7MZAcD/qSfCYV7hnGlfQo+pjumDPPTCoP7aZf1jfzQuaGLLkmo+5kA+Sb6QpwcjpAQm4Uj2Qu3w9WwhhEdmkjyB59VD2ZOWhMkmsT7ORWEYCjO27yh5lQdblz585aXl4eCBjSgRozJ0cv5OTZCxRtgrissk7drdosCFLjsFVtS8tdvv3tb9eRI0fqwIED7d/m5+fr/PPPr2uvvbaqqq699tpaWFhoHV1V1YEDB2p0dLQOHTrUXvPsZz+7zY1VVV1wwQV166231t13333aZ6+urtbS0tLAT1W1qHdiYqLdH6faiQHs9Xot/06oGb+kYDJnx5lkgUhV/w3GEDVB8Kykp2wJYLAJEyURKaUQUyyUU0ZSp0NQrkthSqepX6haTsa8UWyRGsOe95fvZEQhX/PFaVHQfBNzOm/KSxmr+ucsMuqZzzSfCUAooTGaO04W7Ws+RB7pMNLIdecNLZfRkQjGHDCOZCKfY32qNikguduqfmVcRpW+l1WEDBSkjzXQj9wekn8TWXMSdIGMVfUjHU5WX+kXx4USZ+ScGJMOmKHOfC30PjMz04JFc+VaRjnBIfaiqtrvkS0G1ItTs+DIAdWZR0vHhjbL4grRvTXOPFJGPMao/6Jq8uT/bE+eJNKlUnu9Xgswk1kCPnw/2SXzJA1iTc2LdSYrCorMpby1+aOHyS4sLS0NODfP9G+v16terzfAvLhfHkJBJzhe988XCVf186SLi4vttVvVttTZHTlypKqq9uzZM/D3PXv2tJ8dOXKkzjrrrIHPx8fH62EPe9jANae7Rz6j2y699NKan59vf84+++yqqpYCgIjS4FGQ8fHxFlFxNhaXgc+IxvmMGcIz4kL3qsEoIQ0hBeZQCOnGxkb1er32nErRYjom88WxoDeSxvTsqn4OD6qUo1EMkjRcVQ3Qqpn3MI6MYLr5qDS6lJMSGqc+ZUQ1NjbWHmOWz2Wgk+5xAkQWvlT1D8hOQ5ngBdpMJ17Vf7VJ9ss9HLuWuTYn1nOuWc3IqWXOjfHhlESxHEJGA6L30dHR1lCk8WaEHGGWDALQk1FEVf+9aeYiKwQ1spdADTVP7vTZOOhSt+AHqDA/ubnfmqER05kCU9YqIzFgUrSZMpj5r6wqFB0CQn5sT8j5A+BEmsZKr9bW1gZO4iFfnIb7ZI6JPeieXkTmPDNBrvGzHxMTE7W0tNQ6exE0h5iVmhMTm+e/dk9OMcf6lpWfHGpXV5wkBWxnwDAzMzMQ9WM6UP3WONMegoJujm5kpP9Oz4mJ/vaeZF62sm2bfXZvfvOba3Fxsf25/fbbq6p/SogJ7hptwpo5HCgkaRELl5GUg2oJuujIwnMya2tr7X6djPQ4gaxoYzSzCIOTYHwIqeN2upRfcu2MQx4sPDIyMlAwAkmmY5BDSvqJM4Iwc5+cKByKRu0qKeYE7YHTV2duVvX3WGV0VtV/dx4UnjRQV1HX1tZah5jfzX08xq8QqGoQfebaJ+JnQFT+MUoMYYKNpIS83SGfnwUXDBIjljlYhsv8MML+RnbX19cHTk7JqkORbBZTWC9Ve5nfMj+a8xhzHUTLefyU/gMp+Z4yz0mAlbm0zI052YMcVg1W7maxD1nxDrSMnHNNM2+YAJLzWF5ebvORmUfK/gIcvi8fZyzWI2lsNkAfMo1Ch0WaqSMMvnnXn3vLc+UcZyTVNE2bb02aN+cjC1/ourxnjt2RcWQTaMjiqlxPnwGb7IEx6Cd5AZymp6db/dqqtqXObu/evVVVdccddwz8/Y477mg/27t3b915550Dn6+vr9ddd901cM3p7pHP6LYdO3bU3NzcwE9VDeRfoBXKmQi9aZr2ZPykJar6R4GJCn03EUtVfx9J8tdoiO6eFol0v0vkokySbskS3S7aTCTF4BDyFN5EkYxMVbV5JAI9NTXVKotrnS3KCRDgLNtPGgdqz9NYRkZGWrpUn6yJNchohwIAAOQkDVzSKVrSo5LgHEJSQNYiwQzHZ4xAkbEwpAo2yIT1yKO8/CsazdxoVo/Ozc21fTT/PkPXifZcl4dyuxdKSMSvcIVMOvounYWxWxt91H9GlwHLdyuKfDk/gMe8AwCZrwO80vgnyDTX9IkO+HtGY0kjYyNy7TPnRt+TNsTC5L5UoE8ONQGx+c58d66l58krMvbsT8qjCI6+Gw+9sE5ZxEHWRawAjPwlgMDmmQPAsqra9ZHzoy+ZgvB30Zv109+swjU+cgoMWedkLcy7sWZOEttkDkSU9HGr2pY6u3PPPbf27t1bV111Vfu3paWlOnToUO3fv7+qqvbv319Hjx6tG2+8sb3m6quvro2NjTr//PPba6655po2kqmquvLKK+uxj31se17h99osnt/9Pzc0+n1paak1TElRVlVrZHLBkpayyKm8iVIoHaeUURGBRh+ghiiS36v6h/ZmgjhLfJNuZSiTEoJcIdaq/huj/T2pVQpMAeUO5+bmamZmpjVGHKb7EWjPTSq1W1CTEQuDxjAZRxqIpF6VxTPInpeG1H2y0AMCRsFw2mQBSGma/rmHgIa1zxwqxUUxMwSZ/+CsGM+k24wzq0vlxVJ29A1liVrtUrdeQmvu/Z4AIGUo945yAiKzBDmAGCDCiXGimRMi14Cd72bhjr7la5y6nxl7VR8kVVULFslagoYuqAEiABZRMJnKnKbijczbGYdqydwXllWP5FTOkNzRw3SA5DUBLUBE11K2RKzuz8k66YWeJx1sbnLLAcedzJE5QL9m8RZdNR9ZNZy2in4YV8pq1SClbg5yvPpWVW2h2la2++zser1eHT58uA4fPlxVm0Uphw8frttuu61GRkbqta99bf3+7/9+ffrTn66vfvWr9Yu/+Iu1b9++evGLX1xVVY9//OPrec97Xr3yla+s6667rv7pn/6pLrnkknrpS19a+/btq6qqn//5n6/Jycl6xSteUbfcckv95V/+Zb3nPe+p173udfd5gASeoltIlZVV/ddiNE3THjuVDiIN+vT0dItw0xFZYAaYIiVNxtBk4pjAJA0EJUPdWXiR/HkWaRAefU2Ei1atqjbqdH/Phs6zBDxzUeaSwMrpcDqUxzOrBpPdaWA9z1i8roTBp6QMcB6zldVfEGBGne6RSup+6fQykiAX9h6ZCzRdVX/j9+TkZGtY3IszzMixa6zNBUeY7/MSzUDTUDxKM6vgqvoHcqdzJ3NASVJfeW0a45wP4yMDXf3Ie2TOJt+Dl4VFmWOUN5+ammoBkvkBEBhZhpmc0NVcR99J+QZYrLv+oQVzOwZHnWMbHe2fJUv2cm4Ajqz+9Qx9yMhzbGysZmZm2gpWzpLDs27ARlKvHKf75hqS75QVcplzxtaoNBeJA8fJPhhzUrjJfKX+uze7RB5PnTo18M7ALKIzH+vr67W4uNj2B/ARAHTXJnOzW9HucwbwhhtuqJ/6qZ9q/88BXXTRRfXRj3603vCGN9TKykq96lWvqqNHj9Yzn/nM+tznPteG/1VVl19+eV1yySX1nOc8p0ZHR+slL3lJvfe9720/n5+fr7//+7+viy++uJ761KfWmWeeWW9729vu87aDqmrzNbhxC2ABk95Lg2CRqqpFJ0mlKCagiIxFVf8EAgLjPpxYUkWME6HNZ3YjxUySEwiKrB9ZtVlVLeLidDMKpDyQo1fcuDaT6ARUbiGpPXOVuQdOJJEbJ6cqFnLLkmrVsZQ/jWBSYqhD1yXVNTMz09KpHKEtFwzg6mr/9PrMMegvKi4V3338jYJmzrKq//qYjPCTcgJ0GMCqvsPKzeM5B6o2E/xkVGOt/S2LS6x5MhUZ+XKqaZySCgMU9Mn6onSTviVb+XsWgBmzUvvsh0pGfUhK2zFdXWOYxrqq/0YSThP41NzXuq2vr7fFYEl15nMTwGYuU5SXa5WbwgEHfaZnfk/7oF+endt+gFwymtWoO3bsqF6v1/aTPMmppjPlJDnTXB+gPSNo1yclzGmhe5NSzvxtsgE5p2xxVbV6mpF7UpkZyW5FG2k8ZZu1paWlmp+fr9/93d9tEQi60dE8BCZprC5CTO4ekquq9nUxVYPbFLqIOSnD5K8pkLLkrDwi/IRDlMWIESLvBOMIsiLQXiyG25iy3DvHx9jkVoek0DxbP7uRjefYu+O+iZyT2vV940wnlrkMygU8UMypqakWwECSqhQ5uqxIYxjtz/J55uYyd8Jxi3Ay2sio0fooGknKkWxkvlD/stBDy+PLvEdMlMGZMH5dCtjp9yKt3EtHdu3Vynyv8xc5eqCA0TQ/ZA6rIKpzreiMfBuDSCipZ+tu7bO039sCzBVAZj0SnCV1nXNMhz0bECNbydSMjm5ulpfj5KTIBMNuA711HR8fb/tMxgFNMpDFa4Dh4uJiC5IyCkydSx1JKpgOZYSX+mhe7GlV4OFMV7JoLtwP8LLeHCJ7lQAQaCOvAGWyOoChdU7GwzNOnDhRc3NzA5S21IB1O3bsWL397W+vxcXFtgbjf9O27dmYJpaRMOn5PjtGKQ0uFOjgX4vYRVqqldI4irCq+nuBkiqAnjkDzpMx7aJ+EYHvc4wiiSwyoMCEhINkJLx9IJG+3ASl8mxCjkdHX1b1ERe0NjY2VnfffXdNTEwMRGSZZ8xoMdE5xUxKJBVen6v6WymcnygK5TjkuHynGxHqS9Jkubk4jSWlm5iYaKv0sq+ed+LEibY4Ymys/4oaxi+3HTi+zBgBC/NhblXMZRFR5ojTiOYetarNY/jIW3ebS+aNOHbFVln96XlJQyVQ4YSBPYaQc8pzHKv6b89w/6wmdHhAghlVy5y56NtLeQEKuU7zmE7A3GUuLdcXDa1f//Ef/zGwv7Xr3BWi0bXp6enq9XrtvJoDOk+2OANrZz6SHh0dHW0PFrCuSakCI5xwVf9ACfMuIt65c+fA+ZPz8/MDOW3jS6Cf60fP2A99YcfYCjas1+u11+lDskr6C8wkgN7Y2Ki77767dW6cvnWt6m/B2Kp4bNtGdt/61rfq0Y9+9IPdjWEbtmEbtmH7X7Tbb7+9fvAHf/B/fZ9tG9k97GEPq6qq2267rebn5x/k3vzfa0tLS3X22WfX7bffviUUwXZrw/n5n9twju69Defn3tv/ND9Ns/nKM4WL/9u2bZ2dUHh+fn4oaPfSck/isN2zDefnf27DObr3Npyfe2/3Nj9bGahsmxNUhm3Yhm3Yhm3Y/rs2dHbDNmzDNmzDtu3btnV2O3bsqN/5nd9pqyOHbbAN5+fe23B+/uc2nKN7b8P5uff2QM/Ptq3GHLZhG7ZhG7Zh07ZtZDdswzZswzZsw6YNnd2wDduwDduwbfs2dHbDNmzDNmzDtu3b0NkN27AN27AN27ZvQ2c3bMM2bMM2bNu+bVtn9/73v78e9ahH1dTUVJ1//vl13XXXPdhdut/bpZdeWj/+4z9es7OzddZZZ9WLX/ziuvXWWweuOXHiRF188cV1xhln1MzMTL3kJS+5x1vhb7vttnrBC15QO3furLPOOqte//rXD7zxeLu0yy67rH0Ho/ZQn5/vfOc79Qu/8At1xhln1PT0dD3pSU+qG264of28aZp629veVg9/+MNrenq6Dhw4UN/85jcH7nHXXXfVwYMHa25urhYWFuoVr3hF9Xq9B3oo90s7depUvfWtb61zzz23pqen69GPfnT93u/93sBhxQ+lObrmmmvqhS98Ye3bt69GRkbqiiuuGPh8q+biK1/5Sj3rWc+qqampOvvss+ud73znfe9ssw3bxz/+8WZycrL58Ic/3Nxyyy3NK1/5ymZhYaG54447Huyu3a/tggsuaD7ykY80N998c3P48OHm+c9/fnPOOec0vV6vvebVr351c/bZZzdXXXVVc8MNNzQ/8RM/0Tz96U9vP19fX2+e+MQnNgcOHGhuuumm5rOf/Wxz5plnNm9+85sfjCHdb+26665rHvWoRzU/+qM/2rzmNa9p//5Qnp+77rqreeQjH9n80i/9UnPo0KHmW9/6VvN3f/d3zb/+67+211x22WXN/Px8c8UVVzRf/vKXm5/+6Z9uzj333Ob48ePtNc973vOaJz/5yc2XvvSl5h//8R+bH/7hH25e9rKXPRhD2vL2jne8oznjjDOaz3zmM823v/3t5hOf+EQzMzPTvOc972mveSjN0Wc/+9nmLW95S/PJT36yqarmU5/61MDnWzEXi4uLzZ49e5qDBw82N998c/Oxj32smZ6ebv7kT/7kPvV1Wzq7pz3tac3FF1/c/v/UqVPNvn37mksvvfRB7NUD3+68886mqpovfOELTdM0zdGjR5uJiYnmE5/4RHvN17/+9aaqmmuvvbZpmk3hHR0dbY4cOdJe84EPfKCZm5trVldXH9gB3E9teXm5ecxjHtNceeWVzU/+5E+2zu6hPj9vfOMbm2c+85n/7ecbGxvN3r17m3e9613t344ePdrs2LGj+djHPtY0TdN87Wtfa6qquf7669tr/vZv/7YZGRlpvvOd79x/nX+A2gte8ILmV37lVwb+9rM/+7PNwYMHm6Z5aM9R19lt1Vz88R//cbN79+4B/XrjG9/YPPaxj71P/dt2NObJkyfrxhtvrAMHDrR/Gx0drQMHDtS11177IPbsgW+Li4tV1X8DxI033lhra2sDc/O4xz2uzjnnnHZurr322nrSk55Ue/bsaa+54IILamlpqW655ZYHsPf3X7v44ovrBS94wcA8VA3n59Of/nSdd9559XM/93N11lln1VOe8pT60z/90/bzb3/723XkyJGB+Zmfn6/zzz9/YH4WFhbqvPPOa685cOBAjY6O1qFDhx64wdxP7elPf3pdddVV9Y1vfKOqqr785S/XF7/4xbrwwgurajhH2bZqLq699tp69rOfXZOTk+01F1xwQd1666119913f8/92XZvPfjP//zPOiFQ6bwAAAXBSURBVHXq1IAxqqras2dP/cu//MuD1KsHvm1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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plot_yolo_datum(yds.next())" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eca207fb-7378-4fa4-b3e3-00a71468c63b", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/torchsig/image_datasets/annotation_tools/yolo_annotation_tool.py b/torchsig/image_datasets/annotation_tools/yolo_annotation_tool.py new file mode 100644 index 0000000..931e9dc --- /dev/null +++ b/torchsig/image_datasets/annotation_tools/yolo_annotation_tool.py @@ -0,0 +1,106 @@ +import os +import numpy as np +from jupyter_bbox_widget import BBoxWidget +import ipywidgets as widgets +from matplotlib import pyplot as plt + +def setup_yolo_directories(root_path): + image_dir = root_path + "images/" + label_dir = root_path + "labels/" + + if not os.path.isdir(root_path): + os.mkdir(root_path) + if not os.path.isdir(image_dir): + os.mkdir(image_dir) + if not os.path.isdir(label_dir): + os.mkdir(label_dir) + +def load_and_process_image(fpath): + img = plt.imread(fpath) + return img +def save_image(img, fpath): + plt.imsave(fpath, img) +def save_yolo_labels(labels, fpath): + with open(fpath,'w') as labels_file: + for label in labels: + labels_file.write(str(label[0])+" "+str(label[1])+" "+str(label[2])+" "+str(label[3])+" "+str(label[4])+"\n") + +def save_as_yolo_data(output_image_dir, output_label_dir, fname, img, bboxes, class_names): + """ + Saves data from the annotator widget as yolo image/label files in the output directory + Inputs: + output_image_dir - the path of the image directory for the new yolo data + output_label_dir - the path of the label directory for the new yolo data + fname - the name of the image being saved + img - the image being saved + bboxes - the bounding boxes to be saved + """ + height, width = img.shape[:2] + labels = [] + for box in bboxes: + cid = class_names.index(box['label']) + cx = (box['x'] + box['width']//2)/width + cy = (box['y'] + box['height']//2)/height + new_width = box['width']/width + new_height = box['height']/height + labels += [[cid, cx, cy, new_width, new_height]] + save_image(img, output_image_dir + fname) + label_fname = fname[:-4] + ".txt" + save_yolo_labels(labels, output_label_dir + label_fname) + +def yolo_annotator(input_image_dir, output_root_path, class_names=['Signal']): + """ + loads and runs an interactive notebook cell with an annotation tool that lets ou label the images in input_image_dir in yolo format and save the outputs to output_root_path + annotations are saved as you label them, and the tool will recognize and skip images which already have labels, so terminating and reruning the tool will pick up labeling on the next unlabeled image + by default the tool uses a single 'signal' class, but an array of string class_names can be passed in + """ + setup_yolo_directories(output_root_path) + fnames = os.listdir(input_image_dir) + annotated_fnames = os.listdir(output_root_path + "images/") # used to make sure we don't annotate already annotated images + fname_ind = 0 + fname = fnames[fname_ind] + while fname in annotated_fnames: + fname_ind += 1 + if fname_ind >= len(fnames): + raise IndexError("There are no more unlabeled images is the target directory. Either remove existing label and image files from the output dataset, specify a new input directory to add new data to the dataset, or specify a new output directory to relabel images in a new dataset.") + fname = fnames[fname_ind] + annotation_tool = BBoxWidget( + image = os.path.join(input_image_dir, fname), + classes=class_names + ) + annotation_tool.fnames = fnames + annotation_tool.annotated_fnames = fnames + annotation_tool.ind = fname_ind + + out_cell = widgets.Output(layout={'border': '1px solid black'}) + + # when Skip button is pressed we move on to the next file + @annotation_tool.on_skip + def skip(): + annotation_tool.ind += 1 + if annotation_tool.ind >= len(annotation_tool.fnames): + out_cell.append_display_data("There are no more unlabeled images is the target directory. Either remove existing label and image files from the output dataset, specify a new input directory to add new data to the dataset, or specify a new output directory to relabel images in a new dataset.") + annotation_tool.close() + print("All input images are labeled") + return "All input images are labeled" + annotation_tool.fname = annotation_tool.fnames[annotation_tool.ind] + if not fname in annotated_fnames: + annotation_tool.image = os.path.join(input_image_dir, annotation_tool.fname) + annotation_tool.bboxes = [] + else: + skip() + + # when Submit button is pressed we save current annotations + # and then move on to the next file + @annotation_tool.on_submit + def submit(): + annotation_tool.fname = annotation_tool.fnames[annotation_tool.ind] + img = load_and_process_image(input_image_dir + annotation_tool.fname) + save_as_yolo_data(output_root_path + "images/", output_root_path + "labels/", annotation_tool.fname, img, annotation_tool.bboxes, annotation_tool.classes) + skip() + + out_cell.append_display_data(annotation_tool) + out_cell.annotation_tool = annotation_tool + + return out_cell + diff --git a/torchsig/image_datasets/datasets/file_loading_datasets.py b/torchsig/image_datasets/datasets/file_loading_datasets.py index bd6abc9..14832b4 100644 --- a/torchsig/image_datasets/datasets/file_loading_datasets.py +++ b/torchsig/image_datasets/datasets/file_loading_datasets.py @@ -4,12 +4,16 @@ import torch from torch.utils.data import Dataset -from torchsig.image_datasets.transforms.impairments import normalize_image +from torchsig.image_datasets.transforms.denoising import normalize_image def load_image_rgb(filepath): f = cv2.imread(filepath) img = cv2.cvtColor(f, cv2.COLOR_BGR2RGB) return img +def load_image_grey(filepath): + f = cv2.imread(filepath) + img = cv2.cvtColor(f, cv2.COLOR_BGR2GRAY) + return img def extract_bounding_boxes(filepath, filter_strength=None): return extract_bounding_boxes_from_image(load_image_rgb(filepath), filter_strength=filter_strength) diff --git a/torchsig/image_datasets/datasets/yolo_datasets.py b/torchsig/image_datasets/datasets/yolo_datasets.py index 3d672ad..68c7347 100644 --- a/torchsig/image_datasets/datasets/yolo_datasets.py +++ b/torchsig/image_datasets/datasets/yolo_datasets.py @@ -1,6 +1,9 @@ import numpy as np import torch from torch.utils.data import Dataset +import os +from torchsig.image_datasets.datasets.file_loading_datasets import load_image_grey +from torchsig.image_datasets.transforms.denoising import normalize_image, isolate_foreground_signal """ A class for wrapping YOLO data; contains a single datum for a YOLO dataset, with image and label data together. @@ -241,4 +244,123 @@ def __getitem__(self, idx): else: full_datum.img = self.transforms(full_datum.img) - return full_datum \ No newline at end of file + return full_datum + +def read_yolo_datum(root_dir, fname): + """ + loads a YOLODatum from a root directory and file name that point to a dataset in yolo format + """ + img = torch.Tensor(load_image_grey(root_dir + "images/" + fname + ".png")[None,:,:]) + labels = [] + labels_in_file = np.loadtxt(root_dir + "labels/" + fname + ".txt", delimiter=" ") + if len(labels_in_file.shape) == 2: + labels = list(labels_in_file) + elif len(labels_in_file.shape) == 1: + labels = [list(labels_in_file)] + return YOLODatum(img, labels) + +def yolo_to_pixels_on_image(img, box): + """ + returns the (x_start, y_start, x_end, y_end) pixels of an input box in the yolo format (cx, cy, width, height) on img + """ + cx, cy, width, height = box + img_width, img_height = img.shape[1:] + x_start = int((cx - width) * img_width) + x_end = int((cx + width) * img_width) + y_start = int((cy - height) * img_height) + y_end = int((cy + height) * img_height) + return (x_start, y_start, x_end, y_end) +def yolo_box_on_image(img, box): + """ + returns an image tensor containing the portion of img that falls within box, where box is a tuple (cx, cy, width, height) in yolo format + """ + x_start, y_start, x_end, y_end = yolo_to_pixels_on_image(img, box) + return img[:, y_start:y_end, x_start:x_end] + +def extract_yolo_boxes(yolo_datum): + """ + returns a list of new YOLODatum objects which each contain a single box from the input object + """ + img, labels = yolo_datum + extracted_boxes = [] + for label in labels: + extracted_boxes += [YOLODatum(yolo_box_on_image(img, label[1:]), int(label[0]))] + return extracted_boxes + +class YOLOFileDataset(Dataset): + """ + A Dataset class for loading image and label files in YOLO format from a root directory + Inputs: + filepath: a string file path to a folder containing the yolo dataset + transforms: either a single function or list of functions from images to images to be applied to each loaded image; used for adding noise and impairments to data; defaults to None + read_black_hot: whether or not to read loaded images as black-hot; this will invert the value of loaded SOIs + """ + def __init__(self, filepath: str, transforms = None): + self.root_filepath = filepath + self.transforms = transforms + + self.fnames = [] + for f in os.listdir(self.root_filepath + "images/"): + if f.endswith(".png"): + self.fnames.append(f[:-4]) + + def __len__(self): + return len(self.fnames) + def __getitem__(self, idx): + image, labels = read_yolo_datum(self.root_filepath, self.fnames[idx]) + + if self.transforms: + if type(self.transforms) == list: + for transform in self.transforms: + image = transform(image) + else: + image = self.transforms(image) + return YOLODatum(image, labels) + def next(self): + return self[np.random.randint(len(self))] + +class YOLOSOIExtractorDataset(Dataset): + """ + A Dataset class for loading marked signals of interest (SOIs) from a yolo format dataset + Inputs: + filepath: a string file path to a folder containing images in which all signals of interest have been marked wit ha colored bounding box + transforms: either a single function or list of functions from images to images to be applied to each SOI; used for adding noise and impairments to data; defaults to None + read_black_hot: whether or not to read loaded images as black-hot; this will invert the value of loaded SOIs + soi_classes: which classes from the yolo dataset are to be considered signals of interest; None for all classes; defaults to None + """ + def __init__(self, filepath: str, transforms = None, read_black_hot = False, soi_classes : list = None, filter_strength=1): + self.root_filepath = filepath + self.transforms = transforms + self.soi_classes = soi_classes + self.filter_strength = filter_strength + self.sois = [] + + fnames = [] + for f in os.listdir(self.root_filepath + "images/"): + if f.endswith(".png"): + fnames.append(f[:-4]) + + for fname in fnames: + datum = read_yolo_datum(self.root_filepath, fname) + new_sois = [soi[0] for soi in extract_yolo_boxes(datum) if not self.soi_classes or int(soi[1][0][0]) in self.soi_classes] # take only the image part + new_sois = [soi for soi in new_sois if np.prod(soi.shape) > 0] # dont allow sois for boxes of null dimensions + if read_black_hot: + new_sois = [normalize_image(soi) for soi in new_sois] + else: + new_sois = [normalize_image(-soi) for soi in new_sois] + self.sois += [isolate_foreground_signal(soi, self.filter_strength) for soi in new_sois] + + + def __len__(self): + return len(self.sois) + def __getitem__(self, idx): + soi = torch.Tensor(self.sois[idx]) + if self.transforms: + if type(self.transforms) == list: + for transform in self.transforms: + soi = transform(soi) + else: + soi = self.transforms(soi) + return soi + def next(self): + return self[np.random.randint(len(self))] diff --git a/torchsig/image_datasets/generate_dataset.py b/torchsig/image_datasets/generate_dataset.py index b858167..c2cc719 100644 --- a/torchsig/image_datasets/generate_dataset.py +++ b/torchsig/image_datasets/generate_dataset.py @@ -16,8 +16,8 @@ # constants/config stuff--------------------------------------------------------------------------------------------------------- -TRAINING_PATH = "./new_dataset_sig53_imgs/training/" -TESTING_PATH = "./new_dataset_sig53_imgs/testing/" +TRAINING_PATH = "./new_dataset_narrowband_imgs/training/" +TESTING_PATH = "./new_dataset_narrowband_imgs/testing/" NUM_TRAINING_DATA = 250000 diff --git a/torchsig/image_datasets/transforms/denoising.py b/torchsig/image_datasets/transforms/denoising.py new file mode 100644 index 0000000..ab69eb3 --- /dev/null +++ b/torchsig/image_datasets/transforms/denoising.py @@ -0,0 +1,39 @@ +import cv2 +import numpy as np +import torch + +def normalize_image(image, axis=None): + """ + returns the infinity norm of an image + Inputs: + image: image to norm as a 2d ndarray + Outputs: + the normalized image + """ + if type(image) != torch.Tensor: + image = torch.Tensor(image) + if axis == None: + ans = image - image.min() + return torch.clip(ans/max(ans.max(),0.0000001), 0, 1) + else: + ans = image - image.min(dim=axis, keepdim=True)[0] + return torch.clip(ans/torch.clamp(ans.max(dim=axis, keepdim=True)[0],min=0.0000001), 0, 1) + +def isolate_foreground_signal(image, filter_strength=0): + ''' + filters image (a tensor of shape [1, width, height] in grayscale) to seperate foreground from background noise, and returns the filtered image tensor; + an integer filter_strength can be passed in to tune the filtration effect + ''' + test_hsv = cv2.cvtColor(cv2.cvtColor((image[0]*255).int().numpy().astype(np.uint8), cv2.COLOR_GRAY2BGR), cv2.COLOR_BGR2HSV) + lower = np.array([0, 0, 0]) + upper = np.array([360, 255, int(255/2)]) # hand tuned, HARD CODED # TODO hard coded considered harmful :( + upper = np.array([360, 255, int(255/2) - filter_strength]) # HARD CODED # TODO hard coded considered harmful :( + + mask = cv2.inRange(test_hsv, lower, upper) + + img_contours, _ = cv2.findContours(mask, cv2.RETR_LIST, cv2.CHAIN_APPROX_NONE) + blank = np.ones(image.shape[1:], np.uint8) * 255 + d = cv2.drawContours(blank, img_contours, -1, (0, 0, 0), -1) + final_image = torch.Tensor(d[None,:,:]) * image + + return final_image \ No newline at end of file diff --git a/torchsig/image_datasets/transforms/impairments.py b/torchsig/image_datasets/transforms/impairments.py index fb89060..ba9d03a 100644 --- a/torchsig/image_datasets/transforms/impairments.py +++ b/torchsig/image_datasets/transforms/impairments.py @@ -2,24 +2,7 @@ import numpy as np import cv2 - -""" -returns the infinity norm of an image -Inputs: - image: image to norm as a 2d ndarray -Outputs: - the normalized image -""" - -def normalize_image(image, axis=None): - if type(image) != torch.Tensor: - image = torch.Tensor(image) - if axis == None: - ans = image - image.min() - return torch.clip(ans/max(ans.max(),0.0000001), 0, 1) - else: - ans = image - image.min(dim=axis, keepdim=True)[0] - return torch.clip(ans/torch.clamp(ans.max(dim=axis, keepdim=True)[0],min=0.0000001), 0, 1) +from torchsig.image_datasets.transforms.denoising import normalize_image def pad_border(image, to_pad): if type(image) != torch.Tensor: diff --git a/torchsig/models/__init__.py b/torchsig/models/__init__.py index 7ff45ed..8c8bb88 100755 --- a/torchsig/models/__init__.py +++ b/torchsig/models/__init__.py @@ -1,4 +1,5 @@ from . import iq_models, model_utils, spectrogram_models from .iq_models.efficientnet import EfficientNet1d -from .iq_models.xcit import XCiT1d +from .iq_models.xcit import XCiT1d, XCiTClassifier from .iq_models.densenet import DenseNet1d +from .iq_models.inceptiontime import InceptionTime \ No newline at end of file diff --git a/torchsig/models/iq_models/__init__.py b/torchsig/models/iq_models/__init__.py index 521ae60..b5f7bae 100755 --- a/torchsig/models/iq_models/__init__.py +++ b/torchsig/models/iq_models/__init__.py @@ -1 +1 @@ -from . import efficientnet, xcit \ No newline at end of file +from . import efficientnet, xcit, inceptiontime \ No newline at end of file diff --git a/torchsig/models/iq_models/efficientnet/efficientnet.py b/torchsig/models/iq_models/efficientnet/efficientnet.py index 7dca1c0..5a09630 100755 --- a/torchsig/models/iq_models/efficientnet/efficientnet.py +++ b/torchsig/models/iq_models/efficientnet/efficientnet.py @@ -156,7 +156,7 @@ def efficientnet_b0( Args: pretrained (bool): - If True, returns a model pre-trained on Sig53 + If True, returns a model pre-trained on TorchSigNarrowband path (str): Path to existing model or where to download checkpoint to @@ -204,7 +204,7 @@ def efficientnet_b2( Args: pretrained (bool): - If True, returns a model pre-trained on Sig53 + If True, returns a model pre-trained on TorchSigNarrowband path (str): Path to existing model or where to download checkpoint to @@ -252,7 +252,7 @@ def efficientnet_b4( Args: pretrained (bool): - If True, returns a model pre-trained on Sig53 + If True, returns a model pre-trained on TorchSigNarrowband path (str): Path to existing model or where to download checkpoint to diff --git a/torchsig/models/iq_models/inceptiontime/__init__.py b/torchsig/models/iq_models/inceptiontime/__init__.py new file mode 100644 index 0000000..55fdaff --- /dev/null +++ b/torchsig/models/iq_models/inceptiontime/__init__.py @@ -0,0 +1 @@ +from .inceptiontime import InceptionTime diff --git a/torchsig/models/iq_models/inceptiontime/inceptiontime.py b/torchsig/models/iq_models/inceptiontime/inceptiontime.py new file mode 100644 index 0000000..b309396 --- /dev/null +++ b/torchsig/models/iq_models/inceptiontime/inceptiontime.py @@ -0,0 +1,155 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +import timm +from torch import Tensor +from typing import Optional, Tuple, List + +import pytorch_lightning as pl +from pytorch_lightning import LightningDataModule, LightningModule, Trainer +from pytorch_lightning.callbacks import ModelCheckpoint, Callback +import matplotlib.pyplot as plt +import numpy as np + +__all__ = ["InceptionTime"] + +# Metric Tracker for Classifiers +class ClassifierMetrics(Callback): + def __init__(self): + self.train_losses = [] + self.val_losses = [] + self.train_accs = [] + self.val_accs = [] + + def on_train_epoch_end(self, trainer, pl_module): + metrics = trainer.callback_metrics + if 'train_loss' in metrics and 'train_acc' in metrics: + self.train_losses.append(metrics['train_loss'].item()) + self.train_accs.append(metrics['train_acc'].item()) + + def on_validation_epoch_end(self, trainer, pl_module): + metrics = trainer.callback_metrics + if 'val_loss' in metrics and 'val_acc' in metrics: + self.val_losses.append(metrics['val_loss'].item()) + self.val_accs.append(metrics['val_acc'].item()) + +class FocalLoss(nn.Module): + def __init__(self, gamma=2.0, alpha=None, reduction='mean', ignore_index=-100): + super(FocalLoss, self).__init__() + self.gamma = gamma + self.alpha = alpha # Can be a scalar or a tensor of shape [num_classes] + self.reduction = reduction + self.ignore_index = ignore_index + + def forward(self, inputs, targets): + log_probs = F.log_softmax(inputs, dim=1) + ce_loss = F.nll_loss(log_probs, targets, weight=self.alpha, reduction='none', ignore_index=self.ignore_index) + probs = torch.exp(-ce_loss) + focal_loss = ((1 - probs) ** self.gamma) * ce_loss + + if self.reduction == 'mean': + return focal_loss.mean() + elif self.reduction == 'sum': + return focal_loss.sum() + else: + return focal_loss + +class InceptionModule(nn.Module): + def __init__(self, in_channels, num_filters=32, kernel_sizes=[9, 19, 39], bottleneck_channels=32, activation=nn.ReLU()): + super(InceptionModule, self).__init__() + + # Determine effective number of input channels after bottleneck + if in_channels > bottleneck_channels: + self.bottleneck = nn.Conv1d(in_channels, bottleneck_channels, kernel_size=1, bias=False) + effective_in_channels = bottleneck_channels + else: + self.bottleneck = nn.Identity() + effective_in_channels = in_channels # Use the actual input channels + + # Convolutional branches with different kernel sizes + self.conv1 = nn.Conv1d(effective_in_channels, num_filters, kernel_size=kernel_sizes[0], padding=kernel_sizes[0] // 2, bias=False) + self.conv2 = nn.Conv1d(effective_in_channels, num_filters, kernel_size=kernel_sizes[1], padding=kernel_sizes[1] // 2, bias=False) + self.conv3 = nn.Conv1d(effective_in_channels, num_filters, kernel_size=kernel_sizes[2], padding=kernel_sizes[2] // 2, bias=False) + + # Max pooling branch + self.maxpool = nn.MaxPool1d(kernel_size=3, stride=1, padding=1) + self.conv4 = nn.Conv1d(in_channels, num_filters, kernel_size=1, bias=False) + + # Batch normalization and activation + self.bn = nn.BatchNorm1d(num_filters * 4) + self.activation = activation + + def forward(self, x): + input_res = x # For the residual connection + + x = self.bottleneck(x) + + conv1 = self.conv1(x) + conv2 = self.conv2(x) + conv3 = self.conv3(x) + pool = self.maxpool(input_res) + conv4 = self.conv4(pool) + + # Concatenate all convolutional outputs + x = torch.cat([conv1, conv2, conv3, conv4], dim=1) + x = self.bn(x) + x = self.activation(x) + return x + +class InceptionTime(LightningModule): + def __init__(self, num_classes, input_channels=2, num_modules=6, learning_rate=1e-3): + super(InceptionTime, self).__init__() + self.save_hyperparameters() + num_filters = 32 + self.learning_rate = learning_rate + # self.criterion = nn.CrossEntropyLoss() + self.criterion = FocalLoss(gamma=2.0, alpha=None, reduction='mean') + + + # Stack multiple Inception modules + self.inception_blocks = nn.ModuleList() + for i in range(num_modules): + in_ch = input_channels if i == 0 else num_filters * 4 + self.inception_blocks.append(InceptionModule(in_ch, num_filters=num_filters)) + + # Global Average Pooling and Fully Connected layer + self.gap = nn.AdaptiveAvgPool1d(1) + self.fc = nn.Linear(num_filters * 4, num_classes) + + def forward(self, x): + for block in self.inception_blocks: + x = block(x) + x = self.gap(x).squeeze(-1) + x = self.fc(x) + return x + + def training_step(self, batch, batch_idx): + x, y = batch + x = x.float() + logits = self(x) + loss = self.criterion(logits, y) + preds = torch.argmax(logits, dim=1) + acc = (preds == y).float().mean() + self.log('train_loss', loss, on_epoch=True) + self.log('train_acc', acc, on_epoch=True) + return loss + + def validation_step(self, batch, batch_idx): + x, y = batch + x = x.float() + logits = self(x) + loss = self.criterion(logits, y) + preds = torch.argmax(logits, dim=1) + acc = (preds == y).float().mean() + self.log('val_loss', loss, on_epoch=True, prog_bar=True) + self.log('val_acc', acc, on_epoch=True, prog_bar=True) + + # def configure_optimizers(self): + # optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) + # return optimizer + + def configure_optimizers(self): + optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) + lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=self.trainer.max_epochs) + return [optimizer], [lr_scheduler] diff --git a/torchsig/models/iq_models/xcit/__init__.py b/torchsig/models/iq_models/xcit/__init__.py index 52ef1e6..06263ee 100755 --- a/torchsig/models/iq_models/xcit/__init__.py +++ b/torchsig/models/iq_models/xcit/__init__.py @@ -1 +1 @@ -from .xcit1d import XCiT1d \ No newline at end of file +from .xcit1d import XCiT1d, XCiTClassifier \ No newline at end of file diff --git a/torchsig/models/iq_models/xcit/xcit.py b/torchsig/models/iq_models/xcit/xcit.py index 9c3071b..8b5dee5 100755 --- a/torchsig/models/iq_models/xcit/xcit.py +++ b/torchsig/models/iq_models/xcit/xcit.py @@ -104,7 +104,7 @@ def xcit_nano( Args: pretrained (bool): - If True, returns a model pre-trained on Sig53 + If True, returns a model pre-trained on TorchSigNarrowband path (str): Path to existing model or where to download checkpoint to @@ -155,7 +155,7 @@ def xcit_tiny12( Args: pretrained (bool): - If True, returns a model pre-trained on Sig53 + If True, returns a model pre-trained on TorchSigNarrowband path (str): Path to existing model or where to download checkpoint to diff --git a/torchsig/models/iq_models/xcit/xcit1d.py b/torchsig/models/iq_models/xcit/xcit1d.py index 07408f8..cd3910c 100755 --- a/torchsig/models/iq_models/xcit/xcit1d.py +++ b/torchsig/models/iq_models/xcit/xcit1d.py @@ -1,83 +1,271 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + import timm -from torch import cat -from torch.nn import Module, Conv1d, Linear +from torch import Tensor +from typing import Optional, Tuple, List -from torchsig.models.model_utils.model_utils_1d.iq_sampling import ConvDownSampler, Chunker +import pytorch_lightning as pl +from pytorch_lightning import LightningDataModule, LightningModule, Trainer +from pytorch_lightning.callbacks import ModelCheckpoint, Callback +import matplotlib.pyplot as plt +import numpy as np -__all__ = ["XCiT1d"] +__all__ = ["XCiT1d", "XCiTClassifier"] -class XCiT1d(Module): - """A 1d implementation of the XCiT architecture from - `"XCiT: Cross-Covariance Image Transformers" `_. +class XCiT1d(nn.Module): + """A 1D implementation of the XCiT architecture. Args: - - input_channels (int): - Number of 1d input channels; e.g., common practice is to split complex number time-series data into 2 channels, representing the real and imaginary parts respectively - - n_features (int): - Number of output features; should be the number of classes when used directly for classification - - xcit_version (str): - Specifies the version of efficientnet to use. See the timm xcit documentation for details. Examples are 'nano_12_p16_224', and 'xcit_tiny_12_p16_224' - - drop_path_rate (float): - Drop path rate for training - - drop_rate (float): - Dropout rate for training - - ds_method (str): - Specifies the downsampling method to use in the model. Currently convolutional downsampling and chunking are supported, using string arguments 'downsample' and 'chunk' respectively - - ds_rate (int): - Specifies the downsampling rate; e.g., ds_rate=2 will downsample the imput by a factor of 2 + input_channels (int): Number of 1D input channels. + n_features (int): Number of output features/classes. + xcit_version (str): Version of XCiT model to use (e.g., 'nano_12_p16_224'). + drop_path_rate (float): Drop path rate for training. + drop_rate (float): Dropout rate for training. + ds_method (str): Downsampling method ('downsample' or 'chunk'). + ds_rate (int): Downsampling rate (e.g., 2 for downsampling by a factor of 2). """ - def __init__(self, + def __init__( + self, input_channels: int, n_features: int, xcit_version: str = "nano_12_p16_224", drop_path_rate: float = 0.0, drop_rate: float = 0.3, ds_method: str = "downsample", - ds_rate: int = 2): - + ds_rate: int = 2 + ): super().__init__() + + # Ensure the model name is correct + model_name = f"xcit_{xcit_version}" if not xcit_version.startswith("xcit_") else xcit_version + + # Create the backbone model self.backbone = timm.create_model( - "xcit_" + xcit_version, + model_name, + pretrained=False, num_classes=n_features, in_chans=input_channels, drop_path_rate=drop_path_rate, drop_rate=drop_rate, ) - + + # Number of features from the backbone W = self.backbone.num_features - self.grouper = Conv1d(W, n_features, 1) + + # Include the grouper Conv1d layer + self.grouper = nn.Conv1d(W, n_features, kernel_size=1) + + # Replace the patch embedding with a 1D version if ds_method == "downsample": self.backbone.patch_embed = ConvDownSampler(input_channels, W, ds_rate) elif ds_method == "chunk": self.backbone.patch_embed = Chunker(input_channels, W, ds_rate) else: - raise ValueError(ds_method + " is not a supported downsampling method; currently 'downsample', and 'chunk' are supported") + raise ValueError( + f"{ds_method} is not a supported downsampling method; currently 'downsample' and 'chunk' are supported" + ) - self.backbone.head = Linear(self.backbone.head.in_features, n_features) + # Replace the classifier head with an identity layer (since we use self.grouper) + self.backbone.head = nn.Identity() - def forward(self, x): + def forward(self, x: Tensor) -> Tensor: mdl = self.backbone B = x.shape[0] - x = self.backbone.patch_embed(x) - Hp, Wp = x.shape[-1], 1 - pos_encoding = mdl.pos_embed(B, Hp, Wp).reshape(B, -1, Hp).permute(0, 2, 1).half() - x = x.transpose(1, 2) + pos_encoding + # Patch embedding + x = self.backbone.patch_embed(x) # Shape: [B, C, L] + + # Define H and W for 1D data + Hp, Wp = x.shape[-1], 1 # Height is sequence length, Width is 1 + + # Obtain positional encoding + pos_encoding = mdl.pos_embed(B, Hp, Wp).reshape(B, -1, Hp).permute(0, 2, 1) + + # Add positional encoding + x = x.transpose(1, 2) + pos_encoding # Shape: [B, Hp, C] + + # Apply transformer blocks for blk in mdl.blocks: x = blk(x, Hp, Wp) - cls_tokens = mdl.cls_token.expand(B, -1, -1) - x = cat((cls_tokens, x), dim=1) + + # Classification token + cls_tokens = mdl.cls_token.expand(B, -1, -1) # Shape: [B, 1, C] + x = torch.cat((cls_tokens, x), dim=1) # Shape: [B, Hp+1, C] + + # Apply class attention blocks for blk in mdl.cls_attn_blocks: x = blk(x) - x = mdl.norm(x) - x = self.grouper(x.transpose(1, 2)[:, :, :1]).squeeze() - if x.dim() == 2: + + # Layer normalization + x = mdl.norm(x) # Shape: [B, Hp+1, C] + + # Apply the grouper Conv1d to the classification token + # Extract the classification token (first token) + cls_token = x[:, 0, :] # Shape: [B, C] + + # Reshape for Conv1d: [B, C, 1] + cls_token = cls_token.unsqueeze(-1) # Shape: [B, C, 1] + + # Apply the grouper Conv1d + x = self.grouper(cls_token).squeeze(-1) # Shape: [B, n_features] + + # If x is 1D (batch size 1), ensure it has the correct shape + if x.dim() == 1: x = x.unsqueeze(0) + return x + +class ConvDownSampler(nn.Module): + def __init__(self, in_chans: int, embed_dim: int, ds_rate: int = 16): + super().__init__() + # Use a single convolutional layer with appropriate stride + self.conv = nn.Conv1d( + in_channels=in_chans, + out_channels=embed_dim, + kernel_size=ds_rate * 2, + stride=ds_rate, + padding=ds_rate // 2, + ) + self.bn = nn.BatchNorm1d(embed_dim) + self.act = nn.GELU() + + def forward(self, x: Tensor) -> Tensor: + x = self.conv(x) + x = self.bn(x) + x = self.act(x) + return x + +class Chunker(nn.Module): + def __init__(self, in_chans: int, embed_dim: int, ds_rate: int = 16): + super().__init__() + self.ds_rate = ds_rate + self.embed = nn.Conv1d(in_chans, embed_dim, kernel_size=7, padding=3) + self.pool = nn.AvgPool1d(kernel_size=ds_rate, stride=ds_rate) + + def forward(self, x: Tensor) -> Tensor: + x = self.embed(x) # Shape: [B, embed_dim, L] + x = self.pool(x) # Downsample by averaging + return x + +class PositionalEncoding1D(nn.Module): + def __init__(self, embed_dim: int): + super().__init__() + self.embed_dim = embed_dim + + def forward(self, x: Tensor) -> Tensor: + B, L, C = x.size() + position = torch.arange(L, device=x.device).unsqueeze(1) # Shape: [L, 1] + div_term = torch.exp(torch.arange(0, C, 2, device=x.device) * (-torch.log(torch.tensor(10000.0)) / C)) + pe = torch.zeros(L, C, device=x.device) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0).expand(B, -1, -1) # Shape: [B, L, C] + return pe + +class XCiTClassifier(LightningModule): + def __init__( + self, + input_channels: int, + num_classes: int, + xcit_version: str = 'tiny_12_p16_224', + ds_method: str = 'downsample', + ds_rate: int = 16, + learning_rate: float = 1e-3, + ): + super().__init__() + self.save_hyperparameters() + self.model = XCiT1d( + input_channels=input_channels, + n_features=num_classes, + xcit_version=xcit_version, + ds_method=ds_method, + ds_rate=ds_rate, + ) + self.learning_rate = learning_rate + # self.criterion = nn.CrossEntropyLoss() + self.criterion = FocalLoss(gamma=2.0, alpha=None, reduction='mean') + + # For logging + self.train_losses = [] + self.val_losses = [] + self.val_accuracies = [] + + def forward(self, x: Tensor) -> Tensor: + return self.model(x) + + def training_step(self, batch, batch_idx) -> Tensor: + x, y = batch + x = x.float() + logits = self(x) + loss = self.criterion(logits, y) + preds = torch.argmax(logits, dim=1) + acc = (preds == y).float().mean() + self.log('train_loss', loss, on_step=False, on_epoch=True) + self.log('train_acc', acc, on_step=False, on_epoch=True) + + self.train_losses.append(loss.item()) + return loss + + def validation_step(self, batch, batch_idx) -> None: + x, y = batch + x = x.float() + logits = self(x) + loss = self.criterion(logits, y) + preds = torch.argmax(logits, dim=1) + acc = (preds == y).float().mean() + self.log('val_loss', loss, prog_bar=True) + self.log('val_acc', acc, prog_bar=True) + self.val_losses.append(loss.item()) + self.val_accuracies.append(acc.item()) + + # def configure_optimizers(self): + # optimizer = torch.optim.AdamW(self.parameters(), lr=self.learning_rate) + # return optimizer + + def configure_optimizers(self): + optimizer = torch.optim.Adam(self.parameters(), lr=self.learning_rate) + lr_scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=self.trainer.max_epochs) + return [optimizer], [lr_scheduler] + +# Metric Tracker for Classifiers +class ClassifierMetrics(Callback): + def __init__(self): + self.train_losses = [] + self.val_losses = [] + self.train_accs = [] + self.val_accs = [] + + def on_train_epoch_end(self, trainer, pl_module): + metrics = trainer.callback_metrics + if 'train_loss' in metrics and 'train_acc' in metrics: + self.train_losses.append(metrics['train_loss'].item()) + self.train_accs.append(metrics['train_acc'].item()) + + def on_validation_epoch_end(self, trainer, pl_module): + metrics = trainer.callback_metrics + if 'val_loss' in metrics and 'val_acc' in metrics: + self.val_losses.append(metrics['val_loss'].item()) + self.val_accs.append(metrics['val_acc'].item()) + +class FocalLoss(nn.Module): + def __init__(self, gamma=2.0, alpha=None, reduction='mean', ignore_index=-100): + super(FocalLoss, self).__init__() + self.gamma = gamma + self.alpha = alpha # Can be a scalar or a tensor of shape [num_classes] + self.reduction = reduction + self.ignore_index = ignore_index + + def forward(self, inputs, targets): + log_probs = F.log_softmax(inputs, dim=1) + ce_loss = F.nll_loss(log_probs, targets, weight=self.alpha, reduction='none', ignore_index=self.ignore_index) + probs = torch.exp(-ce_loss) + focal_loss = ((1 - probs) ** self.gamma) * ce_loss + + if self.reduction == 'mean': + return focal_loss.mean() + elif self.reduction == 'sum': + return focal_loss.sum() + else: + return focal_loss diff --git a/torchsig/models/spectrogram_models/detr/detr.py b/torchsig/models/spectrogram_models/detr/detr.py index 1576556..12a5675 100755 --- a/torchsig/models/spectrogram_models/detr/detr.py +++ b/torchsig/models/spectrogram_models/detr/detr.py @@ -43,7 +43,7 @@ def detr_b0_nano( XCiT from `"XCiT: Cross-Covariance Image Transformers" `_. Args: - pretrained (bool): If True, returns a model pre-trained on WBSig53 + pretrained (bool): If True, returns a model pre-trained on Wideband path (str): Path to existing model or where to download checkpoint to num_classes (int): Number of output classes; if loading checkpoint and number does not equal 1, final layer will not be loaded from checkpoint drop_path_rate_backbone (float): Backbone drop path rate for training @@ -92,7 +92,7 @@ def detr_b2_nano( XCiT from `"XCiT: Cross-Covariance Image Transformers" `_. Args: - pretrained (bool): If True, returns a model pre-trained on WBSig53 + pretrained (bool): If True, returns a model pre-trained on Wideband path (str): Path to existing model or where to download checkpoint to num_classes (int): Number of output classes; if loading checkpoint and number does not equal 1, final layer will not be loaded from checkpoint drop_path_rate_backbone (float): Backbone drop path rate for training @@ -141,7 +141,7 @@ def detr_b4_nano( XCiT from `"XCiT: Cross-Covariance Image Transformers" `_. Args: - pretrained (bool): If True, returns a model pre-trained on WBSig53 + pretrained (bool): If True, returns a model pre-trained on Wideband path (str): Path to existing model or where to download checkpoint to num_classes (int): Number of output classes; if loading checkpoint and number does not equal 1, final layer will not be loaded from checkpoint drop_path_rate_backbone (float): Backbone drop path rate for training @@ -190,7 +190,7 @@ def detr_b0_nano_mod_family( XCiT from `"XCiT: Cross-Covariance Image Transformers" `_. Args: - pretrained (bool): If True, returns a model pre-trained on WBSig53 + pretrained (bool): If True, returns a model pre-trained on Wideband path (str): Path to existing model or where to download checkpoint to num_classes (int): Number of output classes; if loading checkpoint and number does not equal 6, final layer will not be loaded from checkpoint drop_path_rate_backbone (float): Backbone drop path rate for training @@ -239,7 +239,7 @@ def detr_b2_nano_mod_family( XCiT from `"XCiT: Cross-Covariance Image Transformers" `_. Args: - pretrained (bool): If True, returns a model pre-trained on WBSig53 + pretrained (bool): If True, returns a model pre-trained on Wideband path (str): Path to existing model or where to download checkpoint to num_classes (int): Number of output classes; if loading checkpoint and number does not equal 6, final layer will not be loaded from checkpoint drop_path_rate_backbone (float): Backbone drop path rate for training @@ -288,7 +288,7 @@ def detr_b4_nano_mod_family( XCiT from `"XCiT: Cross-Covariance Image Transformers" `_. Args: - pretrained (bool): If True, returns a model pre-trained on WBSig53 + pretrained (bool): If True, returns a model pre-trained on Wideband path (str): Path to existing model or where to download checkpoint to num_classes (int): Number of output classes; if loading checkpoint and number does not equal 6, final layer will not be loaded from checkpoint drop_path_rate_backbone (float): Backbone drop path rate for training diff --git a/torchsig/transforms/functional.py b/torchsig/transforms/functional.py index c7c8ee9..95ac612 100755 --- a/torchsig/transforms/functional.py +++ b/torchsig/transforms/functional.py @@ -1,3 +1,5 @@ +"""Functional transforms +""" from typing import Callable, List, Literal, Optional, Tuple, Union from torchsig.utils.dsp import low_pass, calculate_exponential_filter from numba import complex64, float64, int64, njit @@ -74,6 +76,15 @@ def uniform_discrete_distribution( choices: List, random_generator: Optional[np.random.Generator] = None ): + """Unifrom Discrete Distribution + + Args: + choices (List): List of discrete variables to sample from. + random_generator (Optional[np.random.Generator], optional): Random Generator to use. Defaults to None (new generator created internally) + + Returns: + _type_: _description_ + """ random_generator = random_generator if random_generator else np.random.default_rng() return partial(random_generator.choice, choices) @@ -83,6 +94,16 @@ def uniform_continuous_distribution( upper: Union[int, float], random_generator: Optional[np.random.Generator] = None, ): + """Uniform Continuous Distribution + + Args: + lower (Union[int, float]): Lowest number possible in distribution. + upper (Union[int, float]): Highest number possible in distribution. + random_generator (Optional[np.random.Generator], optional): Random Generator to use. Defaults to None (new generator created internally) + + Returns: + _type_: _description_ + """ random_generator = random_generator if random_generator else np.random.default_rng() return partial(random_generator.uniform, lower, upper) @@ -101,6 +122,15 @@ def to_distribution( ], random_generator: Optional[np.random.Generator] = None, ): + """Create Numpy Random Generator(s) over a distribution. + + Args: + param (Union[ int, float, str, Callable, List[int], List[float], List[str], Tuple[int, int], Tuple[float, float], ]): Range, type, or variables specifying random distribution. + random_generator (Optional[np.random.Generator], optional): Random generator to use. Defaults to None (new generator created internally). + + Returns: + _type_: _description_ + """ random_generator = random_generator if random_generator else np.random.default_rng() if isinstance(param, Callable): # type: ignore return param @@ -181,11 +211,10 @@ def resample( tensor (:class:`numpy.ndarray`): tensor to be resampled. - up_rate (:class:`int`): - rate at which to up-sample the tensor - - down_rate (:class:`int`): - rate at which to down-sample the tensor + resamp_rate(:class:`float`): + the resampling rate. to interpolate, resamp_rate > 1.0, to decimate + resamp_rate < 1.0. can accept a float number for irrational + resampling rates num_iq_samples (:class:`int`): number of IQ samples to have after resampling @@ -193,26 +222,25 @@ def resample( keep_samples (:class:`bool`): boolean to specify if the resampled data should be returned as is - anti_alias_lpf (:class:`bool`)): - boolean to specify if an additional anti aliasing filter should be - applied - Returns: Tensor: Resampled tensor """ + coeffs_filename = "saved_coefficients.npy" + coeffs_fullpath = f"{DIR_PATH}/{coeffs_filename}" + max_uprate = 5000 - try: - with open(f'{DIR_PATH}/resamp_fil.p', 'rb') as fh: - resamp_fil = pickle.load(fh) - except: + + # save/load coefficients when possible (expensive computation) + # saves into saved_coefficients.npy file + if os.path.exists(coeffs_fullpath): + resamp_fil = np.load(coeffs_fullpath) + else: taps_phase = 32 fc = 0.95 / max_uprate resamp_fil = calculate_exponential_filter(P=max_uprate, num_taps=taps_phase * max_uprate, fc=fc, K=24.06) - with open(f'{DIR_PATH}/resamp_fil.p', 'wb') as fh: - pickle.dump(resamp_fil, fh) - + np.save(coeffs_fullpath, resamp_fil) # Resample resampled = sp.upfirdn(resamp_fil * max_uprate, tensor, up=max_uprate, down=max_uprate//resamp_rate) @@ -693,9 +721,7 @@ def time_crop(tensor: np.ndarray, start: int, length: int) -> np.ndarray: if np.max(start) >= tensor.shape[0] or length == 0: return np.empty(shape=(1, 1)) - crop_len = min(length, tensor.shape[0] - np.max(start)) - - return tensor[start : start + crop_len] + return tensor[start : start + length] def freq_shift(tensor: np.ndarray, f_shift: float) -> np.ndarray: @@ -1581,12 +1607,13 @@ def spec_translate( def spectrogram_image( tensor: np.ndarray, + black_hot: bool = True ) -> np.ndarray: """tensor to image Args: - tensor (:class:`numpy.ndarray`)): - (batch_size, vector_length, ...)-sized tensor + tensor (numpy.ndarray): (batch_size, vector_length, ...)-sized tensor + black_hot (bool, optional): toggles black hot spectrogram. Defaults to False (white-hot). Returns: @@ -1597,4 +1624,8 @@ def spectrogram_image( img = np.zeros((spec.shape[0], spec.shape[1], 3), dtype=np.float32) img = cv2.normalize(spec, img, 0, 255, cv2.NORM_MINMAX) img = cv2.cvtColor(img.astype(np.uint8), cv2.COLOR_GRAY2BGR) + + if black_hot: + img = cv2.bitwise_not(img, img) + return img diff --git a/torchsig/transforms/target_transforms.py b/torchsig/transforms/target_transforms.py index 0363222..02a5cec 100755 --- a/torchsig/transforms/target_transforms.py +++ b/torchsig/transforms/target_transforms.py @@ -1,3 +1,6 @@ +"""Signal Transforms on metadata/label +""" + from torchsig.utils.types import SignalMetadata, RFMetadata, ModulatedRFMetadata from torchsig.utils.types import ( meta_bound_frequency, @@ -7,7 +10,7 @@ has_modulated_rf_metadata, has_rf_metadata, ) -from torchsig.datasets.signal_classes import sig53 +from torchsig.datasets.signal_classes import torchsig_signals from torchsig.transforms.transforms import Transform from typing import Any, Dict, List, Optional, Tuple, Union import numpy as np @@ -107,7 +110,7 @@ class DescToFamilyName(Transform): or a list of the classes present if there are multiple classes """ - class_family_dict: Dict[str, str] = sig53.family_dict + class_family_dict: Dict[str, str] = torchsig_signals.family_dict def __init__(self, class_family_dict: Optional[Dict[str, str]] = None,family_list: Optional[List[str]] = None,) -> None: super(DescToFamilyName, self).__init__() @@ -298,7 +301,7 @@ def __call__(self, metadata: List[SignalMetadata]) -> np.ndarray: class DescToMaskFamily(Transform): """Transform to transform SignalMetadatas into spectrogram masks with different channels for each class's metadata: SignalMetadata family. If no `class_family_dict` - provided, the default mapping for the WBSig53 modulation families is used. + provided, the default mapping for the Wideband modulation families is used. Args: class_family_dict (:obj:`dict`): @@ -312,7 +315,7 @@ class DescToMaskFamily(Transform): """ - class_family_dict: Dict[str, str] = sig53.family_dict + class_family_dict: Dict[str, str] = torchsig_signals.family_dict def __init__( self, @@ -767,7 +770,7 @@ def __init__( num_classes: Optional[int] = None, ) -> None: super(DescToClassEncoding, self).__init__() - self.class_list = sig53.class_list if class_list is None else class_list + self.class_list = torchsig_signals.class_list if class_list is None else class_list self.num_classes = len(self.class_list) if num_classes is None else num_classes def __call__(self, metadata: List[SignalMetadata]) -> np.ndarray: @@ -1017,7 +1020,7 @@ class DescToBBoxFamilyDict(Transform): """ - class_family_dict: Dict[str, str] = sig53.family_dict + class_family_dict: Dict[str, str] = torchsig_signals.family_dict def __init__( self, @@ -1195,7 +1198,7 @@ class DescToSignalFamilyInstMaskDict(Transform): """Transform to transform SignalMetadatas into the class mask format using dictionaries of labels and masks, similar to the COCO image dataset. The labels with this target transform are set to be the class's family. If - no `class_family_dict` is provided, the default mapping for the WBSig53 + no `class_family_dict` is provided, the default mapping for the Wideband modulation families is used. Args: @@ -1210,7 +1213,7 @@ class DescToSignalFamilyInstMaskDict(Transform): """ - class_family_dict: Dict[str, str] = sig53.family_dict + class_family_dict: Dict[str, str] = torchsig_signals.family_dict def __init__( self, @@ -1307,14 +1310,21 @@ def __call__( class ListTupleToDesc(Transform): - + """Transform to transform list of tuples into SignalMetadata. + List of tuples contain + the modulation, start time, stop time, center frequency, bandwidth, and SNR + Args: + sample_rate (float): Signal sample rate. + num_iq_samples (int): Number of IQ samples. + class_list (List[str]): List of signal classes. + """ def __init__( self, sample_rate: float, num_iq_samples: int, class_list: List[str], - ) -> None: + ) -> None: super(ListTupleToDesc, self).__init__() self.sample_rate = sample_rate self.num_iq_samples = num_iq_samples diff --git a/torchsig/transforms/transforms.py b/torchsig/transforms/transforms.py index 9138f1d..753ccf6 100755 --- a/torchsig/transforms/transforms.py +++ b/torchsig/transforms/transforms.py @@ -1,3 +1,6 @@ +"""Signal Transforms on data/IQ +""" + from typing import Any, Callable, List, Literal, Optional, Tuple, Union from torchsig.transforms import functional as F from torchsig.transforms.functional import ( @@ -9,7 +12,7 @@ ) from torchsig.utils.dataset import SignalDataset from torchsig.utils.types import * -from torchsig.utils.dsp import low_pass +from torchsig.utils.dsp import low_pass, MAX_SIGNAL_UPPER_EDGE_FREQ, MAX_SIGNAL_LOWER_EDGE_FREQ from scipy import signal as sp from copy import deepcopy import numpy as np @@ -584,33 +587,56 @@ def __call__(self, signal: Signal) -> Signal: def parameters(self) -> tuple: return (self.rate_ratio(),) + def check_bounds(self, meta: dict, try_new_rate: float): + """Checks single metadata entry is in bounds, returns rate that keeps in bounds. + + Args: + meta (dict): SignalMetadata entry. + try_new_rate (float): New resampling rate to check. + + Returns: + new_rate: Resampling rate that keeps signal in bounds. + """ + # we assume original signal is within bounds + assert meta["lower_freq"] is not None + assert meta["upper_freq"] is not None + assert meta["bandwidth"] < 1.0 + + new_rate = try_new_rate + + test_lf = meta["lower_freq"] / try_new_rate + test_hf = meta["upper_freq"] / try_new_rate + + if test_lf < MAX_SIGNAL_LOWER_EDGE_FREQ or test_hf > MAX_SIGNAL_UPPER_EDGE_FREQ: # out of bounds + new_rate *= 2 + + return new_rate def check_time_freq_bounds(self, signal: Signal, new_rate: float) -> float: - """ - Method checks frequency mins and maxes and adjust the new_rate to ensure - frequency bounds stay within the +-.5 boundary. - """ + """Method checks frequency mins and maxes and adjust the new_rate to ensure + frequency bounds stay within the (approximately) +-.5 boundary. + + Args: + signal (Signal): Signal to transform. + new_rate (float): Possible new_rate to resample Signal. + + Returns: + float: New rate to resample Signal, within bounds. + """ ret_list = [] for meta in signal["metadata"]: - if meta["lower_freq"] is None: - print(f'class_name no lower_freq -> {meta["class_name"]}') - test_lf = meta["lower_freq"] / new_rate - test_hf = meta["upper_freq"] / new_rate - if test_lf < -.5 or test_hf > .5: - if test_lf < -.5: - ret_rate = meta['lower_freq'] / -.5 - else: - ret_rate = meta['upper_freq'] / .5 - ret_list.append(ret_rate) - else: - ret_list.append(new_rate) + new_rate = self.check_bounds(meta, new_rate) + ret_list.append(new_rate) try: new_rate = np.max(ret_list) - except: + except Exception as error: for meta in signal['metadata']: print(f"{meta['lower_freq']} {meta['upper_freq']} {meta['class_name']}") + print(error) + raise ValueError("Unable to run: new_rate = np.max(ret_list)") + for meta in signal["metadata"]: start = meta["start"] * new_rate if start > 1 - self.min_time: @@ -618,6 +644,7 @@ def check_time_freq_bounds(self, signal: Signal, new_rate: float) -> float: return new_rate + def transform_data(self, signal: Signal, params: tuple) -> Signal: if not has_rf_metadata(signal["metadata"]): return signal @@ -1572,10 +1599,12 @@ def check_time_bounds(self, signal: Signal, params: tuple) -> float: """ start_list = [] start, crop_length = params + for meta in signal["metadata"]: original_start_sample = meta["start"] * data_shape(signal["data"])[0] original_stop_sample = meta["stop"] * data_shape(signal["data"])[0] - new_start_sample = original_start_sample - start + #new_start_sample = original_start_sample - start + new_start_sample = start new_stop_sample = original_stop_sample - start start_clip = np.clip(float(new_start_sample / crop_length), a_min=0.0, a_max=1.0) stop_clip = np.clip(float(new_stop_sample / crop_length), a_min=0.0, a_max=1.0) @@ -1583,7 +1612,7 @@ def check_time_bounds(self, signal: Signal, params: tuple) -> float: if duration < .001: start_list.append(int(meta["start"] * data_shape(signal["data"])[0])) else: - start_list.append(start) + start_list.append(int(new_start_sample)) return np.min(start_list), crop_length @@ -1593,14 +1622,12 @@ def transform_data(self, signal: Signal, params: tuple) -> Signal: if len(signal["metadata"]) == 0: return signal - if signal["metadata"][0]["num_samples"] == self.crop_length: + if len(signal["data"]["samples"]) == self.crop_length: return signal params = self.check_time_bounds(signal, params) # if signal["metadata"][0]["num_samples"] < self.crop_length: if data_shape(signal["data"])[0] < self.crop_length: - - pdb.set_trace() raise ValueError( "Input data length {} is less than requested length {}".format( data_shape(signal["data"])[0], self.crop_length @@ -3038,9 +3065,9 @@ class DatasetWidebandCutMix(SignalTransform): Example: >>> import torchsig.transforms as ST - >>> from torchsig.datasets.wideband_sig53 import WidebandSig53 + >>> from torchsig.datasets.torchsig_wideband import TorchSigWideband >>> # Add signals from the `ModulationsDataset` - >>> dataset = WidebandSig53('.') + >>> dataset = TorchSigWideband('.') >>> transform = ST.DatasetWidebandCutMix(dataset=dataset,alpha=(0.2,0.7)) """ @@ -3191,9 +3218,9 @@ class DatasetWidebandMixUp(SignalTransform): Example: >>> import torchsig.transforms as ST - >>> from torchsig.datasets.wideband_sig53 import WidebandSig53 - >>> # Add signals from the `WidebandSig53` Dataset - >>> dataset = WidebandSig53('.') + >>> from torchsig.datasets.torchsig_wideband import TorchSigWideband + >>> # Add signals from the `TorchSigWideband` Dataset + >>> dataset = TorchSigWideband('.') >>> transform = ST.DatasetWidebandMixUp(dataset=dataset,alpha=(0.4,0.6)) """ diff --git a/torchsig/utils/cm_plotter.py b/torchsig/utils/cm_plotter.py index 75969b7..41d6e3a 100755 --- a/torchsig/utils/cm_plotter.py +++ b/torchsig/utils/cm_plotter.py @@ -1,3 +1,6 @@ +"""Confusion Matrix Plotter +""" + from sklearn.metrics import confusion_matrix from matplotlib import pyplot as plt from typing import Optional diff --git a/torchsig/utils/dataset.py b/torchsig/utils/dataset.py index 473e4f2..b4cd235 100755 --- a/torchsig/utils/dataset.py +++ b/torchsig/utils/dataset.py @@ -1,3 +1,5 @@ +"""Signal Dataset base classes +""" from typing import Any, Callable, List, Optional, Tuple, Union from torchsig.utils.types import SignalCapture, SignalData from copy import deepcopy diff --git a/torchsig/utils/dsp.py b/torchsig/utils/dsp.py index 6f2049a..a4a1c01 100755 --- a/torchsig/utils/dsp.py +++ b/torchsig/utils/dsp.py @@ -1,8 +1,10 @@ +"""Digital Signal Processing (DSP) Utils +""" + import scipy from scipy import signal as sp import numpy as np - def convolve(signal: np.ndarray, taps: np.ndarray) -> np.ndarray: """A modified version of scipy.signal.convolve, which discards trasitional regions @@ -43,8 +45,7 @@ def polyphase_prototype_filter ( num_branches: int ) -> np.ndarray: prototypeFilterPFB *= num_branches return prototypeFilterPFB -def irrational_rate_resampler ( input_signal: np.ndarray, resampler_rate: float ) -> np.ndarray: - # TODO: needs to be estimated, not a fixed value +def rational_rate_resampler ( input_signal: np.ndarray, resampler_rate: float ) -> np.ndarray: numBranchesPFB = 10000 resamplerUpRate = numBranchesPFB resamplerDownRate = int(np.round(numBranchesPFB/resampler_rate)) @@ -110,7 +111,7 @@ def gaussian_taps(samples_per_symbol: int, BT: float = 0.35) -> np.ndarray: def calculate_exponential_filter ( M=1, P=1, fc=.25, num_taps=None, K=4. ): """ - Class used to generate Single Band FIR filter using either the Remez + Function used to generate Single Band FIR filter using either the Remez algorithm or exponential filters. Calculate the filter-coefficients for the finite impulse response @@ -188,3 +189,17 @@ def calculate_exponential_filter ( M=1, P=1, fc=.25, num_taps=None, K=4. ): return b +# signal upper edge cannot exceed this number, calculated as center freq + (bandwidth/2) +# +# these number needs to be slightly less than 0.5 in order to account for transition bandwidth +# such that a filter can be designed with some transition bandwidth (see: function +# antiAliasingFilter()) +# +# additionally, 0.48 is close enough to 0.5 such that signals can still press up against the +# edge of the -fs/2 and +fs/2 boundary to simulate a receiver being offtuned. +# +# these values need to be treated as constants! +# they cannot be changed on the fly at run-time +MAX_SIGNAL_UPPER_EDGE_FREQ = 0.48 +MAX_SIGNAL_LOWER_EDGE_FREQ = -MAX_SIGNAL_UPPER_EDGE_FREQ + diff --git a/torchsig/utils/index.py b/torchsig/utils/index.py index 885d5cb..a844651 100755 --- a/torchsig/utils/index.py +++ b/torchsig/utils/index.py @@ -1,3 +1,6 @@ +"""SigMF File indexing +""" + from torchsig.utils.types import * from typing import Any, Dict, List, Tuple from copy import deepcopy diff --git a/torchsig/utils/narrowband_trainer.py b/torchsig/utils/narrowband_trainer.py new file mode 100644 index 0000000..1ab7815 --- /dev/null +++ b/torchsig/utils/narrowband_trainer.py @@ -0,0 +1,462 @@ +"""Narrowband Trainer for IQ Classification on Narrowband. +""" + +import os +import numpy as np +import torch +import matplotlib.pyplot as plt +from sklearn.metrics import confusion_matrix, ConfusionMatrixDisplay + +# TorchSig imports +from torchsig.transforms.target_transforms import DescToClassIndex +from torchsig.transforms.transforms import ( + RandomPhaseShift, + Normalize, + ComplexTo2D, + Compose, +) +from torchsig.datasets.torchsig_narrowband import TorchSigNarrowband +from torchsig.datasets.datamodules import NarrowbandDataModule + +# PyTorch Lightning imports +from pytorch_lightning import LightningDataModule, LightningModule, Trainer +from pytorch_lightning.callbacks import ModelCheckpoint, Callback + + +class MetricsLogger(Callback): + """ + PyTorch Lightning Callback to log training and validation metrics. + + Attributes: + metrics (dict): A dictionary to store training and validation loss and accuracy. + """ + + def __init__(self): + super().__init__() + self.metrics = { + 'train_loss': [], + 'val_loss': [], + 'train_acc': [], + 'val_acc': [], + } + + def on_train_epoch_end(self, trainer, pl_module): + """ + Called when the train epoch ends. Logs the training loss and accuracy. + + Args: + trainer (Trainer): The PyTorch Lightning trainer. + pl_module (LightningModule): The model being trained. + """ + metrics = trainer.callback_metrics + if 'train_loss' in metrics: + self.metrics['train_loss'].append(metrics['train_loss'].item()) + if 'train_acc' in metrics: + self.metrics['train_acc'].append(metrics['train_acc'].item()) + + def on_validation_epoch_end(self, trainer, pl_module): + """ + Called when the validation epoch ends. Logs the validation loss and accuracy. + + Args: + trainer (Trainer): The PyTorch Lightning trainer. + pl_module (LightningModule): The model being validated. + """ + metrics = trainer.callback_metrics + if 'val_loss' in metrics: + self.metrics['val_loss'].append(metrics['val_loss'].item()) + if 'val_acc' in metrics: + self.metrics['val_acc'].append(metrics['val_acc'].item()) + + +class NarrowbandTrainer: + """ + A trainer class for I/Q Signal Modulation Classification using narrowband datasets. + + This class encapsulates data preparation, model initialization, training, + validation, prediction, and plotting functionalities. + + Attributes: + model_name (str): Name of the model to use. + num_epochs (int): Number of training epochs. + batch_size (int): Batch size for training. + num_workers (int): Number of workers for data loading. + learning_rate (float): Learning rate for the optimizer. + data_path (str): Path to the dataset. + impaired (bool): Whether to use the impaired dataset. + qa (bool): Whether to use QA configuration. + checkpoint_path (str): Path to a checkpoint file to load the model weights. + datamodule (LightningDataModule): Custom data module if provided. + checkpoint_dir (str): Directory to save checkpoints. + plots_dir (str): Directory to save plots. + class_list (list): List of class names. + num_classes (int): Number of classes. + transform (Compose): Data transformations to apply. + target_transform (DescToClassIndex): Target transformation. + input_channels (int): Number of input channels. + model (LightningModule): The model to train. + trainer (Trainer): PyTorch Lightning trainer. + metrics_logger (MetricsLogger): Callback for logging metrics. + best_model_path (str): Path to the best saved model. + filename_base (str): Base filename for saved plots. + """ + + def __init__(self, model_name='inception', num_epochs=10, batch_size=32, + num_workers=16, learning_rate=1e-3, input_channels = 2, data_path='../datasets/narrowband_test_QA', + impaired=True, qa=True, checkpoint_path=None, datamodule=None): + """ + Initializes the NarrowbandTrainer with specified parameters. + + Args: + model_name (str): Name of the model to use. + num_epochs (int): Number of training epochs. + batch_size (int): Batch size for training. + num_workers (int): Number of workers for data loading. + learning_rate (float): Learning rate for the optimizer. + input_channels (int): Number of input channels into model. + data_path (str): Path to the dataset. + impaired (bool): Whether to use the impaired dataset. + qa (bool): Whether to use QA configuration. + checkpoint_path (str): Path to a checkpoint file to load the model weights. + datamodule (LightningDataModule): Custom data module instance. + """ + # Set random seed for reproducibility + seed = 1234567890 + torch.manual_seed(seed) + np.random.seed(seed) + + # Data Parameters + self.data_path = data_path + self.batch_size = batch_size + self.num_workers = num_workers + self.impaired = impaired + self.qa = qa + self.datamodule = datamodule # Accept custom datamodule + + # Model Parameters + self.model_name = model_name + self.learning_rate = learning_rate + self.input_channels = input_channels + self.num_epochs = num_epochs + self.checkpoint_path = checkpoint_path # Added checkpoint_path + + # Other parameters + self.checkpoint_dir = './checkpoints' + os.makedirs(self.checkpoint_dir, exist_ok=True) + self.plots_dir = './plots' + os.makedirs(self.plots_dir, exist_ok=True) + + # Prepare data module if not provided + self.prepare_data() + + # Prepare model + self.prepare_model() + + # Initialize trainer + self.trainer = None # Will be initialized in the train() method + + def prepare_data(self): + """ + Prepares the data module for training and validation. + + Uses the provided datamodule or creates a new one if not provided. + Sets up data transformations, target transformations, and initializes + the NarrowbandDataModule. + """ + if self.datamodule is None: + # Get the class list and number of classes + self.class_list = list(TorchSigNarrowband._idx_to_name_dict.values()) + self.num_classes = len(self.class_list) + + # Specify Transforms + self.transform = Compose( + [ + RandomPhaseShift(phase_offset=(-1, 1)), + Normalize(norm=np.inf), + ComplexTo2D(), + ] + ) + self.target_transform = DescToClassIndex(class_list=self.class_list) + + self.datamodule = NarrowbandDataModule( + root=self.data_path, + qa=self.qa, + impaired=self.impaired, + transform=self.transform, + target_transform=self.target_transform, + batch_size=self.batch_size, + num_workers=self.num_workers, + ) + + else: + # Use the provided datamodule + self.class_list = self.datamodule.class_list + self.num_classes = len(self.class_list) + # Assume transforms are set within the provided datamodule + print("Using custom datamodule provided.") + + def prepare_model(self): + """ + Initializes the model based on the specified model name. + + If a checkpoint path is provided, loads the model directly from the checkpoint. + + Raises: + ValueError: If the specified model name is not supported. + """ + # Map model names to their corresponding classes + self.available_models = { + 'xcit': 'XCiTClassifier', + 'inception': 'InceptionTime', + 'MyNewModel': 'MyNewModel', + } + + if self.model_name not in self.available_models: + raise ValueError(f"Model {self.model_name} is not supported.") + + # Determine the model class + if self.model_name == 'xcit': + from torchsig.models import XCiTClassifier + ModelClass = XCiTClassifier + model_kwargs = { + 'input_channels': self.input_channels, + 'num_classes': self.num_classes, + 'xcit_version': 'tiny_12_p16_224', + 'ds_method': 'downsample', + 'ds_rate': 16, + 'learning_rate': self.learning_rate, + } + elif self.model_name == 'inception': + from torchsig.models import InceptionTime + ModelClass = InceptionTime + model_kwargs = { + 'input_channels': self.input_channels, + 'num_classes': self.num_classes, + 'learning_rate': self.learning_rate, + } + elif self.model_name == 'MyNewModel': + from my_models import MyNewModel + ModelClass = MyNewModel + model_kwargs = { + 'input_channels': self.input_channels, + 'num_classes': self.num_classes, + 'learning_rate': self.learning_rate, + } + + # Load model from checkpoint if provided + if self.checkpoint_path: + if not os.path.isfile(self.checkpoint_path): + raise FileNotFoundError(f"Checkpoint file {self.checkpoint_path} not found.") + # Load model directly from checkpoint + self.model = ModelClass.load_from_checkpoint( + checkpoint_path=self.checkpoint_path, + **model_kwargs + ) + print(f"Loaded model from checkpoint: {self.checkpoint_path}") + else: + # Initialize a new model instance + self.model = ModelClass(**model_kwargs) + + def train(self): + """ + Trains the model using the prepared data and model. + + If a checkpoint was loaded, continues training (fine-tuning) from the checkpoint. + + Sets up callbacks, initializes the PyTorch Lightning trainer, and + starts the training process. After training, it plots metrics and + the confusion matrix. + """ + # Callbacks + checkpoint_callback = ModelCheckpoint( + monitor='val_acc', + dirpath=self.checkpoint_dir, + filename=self.model_name + '-{epoch:02d}-{val_acc:.2f}', + save_top_k=1, + mode='max', + ) + + # Metrics Logger Callback + self.metrics_logger = MetricsLogger() + + # Trainer + self.trainer = Trainer( + max_epochs=self.num_epochs, + callbacks=[checkpoint_callback, self.metrics_logger], + accelerator='gpu' if torch.cuda.is_available() else 'cpu', + devices=1, + # No need to specify resume_from_checkpoint when using load_from_checkpoint + ) + + # Train + self.trainer.fit(self.model, self.datamodule) + + # Get the best checkpoint filename base + self.best_model_path = checkpoint_callback.best_model_path + self.filename_base = os.path.splitext(os.path.basename(self.best_model_path))[0] + + # Plot metrics + self.plot_metrics() + + # Plot confusion matrix + self.plot_confusion_matrix() + + def plot_metrics(self): + """ + Plots training and validation loss and accuracy over epochs. + + Uses the metrics logged during training to create plots and saves + them in the specified plots directory. + """ + metrics = self.metrics_logger.metrics + plots_dir = self.plots_dir + filename_base = self.filename_base + epochs = range(1, len(metrics['train_loss']) + 1) + + # Plot Loss + plt.figure() + plt.plot(epochs, metrics['train_loss'], label='Training Loss') + + if metrics['val_loss']: + val_epochs = range(1, len(metrics['val_loss']) + 1) + plt.plot(val_epochs, metrics['val_loss'], label='Validation Loss') + else: + print("Validation loss is empty. Skipping validation loss plot.") + + plt.title('Loss over Epochs') + plt.xlabel('Epoch') + plt.ylabel('Loss') + plt.legend() + plt.grid(True) + self.loss_plot_path = os.path.join(plots_dir, f'{filename_base}_loss.png') + plt.savefig(self.loss_plot_path) + plt.close() + + # Plot Accuracy + plt.figure() + plt.plot(epochs, metrics['train_acc'], label='Training Accuracy') + + if metrics['val_acc']: + val_epochs = range(1, len(metrics['val_acc']) + 1) + plt.plot(val_epochs, metrics['val_acc'], label='Validation Accuracy') + else: + print("Validation accuracy is empty. Skipping validation accuracy plot.") + + plt.title('Accuracy over Epochs') + plt.xlabel('Epoch') + plt.ylabel('Accuracy') + plt.legend() + plt.grid(True) + self.acc_plot_path = os.path.join(plots_dir, f'{filename_base}_accuracy.png') + plt.savefig(self.acc_plot_path) + plt.close() + + def plot_confusion_matrix(self): + """ + Plots a normalized confusion matrix based on validation data. + + Saves the confusion matrix plot in the specified plots directory. + """ + model = self.model + datamodule = self.datamodule + class_list = self.class_list + plots_dir = self.plots_dir + filename_base = self.filename_base + + # Ensure model is on the correct device + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + model = model.to(device) + model.eval() + + all_preds = [] + all_labels = [] + + val_loader = datamodule.val_dataloader() + with torch.no_grad(): + for batch in val_loader: + x, y = batch + x = x.float().to(device) + y = y.to(device) + logits = model(x) + preds = torch.argmax(logits, dim=1) + all_preds.extend(preds.cpu().numpy()) + all_labels.extend(y.cpu().numpy()) + + if not all_preds or not all_labels: + print("No predictions or labels available to plot confusion matrix.") + return + + # Compute confusion matrix + cm = confusion_matrix(all_labels, all_preds, normalize='true') + + # Set up the figure size to make labels readable + plt.figure(figsize=(12, 10)) + + # Plot the confusion matrix without annotations + disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=class_list) + disp.plot(include_values=False, cmap='Blues', ax=plt.gca()) + + # Increase font sizes for labels and ticks + plt.title('Normalized Confusion Matrix', fontsize=16) + plt.xlabel('Predicted Label', fontsize=14) + plt.ylabel('True Label', fontsize=14) + plt.xticks(rotation=90, fontsize=12) + plt.yticks(fontsize=12) + + # Adjust layout to prevent labels from being cut off + plt.tight_layout() + + # Save the plot + self.cm_plot_path = os.path.join(plots_dir, f'{filename_base}_confusion_matrix.png') + plt.savefig(self.cm_plot_path, bbox_inches='tight', dpi=300) + plt.close() + + def validate(self): + """ + Validates the model using the validation dataset. + + This method can be customized to perform specific validation tasks + and compute metrics as needed. + """ + # Ensure model is on the correct device + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.model = self.model.to(device) + self.model.eval() + + val_loader = self.datamodule.val_dataloader() + total_correct = 0 + total_samples = 0 + + with torch.no_grad(): + for batch in val_loader: + x, y = batch + x = x.float().to(device) + y = y.to(device) + logits = self.model(x) + preds = torch.argmax(logits, dim=1) + total_correct += (preds == y).sum().item() + total_samples += y.size(0) + + accuracy = total_correct / total_samples + print(f'Validation Accuracy: {accuracy * 100:.2f}%') + return accuracy + + def predict(self, data): + """ + Predicts the class labels for the given data. + + Args: + data (torch.Tensor): Input data tensor of shape [batch_size, channels, length]. + + Returns: + np.ndarray: Predicted class indices. + """ + # Ensure model is on the correct device + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + self.model = self.model.to(device) + self.model.eval() + with torch.no_grad(): + data = data.to(device) + logits = self.model(data) + preds = torch.argmax(logits, dim=1) + return preds.cpu().numpy() diff --git a/torchsig/utils/optuna/PyTorchLightningCallback.py b/torchsig/utils/optuna/PyTorchLightningCallback.py new file mode 100644 index 0000000..4df41db --- /dev/null +++ b/torchsig/utils/optuna/PyTorchLightningCallback.py @@ -0,0 +1,53 @@ +from pytorch_lightning import LightningModule +from pytorch_lightning import Trainer +from pytorch_lightning.callbacks import Callback +import optuna +import warnings + +# https://github.com/optuna/optuna-examples/issues/166#issuecomment-1403112861 +class PyTorchLightningPruningCallback(Callback): + """PyTorch Lightning callback to prune unpromising trials. + See `the example `__ + if you want to add a pruning callback which observes accuracy. + Args: + trial: + A :class:`~optuna.trial.Trial` corresponding to the current evaluation of the + objective function. + monitor: + An evaluation metric for pruning, e.g., ``val_loss`` or + ``val_acc``. The metrics are obtained from the returned dictionaries from e.g. + ``pytorch_lightning.LightningModule.training_step`` or + ``pytorch_lightning.LightningModule.validation_epoch_end`` and the names thus depend on + how this dictionary is formatted. + """ + + def __init__(self, trial: optuna.trial.Trial, monitor: str) -> None: + super().__init__() + + self._trial = trial + self.monitor = monitor + + def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None: + # When the trainer calls `on_validation_end` for sanity check, + # do not call `trial.report` to avoid calling `trial.report` multiple times + # at epoch 0. The related page is + # https://github.com/PyTorchLightning/pytorch-lightning/issues/1391. + if trainer.sanity_checking: + return + + epoch = pl_module.current_epoch + + current_score = trainer.callback_metrics.get(self.monitor) + if current_score is None: + message = ( + "The metric '{}' is not in the evaluation logs for pruning. " + "Please make sure you set the correct metric name.".format(self.monitor) + ) + warnings.warn(message) + return + + self._trial.report(current_score, step=epoch) + if self._trial.should_prune(): + message = "Trial was pruned at epoch {}.".format(epoch) + raise optuna.TrialPruned(message) \ No newline at end of file diff --git a/torchsig/utils/optuna/tuner.py b/torchsig/utils/optuna/tuner.py new file mode 100644 index 0000000..4f12f94 --- /dev/null +++ b/torchsig/utils/optuna/tuner.py @@ -0,0 +1,146 @@ +"""Optuna Optimizer classes + +Example + >>> from torchsig.utils.optuna.tuner import OptunaOptimizer + override the `objective()` function + >>> opt = YourOptunaOptimizer(n_trials=5, epochs=10, name="DocString Test") + >>> opt.run_optimization() +""" + + +import torch +from torch.utils.data import DataLoader +import os +import yaml +import pytorch_lightning as pl +import yaml +import optuna +import copy +from torchsig.utils.yolo_train import Yolo_Trainer +from typing import Tuple, Union + + +class OptunaOptimizer(): + """Optuna Optimizer abstract base class + + Runs Optuna optimization workflow. Requires subclasses to override the `objective` function. + + Args: + n_trials (int, optional): Number of hyperparameter trials to run. Defaults to 5. + epochs (int, optional): Number of epochs to run per trial. Defaults to 5. + name (str, optional): Name of optuna optimization session. Defaults to "Test". + """ + + def __init__(self, n_trials:int=5, epochs:int=5, name:str="Test"): + self.n_trials = n_trials + self.epochs = epochs + self.name = name + self.best_params = None + + def run_optimization(self, ret_params: bool = True) -> Union[optuna.study.Study, Tuple[optuna.study.Study, dict]]: + """Runs Optuna optimization flow. Includes: + - creating pruners + - creating optuna study + - running the optimization + - printing out best trial and params + + Args: + ret_params (bool, optional): Whether to return best parameters found. Defaults to True. + + Returns: + optuna.study.Study | Tuple[optuna.study.Study, dict]: Returns optuna Study and optionally a dictionary of best parameters. + """ + pruner = optuna.pruners.HyperbandPruner(min_resource=1, max_resource=self.epochs) + study = optuna.create_study(study_name=self.name, direction="minimize", pruner=pruner) + study.optimize(self.objective, n_trials=self.n_trials) + + print("\n\nNumber of finished trials: {}".format(len(study.trials))) + + print("Best trial:") + trial = study.best_trial + + print(" Value: {}".format(trial.value)) + + print(" Params: ") + for key, value in trial.params.items(): + print(" {}: {}".format(key, value)) + + self.best_params = study.best_params + + if ret_params: + return study, trial.params + else: + return study + + def objective(self, trial: optuna.trial.Trial) -> float: + """Optuna Objective function to optimize. Runs once per trial. + + Args: + trial (optuna.trial.Trial): Current trial. + + Raises: + NotImplementedError: Subclasses must implement this method. + + Returns: + float: Score to evaluate performance. Usually set to validation loss. + """ + raise NotImplementedError("Define optuna objective function.") + + +class YoloOptunaOptimizer(OptunaOptimizer): + """YOLO Optuna Optimizer + + Args: + overrides (dict): YOLO overrides dictionary. + """ + + def __init__(self, overrides: dict, **kwargs): + super().__init__(**kwargs) + + self.original_overrides = copy.deepcopy(overrides) + self.overrides = overrides + self.overrides['verbose'] = False + self.overrides['epochs'] = self.epochs + self.overrides['save'] = False + #self.overrides['device'] = 0 + # self.overrides['workers'] = len(os.sched_getaffinity(0)) - 1 + self.overrides['workers'] = 1 + + def objective(self, trial: optuna.trial.Trial) -> float: + lr = trial.suggest_float("lr", 1e-5, 1e-2) + cos_lr = trial.suggest_categorical("cos_lr", [False, True]) + freeze = trial.suggest_int("freeze", 0, 5) + imgsz_power2 = trial.suggest_int("imgsz_power2", 6, 9) # 2^6=64 to 2^9=512 + imgsz = 2 ** imgsz_power2 + optimizer = trial.suggest_categorical("optimizer", ["SGD", "Adam", "AdamW"]) + + trial_overrides = copy.deepcopy(self.overrides) + trial_overrides['lr0'] = lr + trial_overrides['cos_lr'] = cos_lr + trial_overrides['freeze'] = freeze + trial_overrides['imgsz'] = imgsz + trial_overrides['optimizer'] = optimizer + + + trainer = Yolo_Trainer(overrides=trial_overrides) + + trainer.train() + + return trainer.fitness + + def get_optimized_overrides(self) -> dict: + """Returns YOLO dictionary with best parameters set. + + Returns: + dict: YOLO overrides dict with best params. + """ + overrides_optimized = self.original_overrides + for k in self.best_params.keys(): + if k =="imgsz_power2": + overrides_optimized["imgsz"] = 2 ** self.best_params[k] + elif k == "lr": + overrides_optimized["lr0"] = self.best_params[k] + else: + overrides_optimized[k] = self.best_params[k] + + return overrides_optimized diff --git a/torchsig/utils/reader.py b/torchsig/utils/reader.py index 241064e..653c013 100755 --- a/torchsig/utils/reader.py +++ b/torchsig/utils/reader.py @@ -1,3 +1,6 @@ +"""SigMF reader +""" + from torchsig.utils.types import * import numpy as np diff --git a/torchsig/utils/types.py b/torchsig/utils/types.py index 15bda1e..f39bd3e 100755 --- a/torchsig/utils/types.py +++ b/torchsig/utils/types.py @@ -1,3 +1,6 @@ +"""Signal, SignalData, and SignalMetadata classes +""" + from typing import List, Optional, TypedDict from torch import Tensor import numpy as np diff --git a/torchsig/utils/visualize.py b/torchsig/utils/visualize.py index 30f8306..8576a7b 100755 --- a/torchsig/utils/visualize.py +++ b/torchsig/utils/visualize.py @@ -1,5 +1,7 @@ +"""Visualizer and Plotting Utils +""" from typing import Any, Callable, Iterable, List, Optional, Tuple, Union -from torchsig.datasets.signal_classes import sig53 +from torchsig.datasets.signal_classes import torchsig_signals from matplotlib.figure import Figure from matplotlib import pyplot as plt from matplotlib import patches @@ -411,7 +413,7 @@ class MaskClassVisualizer(Visualizer): def __init__(self, class_list=None, **kwargs) -> None: super(MaskClassVisualizer, self).__init__(**kwargs) - self.class_list = sig53.class_list if class_list is None else class_list + self.class_list = torchsig_signals.class_list if class_list is None else class_list def __next__(self) -> Figure: iq_data, targets = next(self.data_iter) diff --git a/torchsig/utils/writer.py b/torchsig/utils/writer.py index 06a1f4d..97c9572 100755 --- a/torchsig/utils/writer.py +++ b/torchsig/utils/writer.py @@ -1,3 +1,5 @@ +"""Dataset Writer Utils +""" from torchsig.utils.dataset import SignalDataset from torch.utils.data import DataLoader from torchsig.utils.dataset import collate_fn diff --git a/torchsig/utils/yolo_classify.py b/torchsig/utils/yolo_classify.py index 08749df..ce4cd86 100644 --- a/torchsig/utils/yolo_classify.py +++ b/torchsig/utils/yolo_classify.py @@ -1,3 +1,5 @@ +""" YOLO Classification Utils +""" from torchsig.datasets.modulations import ModulationsDataset from torchsig.transforms.target_transforms import DescToFamilyName from torchsig.transforms.transforms import Compose as CP @@ -43,10 +45,12 @@ def __init__(self, root, args, augment=False, image_transform=None): # Load the dataset configuration from the root file with open(root, 'r') as file: self.config = yaml.safe_load(file) - + if augment: + self.num_samples = self.config['num_samples'] + else: + self.num_samples = self.config['num_samples'] // 10 # Create a list of class names self.class_list = [item[1] for item in self.config['names'].items()] - # Determine whether to map descriptions to family names if self.config['family']: self.class_to_idx_dict = {v: k for k, v in self.config['families'].items()} @@ -61,7 +65,7 @@ def __init__(self, root, args, augment=False, image_transform=None): use_class_idx=False, level=self.config['level'], num_iq_samples=args.imgsz**2, - num_samples=self.config['num_samples'], + num_samples=self.num_samples, include_snr=self.config['include_snr'], target_transform=target_transform ) @@ -171,6 +175,8 @@ def __init__(self, cfg=DEFAULT_CFG, overrides=None, _callbacks=None, image_trans self.image_transform = image_transform super().__init__(cfg, overrides, _callbacks) + + def build_dataset(self, img_path, mode="train", batch=None): """ Creates a dataset for training or validation. @@ -183,7 +189,7 @@ def build_dataset(self, img_path, mode="train", batch=None): Returns: TorchsigClassificationDataset: The constructed dataset. """ - print(f'args -> {img_path}') + print(f'mode -> {mode}') return TorchsigClassificationDataset( root=img_path, args=self.args, augment=mode == "train", image_transform=self.image_transform ) diff --git a/torchsig/utils/yolo_train.py b/torchsig/utils/yolo_train.py index 09a67ed..a0b6c9a 100644 --- a/torchsig/utils/yolo_train.py +++ b/torchsig/utils/yolo_train.py @@ -1,6 +1,8 @@ +"""YOLO Training Utils +""" from torchsig.transforms.target_transforms import DescToBBoxYoloSignalDict, DescToBBoxFamilyDict from torchsig.transforms import Spectrogram, Normalize, SpectrogramImage -from torchsig.datasets.wideband_sig53 import WidebandSig53 +from torchsig.datasets.torchsig_wideband import TorchSigWideband from torchsig.transforms.transforms import Compose as CP from torchsig.datasets import conf import torchaudio @@ -26,7 +28,7 @@ class TorchsigDataset(YOLODataset): def __init__(self, *args, mode='train', imgsz=640, hyp=DEFAULT_CFG, data=None, task="detect", **kwargs): """ Initializes the TorchsigDataset, which inherits from YOLODataset. This custom dataset class is tailored - to handle spectrogram data and corresponding bounding box labels using the WidebandSig53 dataset. + to handle spectrogram data and corresponding bounding box labels using the TorchSigWideband dataset. Args: mode (str): Indicates whether the dataset is for training ('train') or validation/testing ('val' or 'test'). @@ -54,8 +56,8 @@ def __init__(self, *args, mode='train', imgsz=640, hyp=DEFAULT_CFG, data=None, t DescToBBoxFamilyDict() ]) - # Initialize the WidebandSig53 dataset with the defined transforms - self.wbsig53 = WidebandSig53( + # Initialize the TorchSigWideband dataset with the defined transforms + self.wideband_dataset = TorchSigWideband( root=self.root, train=self.train, impaired=True, @@ -85,7 +87,7 @@ def get_labels(self, batch_size=32, num_threads=32): x = load_dataset_cache_file(cache_path) else: x = {'labels': []} # Initialize a dictionary to store labels - num_samples = self.wbsig53.length + num_samples = self.wideband_dataset.length # Create a list of (start_idx, end_idx) tuples for each batch batches = [(i, min(i + batch_size, num_samples)) for i in range(0, num_samples, batch_size)] @@ -93,7 +95,7 @@ def get_labels(self, batch_size=32, num_threads=32): # Use multithreading to process each batch with concurrent.futures.ThreadPoolExecutor(max_workers=num_threads) as executor: with tqdm(total=len(batches), desc=f"Fetching labels for: {self.mode}") as pbar: - for batch_labels in executor.map(lambda b: process_batch(*b, self.wbsig53, self.root, self.mode, self.imgsz), batches): + for batch_labels in executor.map(lambda b: process_batch(*b, self.wideband_dataset, self.root, self.mode, self.imgsz), batches): x['labels'].extend(batch_labels) pbar.update(1) print(f'Caching labels to {cache_path}') @@ -155,7 +157,7 @@ def get_image_and_label(self, index): and other relevant information. """ label = deepcopy(self.labels[index]) # Deep copy label to avoid modifications to the original - data, _ = self.wbsig53[label['idx']] # Retrieve image and label using the dataset index + data, _ = self.wideband_dataset[label['idx']] # Retrieve image and label using the dataset index label.pop("shape", None) # Remove the shape key as it's used only for rectangle mode @@ -256,7 +258,7 @@ def process_batch(start_idx, end_idx, dataset, root, mode, imgsz): Args: start_idx (int): Start index for the batch. end_idx (int): End index for the batch. - wbsig53 (Dataset): The dataset object. + wideband_dataset (Dataset): The dataset object. root (str): Root directory for the image files. mode (str): Mode indicating the dataset type. imgsz (int): Image size. diff --git a/torchsig/utils/yolo_val.py b/torchsig/utils/yolo_val.py index 1603812..8030c9b 100644 --- a/torchsig/utils/yolo_val.py +++ b/torchsig/utils/yolo_val.py @@ -1,3 +1,5 @@ +"""YOLO Validation Utils +""" # Ultralytics YOLO 🚀, AGPL-3.0 license import torch diff --git a/torchsig/utils/yolo_validator.py b/torchsig/utils/yolo_validator.py index 07ae4c5..11c7a71 100644 --- a/torchsig/utils/yolo_validator.py +++ b/torchsig/utils/yolo_validator.py @@ -1,3 +1,5 @@ +""" YOLO Validator Class +""" # Ultralytics YOLO 🚀, AGPL-3.0 license """ Check a model's accuracy on a test or val split of a dataset.