Docker README corrections and pretrained model checking

README.md

@@ -11,6 +11,9 @@ The repository provides sample notebooks, data loaders for seismic data, utiliti
 
 DeepSeismic currently focuses on Seismic Interpretation (mainly facies classification) with experimental code provided around Seismic Imaging in the contrib folder.
 
+For a quick overview you can watch the video showcasing the latest 0.2 release of this repository by clicking on the GIF below:
+[![watch the video](./assets/ds.gif)](https://www.screencast.com/t/BRIad19jv)
+
 ### Quick Start
 
 Our repo is Docker-enabled and we provide a Docker file which you can use to quickly demo our codebase. If you are in a hurry and just can't wait to run our code, follow the [Docker README](https://github.com/microsoft/seismic-deeplearning/blob/master/docker/README.md) to build and run our repo from [Dockerfile](https://github.com/microsoft/seismic-deeplearning/blob/master/docker/Dockerfile).

cv_lib/cv_lib/segmentation/models/patch_deconvnet.py

@@ -305,7 +305,7 @@ def get_seg_model(cfg, **kwargs):
     ), f"Patch deconvnet is not implemented to accept {cfg.MODEL.IN_CHANNELS} channels. Please only pass 1 for cfg.MODEL.IN_CHANNELS"
     model = patch_deconvnet(n_classes=cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)

cv_lib/cv_lib/segmentation/models/patch_deconvnet_skip.py

@@ -305,7 +305,7 @@ def get_seg_model(cfg, **kwargs):
     ), f"Patch deconvnet is not implemented to accept {cfg.MODEL.IN_CHANNELS} channels. Please only pass 1 for cfg.MODEL.IN_CHANNELS"
     model = patch_deconvnet_skip(n_classes=cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)

cv_lib/cv_lib/segmentation/models/resnet_unet.py

@@ -368,7 +368,7 @@ def get_seg_model(cfg, **kwargs):
     ), f"SEResnet Unet deconvnet is not implemented to accept {cfg.MODEL.IN_CHANNELS} channels. Please only pass 3 for cfg.MODEL.IN_CHANNELS"
     model = Res34Unetv4(n_classes=cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)

cv_lib/cv_lib/segmentation/models/section_deconvnet.py

@@ -305,7 +305,7 @@ def get_seg_model(cfg, **kwargs):
     ), f"Section deconvnet is not implemented to accept {cfg.MODEL.IN_CHANNELS} channels. Please only pass 1 for cfg.MODEL.IN_CHANNELS"
     model = section_deconvnet(n_classes=cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)

cv_lib/cv_lib/segmentation/models/section_deconvnet_skip.py

@@ -305,7 +305,7 @@ def get_seg_model(cfg, **kwargs):
     ), f"Section deconvnet is not implemented to accept {cfg.MODEL.IN_CHANNELS} channels. Please only pass 1 for cfg.MODEL.IN_CHANNELS"
     model = section_deconvnet_skip(n_classes=cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)

cv_lib/cv_lib/segmentation/models/seg_hrnet.py

@@ -444,6 +444,6 @@ def init_weights(
 
 def get_seg_model(cfg, **kwargs):
     model = HighResolutionNet(cfg, **kwargs)
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         model.init_weights(cfg.MODEL.PRETRAINED)
     return model

cv_lib/cv_lib/segmentation/models/unet.py

@@ -114,7 +114,7 @@ def forward(self, x):
 def get_seg_model(cfg, **kwargs):
     model = UNet(cfg.MODEL.IN_CHANNELS, cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)

docker/README.md

@@ -1,15 +1,5 @@
 This Docker image allows the user to run the notebooks in this repository on any Unix based operating system without having to setup the environment or install anything other than the Docker engine. We recommend using [Azure Data Science Virtual Machine (DSVM) for Linux (Ubuntu)](https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-ubuntu-intro) as outlined [here](../README.md#compute-environment). For instructions on how to install the Docker engine, click [here](https://www.docker.com/get-started). 
 
-# Download the HRNet model: 
-
-To run the [`Dutch_F3_patch_model_training_and_evaluation.ipynb`](https://github.com/microsoft/seismic-deeplearning/blob/master/examples/interpretation/notebooks/Dutch_F3_patch_model_training_and_evaluation.ipynb), you will need to manually download the [HRNet-W48-C](https://1drv.ms/u/s!Aus8VCZ_C_33dKvqI6pBZlifgJk) pretrained model. You can follow the instructions [here](../README.md#pretrained-models). 
-
-If you are using an Azure Virtual Machine to run this code, you can download the model to your local machine, and then copy it to your Azure VM through the command below. Please make sure you update the `<azureuser>` and `<azurehost>` feilds.
-```bash
-scp hrnetv2_w48_imagenet_pretrained.pth <azureuser>@<azurehost>:/home/<azureuser>/seismic-deeplearning/docker/hrnetv2_w48_imagenet_pretrained.pth
-```
-Once you have the model downloaded (ideally under the `docker` directory), you can proceed to build the Docker image. 
-
 # Build the Docker image:
 
 In the `docker` directory, run the following command to build the Docker image and tag it as `seismic-deeplearning`: 
@@ -22,13 +12,52 @@ This process will take a few minutes to complete.
 # Run the Docker image:
 Once the Docker image is built, you can run it anytime using the following command:
 ```bash
-sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/username/seismic-deeplearning/docker/hrnetv2_w48_imagenet_pretrained.pth seismic-deeplearning
+sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G seismic-deeplearning
 ```
-If you have saved the pretrained model in a different directory, make sure you replace `$PWD/hrnetv2_w48_imagenet_pretrained.pth` with the **absolute** path to the pretrained HRNet model. The command above will run a Jupyter Lab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run. 
+The command above will run a [JupyterLab](https://jupyterlab.readthedocs.io/en/stable/) instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run.
+
+We recommend using [Google Chrome](https://www.google.com/chrome/) web browser for any visualizations shown in the notebook.
+
+You can alternatively use [Jupyter](https://jupyter.org/) notebook instead of Jupyter Lab by changing the last line in the Dockerfile from
+```bash
+jupyter lab --allow-root --ip 0.0.0.0 --port 9000
+```  
+to
+```bash
+jupyter notebook --allow-root --ip 0.0.0.0 --port 9000
+```
+and rebuilding the Docker image.
 
 # Run TensorBoard:
 To run Tensorboard to visualize the logged metrics and results, open a terminal in Jupyter Lab, navigate to the parent of the `output` directory of your model, and run the following command: 
 ```bash 
 tensorboard --logdir output/ --port 9001 --bind_all
 ```
 Make sure your VM has the port 9001 allowed in the networking rules, and then you can open TensorBoard by navigating to `http://<vm_ip_address>:9001/` on your browser where `<vm_ip_address>` is your public VM IP address (or private VM IP address if you are using a VPN).
+
+# Experimental
+
+We also offer the ability so use a semantic segmentation [HRNet](https://github.com/HRNet/HRNet-Semantic-Segmentation) model with the repository from 
+[Microsoft Research](https://www.microsoft.com/en-us/research/). Its use is currently experimental. 
+
+## Download the HRNet model: 
+
+To run the [`Dutch_F3_patch_model_training_and_evaluation.ipynb`](https://github.com/microsoft/seismic-deeplearning/blob/master/examples/interpretation/notebooks/Dutch_F3_patch_model_training_and_evaluation.ipynb), you will need to manually download the [HRNet-W48-C](https://1drv.ms/u/s!Aus8VCZ_C_33dKvqI6pBZlifgJk) pretrained model. You can follow the instructions [here](../README.md#pretrained-models). 
+
+If you are using an Azure Virtual Machine to run this code, you can download the HRNet model to your local machine, and then copy it to your Azure VM through the command below. Please make sure you update the `<azureuser>` and `<azurehost>` feilds.
+```bash
+scp hrnetv2_w48_imagenet_pretrained.pth <azureuser>@<azurehost>:/home/<azureuser>/seismic-deeplearning/docker/hrnetv2_w48_imagenet_pretrained.pth
+```
+
+## Run the Docker image:
+
+Once you have the model downloaded (ideally under the `docker` directory), you can proceed to build the Docker image: go to the [Build the Docker image](#build-the-docker-image) section above to do so.
+
+Once the Docker image is built, you can run it anytime using the following command:
+```bash
+sudo docker run --rm -it -p 9000:9000 -p 9001:9001 --gpus=all --shm-size 11G --mount type=bind,source=$PWD/hrnetv2_w48_imagenet_pretrained.pth,target=/home/username/seismic-deeplearning/docker/hrnetv2_w48_imagenet_pretrained.pth seismic-deeplearning
+```
+
+If you have saved the pretrained model in a different directory, make sure you replace `$PWD/hrnetv2_w48_imagenet_pretrained.pth` with the **absolute** path to the pretrained HRNet model. 
+The command above will run a Jupyter Lab instance that you can access by clicking on the link in your terminal. You can then navigate to the notebook or script that you would like to run.
+

interpretation/deepseismic_interpretation/models/texture_net.py

@@ -157,7 +157,7 @@ def getFeatures(self, x, layer_no):
 def get_seg_model(cfg, **kwargs):
     model = TextureNet(n_classes=cfg.DATASET.NUM_CLASSES)
     # load the pre-trained model
-    if "PRETRAINED" in cfg.MODEL.keys():
+    if "PRETRAINED" in cfg.MODEL.keys() and os.path.exists(cfg.MODEL.PRETRAINED) and os.path.isfile(cfg.MODEL.PRETRAINED):
         trained_model = torch.load(cfg.MODEL.PRETRAINED)
         trained_model = {k.replace("module.", ""): v for (k, v) in trained_model.items()}
         model.load_state_dict(trained_model, strict=True)