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SEGMENTATION.md

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Semantic segmentation

Before diving into this, please make sure you followed the instructions to prepare datasets in DATASET.md

Execution is based on config files

Training:

Some models' ImageNet pre-trained weights need to be manually downloaded, refer to this table.

python main_semseg.py --train \
                      --config=<config file path> \
                      --mixed-precision  # Optional, enable mixed precision \
                      --cfg-options=<overwrite cfg dict>  # Optional

Your <overwrite cfg dict> is used to manually override config file options in commandline so you don't have to modify config file each time. It should look like this (the quotation marks are necessary!): "train.batch_size=8 train.workers=4 model.classifier_cfg.num_classes=21"

Some options can be used by shortcuts, such as --batch-size will set both train.batch_size and test.batch_size, for more info:

python main_semseg.py --help

Example shells are provided in tools/shells.

Distributed Training

We support multi-GPU training with Distributed Data Parallel (DDP):

python -m torch.distributed.launch --nproc_per_node=<number of GPU per-node> --use_env main_semseg.py <your normal args>

With DDP, batch size and number of workers are per-GPU.

Testing:

Training contains online evaluations and the best model is saved.

To evaluate a trained model:

python main_semseg.py --val \  # No test set labels available
                      --config=<config file path> \
                      --mixed-precision  # Optional, enable mixed precision \
                      --cfg-options=<overwrite cfg dict>  # Optional

To test a downloaded pt file, try add --checkpoint=<pt file path>.

Detail results will be saved to <save_dir>/<exp_name>/.

Overall result will be saved to log.txt.

Recommend workers=0 batch_size=1 for high precision inference.

Notes:

  1. Cityscapes dataset is down-sampled by 2 when training at 256 x 512, to specify different sizes, modify them in config files if needed.

  2. All segmentation results reported are from single model without CRF and without multi-scale testing.