64 lines
2.3 KiB
Markdown
64 lines
2.3 KiB
Markdown
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# Semantic segmentation
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**Before diving into this, please make sure you followed the instructions to prepare datasets in [DATASET.md](./DATASET.md)**
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**Execution is based on [config files](../configs/README.md)**
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## Training:
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Some models' ImageNet pre-trained weights need to be manually downloaded, refer to [this table](./IMAGENET_MODELS.md).
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```
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python main_semseg.py --train \
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--config=<config file path> \
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--mixed-precision # Optional, enable mixed precision \
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--cfg-options=<overwrite cfg dict> # Optional
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```
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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"`
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Some options can be used by shortcuts, such as `--batch-size` will set both `train.batch_size` and `test.batch_size`, for more info:
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```
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python main_semseg.py --help
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```
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Example shells are provided in [tools/shells](../tools/shells/).
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## Distributed Training
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We support multi-GPU training with Distributed Data Parallel (DDP):
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```
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python -m torch.distributed.launch --nproc_per_node=<number of GPU per-node> --use_env main_semseg.py <your normal args>
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```
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With DDP, batch size and number of workers are **per-GPU**. Do not forget to set device args like `world_size` in your config.
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## Testing:
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Training contains online evaluations and the best model is saved.
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To evaluate a trained model:
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```
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python main_semseg.py --val \ # No test set labels available
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--config=<config file path> \
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--mixed-precision # Optional, enable mixed precision \
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--cfg-options=<overwrite cfg dict> # Optional
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```
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To test a downloaded pt file, try add `--checkpoint=<pt file path>`.
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Detail results will be saved to `<save_dir>/<exp_name>/`.
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Overall result will be saved to `log.txt`.
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Recommend `workers=0 batch_size=1` for high precision inference.
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## Notes:
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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.
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2. All segmentation results reported are from single model without CRF and without multi-scale testing.
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