81 lines
3.0 KiB
Markdown
81 lines
3.0 KiB
Markdown
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# Lane detection
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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_landet.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.lane_classifier_cfg.dropout=0.1"`
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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_landet.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_landet.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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### Evaluation:
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**Important Notice: Do not simoutanously run multiple evaluation on CULane, since the eval use the same pytorch-auto-drive/output cache directory, the results could be overwritten! Same goes for LLAMAS!**
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1. Predict lane lines:
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```
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python main_landet.py --test \ # Or --val for validation
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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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Note that LLAMAS doesn't have test set labels.
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2. Test with official scripts on `<my_dataset>`:
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```
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./autotest_<my_dataset>.sh <exp_name> <mode> <save_dir>
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```
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`<mode>` includes `test` and `val`.
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`<save_dir>` and `<exp_name>` are recommended to set the same as in config file, so detail evaluation 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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### Fast evaluation in mIoU [Not Recommended]:
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Training contains online fast validations by using `val_num_steps` and the best model is saved, but we find that the best checkpoint is usually the last, so probably no need for validations. For log details you can checkout tensorboard.
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To validate a trained model on mean IoU, you can use either mixed-precision or fp32 for any model trained with/without mixed-precision:
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```
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python main_landet.py --valfast \
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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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