ErfNet

This implements training of ErfNet on the cityscapes dataset, mainly modified from Eromera/erfnet_pytorch.

ErfNet Detail

• The original network structure contains ConvTranspose2d, but the operator can not converge in multi-p environment, so it is modified to interpolation and convolution.
• If you want to transfer learning based on the original weight, you only need to modify the fnum value of finetune script to the number of categories corresponding to your dataset.

Requirements

• Install PyTorch (pytorch.org)

• Download the Cityscapes dataset from https://www.cityscapes-dataset.com/

  • Download the "leftImg8bit" for the RGB images and the "gtFine" for the labels.

  • Please note that for training you should use the "_labelTrainIds" and not the "_labelIds", you can download the cityscapes scripts and use the conversor to generate trainIds from labelIds

Expected dataset structure for cityscapes:

cityscapes/
  gtFine/
    train/
      aachen/
        color.png, instanceIds.png, labelIds.png, polygons.json,
        labelTrainIds.png
      ...
    val/
    test/
    # below are generated Cityscapes panoptic annotation
    cityscapes_panoptic_train.json
    cityscapes_panoptic_train/
    cityscapes_panoptic_val.json
    cityscapes_panoptic_val/
    cityscapes_panoptic_test.json
    cityscapes_panoptic_test/
  leftImg8bit/
    train/
    val/
    test/

Install cityscapes scripts by:

pip install git+https://github.com/mcordts/cityscapesScripts.git

Note: to create labelTrainIds.png, first prepare the above structure, then run cityscapesescript with:

CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createTrainIdLabelImgs.py

These files are not needed for instance segmentation.

Note: to generate Cityscapes panoptic dataset, run cityscapesescript with:

CITYSCAPES_DATASET=/path/to/abovementioned/cityscapes python cityscapesscripts/preparation/createPanopticImgs.py

These files are not needed for semantic and instance segmentation.

• Download the erfnet_encoder_pretrained.pth.tar and put it into the ./trained_models folder.

Training

To train a model, run train/main.py with the desired model architecture and the path to the market dataset:

# training 1p performance
bash test/train_performance_1p.sh --data_path=real_data_path

# training 8p accuracy
bash test/train_full_8p.sh --data_path=real_data_path

# training 8p performance
bash test/train_performance_8p.sh --data_path=real_data_path

#test 8p accuracy
bash test/train_eval_8p.sh --data_path=real_data_path

# finetune
bash test/train_finetune_1p.sh --data_path=real_data_path

# Online inference demo
python demo.py
 

ErfNet training result

名称	iou	fps
GPU-1p	-	14.52
GPU-8p	-	94.64
NPU-1p	-	24.08
NPU-8p	71.47	143.15

Statement

For details about the public address of the code in this repository, you can get from the file public_address_statement.md