Context Encoding for Semantic Segmentation (EncNet)

Install Package

• Clone the GitHub repo:

  git clone https://github.com/zhanghang1989/PyTorch-Encoding
  

• Install PyTorch Encoding (if not yet). Please follow the installation guide Installing PyTorch Encoding.

Test Pre-trained Model

Hint

The model names contain the training information. For instance FCN_ResNet50_PContext:

• FCN indicate the algorithm is “Fully Convolutional Network for Semantic Segmentation”
• ResNet50 is the name of backbone network.
• PContext means the PASCAL in Context dataset.

How to get pretrained model, for example FCN_ResNet50_PContext:

model = encoding.models.get_model('FCN_ResNet50_PContext', pretrained=True)

Prepare the datasets by runing the scripts in the scripts/ folder, for example preparing PASCAL Context dataset:

python scripts/prepare_pcontext.py

The test script is in the experiments/segmentation/ folder. For evaluating the model (using MS), for example Encnet_ResNet50_PContext:

python test.py --dataset PContext --model-zoo Encnet_ResNet50_PContext --eval
# pixAcc: 0.792, mIoU: 0.510: 100%|████████████████████████| 1276/1276 [46:31<00:00,  2.19s/it]

The command for training the model can be found by clicking cmd in the table.

Model	pixAcc	mIoU	Command
Encnet_ResNet50_PContext	79.2%	51.0%	cmd
EncNet_ResNet101_PContext	80.7%	54.1%	cmd
EncNet_ResNet50_ADE	80.1%	41.5%	cmd
EncNet_ResNet101_ADE	81.3%	44.4%	cmd
EncNet_ResNet101_VOC	N/A	85.9%	cmd

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset PContext --model FCN CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset PContext --model EncNet --aux --se-loss CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset PContext --model EncNet --aux --se-loss --backbone resnet101 CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ADE20K --model PSP --aux CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ADE20K --model EncNet --aux --se-loss CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset ADE20K --model EncNet --aux --se-loss --backbone resnet101 --base-size 640 --crop-size 576 # First finetuning COCO dataset pretrained model on augmented set # You can also train from scratch on COCO by yourself CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset Pascal_aug --model-zoo EncNet_Resnet101_COCO --aux --se-loss --lr 0.001 --syncbn --ngpus 4 --checkname res101 --ft # Finetuning on original set CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset Pascal_voc --model encnet --aux --se-loss --backbone resnet101 --lr 0.0001 --syncbn --ngpus 4 --checkname res101 --resume runs/Pascal_aug/encnet/res101/checkpoint.params --ft

Quick Demo

import torch
import encoding

# Get the model
model = encoding.models.get_model('Encnet_ResNet50_PContext', pretrained=True).cuda()
model.eval()

# Prepare the image
url = 'https://github.com/zhanghang1989/image-data/blob/master/' + \
      'encoding/segmentation/pcontext/2010_001829_org.jpg?raw=true'
filename = 'example.jpg'
img = encoding.utils.load_image(
    encoding.utils.download(url, filename)).cuda().unsqueeze(0)

# Make prediction
output = model.evaluate(img)
predict = torch.max(output, 1)[1].cpu().numpy() + 1

# Get color pallete for visualization
mask = encoding.utils.get_mask_pallete(predict, 'pcontext')
mask.save('output.png')

Train Your Own Model

• Prepare the datasets by runing the scripts in the scripts/ folder, for example preparing PASCAL Context dataset:

  python scripts/prepare_pcontext.py
  

• The training script is in the experiments/segmentation/ folder, example training command:

  CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py --dataset pcontext --model encnet --aux --se-loss
  

• Detail training options, please run python train.py -h. Commands for reproducing pre-trained models can be found in the table.

Hint

The validation metrics during the training only using center-crop is just for monitoring the training correctness purpose. For evaluating the pretrained model on validation set using MS, please use the command:

CUDA_VISIBLE_DEVICES=0,1,2,3 python test.py --dataset pcontext --model encnet --aux --se-loss --resume mycheckpoint --eval

Citation

Note

• Hang Zhang, Kristin Dana, Jianping Shi, Zhongyue Zhang, Xiaogang Wang, Ambrish Tyagi, Amit Agrawal. “Context Encoding for Semantic Segmentation” The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2018:

  @InProceedings{Zhang_2018_CVPR,
  author = {Zhang, Hang and Dana, Kristin and Shi, Jianping and Zhang, Zhongyue and Wang, Xiaogang and Tyagi, Ambrish and Agrawal, Amit},
  title = {Context Encoding for Semantic Segmentation},
  booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
  month = {June},
  year = {2018}
  }