DeepLabv3Plus-Pytorch

DeepLabv3, DeepLabv3+ with pretrained models for Pascal VOC & Cityscapes.

Quick Start

1. Available Architectures

Specify the model architecture with '--model ARCH_NAME' and set the output stride using '--output_stride OUTPUT_STRIDE'.

DeepLabV3	DeepLabV3+
deeplabv3_resnet50	deeplabv3plus_resnet50
deeplabv3_resnet101	deeplabv3plus_resnet101
deeplabv3_mobilenet	deeplabv3plus_mobilenet
deeplabv3_hrnetv2_48	deeplabv3plus_hrnetv2_48
deeplabv3_hrnetv2_32	deeplabv3plus_hrnetv2_32

All pretrained models: Dropbox, Tencent Weiyun

Note: The HRNet backbone was contributed by @timothylimyl. A pre-trained backbone is available at google drive.

2. Load the pretrained model:

model.load_state_dict( torch.load( CKPT_PATH )['model_state']  )

3. Visualize segmentation outputs:

outputs = model(images)
preds = outputs.max(1)[1].detach().cpu().numpy()
colorized_preds = val_dst.decode_target(preds).astype('uint8') # To RGB images, (N, H, W, 3), ranged 0~255, numpy array
# Do whatever you like here with the colorized segmentation maps
colorized_preds = Image.fromarray(colorized_preds[0]) # to PIL Image

4. Atrous Separable Convolution

Note: pre-trained models in this repo do not use Seperable Conv.

Atrous Separable Convolution is supported in this repo. We provide a simple tool network.convert_to_separable_conv to convert nn.Conv2d to AtrousSeparableConvolution. Please run main.py with '--separable_conv' if it is required. See 'main.py' and 'network/_deeplab.py' for more details.

5. Prediction

Single image:

python predict.py --input datasets/data/cityscapes/leftImg8bit/train/bremen/bremen_000000_000019_leftImg8bit.png  --dataset cityscapes --model deeplabv3plus_mobilenet --ckpt checkpoints/best_deeplabv3plus_mobilenet_cityscapes_os16.pth --save_val_results_to test_results

Image folder:

python predict.py --input datasets/data/cityscapes/leftImg8bit/train/bremen  --dataset cityscapes --model deeplabv3plus_mobilenet --ckpt checkpoints/best_deeplabv3plus_mobilenet_cityscapes_os16.pth --save_val_results_to test_results

Results

1. Performance on Pascal VOC2012 Aug (21 classes, 513 x 513)

Training: 513x513 random crop
validation: 513x513 center crop

Model	Batch Size	FLOPs	train/val OS	mIoU	Dropbox	Tencent Weiyun
DeepLabV3-MobileNet	16	6.0G	16/16	0.701	Download	Download
DeepLabV3-ResNet50	16	51.4G	16/16	0.769	Download	Download
DeepLabV3-ResNet101	16	72.1G	16/16	0.773	Download	Download
DeepLabV3Plus-MobileNet	16	17.0G	16/16	0.711	Download	Download
DeepLabV3Plus-ResNet50	16	62.7G	16/16	0.772	Download	Download
DeepLabV3Plus-ResNet101	16	83.4G	16/16	0.783	Download	Download

2. Performance on Cityscapes (19 classes, 1024 x 2048)

Training: 768x768 random crop
validation: 1024x2048

Model	Batch Size	FLOPs	train/val OS	mIoU	Dropbox	Tencent Weiyun
DeepLabV3Plus-MobileNet	16	135G	16/16	0.721	Download	Download
DeepLabV3Plus-ResNet101	16	N/A	16/16	0.762	Download	Comming Soon

Segmentation Results on Pascal VOC2012 (DeepLabv3Plus-MobileNet)

Segmentation Results on Cityscapes (DeepLabv3Plus-MobileNet)

Visualization of training

Pascal VOC

1. Requirements

pip install -r requirements.txt

2. Prepare Datasets

2.1 Standard Pascal VOC

You can run train.py with "--download" option to download and extract pascal voc dataset. The defaut path is './datasets/data':

/datasets
    /data
        /VOCdevkit 
            /VOC2012 
                /SegmentationClass
                /JPEGImages
                ...
            ...
        /VOCtrainval_11-May-2012.tar
        ...

2.2 Pascal VOC trainaug (Recommended!!)

See chapter 4 of [2]

    The original dataset contains 1464 (train), 1449 (val), and 1456 (test) pixel-level annotated images. We augment the dataset by the extra annotations provided by [76], resulting in 10582 (trainaug) training images. The performance is measured in terms of pixel intersection-over-union averaged across the 21 classes (mIOU).

./datasets/data/train_aug.txt includes the file names of 10582 trainaug images (val images are excluded). Please to download their labels from Dropbox or Tencent Weiyun. Those labels come from DrSleep's repo.

Extract trainaug labels (SegmentationClassAug) to the VOC2012 directory.

/datasets
    /data
        /VOCdevkit  
            /VOC2012
                /SegmentationClass
                /SegmentationClassAug  # <= the trainaug labels
                /JPEGImages
                ...
            ...
        /VOCtrainval_11-May-2012.tar
        ...

3. Training on Pascal VOC2012 Aug

3.1 Visualize training (Optional)

Start visdom sever for visualization. Please remove '--enable_vis' if visualization is not needed.

# Run visdom server on port 28333
visdom -port 28333

3.2 Training with OS=16

Run main.py with "--year 2012_aug" to train your model on Pascal VOC2012 Aug. You can also parallel your training on 4 GPUs with '--gpu_id 0,1,2,3'

Note: There is no SyncBN in this repo, so training with multple GPUs and small batch size may degrades the performance. See PyTorch-Encoding for more details about SyncBN

python main.py --model deeplabv3plus_mobilenet --enable_vis --vis_port 28333 --gpu_id 0 --year 2012_aug --crop_val --lr 0.01 --crop_size 513 --batch_size 16 --output_stride 16

3.3 Continue training

Run main.py with '--continue_training' to restore the state_dict of optimizer and scheduler from YOUR_CKPT.

python main.py ... --ckpt YOUR_CKPT --continue_training

3.4. Testing

Results will be saved at ./results.

python main.py --model deeplabv3plus_mobilenet --enable_vis --vis_port 28333 --gpu_id 0 --year 2012_aug --crop_val --lr 0.01 --crop_size 513 --batch_size 16 --output_stride 16 --ckpt checkpoints/best_deeplabv3plus_mobilenet_voc_os16.pth --test_only --save_val_results

Cityscapes

1. Download cityscapes and extract it to 'datasets/data/cityscapes'

/datasets
    /data
        /cityscapes
            /gtFine
            /leftImg8bit

2. Train your model on Cityscapes

python main.py --model deeplabv3plus_mobilenet --dataset cityscapes --enable_vis --vis_port 28333 --gpu_id 0  --lr 0.1  --crop_size 768 --batch_size 16 --output_stride 16 --data_root ./datasets/data/cityscapes 

Reference

[1] Rethinking Atrous Convolution for Semantic Image Segmentation

[2] Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation