PDANet : Pyramid Dual Attention Network for Semantic Segmentation

By Guoqiang Liu, lgqfhwy@zju.edu.cn.

This code is an implementation of the experiments on ADE20K in the PDANet. We implement our method based on open source semantic-segmentation-pytorch

Environment

The code is developed under the following configurations.

• Hardware: 1-8 GPUs (with at least 12G GPU memories) (change [--gpus GPUS] accordingly)
• Software: Ubuntu 16.04.3 LTS, CUDA>=8.0, Python>=3.5, PyTorch>=0.4.0

Quick start:

1. Here is a simple demo to do inference on a single image:

chmod +x demo_test.sh
./demo_test.sh

This script downloads a trained model (ResNet50dilated + PPM_deepsup) and a test image, runs the test script, and saves predicted segmentation (.png) to the working directory.

2. Train a model (default: ResNet18dilated + PDAM). During training, checkpoints will be saved in folder ckpt.

python3 train.py --gpus GPUS

• To choose which gpus to use, you can either do --gpus 0-7, or --gpus 0,2,4,6.

For example:

• Train resnet18dilated + PDAM (Pyramid Dual Attention Module)

python3 train.py --gpus GPUS \
--arch_decoder pdam \

Evaluation

1. Evaluate a trained model on the validation set. --id is the folder name under ckpt directory. --suffix defines which checkpoint to use, for example _epoch_20.pth. Add --visualize option to output visualizations as shown in teaser.

python3 eval_multipro.py --gpus GPUS --id MODEL_ID --suffix SUFFIX

For example:

• Evaluate ResNet18dilated + PDAM

python3 eval_multipro.py --gpus GPUS \
    --id MODEL_ID --suffix SUFFIX --arch_encoder resnet18dilated --arch_decoder pdam \