Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR, 2021)

Related tags

Deep LearningCSG
Overview

CSG-lightning

Unofficial Pytorch Lightning implementation of Contrastive Syn-to-Real Generalization (ICLR 2021).

Based on:

Environment Setup

Tested in a Python 3.8 environment in Linux and Windows with:

Installing the dependencies:

pip install pytorch-lightning lightning-bolts torchmetrics

Classification (VisDA17)

Dataset Setup

Download VisDA17 dataset from official website or, use the provided script for your convenience.

# The script downloads and extracts VisDA17 dataset.
# Note: It takes a very long time to download full dataset.
python datasets/prepare_visda17.py

If you downloaded the dataset manually, extract and place them as below.

πŸ“‚ datasets
 ┣ πŸ“‚ visda17
 ┃ ┣ πŸ“‚ train
 ┃ ┃ πŸ“‚ validation
 β”— β”— πŸ“‚ test

How to run

Training

Simply run:

python run.py

or with options,

usage: run.py [-h] [-o OUTPUT] [-r ROOT] [-e EPOCHS] [-lr LEARNING_RATE] [-bs BATCH_SIZE] [-wd WEIGHT_DECAY] [--task {classification,segmentation}] [--encoder {resnet101,deeplab50,deeplab101}] [--momentum MOMENTUM] [--num-classes NUM_CLASSES] [--eval-only] [--gpus GPUS]
              [--resume RESUME] [--dev-run] [--exp-name EXP_NAME] [--augmentation AUGMENTATION] [--seed SEED] [--fc-dim FC_DIM] [--no-apool] [--single-network] [--stages STAGES [STAGES ...]] [--emb-dim EMB_DIM] [--emb-depth EMB_DEPTH] [--num-patches NUM_PATCHES]
              [--moco-weight MOCO_WEIGHT] [--moco-queue-size MOCO_QUEUE_SIZE] [--moco-momentum MOCO_MOMENTUM] [--moco-temperature MOCO_TEMPERATURE]

Evaluation

python run.py --eval-only --resume https://github.com/ryanking13/CSG/releases/download/v0.2/csg_resnet101.ckpt

Results

Model Accuracy
CSG (from paper) 64.1
CSG (reimpl) 67.1

Semantic Segmentation

Dataset Setup (GTA5 ==> Cityscapes)

Download GTA5 and Cityscapes datasets.

Place them as below.

πŸ“‚ datasets
 ┣ πŸ“‚ GTA5
 ┃ ┣ πŸ“‚ images 
 ┃ ┃ ┣ πŸ“œ 00001.png
 ┃ ┃ ┣ ...
 ┃ ┃ β”— πŸ“œ 24966.png
 ┃ ┃ ┣ πŸ“‚ labels
 ┃ ┃ ┣ πŸ“œ 00001.png
 ┃ ┃ ┣ ...
 ┃ ┃ β”— πŸ“œ 24966.png
 ┣ πŸ“‚ cityscapes
 ┃ ┣ πŸ“‚ leftImg8bit
 ┃ ┃ ┣ πŸ“‚ train
 ┃ ┃ ┃ πŸ“‚ val
 β”— β”— β”— πŸ“‚ test
 ┃ ┣ πŸ“‚ gtFine 
 ┃ ┃ ┣ πŸ“‚ train
 ┃ ┃ ┃ πŸ“‚ val
 β”— β”— β”— πŸ“‚ test

How to run

Training

Simply run:

./run_seg.sh

Evaluation

./run_seg --eval-only --resume https://github.com/ryanking13/CSG/releases/download/v0.2/csg_deeplab50.ckpt

Results

Model IoU
CSG (from paper) 35.27
CSG (reimpl) 34.71

Differences from official implementation

  • Warmup LR scheduler
  • No layerwise LR modification
  • RandAugment augmentation types

Known Issues

  • I got error Distributed package doesn't have NCCL built in

On windows, nccl is not supported, try:

set PL_TORCH_DISTRIBUTED_BACKEND=gloo
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