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.

๐Ÿ“‚ datasets
 โ”ฃ ๐Ÿ“‚ visda17
 โ”ƒ โ”ฃ ๐Ÿ“‚ train
 โ”ƒ โ”ƒ ๐Ÿ“‚ validation
 โ”— โ”— ๐Ÿ“‚ 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.

๐Ÿ“‚ datasets
 โ”ฃ ๐Ÿ“‚ GTA5
 โ”ƒ โ”ฃ ๐Ÿ“‚ images 
 โ”ƒ โ”ƒ โ”ฃ ๐Ÿ“œ 00001.png
 โ”ƒ โ”ƒ โ”ฃ ...
 โ”ƒ โ”ƒ โ”— ๐Ÿ“œ 24966.png
 โ”ƒ โ”ƒ โ”ฃ ๐Ÿ“‚ labels
 โ”ƒ โ”ƒ โ”ฃ ๐Ÿ“œ 00001.png
 โ”ƒ โ”ƒ โ”ฃ ...
 โ”ƒ โ”ƒ โ”— ๐Ÿ“œ 24966.png
 โ”ฃ ๐Ÿ“‚ cityscapes
 โ”ƒ โ”ฃ ๐Ÿ“‚ leftImg8bit
 โ”ƒ โ”ƒ โ”ฃ ๐Ÿ“‚ train
 โ”ƒ โ”ƒ โ”ƒ ๐Ÿ“‚ val
 โ”— โ”— โ”— ๐Ÿ“‚ test
 โ”ƒ โ”ฃ ๐Ÿ“‚ gtFine 
 โ”ƒ โ”ƒ โ”ฃ ๐Ÿ“‚ train
 โ”ƒ โ”ƒ โ”ƒ ๐Ÿ“‚ val
 โ”— โ”— โ”— ๐Ÿ“‚ 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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