chaowentao/ntire2021_compress

Prerequisites

Linux (Windows is not officially supported)
Python 3.6+
PyTorch 1.3 or higher
CUDA 9.0 or higher
NCCL 2
GCC 4.9 or higher
mmcv(script will install mmcv automatically)

A from-scratch setup script(just do it)

Here is a full script for setting up mmediting with conda.

conda create -n open-mmlab python=3.7 -y
source activate open-mmlab

conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

git clone https://github.com/open-mmlab/mmediting.git
cd mmediting
pip install -r requirements.txt
pip install -v -e .

Test a dataset

MMEditing implements distributed testing with MMDistributedDataParallel.

Test with single/multiple GPUs

You can use the following commands to test a dataset with single/multiple GPUs.

# single-gpu testing
python tools/test.py ${CONFIG_FILE} ${CHECKPOINT_FILE} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]

# multi-gpu testing
./tools/dist_test.sh ${CONFIG_FILE} ${CHECKPOINT_FILE} ${GPU_NUM} [--out ${RESULT_FILE}] [--save-path ${IMAGE_SAVE_PATH}]

For example,

# single-gpu testing
python tools/test.py configs/example_config.py work_dirs/example_exp/example_model_20200202.pth --out work_dirs/example_exp/results.pkl

# multi-gpu testing
./tools/dist_test.sh configs/example_config.py work_dirs/example_exp/example_model_20200202.pth gpu_num --save-path work_dirs/example_exp/results/

Data and Model Preperation

You should download the data from the wedsite, and put it in data floder. You also should download the model from the wedsite, and put it in works_dir floder. May be you should change config.py 'lq', 'gt' and 'anno' path. After have 3 results, you should ensemble it. ./tools/image_merge_cal_psnr.py

Compress1 && Compress2 test cmd


./tools/dist_test.sh configs/restorers/edvr/edvr_g8_600k_large_finetune_compress.py work_dirs/edvr_g8_600k_large_fintune_compress/iter_150000.pth 8 --save-path=./work_dirs/edvr_g8_600k_large_finetune_compress/results_test/ --multi-scale

Compress3 test cmd

./tools/dist_test.sh configs/restorers/edvr/edvr_g8_600k_large_finetune_compress3.py work_dirs/edvr_g8_600k_large_fintune_compress3/iter_150000.pth 8 --save-path=./work_dirs/edvr_g8_600k_large_compress3/results_test/ --multi-scale

Detailed Installation

a. Create a conda virtual environment and activate it.

conda create -n open-mmlab python=3.7 -y
conda activate open-mmlab

b. Install PyTorch and torchvision following the official instructions, e.g.,

conda install pytorch torchvision -c pytorch

Note: Make sure that your compilation CUDA version and runtime CUDA version match. You can check the supported CUDA version for precompiled packages on the PyTorch website.

E.g. 1 If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5, you need to install the prebuilt PyTorch with CUDA 10.1.

conda install pytorch cudatoolkit=10.1 torchvision -c pytorch

E.g. 2 If you have CUDA 9.2 installed under /usr/local/cuda and would like to install PyTorch 1.3.1., you need to install the prebuilt PyTorch with CUDA 9.2.

conda install pytorch=1.3.1 cudatoolkit=9.2 torchvision=0.4.2 -c pytorch

you can use more CUDA versions such as 9.0.

c. Clone the mmediting repository.

git clone https://github.com/open-mmlab/mmediting.git
cd mmediting

d. Install build requirements and then install mmediting.

pip install -r requirements.txt
pip install -v -e .  # or "python setup.py develop"

If you build mmediting on macOS, replace the last command with

CC=clang CXX=clang++ CFLAGS='-stdlib=libc++' pip install -e .

Note:

The git commit id will be written to the version number with step d, e.g. 0.6.0+2e7045c. The version will also be saved in trained models. It is recommended that you run step d each time you pull some updates from github. If C++/CUDA codes are modified, then this step is compulsory.

Important: Be sure to remove the ./build folder if you reinstall mmedit with a different CUDA/PyTorch version.
```
pip uninstall mmedit
rm -rf ./build
find . -name "*.so" | xargs rm
```
Following the above instructions, mmediting is installed on dev mode, any local modifications made to the code will take effect without the need to reinstall it (unless you submit some commits and want to update the version number).
If you would like to use opencv-python-headless instead of opencv-python, you can install it before installing MMCV.
Some models (such as EDVR in restorers) depend on CUDA ops in mmcv-full which is listed in requirements.txt. Install it with the default command pip install -r requirements.txt need to compile CUDA ops locally and it may take up to 10 mins. Another option is to install pre-compiled mmcv-full, visit MMCV github page for concrete instructions. Moreover, if the model you intend to use does not depend on CUDA ops, you could also install the lite version of mmcv with pip install mmcv in which CUDA ops is excluded.

chaowentao / ntire2021_compress