- Use the template by clicking the green button or directly clone this repo
- Install miniconda/anaconda, a package for package/environment management
wget repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh
bash Miniconda3-latest-Linux-x86_64.sh
- Build conda environment from file
cd pytorch-golden-template
conda create -n PytorchGoldenTemplate python">=3.6" pytorch">=1.0" torchvision tensorboard pillow">=6.1" pandas coloredlogs imageio scipy -c pytorch
source activate PytorchGoldenTemplate
pip install attrdict tensorboardX
# Or
conda env create -f environment.yaml
- Update submodules
git submodule update --init --recursive
- Activate the environment
source activate <environemnt name>
usage: main.py [-h] [-tc TEMPLATE_CONFIG]
[-sc SPECIFIED_CONFIGS [SPECIFIED_CONFIGS ...]] [-r RESUME]
[-p PRETRAINED] [-d DEVICE] [--mode {train,test,eval}]
[--saved_keys SAVED_KEYS [SAVED_KEYS ...]]
[--ckpts_subdir CKPTS_SUBDIR] [--outputs_subdir OUTPUTS_SUBDIR]
PyTorch Template
optional arguments:
-h, --help show this help message and exit
-tc TEMPLATE_CONFIG, --template_config TEMPLATE_CONFIG
Template configuraion file. It should contain all
default configuration and will be overwritten by
specified config.
-sc SPECIFIED_CONFIGS [SPECIFIED_CONFIGS ...], --specified_configs SPECIFIED_CONFIGS [SPECIFIED_CONFIGS ...]
Specified configuraion files. They serve as experiemnt
controls and will overwrite template configs.
-r RESUME, --resume RESUME
path to latest checkpoint (default: None)
-p PRETRAINED, --pretrained PRETRAINED
path to pretrained checkpoint (default: None)
-d DEVICE, --device DEVICE
indices of GPUs to enable (default: all)
--mode {train,test,eval}
--saved_keys SAVED_KEYS [SAVED_KEYS ...]
Specify the keys to save at testing mode.
--ckpts_subdir CKPTS_SUBDIR
Subdir name for ckpts saving.
--outputs_subdir OUTPUTS_SUBDIR
Subdir name for outputs saving.
Example: train a new model
python main.py
Example: train a new GAN model
python main.py -tc configs/template_GAN_train_config.json
Example: resume from a checkpoint, inference and save outputs
python main.py --resume ./saved/ckpts/template_config+CrossEntropy/0723_180600/ckpt-ep1-valid_mnist_avg_loss0.2885-best.pth --mode test
Example: generate testing results
# For submission to the leaderboard, etc.
python main.py --mode test -p <pretrained_weight>
Example: evaluate results
# You could evaluate results by other models (corresponding data loader needs to be defined)
# See the result_data_loaders in configs/template_eval_config.json for details
python main.py --mode eval
By default, Tensorboard logs are recoded under saved/runs.
Please run tensorboard --logidr saved/runs and go to localhost:6006 to see the training progress on tensorboard.
- Ya-Liang Chang (Allen) amjltc295
- Zhe Yu Liu Nash2325138