A clear PyTorch template for swift model building.
- Well-organized project template out of the box.
- Automatically record the model version (by saving the git commit hash) for later reproduction.
- Automatically start TensorBoard for you.
- Use JSON file or command line arguments to specify arguments.
- The results of each experiment are properly stored.
- Modify the model structures
models/build.py. - Update the loss functions used
solver/loss.py. - Update the data loading process
data/dataset.py&data/loader.py. - Add metrics that can measure your model's performance
metrics/eval.py. - Update sampling functions & logging functions, so you can see the results with TensorBoard
solver/solver.py! - Add a shell script that run your model
scripts/{exp_id}-model_key_config.sh. - Start training, evaluating or inference by running the above script!
+--- .gitignore
+--- archive (generated files & dataset)
| +--- README.md
+--- bin (utility script)
| +--- README.md
| +--- template.py
+--- config.py (options)
+--- data (data fetching related)
| +--- dataset.py
| +--- fetcher.py
| +--- loader.py
| +--- README.md
+--- expr (experiment directory)
+--- main.py (everything start from here)
+--- metrics (metric used)
| +--- eval.py
| +--- fid.py
| +--- README.md
+--- models (model architecture related)
| +--- build.py (the wrapper for models)
| +--- discriminator.py
| +--- generator.py
| +--- layers.py
| +--- mapping_network.py
| +--- README.md
+--- README.md
+--- requirements.txt
+--- scripts (training related shell scripts)
| +--- train.sh
+--- solver (training related)
| +--- loss.py
| +--- misc.py
| +--- solver.py
| +--- utils.py
+--- utils (utility functions)
| +--- checkpoint.py
| +--- file.py
| +--- image.py
| +--- logger.py
| +--- misc.py
| +--- model.py
I referred StarGAN v2's official implementation when crafting this template, so don't be surprised if you find some code is similar.
BTW, if you want to deploy your model, you may want to check out this template.