Initial commit

.gitignore

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+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
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+lib/
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+parts/
+sdist/
+var/
+wheels/
+pip-wheel-metadata/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
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+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
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+# PyBuilder
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+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+.python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
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+# Spyder project settings
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+/site
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+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# experiments and results
+experiments/
+results/
+
+# pycharm
+.idea/

LICENSE

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+MIT License
+
+Copyright (c) 2021 Dongliang Cao
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

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+# pytorch-framework
+PyTorch common framework to accelerate network implementation, training and validation.
+
+This framework is inspired by works from [MMLab](https://github.com/open-mmlab), which modularize the data, network,
+loss, metric, etc. to make the framework to be flexible, easy to modify and to extend.
+
+
+## How to use
+```bash
+# install necessary libs
+pip install -r requirements.txt
+```
+The framework contains six different subfolders:
+- networks: all networks should be implemented under the networks folder with {NAME}_network.py filename. 
+- datasets: all datasets should be implemented under the datasets folder with {NAME}_dataset.py filename.
+- losses: all losses should be implemented under the losses folder with {NAME}_loss.py filename.
+- metrics: all metrics should be implemented under the metrics folder with {NAME}_metric.py filename.
+- models: all models should be implemented under the models folder with {NAME}_model.py filename.
+- utils: all util functions should be implemented under the utils folder with {NAME}_util.py filename.
+
+The training and validation procedure can be defined in the specified .yaml file.
+```bash
+# training 
+CUDA_VISIBLE_DEVICES=gpu_ids python train.py --opt options/train.yaml
+
+# validation/test
+CUDA_VISIBLE_DEVICES=gpu_ids python test.py --opt options/test.yaml
+```
+
+In the .yaml file for training, you can define all the things related to training such as the experiment name, model, 
+dataset, network, loss, optimizer, metrics and other hyper-parameters. Here is an example to train VGG16 for image classification:
+```bash
+# general setting
+name: vgg_train
+backend: dp # DataParallel
+type: ClassifierModel
+num_gpu: auto
+
+# path to resume network
+path:
+  resume_state: ~
+
+# datasets
+datasets:
+  train_dataset:
+    name: TrainDataset
+    type: ImageNet
+    data_root: ../data/train_data
+  val_dataset:
+    name: ValDataset
+    type: ImageNet
+    data_root: ../data/val_data
+  # setting for train dataset
+  batch_size: 8
+
+# network setting
+networks:
+  classifier:
+    type: VGG16
+    num_classes: 1000
+
+# training setting
+train:
+  total_iter: 10000
+  optims:
+    classifier:
+      type: Adam
+      lr: 1.0e-4
+  schedulers:
+    classifier:
+      type: none
+  losses:
+    ce_loss:
+      type: CrossEntropyLoss
+
+# validation setting
+val:
+  val_freq: 10000
+
+# log setting
+logger:
+  print_freq: 100
+  save_checkpoint_freq: 10000
+```
+In the .yaml file for validation, you can define all the things related to validation such as: model, dataset, metrics.
+Here is an example:
+```bash
+# general setting
+name: test
+backend: dp # DataParallel
+type: ClassifierModel
+num_gpu: auto
+manual_seed: 1234
+
+# path
+path:
+  resume_state: experiments/train/models/final.pth
+  resume: false
+
+# datasets
+datasets:
+  val_dataset:
+    name: ValDataset
+    type: ImageNet
+    data_root: ../data/test_data
+
+# network setting
+networks:
+  classifier:
+    type: VGG
+    num_classes: 1000
+
+# validation setting
+val:
+  metrics:
+    accuracy:
+      type: calculate_accuracy
+```
+
+## Framework Details
+The core of the framework is the **BaseModel** in the [base_model.py](models/base_model.py). The **BaseModel** controls 
+the whole training/validation procedure from initialization over training/validation iteration to results saving.
+- Initialization:
+In the model initialization, it will read the configuration in the .yaml file and construct the 
+corresponding networks, datasets, losses, optimizers, metrics, etc. 
+- Training/Validation:
+In the training/validation procedure, you can refer the training process in the [train.py](./train.py) and the validation process in the [test.py](./test.py).
+- Results saving:
+The model will automatically save the state_dict for networks, optimizers and other hyperparameters during the training.
+
+The configuration of the framework is down by **Register** in the [registry.py](utils/registry.py). The **Register**
+ has a object map (key-value pair). The key is the name of the object, the value is the class of the object. 
+There are total 4 different registers for networks, datasets, losses and metrics.
+Here is an example to register a new network:
+```bash
+import torch
+import torch.nn as nn
+
+from utils.registry import NETWORK_REGISTRY
+
+@NETWORK_REGISTRY.register()
+class MyNet(nn.Module):
+  ...
+```