llien30 / pytorch_template

Pytorch Implementation example of Image Classification with flowers recognition dataset

Image Classification Template

project for pytorch implementation example of image classification

Requirements

• python >= 3.7
• pytorch >= 1.0
• pyyaml
• scikit-learn
• wandb
• pre-commit (for pre-commit formatting, type check and testing)

Please run pip install -r requirements.txt to install the necessary packages.

Dataset

Flowers Recognition Dataset Download the dataset from HERE．

Directory Structure

.
├── FOR_AOLAB_MEMBERS.md
├── LICENSE
├── README.md
├── dataset/
│   └── flowers/
├── requirements.txt
├── .gitignore
├── .pre-commit-config
└── src/
    ├── csv
    ├── libs/
    ├── utils
    ├── notebook/
    ├── result/
    ├── scripts/
    │   └── experiment.sh
    ├── train.py
    └── evaluate.py

Features

• configuration class using dataclasses.dataclass (libs/config.py)

  • type check.
  • detection of unnecessary / extra parameters in a specified configuration.
  • dataclass is an immutable object, which prevents the setting from being changed by mistake.

• automatically generating configuration files (utils/make_configs.py)

  • e.g.) run this command

  python utils/make_configs.py --model resnet18 resnet30 resnet50 --learning_rate 0.001 0.0001

  then you can get all of the combinations with model and learning_rate (total 6 config files) while the other parameters are set by default as described in libs/config.py.

• running all the experiments by running shell scripts (scripts/experiment.sh)

• support type annotation (typing)

• code formatting with black, isort and flake8

Experiment

Please see scripts/experiment.sh for the detail. You can set configurations and run all the experiments by the below command.

sh scripts/experiment.sh

Formatting

• black
• flake8
• isort

TODO

• pytorch implementation of image classification
• configuration class using dataclasses.dataclass
• auto generation of config yaml files
• shell script to run all the experiment
• support typing (type annotation)
• test code (run testing with pre-commit check)
• mypy (pre-commit check)
• formatting (pre-commit isort, black and flake8)
• calculate cyclomatic complexity / expression complexity / cognitive complexity (flake8 extension)
• CI for testing using GitHub Actions

License

This repository is released under the MIT License