hechengjin0/pytorch_3t27t

PyTorch 3T27T

3T27T: Image-to-image translation of 3T to 7T brain MRI using Generative Adversarial Networks - a step towards longitudinal harmonization

Project Organization

├── LICENSE
├── Makefile             <- Makefile with commands like `make data` or `make train`
├── README.md            <- The top-level README for developers using this project.
│
├── references/          <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports              <- Generated analysis as HTML, PDF, LaTeX, etc.
│   └── figures/         <- Generated graphics and figures to be used in reporting
│
├── docs/                <- A default Sphinx project; see sphinx-doc.org for details
│
├── data                 <- directory for storing input data
│   ├── external/        <- Data from third party sources.
│   ├── interim/         <- Intermediate data that has been transformed.
│   ├── processed/       <- The final, canonical data sets for modeling.
│   └── raw/             <- The original, immutable data dump.
│
├── experiments                                     <- directory for storing experimental setups
│   ├── A01_MLP-XOR-implementation/
│   ├── A02_clustering-interleaving-moons/
│   ├── A03_MNIST-handwritten-digit-classification/
│   └── A04_fashion-MNIST-clothing-classification   <- Aims. Naming convention is
│           │                                          aim ID (A + ordered two-digit integer)
│           │                                          + "_" + short "-" delimited description
│           ├── E01_linear-model/
│           ├── E02_MLP/
│           └── E03_Alpha                              <- Experiments. Naming convention is
│                │                                     experiment ID
│                │                                     (E + ordered two-digit integer)
│                │                                     + "_" + short "-" delimited description
│                ├── S0001_pilot/
│                ├── S0002_dec-lr/
│                └── S0003_inc-batch                <- Setups. Naming convention is setup ID
│                      │                               (S + ordered four-digit integer)
│                      │                               + "_" + short "-" delimited description
│                      └── A04-E03-S0003.yml        <- YAML configuration file.
│                                                      Naming convention is aim ID +
│                                                      "-" + experiment ID + "-" setup ID
│
│
├── notebooks/                     <- Jupyter notebooks. Naming convention is
│                                     a number (for ordering) + "_" + the creator's initials
│                                     + "_" experiment ID + "_" + short `-` delimited
│                                     description, e.g. `1.0_ed_E01_results-visualization`
│
├── trials                         <- directory for storing experimental trials
│    ├── A01-E01-S0001/            <- Name of the YAML config file
│    ├── A02-E01-S0001/
│    ├── A03-E01-S0001/
│    └── A04-E03-S0003
│         ├── timestamp1/           <- timestamp, in YYYYMMDDhhmmssUTC format,
│         │                            i.e., year (YYYY), month (MM), day (DD),
│         │                            hour (hh), minute (mm), second (ss), all according to
│         │                            the Coordinated Universtal Time (UTC) standard
│         └── timestamp2
│                    ├── etc/       <- stores all information necessary to reproduce this trial,
│                    │                 e.g., the YAML config file, resuming session, seeds
│                    ├── logs/      <- stores all logging information
│                    ├── runs/      <- stores Tensorboard runs
│                    └── ckpts/     <- stores checkpoints
│
│
├── observations                    <- directory for storing experimental observations
│    ├── A01-E01-S0001/
│    ├── A02-E01-S0001/
│    ├── A03-E01-S0001/
│    └── A04-E03-S0003
│         ├── timestamp1/
│         └── timestamp2/
│
│
├── tox.ini              <- tox file with settings for running tox; see tox.readthedocs.io
│
├── requirements.txt     <- The requirements file for reproducing the analysis environment,
│                           e.g., generated with `pip freeze > requirements.txt`
├── requirements-dev.txt <- The requirements file with the devel dependencies
│
├── setup.py             <- makes project pip installable (pip install -e .)
│                           so package_name can be imported
├── tests/               <- folder with test code
│
├── logging.yml          <- logging configuration
│
└── package_name                  <- package directory
    ├── __init__.py               <- Makes package_name a Python package
    ├── cli.py                    <- command line interface
    ├── package_name.py           <- package script
    ├── main_foo.py               <- training script for model "foo"
    ├── main_alpha.py             <- training script for model "alpha"
    │
    ├── base                      <- abstract base classes
    │   ├── __init__.py           <- Makes the abstract base class a Python subpackage
    │   ├── transform.py          <- abstract base class for data transformations
    │   ├── dataset.py            <- abstract base class for datasets
    │   ├── dataloader.py         <- abstract base class for data loaders
    │   ├── arch.py               <- abstract base class for models' archtectures
    │   ├── loss.py               <- abstract base class for losses
    │   ├── metric.py             <- abstract base class for metrics
    │   ├── optimizer.py          <- abstract base class for optimizers
    │   ├── scheduler.py          <- abstract base class for schedulers
    │   └── trainer.py            <- abstract base class for trainers
    │
    ├── utils                     <- utilities
    │    ├── seeder.py            <- manages reproducibility
    │    ├── logger.py            <- trainning logger
    │    ├── monitor.py           <- Tensorboard visualization support
    │    └── backer.py            <- manages paths for saving models + logs
    │
    ├── model_foo/                <- each model is a subpackage
    └── model_alpha               <- model "alpha"
         ├── __init__.py          <- Makes model_alpha a Python subpackage
         ├── dataset.py
         ├── transform.py
         ├── dataloader.py
         ├── loss.py
         ├── metric.py
         ├── arch.py
         ├── optimizer.py
         ├── scheduler.py
         └── trainer.py

Project based on the cookiecutter data science project template. #cookiecutterdatascience

Some features inspired by A template for small scientific python projects. #shablona

Package scaffold inspired by cookiecutter PyTorch Deep Learning template. #cookiecutterpytorch

Config file format

---

Config files are in .yml format:

name: A04-E03-S0003
seed: 12345
target_devices: [0]
save_dir: trials/

arch:
    type: Alpha
    args: {}

transform:
    type: MNISTTransform
    args: {}

dataloader:
    type: MNISTDataloader
    args:
        batch_size: 128
        data_dir: data/
        num_workers: 2
        shuffle: true
        validation_split: 0.1

loss: nll_loss

lr_scheduler:
    type: StepLR
    args:
        gamma: 0.1
        step_size: 50

metrics:
    - top_1_acc
    - top_3_acc

optimizer:
    type: Adam
    args:
        lr: 0.001
        weight_decay: 0

training:
    early_stop: 10
    epochs: 100
    monitor: min val_loss
    save_period: 1
    tensorboard: true

testing:
    data_dir: data/
    batch_size: 128
    num_workers: 8

Add additional configurations as needed.

Test the newly create repo

---

1. Create virtual environment

$ make virtual_environment

2. Instal development requirements

$ make install_requirements

3. Run MNIST hello world example

$ conda activate <your_repo_name>
$ <your_repo_name> train-hello

Checkpoints

---

You can specify the name of the training session in config files:

name: A04-E03-S0003

The checkpoints will be saved in

<save_dir>/<trial>/<timestamp>/ckpts/ckpt_epoch-<n>_<trial>_<timestamp>,

where <save_dir> and <trial> are as defined in the config file, <n> is an integer identifying the checkpoint, and timestamp is a datetime footprint in the YYYYMMDDhhmmssUTC format, with year (YYYY), month (MM), day (DD), hour (hh), minute (mm), second (ss), all according to the Coordinated Universtal Time (UTC) standard. A copy of config file will be saved in

<save_dir>/<trial>/<timestamp>/etc/config_<trial>_<timestamp>.yml.

Note: checkpoints contain:

checkpoint = {
'arch': arch,
'epoch': epoch,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'monitor_best': self.mnt_best,
'config': self.config
}

Tensorboard Visualization

---

This template supports Tensorboard visualization

1. Run training

Set tensorboard option in config file true.

2. Open tensorboard server

Type tensorboard --logdir <save_dir>/ at the project root, then server will open at http://localhost:6006

By default, values of loss and metrics specified in config file, input images, and histogram of model parameters will be logged. If you need more visualizations, use add_scalar('tag', data), add_image('tag', image), etc in the trainer._train_epoch method. add_something() methods in this template are basically wrappers for those of tensorboard.SummaryWriter module.

Note: You don't have to specify current steps, since TensorboardWriter class defined at logger/monitor.py will track current steps automatically.