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https://github.com/sovit-123/pytorch-dcgan-framework
A small PyTorch framework to try out and train DCGAN on different datasets.
https://github.com/sovit-123/pytorch-dcgan-framework
convolutional-neural-networks deep-learning generative-adversarial-network pytorch
Last synced: 7 days ago
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A small PyTorch framework to try out and train DCGAN on different datasets.
- Host: GitHub
- URL: https://github.com/sovit-123/pytorch-dcgan-framework
- Owner: sovit-123
- Created: 2021-10-21T11:40:59.000Z (over 3 years ago)
- Default Branch: main
- Last Pushed: 2021-11-22T01:12:54.000Z (about 3 years ago)
- Last Synced: 2024-12-08T19:08:10.139Z (2 months ago)
- Topics: convolutional-neural-networks, deep-learning, generative-adversarial-network, pytorch
- Language: Python
- Homepage:
- Size: 181 MB
- Stars: 1
- Watchers: 2
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# pytorch-dcgan-framework
## DCGAN Training on Different Datasets using PyTorch
* Currently, the generator generates 64x64 resolution images.
## Current Features/Supports
* You can resume training from any saved model.
* TensorBoard logging of loss graphs.
* Resuming training will also create new TensorBoard run where the old plots will be generated first, and then continue.
* The generator model is from the paper [Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks](https://arxiv.org/abs/1511.06434v2). I tried to replicate it as well as I could.
* The discriminator network follows all the rules from the [official paper](https://arxiv.org/abs/1511.06434v2) although there can be a few flexibilties to the size and depth of the network. Still, the core rules are all from the paper.
* Option to save GIFs of generated images after training ends (See `config.py`). **If trained for high number of epochs (>500), it will require a lot of RAM as all the saved images from the 500 epochs will be loaded directly to memory to create teh GIF**. Therefore provided option to turn it off.
## Current Datasets Supported
* MNIST
* Fashion MNIST
* CIFAR10
* CELEBA
* Abstract Art Gallery
## Dataset Directory
The datasets are one folder back from the working project directory. Relative path from current project directory:
* `../input/data`
Following is the structure showing how all the datasets are arranged:
```
input
|───data
├───abstract_art_gallery
│ ├───Abstract_gallery
│ │ └───Abstract_gallery
│ └───Abstract_gallery_2
│ └───Abstract_gallery_2
├───celeba
│ └───img_align_celeba
├───cifar-10-batches-py
├───FashionMNIST
│ └───raw
├───MNIST
│ └───raw
```
* MNIST, Fashion MNIST, and CIFAR10 data are directly downloaded from PyTorch `torchvision` module.
* To train on CelebA and Abstract Art Gallery dataset, you need to download them and arrange them proper directory first.
* [Download CelebA dataset](https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg?resourcekey=0-rJlzl934LzC-Xp28GeIBzQ).
* Download the `img_align_celeba.zip` file.
* [Download Abstract Art Gallery dataset](https://www.kaggle.com/bryanb/abstract-art-gallery).
## Project Directory
```
.
├── config.py
├── datasets.py
├── models.py
├── outputs_ABSTRACT_ART
├── outputs_CELEBA
├── outputs_CIFAR10
├── outputs_FashionMNIST
├── outputs_MNIST
├── README.md
├── runs
├── train.py
└── utils.py
```
## Training Configurations
* The training configuration for MNIST and Fashion MNIST datasets are the same.
* Just change the `DATASET` to `'MNIST'` or `'FashionMNIST'`. `N_CHANNELS` should be `1` for grayscale images.
```python
import torch
BATCH_SIZE = 128
EPOCHS = 50
EPOCH_START = 0
NUM_WORKERS = 4
MULT_FACTOR = 1
IMAGE_SIZE = 64*MULT_FACTOR
# image channels
N_CHANNELS = 1
# SAMPLE_SIZE is the total number of images in row x column form...
# if SAMPLE_SIZE = 64, then 8x8 image grids will be saved to disk...
# if SAMPLE_SIZE = 128, then 16x8 image grids will be saved to disk...
SAMPLE_SIZE = 64
# latent vector size
NZ = 100
# number of steps to apply to the discriminator
K = 1
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# one of 'MNIST', 'FashionMNIST', 'CIFAR10', 'CELEBA', ...
# ... 'ABSTRACT_ART'
DATASET = 'MNIST'
# for printing metrics
PRINT_EVERY = 100
# for optimizer
BETA1 = 0.5
BETA2 = 0.999
LEARNING_RATE = 0.0002
# Epcoh nterval at which to save the Generator Model.
MODEL_SAVE_INTERVAL = 25
# Provide path to a trained model to resume training, else keep `None`.
# GEN_MODEL_PATH = 'outputs_MNIST/generator_final.pth'
# DISC_MODEL_PATH = 'outputs_MNIST/discriminator_final.pth'
GEN_MODEL_PATH = None
DISC_MODEL_PATH = None
# Whether to create GIF from all the generated images at the end or not,
# might need a considerable amoung of RAM as all the generated images will
# be loaded to at once. Give values as either `True` or `False`.
CREATE_GIF = False
```
* For CIFAR10 and other colored images datasets, change the `N_CHANNELS` to 3, RGB images.
* ```python
import torch
BATCH_SIZE = 128
EPOCHS = 50
EPOCH_START = 0
NUM_WORKERS = 4
MULT_FACTOR = 1
IMAGE_SIZE = 64*MULT_FACTOR
# image channels
N_CHANNELS = 3
# SAMPLE_SIZE is the total number of images in row x column form...
# if SAMPLE_SIZE = 64, then 8x8 image grids will be saved to disk...
# if SAMPLE_SIZE = 128, then 16x8 image grids will be saved to disk...
SAMPLE_SIZE = 64
# latent vector size
NZ = 100
# number of steps to apply to the discriminator
K = 1
DEVICE = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
# one of 'MNIST', 'FashionMNIST', 'CIFAR10', 'CELEBA', ...
# ... 'ABSTRACT_ART'
DATASET = 'CELEBA'
# for printing metrics
PRINT_EVERY = 100
# for optimizer
BETA1 = 0.5
BETA2 = 0.999
LEARNING_RATE = 0.0002
# Epcoh nterval at which to save the Generator Model.
MODEL_SAVE_INTERVAL = 25
# Provide path to a trained model to resume training, else keep `None`.
# GEN_MODEL_PATH = 'outputs_MNIST/generator_final.pth'
# DISC_MODEL_PATH = 'outputs_MNIST/discriminator_final.pth'
GEN_MODEL_PATH = None
DISC_MODEL_PATH = None
# Whether to create GIF from all the generated images at the end or not,
# might need a considerable amoung of RAM as all the generated images will
# be loaded to at once. Give values as either `True` or `False`.
CREATE_GIF = False
```