DCGAN-huggingface

An implementation of DCGAN, leveraging the HuggingFace ecosystem for processing data and pushing the model to the hub.

To train the model with the default parameters (5 epochs, 64x64 images, etc.) on MNIST, simply do:

python train.py

This will create a local "images" directory, containing generated images over the course of the training.

To train on another dataset available on the hub, simply do:

python train.py --dataset cifar-10

Training on your own data

You can of course also train on your own images. For this, one can leverage Datasets' ImageFolder. Make sure to authenticate with the hub first, by running the huggingface-cli login command in a terminal, or the following in case you're working in a notebook:

from huggingface_hub import notebook_login

notebook_login()

Next, run the following in a notebook/script:

from datasets import load_dataset

# first: load dataset
# option 1: from local folder
dataset = load_dataset("imagefolder", data_dir="path_to_folder")
# option 2: from remote URL (e.g. a zip file)
dataset = load_dataset("imagefolder", data_files="URL to .zip file")

# next: push to the hub (assuming git-LFS is installed)
dataset.push_to_hub("huggan/my-awesome-dataset")

You can then simply pass the name of the dataset to the script:

python train.py --dataset huggan/my-awesome-dataset

Pushing model to the hub

You can push your trained generator to the hub after training by specifying the push_to_hub flag. Then, you can run the script as follows:

python train.py --push_to_hub --model_name dcgan-mnist

This is made possible by making the generator inherit from PyTorchModelHubMixinavailable in the huggingface_hub library.

This means that after training, generating a new image can be done as follows:

import torch
import torch.nn as nn
from huggingface_hub import PyTorchModelHubMixin

class Generator(nn.Module, PyTorchModelHubMixin):
    def __init__(self, num_channels=3, latent_dim=100, hidden_size=64):
        super(Generator, self).__init__()
        self.model = nn.Sequential(
            # input is Z, going into a convolution
            nn.ConvTranspose2d(latent_dim, hidden_size * 8, 4, 1, 0, bias=False),
            nn.BatchNorm2d(hidden_size * 8),
            nn.ReLU(True),
            # state size. (hidden_size*8) x 4 x 4
            nn.ConvTranspose2d(hidden_size * 8, hidden_size * 4, 4, 2, 1, bias=False),
            nn.BatchNorm2d(hidden_size * 4),
            nn.ReLU(True),
            # state size. (hidden_size*4) x 8 x 8
            nn.ConvTranspose2d(hidden_size * 4, hidden_size * 2, 4, 2, 1, bias=False),
            nn.BatchNorm2d(hidden_size * 2),
            nn.ReLU(True),
            # state size. (hidden_size*2) x 16 x 16
            nn.ConvTranspose2d(hidden_size * 2, hidden_size, 4, 2, 1, bias=False),
            nn.BatchNorm2d(hidden_size),
            nn.ReLU(True),
            # state size. (hidden_size) x 32 x 32
            nn.ConvTranspose2d(hidden_size, num_channels, 4, 2, 1, bias=False),
            nn.Tanh()
            # state size. (num_channels) x 64 x 64
        )

    def forward(self, noise):
        pixel_values = self.model(noise)

        return pixel_values

model = Generator.from_pretrained("huggan/dcgan-mnist")

device = "cuda" if torch.cuda.is_available() else "cpu
model.to(device)
 
with torch.no_grad():
    z = torch.randn(1, 100, 1, 1, device=device)
    outputs = model(z)

Citation

This repo is entirely based on PyTorch's official DCGAN tutorial, but with added HuggingFace goodies.

nielsrogge / dcgan-huggingface Goto Github PK