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🦋A PyTorch implementation of BigGAN with pretrained weights and conversion scripts.
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# PyTorch pretrained BigGAN
An op-for-op PyTorch reimplementation of DeepMind's BigGAN model with the pre-trained weights from DeepMind.
## Introduction
This repository contains an op-for-op PyTorch reimplementation of DeepMind's BigGAN that was released with the paper [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://openreview.net/forum?id=B1xsqj09Fm) by Andrew Brock, Jeff Donahue and Karen Simonyan.
This PyTorch implementation of BigGAN is provided with the [pretrained 128x128, 256x256 and 512x512 models by DeepMind](https://tfhub.dev/deepmind/biggan-deep-128/1). We also provide the scripts used to download and convert these models from the TensorFlow Hub models.
This reimplementation was done from the raw computation graph of the Tensorflow version and behave similarly to the TensorFlow version (variance of the output difference of the order of 1e-5).
This implementation currently only contains the generator as the weights of the discriminator were not released (although the structure of the discriminator is very similar to the generator so it could be added pretty easily. Tell me if you want to do a PR on that, I would be happy to help.)
## Installation
This repo was tested on Python 3.6 and PyTorch 1.0.1
PyTorch pretrained BigGAN can be installed from pip as follows:
```bash
pip install pytorch-pretrained-biggan
```
If you simply want to play with the GAN this should be enough.
If you want to use the conversion scripts and the imagenet utilities, additional requirements are needed, in particular TensorFlow and NLTK. To install all the requirements please use the `full_requirements.txt` file:
```bash
git clone https://github.com/huggingface/pytorch-pretrained-BigGAN.git
cd pytorch-pretrained-BigGAN
pip install -r full_requirements.txt
```
## Models
This repository provide direct and simple access to the pretrained "deep" versions of BigGAN for 128, 256 and 512 pixels resolutions as described in the [associated publication](https://openreview.net/forum?id=B1xsqj09Fm).
Here are some details on the models:
- `BigGAN-deep-128`: a 50.4M parameters model generating 128x128 pixels images, the model dump weights 201 MB,
- `BigGAN-deep-256`: a 55.9M parameters model generating 256x256 pixels images, the model dump weights 224 MB,
- `BigGAN-deep-512`: a 56.2M parameters model generating 512x512 pixels images, the model dump weights 225 MB.
Please refer to Appendix B of the paper for details on the architectures.
All models comprise pre-computed batch norm statistics for 51 truncation values between 0 and 1 (see Appendix C.1 in the paper for details).
## Usage
Here is a quick-start example using `BigGAN` with a pre-trained model.
See the [doc section](#doc) below for details on these classes and methods.
```python
import torch
from pytorch_pretrained_biggan import (BigGAN, one_hot_from_names, truncated_noise_sample,
save_as_images, display_in_terminal)
# OPTIONAL: if you want to have more information on what's happening, activate the logger as follows
import logging
logging.basicConfig(level=logging.INFO)
# Load pre-trained model tokenizer (vocabulary)
model = BigGAN.from_pretrained('biggan-deep-256')
# Prepare a input
truncation = 0.4
class_vector = one_hot_from_names(['soap bubble', 'coffee', 'mushroom'], batch_size=3)
noise_vector = truncated_noise_sample(truncation=truncation, batch_size=3)
# All in tensors
noise_vector = torch.from_numpy(noise_vector)
class_vector = torch.from_numpy(class_vector)
# If you have a GPU, put everything on cuda
noise_vector = noise_vector.to('cuda')
class_vector = class_vector.to('cuda')
model.to('cuda')
# Generate an image
with torch.no_grad():
output = model(noise_vector, class_vector, truncation)
# If you have a GPU put back on CPU
output = output.to('cpu')
# If you have a sixtel compatible terminal you can display the images in the terminal
# (see https://github.com/saitoha/libsixel for details)
dispExcerpt of 10,842 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:0df368d6145dd453, topic:neural-network, topic:pytorch
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matched fp:0df368d6145dd453, topic:gan
