Introduction¶
Installation¶
For best performance, we recommend you to install the GPU versions of both TensorFlow and PyTorch, which are used in this library.
First create a new environment with conda
using Python 3.6 or 3.7 (Python 3.8 is currently not supported by TensorFlow):
$ conda create -n mimicry python=3.6
Install TensorFlow (GPU):
$ conda install -c anaconda tensorflow-gpu
Install PyTorch (GPU):
$ conda install pytorch torchvision cudatoolkit=9.2 -c pytorch
For installing the CUDA version matching your drivers, see the official instructions.
Mimicry can be installed using pip
directly:
$ pip install torch-mimicry
Quick Start¶
We provide a sample training script for training the Spectral Normalization GAN on the CIFAR-10 dataset, with the same training hyperparameters that reproduce results in the paper.
import torch
import torch.optim as optim
import torch_mimicry as mmc
from torch_mimicry.nets import sngan
# Data handling objects
device = torch.device('cuda:0' if torch.cuda.is_available() else "cpu")
dataset = mmc.datasets.load_dataset(root='./datasets', name='cifar10')
dataloader = torch.utils.data.DataLoader(
dataset, batch_size=64, shuffle=True, num_workers=4)
# Define models and optimizers
netG = sngan.SNGANGenerator32().to(device)
netD = sngan.SNGANDiscriminator32().to(device)
optD = optim.Adam(netD.parameters(), 2e-4, betas=(0.0, 0.9))
optG = optim.Adam(netG.parameters(), 2e-4, betas=(0.0, 0.9))
# Start training
trainer = mmc.training.Trainer(
netD=netD,
netG=netG,
optD=optD,
optG=optG,
n_dis=5,
num_steps=100000,
lr_decay='linear',
dataloader=dataloader,
log_dir='./log/example',
device=device)
trainer.train()
To evaluate its FID, we can simply run the following:
# Evaluate fid
mmc.metrics.evaluate(
metric='fid',
log_dir='./log/example',
netG=netG,
dataset_name='cifar10',
num_real_samples=50000,
num_fake_samples=50000,
evaluate_step=100000,
device=device)
Alternatively, one could evaluate FID progressively over an interval by swapping the evaluate_step argument for evaluate_range:
# Evaluate fid
mmc.metrics.evaluate(
metric='fid',
log_dir='./log/example',
netG=netG,
dataset_name='cifar10',
num_real_samples=50000,
num_fake_samples=50000,
evaluate_range=(5000, 100000, 5000),
device=device)
We support other datasets and models See datasets and nets for more information.
Visualizations¶
Mimicry provides TensorBoard support for visualizing the following:
- Loss and probability curves for monitoring GAN training
- Randomly generated images for checking diversity.
- Generated images from a fixed set of noise vectors.
$ tensorboard --logdir=./log/example