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sngan_example.py
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sngan_example.py
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"""
Typical usage example.
"""
import torch
import torch.optim as optim
import torch_mimicry as mmc
from torch_mimicry.nets import sngan
if __name__ == "__main__":
# 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=30,
lr_decay='linear',
dataloader=dataloader,
log_dir='./log/example',
device=device)
trainer.train()
# Evaluate fid
mmc.metrics.evaluate(metric='fid',
log_dir='./log/example',
netG=netG,
dataset='cifar10',
num_real_samples=50000,
num_fake_samples=50000,
evaluate_step=30,
device=device)
# Evaluate kid
mmc.metrics.evaluate(metric='kid',
log_dir='./log/example',
netG=netG,
dataset='cifar10',
num_samples=50000,
evaluate_step=30,
device=device)
# Evaluate inception score
mmc.metrics.evaluate(metric='inception_score',
log_dir='./log/example',
netG=netG,
num_samples=50000,
evaluate_step=30,
device=device)