import argparse
import os
import PIL
import torch
import torchvision
import tqdm
from torch.utils import tensorboard
import torch_fidelity
class Generator(torch.nn.Module):
# Adapted from https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
def __init__(self, z_size):
super(Generator, self).__init__()
self.z_size = z_size
self.model = torch.nn.Sequential(
torch.nn.ConvTranspose2d(z_size, 512, 4, stride=1),
torch.nn.BatchNorm2d(512),
torch.nn.ReLU(),
torch.nn.ConvTranspose2d(512, 256, 4, stride=2, padding=(1, 1)),
torch.nn.BatchNorm2d(256),
torch.nn.ReLU(),
torch.nn.ConvTranspose2d(256, 128, 4, stride=2, padding=(1, 1)),
torch.nn.BatchNorm2d(128),
torch.nn.ReLU(),
torch.nn.ConvTranspose2d(128, 64, 4, stride=2, padding=(1, 1)),
torch.nn.BatchNorm2d(64),
torch.nn.ReLU(),
torch.nn.ConvTranspose2d(64, 3, 3, stride=1, padding=(1, 1)),
torch.nn.Tanh(),
)
def forward(self, z):
fake = self.model(z.view(-1, self.z_size, 1, 1))
if not self.training:
fake = 255 * (fake.clamp(-1, 1) * 0.5 + 0.5)
fake = fake.to(torch.uint8)
return fake
class Discriminator(torch.nn.Module):
# Adapted from https://github.com/christiancosgrove/pytorch-spectral-normalization-gan
def __init__(self, sn=True):
super(Discriminator, self).__init__()
sn_fn = torch.nn.utils.spectral_norm if sn else lambda x: x
self.conv1 = sn_fn(torch.nn.Conv2d(3, 64, 3, stride=1, padding=(1, 1)))
self.conv2 = sn_fn(torch.nn.Conv2d(64, 64, 4, stride=2, padding=(1, 1)))
self.conv3 = sn_fn(torch.nn.Conv2d(64, 128, 3, stride=1, padding=(1, 1)))
self.conv4 = sn_fn(torch.nn.Conv2d(128, 128, 4, stride=2, padding=(1, 1)))
self.conv5 = sn_fn(torch.nn.Conv2d(128, 256, 3, stride=1, padding=(1, 1)))
self.conv6 = sn_fn(torch.nn.Conv2d(256, 256, 4, stride=2, padding=(1, 1)))
self.conv7 = sn_fn(torch.nn.Conv2d(256, 512, 3, stride=1, padding=(1, 1)))
self.fc = sn_fn(torch.nn.Linear(4 * 4 * 512, 1))
self.act = torch.nn.LeakyReLU(0.1)
def forward(self, x):
m = self.act(self.conv1(x))
m = self.act(self.conv2(m))
m = self.act(self.conv3(m))
m = self.act(self.conv4(m))
m = self.act(self.conv5(m))
m = self.act(self.conv6(m))
m = self.act(self.conv7(m))
return self.fc(m.view(-1, 4 * 4 * 512))
def hinge_loss_dis(fake, real):
assert fake.dim() == 2 and fake.shape[1] == 1 and real.shape == fake.shape, f"{fake.shape} {real.shape}"
loss = torch.nn.functional.relu(1.0 - real).mean() + \
torch.nn.functional.relu(1.0 + fake).mean() # fmt: skip
return loss
def hinge_loss_gen(fake):
assert fake.dim() == 2 and fake.shape[1] == 1
loss = -fake.mean()
return loss
def train(args):
# set up dataset loader
os.makedirs(args.dir_dataset, exist_ok=True)
ds_transform = torchvision.transforms.Compose(
[torchvision.transforms.ToTensor(), torchvision.transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]
)
ds_instance = torchvision.datasets.CIFAR10(args.dir_dataset, train=True, download=True, transform=ds_transform)
loader = torch.utils.data.DataLoader(
ds_instance, batch_size=args.batch_size, drop_last=True, shuffle=True, num_workers=4, pin_memory=True
)
loader_iter = iter(loader)
# reinterpret command line inputs
device = "cuda" if torch.cuda.is_available() else "cpu"
num_classes = 10 if args.conditional else 0 # unconditional
leading_metric, last_best_metric, metric_greater_cmp = {
"ISC": (torch_fidelity.KEY_METRIC_ISC_MEAN, 0.0, float.__gt__),
"FID": (torch_fidelity.KEY_METRIC_FID, float("inf"), float.__lt__),
"KID": (torch_fidelity.KEY_METRIC_KID_MEAN, float("inf"), float.__lt__),
"PPL": (torch_fidelity.KEY_METRIC_PPL_MEAN, float("inf"), float.__lt__),
"PRC": (torch_fidelity.KEY_METRIC_PRECISION, 0.0, float.__gt__),
}[args.leading_metric]
# create Generator and Discriminator models
G = Generator(args.z_size).to(device).train()
D = Discriminator(not args.disable_sn).to(device).train()
# initialize persistent noise for observed samples
z_vis = torch.randn(64, args.z_size, device=device)
# prepare optimizer and learning rate schedulers (linear decay)
optim_G = torch.optim.Adam(G.parameters(), lr=args.lr, betas=(0.0, 0.9))
optim_D = torch.optim.Adam(D.parameters(), lr=args.lr, betas=(0.0, 0.9))
scheduler_G = torch.optim.lr_scheduler.LambdaLR(optim_G, lambda step: 1.0 - step / args.num_total_steps)
scheduler_D = torch.optim.lr_scheduler.LambdaLR(optim_D, lambda step: 1.0 - step / args.num_total_steps)
# initialize logging
tb = tensorboard.SummaryWriter(log_dir=args.dir_logs)
pbar = tqdm.tqdm(total=args.num_total_steps, desc="Training", unit="batch")
os.makedirs(args.dir_logs, exist_ok=True)
for step in range(args.num_total_steps):
# read next batch
try:
real_img, real_label = next(loader_iter)
except StopIteration:
loader_iter = iter(loader)
real_img, real_label = next(loader_iter)
real_img = real_img.to(device)
real_label = real_label.to(device)
# update Generator
G.requires_grad_(True)
D.requires_grad_(False)
z = torch.randn(args.batch_size, args.z_size, device=device)
optim_D.zero_grad()
optim_G.zero_grad()
fake = G(z)
loss_G = hinge_loss_gen(D(fake))
loss_G.backward()
optim_G.step()
# update Discriminator
G.requires_grad_(False)
D.requires_grad_(True)
for d_iter in range(args.num_dis_updates):
z = torch.randn(args.batch_size, args.z_size, device=device)
optim_D.zero_grad()
optim_G.zero_grad()
fake = G(z)
loss_D = hinge_loss_dis(D(fake), D(real_img))
loss_D.backward()
optim_D.step()
# log
if (step + 1) % 10 == 0:
step_info = {"loss_G": loss_G.cpu().item(), "loss_D": loss_D.cpu().item()}
pbar.set_postfix(step_info)
for k, v in step_info.items():
tb.add_scalar(f"loss/{k}", v, global_step=step)
tb.add_scalar(f"LR/lr", scheduler_G.get_last_lr()[0], global_step=step)
pbar.update(1)
# decay LR
scheduler_G.step()
scheduler_D.step()
# check if it is validation time
next_step = step + 1
if next_step % args.num_epoch_steps != 0:
continue
pbar.close()
G.eval()
print("Evaluating the generator...")
# compute and log generative metrics
metrics = torch_fidelity.calculate_metrics(
input1=torch_fidelity.GenerativeModelModuleWrapper(G, args.z_size, args.z_type, num_classes),
input1_model_num_samples=args.num_samples_for_metrics,
input2="cifar10-train",
isc=True,
fid=True,
kid=True,
ppl=True,
ppl_epsilon=1e-2,
ppl_sample_similarity_resize=64,
prc=True,
)
# log metrics
for k, v in metrics.items():
tb.add_scalar(f"metrics/{k}", v, global_step=next_step)
# log observed images
samples_vis = G(z_vis).detach().cpu()
samples_vis = torchvision.utils.make_grid(samples_vis).permute(1, 2, 0).numpy()
tb.add_image("observations", samples_vis, global_step=next_step, dataformats="HWC")
samples_vis = PIL.Image.fromarray(samples_vis)
samples_vis.save(os.path.join(args.dir_logs, f"{next_step:06d}.png"))
# save the generator if it improved
if metric_greater_cmp(metrics[leading_metric], last_best_metric):
print(f"Leading metric {args.leading_metric} improved from {last_best_metric} to {metrics[leading_metric]}")
last_best_metric = metrics[leading_metric]
dummy_input = torch.zeros(1, args.z_size, device=device)
torch.jit.save(torch.jit.trace(G, (dummy_input,)), os.path.join(args.dir_logs, "generator.pth"))
torch.onnx.export(
G,
dummy_input,
os.path.join(args.dir_logs, "generator.onnx"),
opset_version=11,
input_names=["z"],
output_names=["rgb"],
dynamic_axes={"z": {0: "batch"}, "rgb": {0: "batch"}},
)
# resume training
if next_step <= args.num_total_steps:
pbar = tqdm.tqdm(total=args.num_total_steps, initial=next_step, desc="Training", unit="batch")
G.train()
tb.close()
print(
f"Training finished; the model with best {args.leading_metric} value ({last_best_metric}) is saved as "
f"{args.dir_logs}/generator.onnx and {args.dir_logs}/generator.pth"
)
def main():
dir = os.getcwd()
parser = argparse.ArgumentParser()
parser.add_argument("--batch_size", type=int, default=64)
parser.add_argument("--num_total_steps", type=int, default=100000)
parser.add_argument("--num_epoch_steps", type=int, default=5000)
parser.add_argument("--num_dis_updates", type=int, default=5)
parser.add_argument("--num_samples_for_metrics", type=int, default=50000)
parser.add_argument("--lr", type=float, default=2e-4)
parser.add_argument("--z_size", type=int, default=128, choices=(128,))
parser.add_argument("--z_type", type=str, default="normal")
parser.add_argument("--leading_metric", type=str, default="ISC", choices=("ISC", "FID", "KID", "PPL", "PRC"))
parser.add_argument("--disable_sn", default=False, action="store_true")
parser.add_argument("--conditional", default=False, action="store_true")
parser.add_argument("--dir_dataset", type=str, default=os.path.join(dir, "dataset"))
parser.add_argument("--dir_logs", type=str, default=os.path.join(dir, "logs"))
args = parser.parse_args()
print("Configuration:\n" + ("\n".join([f"{k:>25}: {v}" for k, v in args.__dict__.items()])))
assert not args.conditional, "Conditional mode not implemented"
train(args)
if __name__ == "__main__":
main()