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train.py
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import argparse
import os
import time
from shutil import copyfile
import numpy as np
import torch
import torch.optim as optim
from datasets import PairDataset, denorm
from models import FUnIEDiscriminator, FUnIEGeneratorV1, FUnIEGeneratorV2, TotalGenLoss
from torch.utils.tensorboard import SummaryWriter
from torchvision.utils import make_grid, save_image
from utils import AverageMeter, ProgressMeter
class Trainer(object):
def __init__(self, arch, train_loader, valid_loader, lr, epochs, gen_resume, dis_resume, save_path, is_cuda):
self.train_loader = train_loader
self.valid_loader = valid_loader
self.start_epoch = 0
self.epochs = epochs
self.save_path = save_path
os.makedirs(f"{self.save_path}/viz", exist_ok=True)
self.writer = SummaryWriter(log_dir=self.save_path)
self.is_cuda = is_cuda
self.print_freq = 20
self.best_gen_loss = 1e6
self.gen = {"v1": FUnIEGeneratorV1, "v2": FUnIEGeneratorV2}[arch]()
self.dis = FUnIEDiscriminator()
if gen_resume and dis_resume:
self.load(gen_resume, dis_resume)
if self.is_cuda:
self.gen.cuda()
self.dis.cuda()
self.dis_criterion = torch.nn.MSELoss()
self.gen_criterion = TotalGenLoss(self.is_cuda)
self.dis_optimizer = optim.Adam(
filter(lambda p: p.requires_grad, self.dis.parameters()), lr)
self.gen_optimizer = optim.Adam(
filter(lambda p: p.requires_grad, self.gen.parameters()), lr)
def train(self):
for e in range(self.start_epoch, self.epochs):
self.epoch = e
_, _ = self.train_epoch()
valid_gen_loss, _ = self.validate()
# Save models
self.save(valid_gen_loss)
self.writer.close()
def train_epoch(self):
self.gen.train()
self.dis.train()
batch_time = AverageMeter("Time", "3.3f")
gen_losses = AverageMeter("Generator Loss")
dis_losses = AverageMeter("Discriminator Loss")
progress = ProgressMeter(len(self.train_loader), [
batch_time, gen_losses, dis_losses], prefix="Train: ")
end = time.time()
for batch_idx, (dstd_images, ehcd_images) in enumerate(self.train_loader):
bs = dstd_images.size(0)
valid = torch.ones((bs, 16, 16))
fake = torch.zeros((bs, 16, 16))
if self.is_cuda:
dstd_images = dstd_images.cuda()
ehcd_images = ehcd_images.cuda()
valid = valid.cuda()
fake = fake.cuda()
# -----
# Train the discriminator
# -----
fake_images = self.gen(dstd_images)
real_outputs = self.dis(ehcd_images, dstd_images)
real_d_loss = self.dis_criterion(real_outputs, valid)
fake_outputs = self.dis(fake_images, dstd_images)
fake_d_loss = self.dis_criterion(fake_outputs, fake)
d_loss = (real_d_loss + fake_d_loss) / 2
self.dis_optimizer.zero_grad()
d_loss.backward()
self.dis_optimizer.step()
# -----
# Train the generator
# -----
fake_images = self.gen(dstd_images)
g_loss = self.dis_criterion(fake_images, ehcd_images)
# Total loss
total_gen_loss = 0.2 * g_loss + 0.8 * \
self.gen_criterion(ehcd_images, fake_images)
self.gen_optimizer.zero_grad()
total_gen_loss.backward()
self.gen_optimizer.step()
# Update
dis_losses.update(d_loss.item(), bs)
gen_losses.update(total_gen_loss.item(), bs)
batch_time.update(time.time() - end)
end = time.time()
if batch_idx % self.print_freq == 0:
progress.display(batch_idx)
# Write stats to tensorboard
self.writer.add_scalar("Generator Loss/Train",
gen_losses.avg, self.epoch)
self.writer.add_scalar("Discriminator Loss/Train",
dis_losses.avg, self.epoch)
return gen_losses.avg, dis_losses.avg
def validate(self):
self.gen.eval()
self.dis.eval()
batch_time = AverageMeter("Time", "3.3f")
gen_losses = AverageMeter("Generator Loss")
dis_losses = AverageMeter("Discriminator Loss")
progress = ProgressMeter(len(self.valid_loader), [
batch_time, gen_losses, dis_losses], prefix="Valid: ")
with torch.no_grad():
end = time.time()
for batch_idx, (dstd_images, ehcd_images) in enumerate(self.valid_loader):
bs = dstd_images.size(0)
valid = torch.ones((bs, 16, 16))
fake = torch.zeros((bs, 16, 16))
if self.is_cuda:
dstd_images = dstd_images.cuda()
ehcd_images = ehcd_images.cuda()
valid = valid.cuda()
fake = fake.cuda()
# -----
# Validate the discriminator
# -----
fake_images = self.gen(dstd_images)
real_outputs = self.dis(ehcd_images, dstd_images)
real_d_loss = self.dis_criterion(real_outputs, valid)
fake_outputs = self.dis(fake_images, dstd_images)
fake_d_loss = self.dis_criterion(fake_outputs, fake)
d_loss = (real_d_loss + fake_d_loss) / 2
# -----
# Validate the generator
# -----
g_loss = self.dis_criterion(fake_images, ehcd_images)
# Total loss
total_gen_loss = 0.2 * g_loss + 0.8 * \
self.gen_criterion(ehcd_images, fake_images)
# Update
dis_losses.update(d_loss.item(), bs)
gen_losses.update(total_gen_loss.item(), bs)
batch_time.update(time.time() - end)
end = time.time()
# Vis
if batch_idx == 0:
fake_grid = denorm(make_grid(fake_images.data)).div_(255.)
ehcd_grid = denorm(make_grid(ehcd_images.data)).div_(255.)
dstd_grid = denorm(make_grid(dstd_images.data)).div_(255.)
save_image(
fake_grid, f"{self.save_path}/viz/fake_{self.epoch}.png")
save_image(
ehcd_grid, f"{self.save_path}/viz/ehcd_{self.epoch}.png")
save_image(
dstd_grid, f"{self.save_path}/viz/dstd_{self.epoch}.png")
self.writer.add_image("Viz/Fake", fake_grid, self.epoch)
self.writer.add_image("Viz/Enhance", ehcd_grid, self.epoch)
self.writer.add_image("Viz/Distort", dstd_grid, self.epoch)
if batch_idx % self.print_freq == 0:
progress.display(batch_idx)
# Write stats to tensorboard
self.writer.add_scalar("Generator Loss/Validation",
gen_losses.avg, self.epoch)
self.writer.add_scalar("Discriminator Loss/Validation",
dis_losses.avg, self.epoch)
return gen_losses.avg, dis_losses.avg
def save(self, loss):
# Check if the current model is the best
is_best = loss < self.best_gen_loss
self.best_gen_loss = min(self.best_gen_loss, loss)
# Prepare model info to be saved
model_content = {"best_loss": loss, "epoch": self.epoch}
gen_path = f"{self.save_path}/{self.epoch}_gen.pth.tar"
dis_path = f"{self.save_path}/{self.epoch}_dis.pth.tar"
# Save generator and discriminator
model_content["state_dict"] = self.gen.state_dict()
torch.save(model_content, gen_path)
print(f">>> Save generator to {gen_path}")
model_content["state_dict"] = self.dis.state_dict()
torch.save(model_content, dis_path)
print(f">>> Save discriminator to {dis_path}")
if is_best:
copyfile(gen_path, f"{self.save_path}/best_gen.pth.tar")
copyfile(dis_path, f"{self.save_path}/best_dis.pth.tar")
def load(self, gen_resume, dis_resume):
device = "cuda:0" if self.is_cuda else "cpu"
gen_ckpt = torch.load(gen_resume, map_location=device)
dis_ckpt = torch.load(dis_resume, map_location=device)
assert gen_ckpt["epoch"] == dis_ckpt["epoch"]
self.gen.load_state_dict(gen_ckpt["state_dict"])
self.dis.load_state_dict(dis_ckpt["state_dict"])
self.best_gen_loss = gen_ckpt["best_loss"]
self.start_epoch = gen_ckpt["epoch"]
print(f"At epoch: {self.start_epoch}")
print(f">>> Load generator from {gen_resume}")
print(f">>> Load discriminator from {dis_resume}")
self.start_epoch += 1
if __name__ == "__main__":
# Set seed
np.random.seed(77)
torch.manual_seed(77)
is_cuda = torch.cuda.is_available()
if is_cuda:
torch.cuda.manual_seed(77)
model_names = ["v1", "v2"]
parser = argparse.ArgumentParser(description="PyTorch FUnIE-GAN Training")
parser.add_argument("-d", "--data", default="", type=str, metavar="PATH",
help="path to data (default: none)")
parser.add_argument("-a", "--arch", metavar="ARCH", default="v1",
choices=model_names,
help="model architecture: " +
" | ".join(model_names) +
" (default: v1)")
parser.add_argument("-j", "--workers", default=4, type=int, metavar="N",
help="number of data loading workers (default: 4)")
parser.add_argument("--epochs", default=90, type=int, metavar="N",
help="number of total epochs to run")
parser.add_argument("-b", "--batch-size", default=256, type=int,
metavar="N",
help="mini-batch size (default: 256), this is the total "
"batch size of all GPUs on the current node when "
"using Data Parallel or Distributed Data Parallel")
parser.add_argument("--lr", "--learning-rate", default=0.1, type=float,
metavar="LR", help="initial learning rate")
parser.add_argument("--gen-resume", default="", type=str, metavar="PATH",
help="path to latest generator checkpoint (default: none)")
parser.add_argument("--dis-resume", default="", type=str, metavar="PATH",
help="path to latest discriminator checkpoint (default: none)")
parser.add_argument("--save-path", default="", type=str, metavar="PATH",
help="path to save results (default: none)")
args = parser.parse_args()
# Build data loaders
train_set = PairDataset(args.data, (256, 256), "train")
valid_set = PairDataset(args.data, (256, 256), "valid")
train_loader = torch.utils.data.DataLoader(
train_set, batch_size=args.batch_size, shuffle=True, num_workers=args.workers)
valid_loader = torch.utils.data.DataLoader(
valid_set, batch_size=args.batch_size, shuffle=False, num_workers=args.workers)
# Create trainer
trainer = Trainer(args.arch, train_loader, valid_loader, args.lr, args.epochs,
args.gen_resume, args.dis_resume, args.save_path, is_cuda)
trainer.train()