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| import torch
import dataset
import os
import argparse
from torch.utils.data import DataLoader
import models
import time
import matplotlib.pyplot as plt
import loss
class Trainer:
record = {"train_loss_d": [], "train_loss_g": [], "train_psnr": [], "val_loss": [], "val_psnr": []}
x_epoch = []
def __init__(self, args):
self.args = args
self.device = self.args.device
self.gnet = models.Generator()
self.dnet = models.Discriminator()
batch = self.args.batch
self.train_loader = DataLoader(dataset.SRGANDataset(self.args.data_path, "train"),
batch_size=batch, shuffle=True, drop_last=True)
self.val_loader = DataLoader(dataset.SRGANDataset(self.args.data_path, "val"),
batch_size=batch, shuffle=False, drop_last=True)
self.criterion_g = loss.PerceptualLoss(self.device)
self.regularization = loss.RegularizationLoss()
self.criterion_d = torch.nn.BCELoss()
self.epoch = 0
self.lr = 1e-3
self.best_psnr = 0.
if self.args.resume:
if not os.path.exists(self.args.save_path):
print("No params, start training...")
else:
param_dict = torch.load(self.args.save_path)
self.epoch = param_dict["epoch"]
self.lr = param_dict["lr"]
self.dnet.load_state_dict(param_dict["dnet_dict"])
self.gnet.load_state_dict(param_dict["gnet_dict"])
self.best_psnr = param_dict["best_psnr"]
print("Loaded params from {}\n[Epoch]: {} [lr]: {} [best_psnr]: {}".format(self.args.save_path,
self.epoch, self.lr,
self.best_psnr))
self.dnet.to(self.device)
self.gnet.to(self.device)
self.optimizer_d = torch.optim.Adam(self.dnet.parameters(), lr=self.lr)
self.optimizer_g = torch.optim.Adam(self.gnet.parameters(), lr=self.lr*0.1)
self.real_label = torch.ones([batch, 1, 1, 1]).to(self.device)
self.fake_label = torch.zeros([batch, 1, 1, 1]).to(self.device)
@staticmethod
def calculate_psnr(img1, img2):
return 10. * torch.log10(1. / torch.mean((img1 - img2) ** 2))
def train(self, epoch):
self.dnet.train()
self.gnet.train()
train_loss_d = 0.
train_loss_g = 0.
train_loss_all_d = 0.
train_loss_all_g = 0.
psnr = 0.
total = 0
start = time.time()
print("Start epoch: {}".format(epoch))
for i, (img, label) in enumerate(self.train_loader):
img = img.to(self.device)
label = label.to(self.device)
fake_img = self.gnet(img)
loss_g = self.criterion_g(fake_img, label, self.dnet(fake_img)) + 2e-8*self.regularization(fake_img)
self.optimizer_g.zero_grad()
loss_g.backward()
self.optimizer_g.step()
if i % 2 == 0:
real_out = self.dnet(label)
fake_out = self.dnet(fake_img.detach())
loss_d = self.criterion_d(real_out, self.real_label
) + self.criterion_d(fake_out, self.fake_label)
self.optimizer_d.zero_grad()
loss_d.backward()
self.optimizer_d.step()
train_loss_d += loss_d.item()
train_loss_all_d += loss_d.item()
train_loss_g += loss_g.item()
train_loss_all_g += loss_g.item()
psnr += self.calculate_psnr(fake_img, label).item()
total += 1
if (i+1) % self.args.interval == 0:
end = time.time()
print("[Epoch]: {}[Progress: {:.1f}%]time:{:.2f} dnet_loss:{:.5f} gnet_loss:{:.5f} psnr:{:.4f}".format(
epoch, (i+1)*100/len(self.train_loader), end-start,
train_loss_d/self.args.interval,
train_loss_g/self.args.interval, psnr/total
))
train_loss_d = 0.
train_loss_g = 0.
print("Save params to {}".format(self.args.save_path1))
param_dict = {
"epoch": epoch,
"lr": self.lr,
"best_psnr": self.best_psnr,
"dnet_dict": self.dnet.state_dict(),
"gnet_dict": self.gnet.state_dict()
}
torch.save(param_dict, self.args.save_path)
return train_loss_all_d/len(self.train_loader), train_loss_all_g/len(self.train_loader), psnr/total
def val(self, epoch):
self.gnet.eval()
self.dnet.eval()
print("Test start...")
val_loss = 0.
psnr = 0.
total = 0
start = time.time()
with torch.no_grad():
for i, (img, label) in enumerate(self.train_loader):
img = img.to(self.device)
label = label.to(self.device)
fake_img = self.gnet(img).clamp(0.0, 1.0)
loss = self.criterion_g(fake_img, label, self.dnet(fake_img))
val_loss += loss.item()
psnr += self.calculate_psnr(fake_img, label).item()
total += 1
mpsnr = psnr / total
end = time.time()
print("Test finished!")
print("[Epoch]: {} time:{:.2f} loss:{:.5f} psnr:{:.4f}".format(
epoch, end - start, val_loss / len(self.val_loader), mpsnr
))
if mpsnr > self.best_psnr:
self.best_psnr = mpsnr
print("Save params to {}".format(self.args.save_path))
param_dict = {
"epoch": epoch,
"lr": self.lr,
"best_psnr": self.best_psnr,
"gnet_dict": self.gnet.state_dict(),
"dnet_dict": self.dnet.state_dict()
}
torch.save(param_dict, self.args.save_path1)
return val_loss/len(self.val_loader), mpsnr
def draw_curve(self, fig, epoch, train_loss_d, train_loss_g, train_psnr, val_loss, val_psnr):
ax0 = fig.add_subplot(121, title="loss")
ax1 = fig.add_subplot(122, title="psnr")
self.record["train_loss_d"].append(train_loss_d)
self.record["train_loss_g"].append(train_loss_g)
self.record["train_psnr"].append(train_psnr)
self.record["val_loss"].append(val_loss)
self.record["val_psnr"].append(val_psnr)
self.x_epoch.append(epoch)
ax0.plot(self.x_epoch, self.record["train_loss_d"], "bo-", label="train_d")
ax0.plot(self.x_epoch, self.record["train_loss_g"], "go-", label="train_g")
ax0.plot(self.x_epoch, self.record["val_loss"], "ro-", label="val_g")
ax1.plot(self.x_epoch, self.record["train_psnr"], "bo-", label="train")
ax1.plot(self.x_epoch, self.record["val_psnr"], "ro-", label="val")
if epoch == 0:
ax0.legend()
ax1.legend()
fig.savefig(r"./train_fig/train_{}.jpg".format(epoch))
def lr_update(self):
for param_group in self.optimizer_d.param_groups:
param_group['lr'] = self.lr * 0.1
self.lr = self.optimizer_d.param_groups[0]["lr"]
for param_group in self.optimizer_g.param_groups:
param_group['lr'] = self.lr
print("===============================================")
print("Learning rate has adjusted to {}".format(self.lr))
def main(args):
t = Trainer(args)
fig = plt.figure()
for epoch in range(t.epoch, t.epoch + args.num_epochs):
train_loss_d, train_loss_g, train_psnr = t.train(epoch)
val_loss, val_psnr = t.val(epoch)
t.draw_curve(fig, epoch, train_loss_d, train_loss_g, train_psnr, val_loss, val_psnr)
# if (epoch + 1) % 10 == 0:
# t.lr_update()
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="Training SRGAN with celebA")
parser.add_argument("--device", default="cuda", type=str)
parser.add_argument("--data_path", default=r"T:\srgan", type=str)
parser.add_argument("--resume", default=False, type=bool)
parser.add_argument("--num_epochs", default=100, type=int)
parser.add_argument("--save_path", default=r"./weight01.pt", type=str)
parser.add_argument("--save_path1", default=r"./weight00.pt", type=str)
parser.add_argument("--interval", default=20, type=int)
parser.add_argument("--batch", default=8, type=int)
args1 = parser.parse_args()
main(args1)
|
