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train.py
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train.py
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import argparse
import os
import pandas as pd
import torch.optim as optim
import torch.utils.data
import torchvision.utils as utils
from torch.autograd import Variable
from torch.utils.data import DataLoader
from tqdm import tqdm
import torch
from data import TrainDatasetFromFolder, ValDatasetFromFolder, display_transform
from loss import GeneratorLoss, PixelLoss
from models.common import ssim, psnr
from models.discriminator import Discriminator
from models import get_generator
parser = argparse.ArgumentParser(description="Train Super Resolution Models")
parser.add_argument(
"--model_name", default="espcn", type=str, help="name of the super resolution model"
)
parser.add_argument(
"--crop_size", default=88, type=int, help="training images crop size"
)
parser.add_argument(
"--upscale_factor",
default=4,
type=int,
choices=[2, 4, 8],
help="super resolution upscale factor",
)
parser.add_argument("--num_epochs", default=100, type=int, help="train epoch number")
parser.add_argument("--num_gpu", default=1, type=int, help="Number of GPUs to use")
parser.add_argument(
"--warmup_batches",
default=500,
type=int,
help="Number of warmup batches for the generator",
)
if __name__ == "__main__":
opt = parser.parse_args()
CROP_SIZE = opt.crop_size
UPSCALE_FACTOR = opt.upscale_factor
NUM_EPOCHS = opt.num_epochs
MODEL_NAME = opt.model_name
N_GPU = opt.num_gpu
WARMUP_BATCHES = opt.warmup_batches
train_set = TrainDatasetFromFolder(
"data/DIV2K_train_HR", crop_size=CROP_SIZE, upscale_factor=UPSCALE_FACTOR
)
val_set = ValDatasetFromFolder("data/DIV2K_valid_HR", upscale_factor=UPSCALE_FACTOR)
train_loader = DataLoader(
dataset=train_set, num_workers=4, batch_size=64, shuffle=True
)
val_loader = DataLoader(dataset=val_set, num_workers=4, batch_size=1, shuffle=False)
netG = get_generator(MODEL_NAME)(N_GPU, UPSCALE_FACTOR)
optimizerG = optim.Adam(netG.parameters())
print("# generator parameters:", sum(param.numel() for param in netG.parameters()))
pixel_criterion = PixelLoss()
if torch.cuda.is_available():
netG.cuda()
pixel_criterion.cuda()
print(f"# pretraining the generator for {WARMUP_BATCHES} steps")
for epoch in range(1, WARMUP_BATCHES + 1):
train_bar = tqdm(train_loader)
running_results = {"batch_sizes": 0, "loss": 0}
netG.train()
for data, target in train_bar:
batch_size = data.size(0)
running_results["batch_sizes"] += batch_size
real_img = Variable(target)
if torch.cuda.is_available():
real_img = real_img.cuda()
z = Variable(data)
if torch.cuda.is_available():
z = z.cuda()
fake_img = netG(z)
netG.zero_grad()
pre_g_loss = pixel_criterion(fake_img, real_img)
pre_g_loss.backward()
optimizerG.step()
running_results["loss"] += pre_g_loss.item() * batch_size
train_bar.set_description(
desc="[%d/%d] Loss: %.4f"
% (
epoch,
WARMUP_BATCHES,
running_results["loss"] / running_results["batch_sizes"],
)
)
netG.eval()
out_path = (
"pre_training_results/" + MODEL_NAME + "/SRF_" + str(UPSCALE_FACTOR) + "/"
)
if not os.path.exists(out_path):
os.makedirs(out_path)
if epoch % 20 == 0:
with torch.no_grad():
val_bar = tqdm(val_loader)
valing_results = {
"mse": 0,
"ssims": 0,
"psnr": 0,
"ssim": 0,
"batch_sizes": 0,
}
val_images = []
for val_lr, val_hr_restore, val_hr in val_bar:
batch_size = val_lr.size(0)
valing_results["batch_sizes"] += batch_size
lr = val_lr
hr = val_hr
if torch.cuda.is_available():
lr = lr.cuda()
hr = hr.cuda()
sr = netG(lr)
batch_mse = ((sr - hr) ** 2).data.mean()
valing_results["mse"] += batch_mse * batch_size
batch_ssim = ssim(sr, hr)
valing_results["ssims"] += batch_ssim * batch_size
valing_results["psnr"] = psnr(sr, hr)
valing_results["ssim"] = (
valing_results["ssims"] / valing_results["batch_sizes"]
)
val_bar.set_description(
desc="[converting LR images to SR images] PSNR: %.4f dB SSIM: %.4f"
% (valing_results["psnr"], valing_results["ssim"])
)
val_images.extend(
[
display_transform()(val_hr_restore.squeeze(0)),
display_transform()(hr.data.cpu().squeeze(0)),
display_transform()(sr.data.cpu().squeeze(0)),
]
)
val_images = torch.stack(val_images)
val_images = torch.chunk(val_images, val_images.size(0) // 15)
val_save_bar = tqdm(val_images, desc="[saving pre training results]")
index = 1
for image in val_save_bar:
image = utils.make_grid(image, nrow=3, padding=5)
utils.save_image(
image,
out_path + "epoch_%d_index_%d.png" % (epoch, index),
padding=5,
)
index += 1
torch.save(
netG.state_dict(),
"epochs/"
+ MODEL_NAME
+ "/pre_netG_epoch_%d_%d.pth" % (UPSCALE_FACTOR, epoch),
)
# Reloading the Generator with latest weights after warmup
netG = get_generator(MODEL_NAME)(N_GPU, UPSCALE_FACTOR)
optimizerG = optim.Adam(netG.parameters())
if torch.cuda.is_available():
netG.cuda()
netG.load_state_dict(
torch.load(
"epochs/"
+ MODEL_NAME
+ "/pre_netG_epoch_%d_%d.pth" % (UPSCALE_FACTOR, WARMUP_BATCHES)
)
)
# netG.load_state_dict(
# torch.load(
# "epochs/" + MODEL_NAME + "/pre_netG_epoch_%d_0.pth" % (UPSCALE_FACTOR)
# )
# )
# netG.load_state_dict(
# torch.load(
# "epochs/" + MODEL_NAME + "/netG_epoch_%d_%d.pth" % (UPSCALE_FACTOR, 2000)
# )
# )
netD = Discriminator(N_GPU)
print(
"# discriminator parameters:", sum(param.numel() for param in netD.parameters())
)
# netD.load_state_dict(
# torch.load(
# "epochs/" + MODEL_NAME + "/netD_epoch_%d_%d.pth" % (UPSCALE_FACTOR, 2000)
# )
# )
generator_criterion = GeneratorLoss()
discriminator_criterion = torch.nn.BCELoss()
if torch.cuda.is_available():
netD.cuda()
generator_criterion.cuda()
discriminator_criterion.cuda()
optimizerD = optim.Adam(netD.parameters())
results = {
"d_loss": [],
"g_loss": [],
"d_score": [],
"g_score": [],
"psnr": [],
"ssim": [],
}
for epoch in range(1, NUM_EPOCHS + 1):
train_bar = tqdm(train_loader)
running_results = {
"batch_sizes": 0,
"d_loss": 0,
"g_loss": 0,
"d_score": 0,
"g_score": 0,
}
netG.train()
netD.train()
for data, target in train_bar:
g_update_first = True
batch_size = data.size(0)
running_results["batch_sizes"] += batch_size
############################
# (1) Update D network: maximize D(x)-1-D(G(z))
###########################
real_img = Variable(target)
if torch.cuda.is_available():
real_img = real_img.cuda()
z = Variable(data)
if torch.cuda.is_available():
z = z.cuda()
fake_img = netG(z)
netD.zero_grad()
real_out = netD(real_img)
fake_out = netD(fake_img)
d_loss_real = discriminator_criterion(real_out, torch.ones_like(real_out))
d_loss_fake = discriminator_criterion(fake_out, torch.zeros_like(fake_out))
d_loss = d_loss_real + d_loss_fake
d_loss.backward(retain_graph=True)
optimizerD.step()
############################
# (2) Update G network: minimize 1-D(G(z)) + Perception Loss + Image Loss + TV Loss
###########################
netG.zero_grad()
## The two lines below are added to prevent runetime error in Google Colab ##
fake_img = netG(z)
fake_out = netD(fake_img).mean()
##
g_loss = generator_criterion(fake_out, fake_img, real_img)
g_loss.backward()
fake_img = netG(z)
fake_out = netD(fake_img).mean()
optimizerG.step()
# loss for current batch before optimization
running_results["g_loss"] += g_loss.item() * batch_size
running_results["d_loss"] += d_loss.item() * batch_size
running_results["d_score"] += real_out.mean().item() * batch_size
running_results["g_score"] += fake_out.mean().item() * batch_size
train_bar.set_description(
desc="[%d/%d] Loss_D: %.4f Loss_G: %.4f D(x): %.4f D(G(z)): %.4f"
% (
epoch,
NUM_EPOCHS,
running_results["d_loss"] / running_results["batch_sizes"],
running_results["g_loss"] / running_results["batch_sizes"],
running_results["d_score"] / running_results["batch_sizes"],
running_results["g_score"] / running_results["batch_sizes"],
)
)
if epoch % 200 == 0:
netG.eval()
out_path = (
"training_results/" + MODEL_NAME + "/SRF_" + str(UPSCALE_FACTOR) + "/"
)
if not os.path.exists(out_path):
os.makedirs(out_path)
with torch.no_grad():
val_bar = tqdm(val_loader)
valing_results = {
"mse": 0,
"ssims": 0,
"psnr": 0,
"ssim": 0,
"batch_sizes": 0,
}
val_images = []
for val_lr, val_hr_restore, val_hr in val_bar:
batch_size = val_lr.size(0)
valing_results["batch_sizes"] += batch_size
lr = val_lr
hr = val_hr
if torch.cuda.is_available():
lr = lr.cuda()
hr = hr.cuda()
sr = netG(lr)
batch_mse = ((sr - hr) ** 2).data.mean()
valing_results["mse"] += batch_mse * batch_size
batch_ssim = ssim(hr, sr)
valing_results["ssims"] += batch_ssim * batch_size
valing_results["psnr"] = psnr(sr, hr)
valing_results["ssim"] = (
valing_results["ssims"] / valing_results["batch_sizes"]
)
val_bar.set_description(
desc="[converting LR images to SR images] PSNR: %.4f dB SSIM: %.4f"
% (valing_results["psnr"], valing_results["ssim"])
)
val_images.extend(
[
display_transform()(val_hr_restore.squeeze(0)),
display_transform()(hr.data.cpu().squeeze(0)),
display_transform()(sr.data.cpu().squeeze(0)),
]
)
val_images = torch.stack(val_images)
val_images = torch.chunk(val_images, val_images.size(0) // 15)
val_save_bar = tqdm(val_images, desc="[saving training results]")
index = 1
for image in val_save_bar:
image = utils.make_grid(image, nrow=3, padding=5)
utils.save_image(
image,
out_path + "epoch_%d_index_%d.png" % (epoch, index),
padding=5,
)
index += 1
# save model parameters
torch.save(
netG.state_dict(),
"epochs/"
+ MODEL_NAME
+ "/netG_epoch_%d_%d.pth" % (UPSCALE_FACTOR, epoch),
)
torch.save(
netD.state_dict(),
"epochs/"
+ MODEL_NAME
+ "/netD_epoch_%d_%d.pth" % (UPSCALE_FACTOR, epoch),
)
# save loss\scores\psnr\ssim
# results["d_loss"].append(
# running_results["d_loss"] / running_results["batch_sizes"]
# )
# results["g_loss"].append(
# running_results["g_loss"] / running_results["batch_sizes"]
# )
# results["d_score"].append(
# running_results["d_score"] / running_results["batch_sizes"]
# )
# results["g_score"].append(
# running_results["g_score"] / running_results["batch_sizes"]
# )
# results["psnr"].append(valing_results["psnr"])
# results["ssim"].append(valing_results["ssim"])
# out_path = "statistics/"
# data_frame = pd.DataFrame(
# data={
# "Loss_D": results["d_loss"].cpu(),
# "Loss_G": results["g_loss"].cpu(),
# "Score_D": results["d_score"].cpu(),
# "Score_G": results["g_score"].cpu(),
# "PSNR": results["psnr"].cpu(),
# "SSIM": results["ssim"],
# },
# index=range(1, epoch + 1),
# )
# data_frame.to_csv(
# out_path + "srf_" + str(UPSCALE_FACTOR) + "_train_results.csv",
# index_label="Epoch",
# )