def main_worker(gpu, args):
args.gpu = gpu
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for testing.")
model = configure(args)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# Set eval mode.
model.eval()
cudnn.benchmark = True
# Get image filename.
filename = os.path.basename(args.lr)
# Read all pictures.
lr = Image.open(args.lr)
bicubic = transforms.Resize(
(lr.size[1] * args.upscale_factor, lr.size[0] * args.upscale_factor),
InterpolationMode.BICUBIC)(lr)
lr = process_image(lr, args.gpu)
bicubic = process_image(bicubic, args.gpu)
with torch.no_grad():
sr = model(lr)
if args.hr:
hr = process_image(Image.open(args.hr), args.gpu)
vutils.save_image(hr, os.path.join("tests", f"hr_{filename}"))
images = torch.cat([bicubic, sr, hr], dim=-1)
value = iqa(sr, hr, args.gpu)
print(f"Performance avg results:\n")
print(f"indicator Score\n")
print(f"--------- -----\n")
print(f"MSE {value[0]:6.4f}\n"
f"RMSE {value[1]:6.4f}\n"
f"PSNR {value[2]:6.2f}\n"
f"SSIM {value[3]:6.4f}\n"
f"LPIPS {value[4]:6.4f}\n"
f"GMSD {value[5]:6.4f}\n")
else:
images = torch.cat([bicubic, sr], dim=-1)
vutils.save_image(lr, os.path.join("tests", f"lr_{filename}"))
vutils.save_image(bicubic, os.path.join("tests", f"bicubic_{filename}"))
vutils.save_image(sr, os.path.join("tests", f"sr_{filename}"))
vutils.save_image(images,
os.path.join("tests", f"compare_{filename}"),
padding=10)
def main_worker(gpu, args):
global best_mse_value, best_rmse_value, best_psnr_value, best_ssim_value, best_lpips_value, best_gmsd_value
args.gpu = gpu
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for training.")
cudnn.benchmark = True
model = configure(args)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
# Read all pictures.
lr = process_image(Image.open(args.lr), args.gpu)
hr = process_image(Image.open(args.hr), args.gpu)
model_paths = glob(
os.path.join(f"{args.model_dir}", "Generator_epoch*.pth"))
best_model = model_paths[0]
for model_path in model_paths:
print(f"Process `{model_path}`")
value = inference(lr, hr, model, model_path, gpu)
is_best = value[2] > best_psnr_value
if is_best:
best_model = os.path.basename(model_path)
best_mse_value = value[0]
best_rmse_value = value[1]
best_psnr_value = value[2]
best_ssim_value = value[3]
best_lpips_value = value[4]
best_gmsd_value = value[5]
print("\n##################################################")
print(f"Best model: `{best_model}`.")
print(f"indicator Score")
print(f"--------- -----")
print(f"MSE {best_mse_value:6.4f}"
f"RMSE {best_rmse_value:6.2f}"
f"PSNR {best_psnr_value:6.2f}\n"
f"SSIM {best_ssim_value:6.4f}\n"
f"LPIPS {best_lpips_value:6.4f}\n"
f"GMSD {best_gmsd_value:6.4f}")
print(f"--------- -----")
print("##################################################\n")
def main(args) -> None:
if args.seed is not None:
# In order to make the model repeatable, the first step is to set random seeds, and the second step is to set
# convolution algorithm.
random.seed(args.seed)
torch.manual_seed(args.seed)
# for the current configuration, so as to optimize the operation efficiency.
cudnn.benchmark = True
# Ensure that every time the same input returns the same result.
cudnn.deterministic = True
# Build a super-resolution model, if model path is defined, the specified model weight will be loaded.
model = configure(args)
# Switch model to eval mode.
model.eval()
# Create an image that conforms to the normal distribution.
data = torch.randn([1, 3, args.image_size, args.image_size],
requires_grad=False)
# If there is a GPU, the data will be loaded into the GPU memory.
if args.gpu is not None:
data = data.cuda(args.gpu, non_blocking=True)
# Needs to reconstruct the low resolution image without the gradient information of the reconstructed image.
with torch.no_grad():
start = time.time()
_ = model(data)
# Waits for all kernels in all streams on a CUDA device to complete.
torch.cuda.synchronize()
print(f"Time:{(time.time() - start) * 1000:.2f}ms.")
# Context manager that manages autograd profiler state and holds a summary of results.
with torch.autograd.profiler.profile(enabled=True,
use_cuda=args.gpu) as profile:
_ = model(data)
print(profile.table())
# Open Chrome browser and enter in the address bar `chrome://tracing`
profile.export_chrome_trace("profile.json")
def main_worker(ngpus_per_node, args):
global best_psnr, best_ssim, fixed_lr
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for training.")
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + args.gpu
dist.init_process_group(args.dist_backend,
args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
generator = configure(args)
discriminator = discriminator_for_vgg(args.image_size)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
elif args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
discriminator.cuda(args.gpu)
generator.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(
(args.workers + ngpus_per_node - 1) / ngpus_per_node)
discriminator = nn.parallel.DistributedDataParallel(
discriminator, device_ids=[args.gpu])
generator = nn.parallel.DistributedDataParallel(
generator, device_ids=[args.gpu])
else:
discriminator.cuda()
generator.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
discriminator = nn.parallel.DistributedDataParallel(discriminator)
generator = nn.parallel.DistributedDataParallel(generator)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
discriminator = discriminator.cuda(args.gpu)
generator = generator.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith("alexnet") or args.arch.startswith("vgg"):
discriminator.features = torch.nn.DataParallel(
discriminator.features)
generator.features = torch.nn.DataParallel(generator.features)
discriminator.cuda()
generator.cuda()
else:
discriminator = torch.nn.DataParallel(discriminator).cuda()
generator = torch.nn.DataParallel(generator).cuda()
# Loss = 10 * pixel loss + content loss + 0.005 * adversarial loss
pixel_criterion = nn.L1Loss().cuda(args.gpu)
content_criterion = ContentLoss().cuda(args.gpu)
adversarial_criterion = nn.BCEWithLogitsLoss().cuda(args.gpu)
if args.gpu is not None:
fixed_lr = fixed_lr.cuda(args.gpu)
# All optimizer function and scheduler function.
psnr_optimizer = torch.optim.Adam(generator.parameters(),
lr=args.psnr_lr,
betas=(0.9, 0.99))
psnr_epoch_indices = math.floor(args.psnr_epochs // 4)
psnr_scheduler = torch.optim.lr_scheduler.StepLR(psnr_optimizer,
psnr_epoch_indices, 0.5)
discriminator_optimizer = torch.optim.Adam(discriminator.parameters(),
lr=args.gan_lr,
betas=(0.9, 0.99))
generator_optimizer = torch.optim.Adam(generator.parameters(),
lr=args.gan_lr,
betas=(0.9, 0.99))
interval_epoch = math.ceil(args.gan_epochs // 8)
gan_epoch_indices = [
interval_epoch, interval_epoch * 2, interval_epoch * 4,
interval_epoch * 6
]
discriminator_scheduler = torch.optim.lr_scheduler.MultiStepLR(
discriminator_optimizer, gan_epoch_indices, 0.5)
generator_scheduler = torch.optim.lr_scheduler.MultiStepLR(
generator_optimizer, gan_epoch_indices, 0.5)
# Selection of appropriate treatment equipment.
train_dataset = BaseTrainDataset(os.path.join(args.data, "train"),
args.image_size, args.upscale_factor)
test_dataset = BaseTestDataset(os.path.join(args.data, "test"),
args.image_size, args.upscale_factor)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
train_dataloader = torch.utils.data.DataLoader(
train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
pin_memory=True,
sampler=train_sampler,
num_workers=args.workers)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
# Load pre training model.
if args.netD != "":
discriminator.load_state_dict(torch.load(args.netD))
if args.netG != "":
generator.load_state_dict(torch.load(args.netG))
# The mixed precision training is used in PSNR-oral.
scaler = amp.GradScaler()
logger.info("Turn on mixed precision training.")
# Create a SummaryWriter at the beginning of training.
psnr_writer = SummaryWriter(f"runs/{args.arch}_psnr_logs")
gan_writer = SummaryWriter(f"runs/{args.arch}_gan_logs")
for epoch in range(args.start_psnr_epoch, args.psnr_epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# Train for one epoch for PSNR-oral.
train_psnr(train_dataloader, generator, pixel_criterion,
psnr_optimizer, epoch, scaler, psnr_writer, args)
# Update GAN-oral optimizer learning rate.
psnr_scheduler.step()
# Evaluate on test dataset.
psnr, ssim, gmsd = test(test_dataloader, generator, args.gpu)
psnr_writer.add_scalar("PSNR_Test/PSNR", psnr, epoch)
psnr_writer.add_scalar("PSNR_Test/SSIM", ssim, epoch)
psnr_writer.add_scalar("PSNR_Test/GMSD", gmsd, epoch)
# Check whether the evaluation index of the current model is the highest.
is_best = psnr > best_psnr
best_psnr = max(psnr, best_psnr)
# Save model weights for every epoch.
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
torch.save(generator.state_dict(),
os.path.join("weights", f"PSNR_epoch{epoch}.pth"))
if is_best:
torch.save(generator.state_dict(),
os.path.join("weights", f"PSNR-best.pth"))
# Save the last training model parameters.
torch.save(generator.state_dict(),
os.path.join("weights", f"PSNR-last.pth"))
for epoch in range(args.start_gan_epoch, args.gan_epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# Train for one epoch for GAN-oral.
train_gan(train_dataloader, discriminator, discriminator_optimizer,
generator, generator_optimizer, pixel_criterion,
content_criterion, adversarial_criterion, epoch, scaler,
gan_writer, args)
# Update GAN-oral optimizer learning rate.
discriminator_scheduler.step()
generator_scheduler.step()
# Evaluate on test dataset.
psnr, ssim, gmsd = test(test_dataloader, generator, args.gpu)
gan_writer.add_scalar("GAN_Test/PSNR", psnr, epoch)
gan_writer.add_scalar("GAN_Test/SSIM", ssim, epoch)
gan_writer.add_scalar("GAN_Test/GMSD", gmsd, epoch)
# Check whether the evaluation index of the current model is the highest.
is_best = ssim > best_ssim
best_ssim = max(ssim, best_ssim)
# Save model weights for every epoch.
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
torch.save(
discriminator.state_dict(),
os.path.join("weights", f"Discriminator_epoch{epoch}.pth"))
torch.save(
generator.state_dict(),
os.path.join("weights", f"Generator_epoch{epoch}.pth"))
if is_best:
torch.save(generator.state_dict(),
os.path.join("weights", f"GAN-best.pth"))
# Save the last training model parameters.
torch.save(generator.state_dict(),
os.path.join("weights", f"GAN-last.pth"))
def main_worker(gpu, args):
args.gpu = gpu
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for testing.")
model = configure(args)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
cudnn.benchmark = True
# Set eval mode.
model.eval()
# Get video filename.
filename = os.path.basename(args.file)
# Image preprocessing operation
tensor2pil = transforms.ToPILImage()
video_capture = cv2.VideoCapture(args.file)
# Prepare to write the processed image into the video.
fps = video_capture.get(cv2.CAP_PROP_FPS)
total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
# Set video size
size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
sr_size = (size[0] * args.upscale_factor, size[1] * args.upscale_factor)
pare_size = (sr_size[0] * 2 + 10, sr_size[1] + 10 + sr_size[0] // 5 - 9)
# Video write loader.
sr_writer = cv2.VideoWriter(
os.path.join("videos", f"sr_{args.upscale_factor}x_{filename}"),
cv2.VideoWriter_fourcc(*"MPEG"), fps, sr_size)
compare_writer = cv2.VideoWriter(
os.path.join("videos", f"compare_{args.upscale_factor}x_{filename}"),
cv2.VideoWriter_fourcc(*"MPEG"), fps, pare_size)
# read frame.
with torch.no_grad():
success, raw_frame = video_capture.read()
progress_bar = tqdm(
range(total_frames),
desc="[processing video and saving/view result videos]")
for _ in progress_bar:
if success:
# Read image to tensor and transfer to the specified device for processing.
lr = process_image(raw_frame, args.gpu)
sr = model(lr)
sr = sr.cpu()
sr = sr.data[0].numpy()
sr *= 255.0
sr = (np.uint8(sr)).transpose((1, 2, 0))
# save sr video
sr_writer.write(sr)
# make compared video and crop shot of left top\right top\center\left bottom\right bottom
sr = tensor2pil(sr)
# Five areas are selected as the bottom contrast map.
crop_sr_images = transforms.FiveCrop(size=sr.width // 5 -
9)(sr)
crop_sr_images = [
np.asarray(transforms.Pad(padding=(10, 5, 0, 0))(image))
for image in crop_sr_images
]
sr = transforms.Pad(padding=(5, 0, 0, 5))(sr)
# Five areas in the contrast map are selected as the bottom contrast map
compare_image = transforms.Resize(
(sr_size[1], sr_size[0]),
interpolation=Mode.BICUBIC)(tensor2pil(raw_frame))
crop_compare_images = transforms.FiveCrop(
size=compare_image.width // 5 - 9)(compare_image)
crop_compare_images = [
np.asarray(transforms.Pad((0, 5, 10, 0))(image))
for image in crop_compare_images
]
compare_image = transforms.Pad(padding=(0, 0, 5,
5))(compare_image)
# concatenate all the pictures to one single picture
# 1. Mosaic the left and right images of the video.
top_image = np.concatenate(
(np.asarray(compare_image), np.asarray(sr)), axis=1)
# 2. Mosaic the bottom left and bottom right images of the video.
bottom_image = np.concatenate(crop_compare_images +
crop_sr_images,
axis=1)
bottom_image_height = int(top_image.shape[1] /
bottom_image.shape[1] *
bottom_image.shape[0])
bottom_image_width = top_image.shape[1]
# 3. Adjust to the right size.
bottom_image = np.asarray(
transforms.Resize(
(bottom_image_height,
bottom_image_width))(tensor2pil(bottom_image)))
# 4. Combine the bottom zone with the upper zone.
final_image = np.concatenate((top_image, bottom_image))
# save compare video
compare_writer.write(final_image)
if args.view:
# display video
cv2.imshow("LR video convert SR video ", final_image)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# next frame
success, raw_frame = video_capture.read()
def main(args):
if args.seed is not None:
# In order to make the model repeatable, the first step is to set random seeds, and the second step is to set
# convolution algorithm.
random.seed(args.seed)
torch.manual_seed(args.seed)
logger.warning("You have chosen to seed testing. "
"This will turn on the CUDNN deterministic setting, "
"which can slow down your testing considerably! "
"You may see unexpected behavior when restarting "
"from checkpoints.")
# for the current configuration, so as to optimize the operation efficiency.
cudnn.benchmark = True
# Ensure that every time the same input returns the same result.
cudnn.deterministic = True
# Build a super-resolution model, if model path is defined, the specified model weight will be loaded.
model = configure(args)
# Switch model to eval mode.
model.eval()
# If the GPU is available, load the model into the GPU memory. This speed.
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
# Get video filename.
filename = os.path.basename(args.lr)
# OpenCV video input method open.
video_capture = cv2.VideoCapture(args.file)
# Prepare to write the processed image into the video.
fps = video_capture.get(cv2.CAP_PROP_FPS)
total_frames = int(video_capture.get(cv2.CAP_PROP_FRAME_COUNT))
# Set video window resolution size.
raw_video_size = (int(video_capture.get(cv2.CAP_PROP_FRAME_WIDTH)),
int(video_capture.get(cv2.CAP_PROP_FRAME_HEIGHT)))
sr_video_size = (raw_video_size[0] * args.upscale_factor, raw_video_size[1] * args.upscale_factor)
compare_video_size = (sr_video_size[0] * 2 + 10, sr_video_size[1] + 10 + sr_video_size[0] // 5 - 9)
# Video write loader.
sr_writer_path = os.path.join("videos", f"sr_{args.upscale_factor}x_{filename}")
compare_writer_path = os.path.join("videos", f"compare_{args.upscale_factor}x_{filename}")
sr_writer = cv2.VideoWriter(sr_writer_path, cv2.VideoWriter_fourcc(*"MPEG"), fps, sr_video_size)
compare_writer = cv2.VideoWriter(compare_writer_path, cv2.VideoWriter_fourcc(*"MPEG"), fps, compare_video_size)
# read video frame.
with torch.no_grad():
success, raw_frame = video_capture.read()
for _ in tqdm(range(total_frames), desc="[processing video and saving/view result videos]"):
if success:
# The low resolution image is reconstructed to the super resolution image.
sr = model(process_image(raw_frame, norm=False, gpu=args.gpu))
# Convert N*C*H*W image data to H*W*C image data.
sr = sr.cpu()
sr = sr.data[0].numpy()
sr *= 255.0
sr = (np.uint8(sr)).transpose((1, 2, 0))
# Writer sr video to SR video file.
sr_writer.write(sr)
# Make compared video and crop shot of left top\right top\center\left bottom\right bottom.
sr = transforms.ToPILImage()(sr)
# Five areas are selected as the bottom contrast map.
crop_sr_images = transforms.FiveCrop(sr.width // 5 - 9)(sr)
crop_sr_images = [np.asarray(transforms.Pad(padding=(10, 5, 0, 0))(image)) for image in crop_sr_images]
sr = transforms.Pad(padding=(5, 0, 0, 5))(sr)
# Five areas in the contrast map are selected as the bottom contrast map
compare_image_size = (sr_video_size[1], sr_video_size[0])
compare_image = transforms.Resize(compare_image_size, interpolation=Mode.BICUBIC)(raw_frame)
compare_image = transforms.ToPILImage()(compare_image)
crop_compare_images = transforms.FiveCrop(compare_image.width // 5 - 9)(compare_image)
crop_compare_images = [np.asarray(transforms.Pad((0, 5, 10, 0))(image)) for image in
crop_compare_images]
compare_image = transforms.Pad(padding=(0, 0, 5, 5))(compare_image)
# Concatenate all the pictures to one single picture
# 1. Mosaic the left and right images of the video.
top_image = np.concatenate((np.asarray(compare_image), np.asarray(sr)), axis=1)
# 2. Mosaic the bottom left and bottom right images of the video.
bottom_image = np.concatenate(crop_compare_images + crop_sr_images, axis=1)
bottom_image_height = int(top_image.shape[1] / bottom_image.shape[1] * bottom_image.shape[0])
bottom_image_width = top_image.shape[1]
# 3. Adjust to the right size.
bottom_image_size = (bottom_image_height, bottom_image_width)
bottom_image = np.asarray(transforms.Resize(bottom_image_size)(transforms.ToPILImage()(bottom_image)))
# 4. Combine the bottom zone with the upper zone.
images = np.concatenate((top_image, bottom_image))
# Writer compare video to compare video file.
compare_writer.write(images)
# Display compare video.
if args.view:
cv2.imshow("LR video convert SR video ", images)
if cv2.waitKey(1) & 0xFF == ord("q"):
break
# Read next frame.
success, raw_frame = video_capture.read()
def main(args):
if args.seed is not None:
# In order to make the model repeatable, the first step is to set random seeds, and the second step is to set
# convolution algorithm.
random.seed(args.seed)
torch.manual_seed(args.seed)
logger.warning("You have chosen to seed testing. "
"This will turn on the CUDNN deterministic setting, "
"which can slow down your testing considerably! "
"You may see unexpected behavior when restarting "
"from checkpoints.")
# for the current configuration, so as to optimize the operation efficiency.
cudnn.benchmark = True
# Ensure that every time the same input returns the same result.
cudnn.deterministic = True
# Build a super-resolution model, if model path is defined, the specified model weight will be loaded.
model = configure(args)
# If special choice model path.
if args.model_path is not None:
logger.info(
f"You loaded the specified weight. Load weights from `{os.path.abspath(args.model_path)}`."
)
model.load_state_dict(
torch.load(args.model_path, map_location=torch.device("cpu")))
# Switch model to eval mode.
model.eval()
# If the GPU is available, load the model into the GPU memory. This speed.
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# Setting this flag allows the built-in auto tuner of cudnn to automatically find the most efficient
# algorithm suitable for the current configuration, so as to optimize the operation efficiency.
cudnn.benchmark = True
# Ensure that every time the same input returns the same result.
cudnn.deterministic = True
# Get image filename.
filename = os.path.basename(args.lr)
# Read the low resolution image and enlarge the low resolution image with bicubic method.
# The purpose of bicubic method is to compare the reconstruction results.
lr = Image.open(args.lr)
bicubic_image_size = (lr.size[1] * args.upscale_factor,
lr.size[0] * args.upscale_factor)
bicubic = transforms.Resize(bicubic_image_size,
InterpolationMode.BICUBIC)(lr)
lr = process_image(lr, norm=False, gpu=args.gpu)
bicubic = process_image(bicubic, norm=True, gpu=args.gpu)
# Needs to reconstruct the low resolution image without the gradient information of the reconstructed image.
with torch.no_grad():
sr = model(lr)
# If there is a reference image, a series of evaluation indexes will be output.
if args.hr:
hr = process_image(Image.open(args.hr), norm=True, gpu=args.gpu)
vutils.save_image(hr,
os.path.join("tests", f"hr_{filename}"),
normalize=True)
# Merge three images into one line for visualization.
images = torch.cat([bicubic, sr, hr], dim=-1)
# The reconstructed image and the reference image are evaluated once.
value = iqa(sr, hr, args.gpu)
print(f"Performance avg results:\n")
print(f"Indicator score\n")
print(f"--------- -----\n")
print(f"MSE {value[0]:6.4f}\n"
f"RMSE {value[1]:6.4f}\n"
f"PSNR {value[2]:6.2f}\n"
f"SSIM {value[3]:6.4f}\n"
f"GMSD {value[4]:6.4f}\n")
else:
# Merge two images into one line for visualization.
images = torch.cat([bicubic, sr], dim=-1)
# Save a series of reconstruction results.
vutils.save_image(lr, os.path.join("tests", f"lr_{filename}"))
vutils.save_image(bicubic,
os.path.join("tests", f"bicubic_{filename}"),
normalize=True)
vutils.save_image(sr,
os.path.join("tests", f"sr_{filename}"),
normalize=True)
vutils.save_image(images,
os.path.join("tests", f"compare_{filename}"),
padding=10,
normalize=True)
def main_worker(gpu, ngpus_per_node, args):
global best_psnr
args.gpu = gpu
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for training.")
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url, world_size=args.world_size, rank=args.rank)
# create model
generator = configure(args)
discriminator = discriminator_for_vgg(image_size=args.image_size)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
elif args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
discriminator.cuda(args.gpu)
generator.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
discriminator = nn.parallel.DistributedDataParallel(module=discriminator, device_ids=[args.gpu])
generator = nn.parallel.DistributedDataParallel(module=generator, device_ids=[args.gpu])
else:
discriminator.cuda()
generator.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
discriminator = nn.parallel.DistributedDataParallel(discriminator)
generator = nn.parallel.DistributedDataParallel(generator)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
discriminator = discriminator.cuda(args.gpu)
generator = generator.cuda(args.gpu)
else:
# DataParallel will divide and allocate batch_size to all available GPUs
if args.arch.startswith("alexnet") or args.arch.startswith("vgg"):
discriminator.features = torch.nn.DataParallel(discriminator.features)
generator.features = torch.nn.DataParallel(generator.features)
discriminator.cuda()
generator.cuda()
else:
discriminator = torch.nn.DataParallel(discriminator).cuda()
generator = torch.nn.DataParallel(generator).cuda()
# Loss = 10 * pixel loss + content loss + 0.005 * adversarial loss
pixel_criterion = nn.L1Loss().cuda(args.gpu)
content_criterion = VGGLoss().cuda(args.gpu)
adversarial_criterion = nn.BCEWithLogitsLoss().cuda(args.gpu)
logger.info(f"Losses function information:\n"
f"\tPixel: L1Loss\n"
f"\tContent: VGG19_35th\n"
f"\tAdversarial: BCEWithLogitsLoss")
# All optimizer function and scheduler function.
psnr_optimizer = torch.optim.Adam(generator.parameters(), lr=args.psnr_lr, betas=(0.9, 0.99))
psnr_epoch_indices = math.floor(args.psnr_epochs // 4)
psnr_scheduler = torch.optim.lr_scheduler.StepLR(psnr_optimizer, step_size=psnr_epoch_indices, gamma=0.5)
discriminator_optimizer = torch.optim.Adam(discriminator.parameters(), lr=args.gan_lr, betas=(0.9, 0.99))
generator_optimizer = torch.optim.Adam(generator.parameters(), args.gan_lr, (0.9, 0.99))
interval_epoch = math.ceil(args.gan_epochs // 8)
gan_epoch_indices = [interval_epoch, interval_epoch * 2, interval_epoch * 4, interval_epoch * 6]
discriminator_scheduler = torch.optim.lr_scheduler.MultiStepLR(discriminator_optimizer, milestones=gan_epoch_indices, gamma=0.5)
generator_scheduler = torch.optim.lr_scheduler.MultiStepLR(generator_optimizer, milestones=gan_epoch_indices, gamma=0.5)
logger.info(f"Optimizer information:\n"
f"\tPSNR learning rate: {args.psnr_lr}\n"
f"\tDiscriminator learning rate: {args.gan_lr}\n"
f"\tGenerator learning rate: {args.gan_lr}\n"
f"\tPSNR optimizer: Adam, [betas=(0.9,0.99)]\n"
f"\tDiscriminator optimizer: Adam, [betas=(0.9,0.99)]\n"
f"\tGenerator optimizer: Adam, [betas=(0.9,0.99)]\n"
f"\tPSNR scheduler: StepLR, [step_size=psnr_epoch_indices, gamma=0.5]\n"
f"\tDiscriminator scheduler: MultiStepLR, [milestones=epoch_indices, gamma=0.5]\n"
f"\tGenerator scheduler: MultiStepLR, [milestones=epoch_indices, gamma=0.5]")
logger.info("Load training dataset")
# Selection of appropriate treatment equipment.
train_dataset = BaseTrainDataset(os.path.join(args.data, "train"), args.image_size, args.upscale_factor)
test_dataset = BaseTestDataset(os.path.join(args.data, "test"), args.image_size, args.upscale_factor)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
else:
train_sampler = None
train_dataloader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
pin_memory=True,
sampler=train_sampler,
num_workers=args.workers)
test_dataloader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.batch_size,
shuffle=False,
pin_memory=True,
num_workers=args.workers)
logger.info(f"Dataset information:\n"
f"\tTrain Path: {os.getcwd()}/{args.data}/train\n"
f"\tTest Path: {os.getcwd()}/{args.data}/test\n"
f"\tNumber of train samples: {len(train_dataset)}\n"
f"\tNumber of test samples: {len(test_dataset)}\n"
f"\tNumber of train batches: {len(train_dataloader)}\n"
f"\tNumber of test batches: {len(test_dataloader)}\n"
f"\tShuffle of train: True\n"
f"\tShuffle of test: False\n"
f"\tSampler of train: {bool(train_sampler)}\n"
f"\tSampler of test: None\n"
f"\tWorkers of train: {args.workers}\n"
f"\tWorkers of test: {args.workers}")
# optionally resume from a checkpoint
if args.resume_psnr:
if os.path.isfile(args.resume_psnr):
logger.info(f"Loading checkpoint '{args.resume_psnr}'.")
if args.gpu is None:
checkpoint = torch.load(args.resume_psnr)
else:
# Map model to be loaded to specified single gpu.
checkpoint = torch.load(args.resume_psnr, map_location=f"cuda:{args.gpu}")
args.start_psnr_epoch = checkpoint["epoch"]
best_psnr = checkpoint["best_psnr"]
if args.gpu is not None:
# best_psnr may be from a checkpoint from a different GPU
best_psnr = best_psnr.to(args.gpu)
generator.load_state_dict(checkpoint["state_dict"])
psnr_optimizer.load_state_dict(checkpoint["optimizer"])
logger.info(f"Loaded checkpoint '{args.resume_psnr}' (epoch {checkpoint['epoch']}).")
else:
logger.info(f"No checkpoint found at '{args.resume_psnr}'.")
if args.resume_d or args.resume_g:
if os.path.isfile(args.resume_d) or os.path.isfile(args.resume_g):
logger.info(f"Loading checkpoint '{args.resume_d}'.")
logger.info(f"Loading checkpoint '{args.resume_g}'.")
if args.gpu is None:
checkpoint_d = torch.load(args.resume_d)
checkpoint_g = torch.load(args.resume_g)
else:
# Map model to be loaded to specified single gpu.
checkpoint_d = torch.load(args.resume_d, map_location=f"cuda:{args.gpu}")
checkpoint_g = torch.load(args.resume_g, map_location=f"cuda:{args.gpu}")
args.start_gan_epoch = checkpoint_g["epoch"]
best_psnr = checkpoint_g["best_psnr"]
if args.gpu is not None:
# best_psnr may be from a checkpoint from a different GPU
best_psnr = best_psnr.to(args.gpu)
discriminator.load_state_dict(checkpoint_d["state_dict"])
discriminator_optimizer.load_state_dict(checkpoint_d["optimizer"])
generator.load_state_dict(checkpoint_g["state_dict"])
generator_optimizer.load_state_dict(checkpoint_g["optimizer"])
logger.info(f"Loaded checkpoint '{args.resume_d}' (epoch {checkpoint_d['epoch']}).")
logger.info(f"Loaded checkpoint '{args.resume_g}' (epoch {checkpoint_g['epoch']}).")
else:
logger.info(f"No checkpoint found at '{args.resume_d}' or '{args.resume_g}'.")
cudnn.benchmark = True
# The mixed precision training is used in PSNR-oral.
scaler = amp.GradScaler()
logger.info("Turn on mixed precision training.")
# Create a SummaryWriter at the beginning of training.
psnr_writer = SummaryWriter(f"runs/{args.arch}_psnr_logs")
gan_writer = SummaryWriter(f"runs/{args.arch}_gan_logs")
logger.info(f"Train information:\n"
f"\tPSNR-oral epochs: {args.psnr_epochs}\n"
f"\tGAN-oral epochs: {args.gan_epochs}")
for epoch in range(args.start_psnr_epoch, args.psnr_epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
train_psnr(dataloader=train_dataloader,
model=generator,
criterion=pixel_criterion,
optimizer=psnr_optimizer,
epoch=epoch,
scaler=scaler,
writer=psnr_writer,
args=args)
psnr_scheduler.step()
# Test for every epoch.
psnr, ssim, lpips, gmsd = test(dataloader=test_dataloader, model=generator, gpu=args.gpu)
gan_writer.add_scalar("Test/PSNR", psnr, epoch + 1)
gan_writer.add_scalar("Test/SSIM", ssim, epoch + 1)
gan_writer.add_scalar("Test/LPIPS", lpips, epoch + 1)
gan_writer.add_scalar("Test/GMSD", gmsd, epoch + 1)
is_best = psnr > best_psnr
best_psnr = max(psnr, best_psnr)
if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0):
torch.save({"epoch": epoch + 1,
"arch": args.arch,
"best_psnr": best_psnr,
"state_dict": generator.state_dict(),
"optimizer": psnr_optimizer.state_dict(),
}, os.path.join("weights", f"PSNR_epoch{epoch}.pth"))
if is_best:
torch.save(generator.state_dict(), os.path.join("weights", f"PSNR.pth"))
# Load best model weight.
best_psnr = 0.0
generator.load_state_dict(torch.load(os.path.join("weights", f"PSNR.pth"), map_location=f"cuda:{args.gpu}"))
for epoch in range(args.start_gan_epoch, args.gan_epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
# train for one epoch
train_gan(dataloader=train_dataloader,
discriminator=discriminator,
discriminator_optimizer=discriminator_optimizer,
generator=generator,
generator_optimizer=generator_optimizer,
pixel_criterion=pixel_criterion,
content_criterion=content_criterion,
adversarial_criterion=adversarial_criterion,
epoch=epoch,
scaler=scaler,
writer=gan_writer,
args=args)
discriminator_scheduler.step()
generator_scheduler.step()
# Test for every epoch.
psnr, ssim, lpips, gmsd = test(dataloader=test_dataloader, model=generator, gpu=args.gpu)
gan_writer.add_scalar("Test/PSNR", psnr, epoch + 1)
gan_writer.add_scalar("Test/SSIM", ssim, epoch + 1)
gan_writer.add_scalar("Test/LPIPS", lpips, epoch + 1)
gan_writer.add_scalar("Test/GMSD", gmsd, epoch + 1)
is_best = psnr > best_psnr
best_psnr = max(psnr, best_psnr)
if not args.multiprocessing_distributed or (args.multiprocessing_distributed and args.rank % ngpus_per_node == 0):
torch.save({"epoch": epoch + 1,
"arch": "vgg",
"state_dict": discriminator.state_dict(),
"optimizer": discriminator_optimizer.state_dict()
}, os.path.join("weights", f"Discriminator_epoch{epoch}.pth"))
torch.save({"epoch": epoch + 1,
"arch": args.arch,
"best_psnr": best_psnr,
"state_dict": generator.state_dict(),
"optimizer": generator_optimizer.state_dict()
}, os.path.join("weights", f"Generator_epoch{epoch}.pth"))
if is_best:
torch.save(generator.state_dict(), os.path.join("weights", f"GAN.pth"))
def main_worker(gpu, args):
global total_mse_value, total_rmse_value, total_psnr_value, total_ssim_value, total_lpips_value, total_gmsd_value
args.gpu = gpu
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for testing.")
model = configure(args)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
logger.info("Load testing dataset.")
# Selection of appropriate treatment equipment.
dataset = BaseTestDataset(os.path.join(args.data, "test"), args.image_size,
args.upscale_factor)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers)
logger.info(f"Dataset information:\n"
f"\tPath: {os.getcwd()}/{args.data}/test\n"
f"\tNumber of samples: {len(dataset)}\n"
f"\tNumber of batches: {len(dataloader)}\n"
f"\tShuffle: False\n"
f"\tSampler: None\n"
f"\tWorkers: {args.workers}")
cudnn.benchmark = True
# Set eval mode.
model.eval()
with torch.no_grad():
# Start evaluate model performance.
progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))
for i, (lr, bicubic, hr) in progress_bar:
# Move data to special device.
if args.gpu is not None:
lr = lr.cuda(args.gpu, non_blocking=True)
bicubic = bicubic.cuda(args.gpu, non_blocking=True)
hr = hr.cuda(args.gpu, non_blocking=True)
sr = model(lr)
# Evaluate performance
value = iqa(sr, hr, args.gpu)
total_mse_value += value[0]
total_rmse_value += value[1]
total_psnr_value += value[2]
total_ssim_value += value[3]
total_lpips_value += value[4]
total_gmsd_value += value[5]
progress_bar.set_description(
f"[{i + 1}/{len(dataloader)}] "
f"PSNR: {total_psnr_value / (i + 1):6.2f} "
f"SSIM: {total_ssim_value / (i + 1):6.4f}")
images = torch.cat([bicubic, sr, hr], -1)
vutils.save_image(images,
os.path.join("benchmarks", f"{i + 1}.bmp"),
padding=10)
print(f"Performance average results:\n")
print(f"indicator Score\n")
print(f"--------- -----\n")
print(f"MSE {total_mse_value / len(dataloader):6.4f}\n"
f"RMSE {total_rmse_value / len(dataloader):6.4f}\n"
f"PSNR {total_psnr_value / len(dataloader):6.2f}\n"
f"SSIM {total_ssim_value / len(dataloader):6.4f}\n"
f"LPIPS {total_lpips_value / len(dataloader):6.4f}\n"
f"GMSD {total_gmsd_value / len(dataloader):6.4f}")
def main(args):
# Initialize all evaluation criteria.
total_mse_value, total_rmse_value, total_psnr_value, total_ssim_value, total_gmsd_value = 0.0, 0.0, 0.0, 0.0, 0.0
if args.seed is not None:
# In order to make the model repeatable, the first step is to set random seeds, and the second step is to set
# convolution algorithm.
random.seed(args.seed)
torch.manual_seed(args.seed)
logger.warning("You have chosen to seed testing. "
"This will turn on the CUDNN deterministic setting, "
"which can slow down your testing considerably! "
"You may see unexpected behavior when restarting "
"from checkpoints.")
# for the current configuration, so as to optimize the operation efficiency.
cudnn.benchmark = True
# Ensure that every time the same input returns the same result.
cudnn.deterministic = True
# Build a super-resolution model, if model_ If path is defined, the specified model weight will be loaded.
model = configure(args)
# If special choice model path.
if args.model_path is not None:
logger.info(f"You loaded the specified weight. Load weights from `{os.path.abspath(args.model_path)}`.")
model.load_state_dict(torch.load(args.model_path, map_location=torch.device("cpu")))
# Switch model to eval mode.
model.eval()
# If the GPU is available, load the model into the GPU memory. This speed.
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
# Selection of appropriate treatment equipment.
dataset = CustomTestDataset(root=os.path.join(args.data, "test"),
image_size=args.image_size,
upscale_factor=args.upscale_factor)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
pin_memory=True,
num_workers=args.workers)
# Needs to reconstruct the low resolution image without the gradient information of the reconstructed image.
with torch.no_grad():
# Start evaluate model performance.
progress_bar = tqdm(enumerate(dataloader), total=len(dataloader))
for i, (lr, bicubic, hr) in progress_bar:
# Move data to special device.
if args.gpu is not None:
lr = lr.cuda(args.gpu, non_blocking=True)
bicubic = bicubic.cuda(args.gpu, non_blocking=True)
hr = hr.cuda(args.gpu, non_blocking=True)
# The low resolution image is reconstructed to the super resolution image.
sr = model(lr)
# The reconstructed image and the reference image are evaluated once.
value = iqa(sr, hr, args.gpu)
# The values of various evaluation indexes are accumulated.
total_mse_value += value[0]
total_rmse_value += value[1]
total_psnr_value += value[2]
total_ssim_value += value[3]
total_gmsd_value += value[4]
# Output as scrollbar style.
progress_bar.set_description(f"[{i + 1}/{len(dataloader)}] "
f"PSNR: {total_psnr_value / (i + 1):6.2f} "
f"SSIM: {total_ssim_value / (i + 1):6.4f}")
# Merge three images into one line for visualization.
# Save a series of reconstruction results.
vutils.save_image(torch.cat([bicubic, sr, hr], dim=-1),
os.path.join("benchmarks", f"{i + 1}.bmp"),
padding=10,
normalize=True)
print(f"Performance average results:\n")
print(f"Indicator score\n")
print(f"--------- -----\n")
print(f"MSE {total_mse_value / len(dataloader):6.4f}\n"
f"RMSE {total_rmse_value / len(dataloader):6.4f}\n"
f"PSNR {total_psnr_value / len(dataloader):6.2f}\n"
f"SSIM {total_ssim_value / len(dataloader):6.4f}\n"
f"GMSD {total_gmsd_value / len(dataloader):6.4f}\n")
from rfb_esrgan_pytorch.utils.common import configure
model_names = sorted(name for name in models.__dict__
if name.islower() and not name.startswith("__")
and callable(models.__dict__[name]))
logger = logging.getLogger(__name__)
logging.basicConfig(format="[ %(levelname)s ] %(message)s", level=logging.INFO)
parser = argparse.ArgumentParser(
description=
"Perceptual Extreme Super Resolution Network with Receptive Field Block.")
parser.add_argument("-a",
"--arch",
metavar="ARCH",
default="rfb",
choices=model_names,
help="Model architecture: " + " | ".join(model_names) +
". (Default: `rfb`)")
parser.add_argument("--model-path",
type=str,
metavar="PATH",
required=True,
help="Path to latest checkpoint for model.")
args = parser.parse_args()
model = configure(args)
model.load_state_dict(torch.load(args.model_path)["state_dict"])
torch.save(model.state_dict(), "Generator.pth")
logger.info("Model convert done.")
