def main_worker(gpu, args):
args.gpu = gpu
if args.gpu is not None:
logger.info(f"Use GPU: {args.gpu} for training.")
model = configure(args)
if not torch.cuda.is_available():
logger.warning("Using CPU, this will be slow.")
elif 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),
Mode.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("test", 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("test", f"lr_{filename}"))
vutils.save_image(bicubic, os.path.join("test", f"bicubic_{filename}"))
vutils.save_image(sr, os.path.join("test", f"sr_{filename}"))
vutils.save_image(images,
os.path.join("test", f"compare_{filename}"),
padding=10)
def inference(lr, hr, model, model_path, gpu: int = None):
model.load_state_dict(torch.load(model_path)["state_dict"])
if gpu is not None:
torch.cuda.set_device(gpu)
model = model.cuda(gpu)
# Set eval mode.
model.eval()
with torch.no_grad():
sr = model(lr)
value = iqa(sr, hr, gpu)
return value
def main_worker(gpu, ngpus_per_node, args):
global total_mse_value, total_rmse_value, total_psnr_value
global 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 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)
model = configure(args)
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)
model.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)
model = nn.parallel.DistributedDataParallel(module=model,
device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.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"):
model.features = torch.nn.DataParallel(model.features)
model.cuda()
else:
model = torch.nn.DataParallel(model).cuda()
logger.info("Load testing dataset")
# Selection of appropriate treatment equipment.
dataset = BaseTestDataset(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)
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()
# 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)
with torch.no_grad():
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], dim=-1)
vutils.save_image(images,
os.path.join("benchmark", 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_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], dim=-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}")
