def validate(model, opt):
# ----------------------------------------
# Initialize testing dataset
# ----------------------------------------
# Define the dataset
opt.dataroot = opt.baseroot + 'DIV2K_valid_HR'
testset = dataset.FullResDenoisingDataset(opt)
print('The overall number of images equals to %d' % len(testset))
# Define the dataloader
dataloader = DataLoader(testset, batch_size=1, pin_memory=True)
# ----------------------------------------
# Testing
# ----------------------------------------
overall_psnr = 0
for batch_idx, (noisy_img, img) in enumerate(dataloader):
# To Tensor
noisy_img = noisy_img.cuda()
img = img.cuda()
# Generator output
recon_img = model(noisy_img)
# convert to visible image format
h = img.shape[2]
w = img.shape[3]
img = img.cpu().numpy().reshape(3, h, w).transpose(1, 2, 0)
img = (img + 1) * 128
img = img.astype(np.uint8)
noisy_img = noisy_img.cpu().numpy().reshape(3, h, w).transpose(1, 2, 0)
noisy_img = (noisy_img + 1) * 128
noisy_img = noisy_img.astype(np.uint8)
recon_img = recon_img.detach().cpu().numpy().reshape(3, h,
w).transpose(
1, 2, 0)
recon_img = (recon_img + 1) * 128
recon_img = recon_img.astype(np.uint8)
overall_psnr += cv2.PSNR(recon_img, img)
return overall_psnr / len(testset)
type=int,
default=1,
help='test batch size, always 1')
parser.add_argument('--load_name',
type=str,
default='SGN_epoch1000_batchsize64',
help='test model name')
opt = parser.parse_args()
print(opt)
# ----------------------------------------
# Initialize testing dataset
# ----------------------------------------
# Define the dataset
testset = dataset.FullResDenoisingDataset(opt)
print('The overall number of images equals to %d' % len(testset))
# Define the dataloader
dataloader = DataLoader(testset,
batch_size=opt.batch_size,
pin_memory=True)
# ----------------------------------------
# Testing
# ----------------------------------------
model = utils.create_generator(opt)
for batch_idx, (noisy_img, img) in enumerate(dataloader):
type=float,
default=0,
help='min scaling factor')
parser.add_argument('--sigma',
type=float,
default=0.05,
help='max scaling factor')
opt = parser.parse_args()
print(opt)
# ----------------------------------------
# Initialize testing dataset
# ----------------------------------------
# Define the dataset
testset = dataset.FullResDenoisingDataset(opt, opt.baseroot)
print('The overall number of images equals to %d' % len(testset))
# Define the dataloader
dataloader = DataLoader(testset,
batch_size=opt.batch_size,
pin_memory=True)
# ----------------------------------------
# Testing
# ----------------------------------------
generator = utils.create_generator(opt)
generator = generator.cuda()
for batch_idx, (noisy_img, img, imgname) in enumerate(dataloader):