# input_tensor is tensor, format CxHxW, data range [0.0, 1.0]
input_tensor.unsqueeze_(0)
input_tensor = input_tensor.to(device)
with torch.no_grad():
output_tensor = model(input_tensor)
output_tensor.squeeze_(0)
output_image = tensor_to_image(output_tensor.cpu())
output_image.save("{}/{:03d}.png".format(output_dir, i))
if __name__ == '__main__':
# Define specific options and parse arguments
parser.add_argument("-i",
"--input",
help="input video",
required=True,
type=str)
parser.add_argument("-o",
"--output",
help="output video directory, defaut: /tmp/output",
type=str,
default="/tmp/output")
parser.add_argument('--checkpoint-file',
type=str,
default="output/WDSR-B-f32-b16-r6-x4-best.pth.tar")
args = parser.parse_args()
# Set cuDNN auto-tuner and get device
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
psnr.update(
calc_psnr(sr,
hr,
scale=args.scale,
max_value=args.rgb_range[1]), lr.shape[0])
t.set_postfix(loss='{:.4f}'.format(losses.avg))
t.update(lr.shape[0])
return losses.avg, psnr.avg
if __name__ == '__main__':
# Define specific options and parse arguments
parser.add_argument('--dataset-dir',
type=str,
required=True,
help='DIV2K Dataset Root Directory')
parser.add_argument('--output-dir', type=str, required=True)
parser.add_argument('--checkpoint-file', type=str, required=True)
args = parser.parse_args()
# Set cuDNN auto-tuner and get device for learning
cudnn.benchmark = True
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Set random seed
random.seed(args.seed)
torch.manual_seed(args.seed)
# Create a directory to store weights
sr = forward(lr)
# Quantize results
sr = quantize(sr, args.rgb_range)
# Update PSNR
psnr.update(calc_psnr(sr, hr, scale=args.scale, max_value=args.rgb_range[1]), lr.shape[0])
t.update(lr.shape[0])
print('DIV2K (val) PSNR: {:.4f} dB'.format(psnr.avg))
if __name__ == '__main__':
# Define specific options and parse arguments
parser.add_argument('--dataset-dir', type=str, required=True, help='DIV2K Dataset Root Directory')
parser.add_argument('--checkpoint-file', type=str, required=True)
parser.add_argument('--self-ensemble', action='store_true')
args = parser.parse_args()
# Set cuDNN auto-tuner and get device
cudnn.benchmark = True
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
# Create model
if args.model == 'WDSR-B':
model = WDSR_B(args).to(device)
else:
model = WDSR_A(args).to(device)
# Load weights