# lowfreq3.axis('off')
if n_count % 800 == 0:
highfreq1.imshow(High_noise[0, 0].cpu(), cmap='jet')
highfreq2.imshow(High_origin[0, 0].cpu(), cmap='jet')
highfreq3.imshow(decompose_output[0, 0].cpu().detach().numpy(),
cmap='jet')
lowfreq1.imshow(y_[0, 0].cpu(), cmap='jet')
lowfreq2.imshow(x_[0, 0].cpu(), cmap='jet')
# lowfreq3.imshow(decompose_output[0,1].cpu().detach().numpy(), cmap='jet')
plt.show()
epoch_loss += decompose_loss.item()
if n_count % 10 == 0:
print('%4d %4d / %4d loss = %2.8f loss_hf = %2.8f ' %
(epoch + 1, n_count, xs.size(0) // batch_size,
decompose_loss.item() / batch_size,
decompose_loss.item() / batch_size))
elapsed_time = time.time() - start_time
log('epcoh = %4d , loss = %4.4f , time = %4.2f s' %
(epoch + 1, epoch_loss / n_count, elapsed_time))
np.savetxt('train_result.txt',
np.hstack((epoch + 1, epoch_loss / n_count, elapsed_time)),
fmt='%2.4f')
# torch.save(compose_model.state_dict(), os.path.join(save_dir, 'com_model_%03d.pth' % (epoch+1)))
torch.save(decompose_model.state_dict(),
os.path.join(save_dir, 'model_%03d.pth' % (epoch + 1)))
# torch.save(model, os.path.join(save_dir, 'model_%03d.pth' % (epoch + 1)))
parser.add_argument('--gaussian_noise_level', type=int)
parser.add_argument('--jpeg_quality', type=int)
parser.add_argument('--downsampling_factor', type=int)
opt = parser.parse_args()
if not os.path.exists(opt.outputs_dir):
os.makedirs(opt.outputs_dir)
if opt.arch == 'DnCNN-S':
model = DnCNN(num_layers=17)
elif opt.arch == 'DnCNN-B':
model = DnCNN(num_layers=20)
elif opt.arch == 'DnCNN-3':
model = DnCNN(num_layers=20)
state_dict = model.state_dict()
for n, p in torch.load(opt.weights_path,
map_location=lambda storage, loc: storage).items():
if n in state_dict.keys():
state_dict[n].copy_(p)
else:
raise KeyError(n)
model = model.to(device)
model.eval()
filename = os.path.basename(opt.image_path).split('.')[0]
descriptions = ''
input = pil_image.open(opt.image_path).convert('RGB')
if __name__ == "__main__":
args = get_args()
# device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = DnCNN().to(device)
optimizer = optim.SGD(model.parameters(), lr=args.lr)
criterion = nn.MSELoss()
training_data_loader, testing_data_loader = dataloader(
args.train_dir, args.test_dir, args.crop_size, args.batch_size)
mean = args.noise_mean
stddev = args.noise_std
num_epochs = args.num_epochs
for epoch in range(1, num_epochs + 1):
train(epoch, model, optimizer, training_data_loader, mean, stddev,
criterion)
if epoch % args.eval_interval == 0:
validate(model, testing_data_loader, mean, stddev, criterion)
if epoch % args.save_interval == 0:
save_checkpoint({
'epoch': epoch + 1,
'arch': model,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
})
original2 = fig.add_subplot(gs[1, 0])
original.axis('off')
noised.axis('off')
out.axis('off')
final.axis('off')
original2.axis('off')
if n_count == 0:
original.imshow(batch_x[0].cpu().detach().numpy().squeeze(0), cmap='rainbow')
noised.imshow(batch_y[0].cpu().detach().numpy().squeeze(0), cmap='rainbow')
out.imshow(output[0].cpu().detach().numpy().squeeze(0), cmap='rainbow')
final.imshow((output+low_)[0].cpu().detach().numpy().squeeze(0), cmap='rainbow')
original2.imshow(temp[0].cpu().detach().numpy().squeeze(0), cmap='rainbow')
plt.show()
epoch_loss += loss.item()
loss.backward()
optimizer.step()
if n_count % 10 == 0:
print('%4d %4d / %4d loss = %2.8f final_loss = %2.8f edge_loss = %2.8f' % (
epoch + 1, n_count, xs.size(0) // batch_size, loss.item() / batch_size, loss2.item() / batch_size, loss1.item() / batch_size))
elapsed_time = time.time() - start_time
log('epcoh = %4d , loss = %4.4f , time = %4.2f s' % (epoch + 1, epoch_loss / n_count, elapsed_time))
np.savetxt('train_result.txt', np.hstack((epoch + 1, epoch_loss / n_count, elapsed_time)), fmt='%2.4f')
# torch.save(model.state_dict(), os.path.join(save_dir, 'model_%03d.pth' % (epoch+1)))
torch.save(model.state_dict(), os.path.join(save_dir, 'model_%03d.pth' % (epoch + 1)))
lowfreq3.axis('off')
if n_count % 100 == 0:
highfreq1.imshow(batch_y[0, 0].cpu(), cmap='rainbow')
highfreq2.imshow(batch_x[0, 0].cpu(), cmap='rainbow')
highfreq3.imshow(output[0, 0].cpu().detach().numpy(),
cmap='rainbow')
lowfreq1.imshow(batch_y[0, 1].cpu(), cmap='rainbow')
lowfreq2.imshow(batch_x[0, 1].cpu(), cmap='rainbow')
lowfreq3.imshow(output[0, 1].cpu().detach().numpy(),
cmap='rainbow')
plt.show()
epoch_loss += loss.item()
loss.backward()
optimizer.step()
if n_count % 10 == 0:
print('%4d %4d / %4d loss = %2.8f' %
(epoch + 1, n_count, xs.size(0) // batch_size,
loss.item() / batch_size))
elapsed_time = time.time() - start_time
log('epcoh = %4d , loss = %4.4f , time = %4.2f s' %
(epoch + 1, epoch_loss / n_count, elapsed_time))
np.savetxt('train_result.txt',
np.hstack((epoch + 1, epoch_loss / n_count, elapsed_time)),
fmt='%2.4f')
torch.save(model.state_dict(),
os.path.join(save_dir, 'model_%03d.pth' % (epoch + 1)))
# torch.save(model, os.path.join(save_dir, 'model_%03d.pth' % (epoch + 1)))