def test_image(model, image_path):
img_transforms = transforms.Compose([
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
# size_transform = Compose([
# PadIfNeeded(736, 1280)
# ])
#crop = CenterCrop(720, 720)
img = cv2.imread(image_path)
img_s = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
# img_s = size_transform(image=img)['image']
#img_s = crop(image=img)['image']
img_tensor = torch.from_numpy(np.transpose(img_s / 255, (2, 0, 1)).astype('float32'))
img_tensor = img_transforms(img_tensor)
with torch.no_grad():
img_tensor = Variable(img_tensor.unsqueeze(0).cuda())
result_image = model(img_tensor)
result_image = result_image[0].cpu().float().numpy()
result_image = (np.transpose(result_image, (1, 2, 0)) + 1) / 2.0 * 255.0
result_image = result_image.astype('uint8')
# gt_image = get_gt_image(image_path)
_, filename = os.path.split(image_path)
#save_image(result_image,filename)
psnr = PSNR(result_image, img_s)
pilFake = Image.fromarray(result_image)
pilReal = Image.fromarray(img_s)
ssim = calculate_ssim(result_image, img_s)
return psnr, ssim
def test_image(model, image_path):
img_transforms = transforms.Compose([
transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])
])
size_transform = Compose([
PadIfNeeded(736, 1280)
])
crop = CenterCrop(720, 1280)
img = cv2.imread(image_path + '_blur_err.png')
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_s = size_transform(image=img)['image']
img_tensor = torch.from_numpy(np.transpose(img_s / 255, (2, 0, 1)).astype('float32'))
img_tensor = img_transforms(img_tensor)
with torch.no_grad():
img_tensor = Variable(img_tensor.unsqueeze(0).cuda())
result_image = model(img_tensor)
result_image = result_image[0].cpu().float().numpy()
result_image = (np.transpose(result_image, (1, 2, 0)) + 1) / 2.0 * 255.0
result_image = crop(image=result_image)['image']
result_image = result_image.astype('uint8')
# gt_image = get_gt_image(image_path)
gt_image = cv2.cvtColor(cv2.imread(image_path + '_ref.png'), cv2.COLOR_BGR2RGB)
_, file = os.path.split(image_path)
psnr = PSNR(result_image, gt_image)
pilFake = Image.fromarray(result_image)
pilReal = Image.fromarray(gt_image)
ssim = SSIM(pilFake).cw_ssim_value(pilReal)
sample_img_names = set(["010221", "024071", "033451", "051271", "060201",
"070041", "090541", "100841", "101031", "113201"])
if file[-3:] == '001' or file in sample_img_names:
print('test_{}: PSNR = {} dB, SSIM = {}'.format(file, psnr, ssim))
result_image = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
cv2.imwrite(os.path.join('./test', 'test_'+image_path[-6:]+'.png'), result_image)
return psnr, ssim
def get_acc(self, output, target):
fake = self.tensor2im(output.data)
real = self.tensor2im(target.data)
psnr = PSNR(fake, real)
ssim = SSIM(fake, real, multichannel=True)
return psnr, ssim
def test_image(model, save_path, image_path):
img_transforms = transforms.Compose(
[transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5])])
size_transform = Compose([PadIfNeeded(736, 1280)])
crop = CenterCrop(720, 1280)
img = cv2.imread(image_path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img_s = size_transform(image=img)['image']
img_tensor = torch.from_numpy(
np.transpose(img_s / 255, (2, 0, 1)).astype('float32'))
img_tensor = img_transforms(img_tensor)
with torch.no_grad():
img_tensor = Variable(img_tensor.unsqueeze(0).cuda())
result_image = model(img_tensor)
result_image = result_image[0].cpu().float().numpy()
result_image = (np.transpose(result_image, (1, 2, 0)) + 1) / 2.0 * 255.0
result_image = crop(image=result_image)['image']
result_image = result_image.astype('uint8')
gt_image = get_gt_image(image_path)
lap = estimate_blur(result_image)
lap_sharp = estimate_blur(gt_image)
lap_blur = estimate_blur(img)
_, filename = os.path.split(image_path)
psnr = PSNR(result_image, gt_image)
pilFake = Image.fromarray(result_image)
pilReal = Image.fromarray(gt_image)
ssim = SSIM(pilFake).cw_ssim_value(pilReal)
#result_image = np.hstack((img_s, result_image, gt_image))
#cv2.imwrite(os.path.join(save_path, filename), cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR))
return psnr, ssim, lap, lap_blur, lap_sharp
def train(opt, data_loader, model):
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
results = model.get_current_visuals()
psnrMetric = PSNR(results['Restored_Train'],
results['Sharp_Train'])
print('PSNR on Train = %f' % (psnrMetric))
visualizer.display_current_results(results, epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors,
t)
message = '(epoch: %d, iters: %d, time: %.3f) ' % (
epoch, epoch_iter, t)
for k, v in errors.items():
message += '%s: %.3f ' % (k, v)
print(message)
print(message, file=open("output.txt", "a"))
if opt.display_id > 0:
visualizer.plot_current_errors(
epoch,
float(epoch_iter) / dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print(
'saving the latest model (epoch %d, total_steps %d)'
% (epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print(
'saving the model at the end of epoch %d, iters %d'
% (epoch, total_steps))
model.save('latest')
model.save(epoch)
print(
'End of epoch %d / %d \t Time Taken: %d sec'
% (epoch, opt.niter + opt.niter_decay,
time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()
def get_images_and_metrics(self, inp, output, target) -> (float, float, np.ndarray):
inp = self.tensor2im(inp)
fake = self.tensor2im(output.data)
real = self.tensor2im(target.data)
psnr = PSNR(fake, real)
ssim = SSIM(fake, real, multichannel=True)
vis_img = np.hstack((inp, fake, real))
return psnr, ssim, vis_img
def train(opt, data_loader, model, visualizer):
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = 0
saving_flag = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
psnr_all = 0
cnt = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters()
results = model.get_current_visuals()
psnrMetric = PSNR(results['Restored_Train'],
results['Sharp_Train'])
psnr_all = psnr_all + psnrMetric
cnt += 1
if total_steps % opt.display_freq == 0:
#print('PSNR on Train = %f' % (psnrMetric))
visualizer.display_current_results(results, epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(
epoch,
float(epoch_iter) / dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if psnr_all / cnt > 28.: ## negelete some unimportant checkpoint
saving_flag = 1
if epoch % opt.save_epoch_freq == 0 or saving_flag:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest')
model.save(epoch)
saving_flag = 0
print('End of epoch %d / %d \t Time Taken: %d sec avg.psnr=%f dB' %
(epoch, opt.niter + opt.niter_decay,
time.time() - epoch_start_time, psnr_all / cnt))
if epoch > opt.niter:
model.update_learning_rate()
def train(opt, data_loader, model, visualizer):
logger = Logger('./checkpoints/log/')
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.optimize_parameters()
if total_steps % opt.display_freq == 0:
results = model.get_current_visuals()
ssim = pytorch_ssim.ssim(results['fake_B'], results['real_B']).item()
psnrMetric = PSNR(results['Restored_Train'],results['Sharp_Train'])
print('PSNR = %f, SSIM = %.4f' % (psnrMetric, ssim))
results.pop('fake_B') # 计算完SSIM，就去掉多余项
results.pop('real_B')
visualizer.display_current_results(results,epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
for tag, value in errors.items():
logger.scalar_summary(tag, value, epoch)
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(epoch, float(epoch_iter)/dataset_size, opt, errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest')
model.save(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay, time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()
def get_images_and_metrics(self, inps, outputs,
targets) -> (float, float, np.ndarray):
psnr = 0
ssim = 0
for i in range(len(inps)):
inp = inps[i:i + 1]
output = outputs[i:i + 1]
target = targets[i:i + 1]
inp = self.tensor2im(inp.data)
fake = self.tensor2im(output.data)
real = self.tensor2im(target.data)
psnr += PSNR(fake, real)
ssim += SSIM(fake, real, multichannel=True)
vis_img = np.hstack((inp, fake, real))
return psnr / len(inps), ssim / len(inps), vis_img
def forward(self):
self.fake_t1fse = self.netG(self.real_A, self.label_t1fse)
self.fake_t2fse = self.netG(self.real_A, self.label_t2fse)
self.fake_t1flair = self.netG(self.real_A, self.label_t1flair)
self.fake_t2flair = self.netG(self.real_A, self.label_t2flair)
self.fake_pdfse = self.netG(self.real_A, self.label_pdfse)
self.fake_stir = self.netG(self.real_A, self.label_stir)
self.loss_PSNR_t1fse = PSNR(self.real_t1fse, self.fake_t1fse)
self.loss_PSNR_t2fse = PSNR(self.real_t2fse, self.fake_t2fse)
self.loss_PSNR_t1flair = PSNR(self.real_t1flair, self.fake_t1flair)
self.loss_PSNR_t2flair = PSNR(self.real_t2flair, self.fake_t2flair)
self.loss_PSNR_pdfse = PSNR(self.real_pdfse, self.fake_pdfse)
self.loss_PSNR_stir = PSNR(self.real_stir, self.fake_stir)
self.loss_PSNR = self.loss_PSNR_t1fse + self.loss_PSNR_t2fse + self.loss_PSNR_t1flair + self.loss_PSNR_t2flair + self.loss_PSNR_pdfse + self.loss_PSNR_stir
def train(opt, data_loader, model, visualizer):
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = 0
for epoch in range(model.s_epoch, opt.e_iter):
epoch_start_time = time.time()
epoch_iter = 0
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batchSize
epoch_iter += opt.batchSize
model.set_input(data)
model.train_update()
if total_steps % opt.display_freq == 0:
results = model.get_current_visuals()
psnrMetric = PSNR(results['Restored_Train'],
results['Sharp_Train'])
print('PSNR on Train = %f' % psnrMetric)
visualizer.display_current_results(results, epoch)
if total_steps % opt.print_freq == 0:
errors = model.get_current_errors()
t = (time.time() - iter_start_time) / opt.batchSize
visualizer.print_current_errors(epoch, epoch_iter, errors, t)
if opt.display_id > 0:
visualizer.plot_current_errors(
epoch,
float(epoch_iter) / dataset_size, opt, errors)
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.e_iter, time.time() - epoch_start_time))
# test
avgPSNR = 0.0
avgSSIM = 0.0
counter = 0
for i, data in enumerate(dataset):
if i >= opt.how_many:
break
counter = i
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
if opt.dataset_mode != 'single':
real_B = util.tensor2im(data['B'])
avgPSNR += PSNR(visuals['fake_B'], real_B)
pilFake = Image.fromarray(visuals['fake_B'])
pilReal = Image.fromarray(real_B)
avgSSIM += SSIM(pilFake).cw_ssim_value(pilReal)
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path)
if opt.dataset_mode != 'single':
avgPSNR /= counter
avgSSIM /= counter
with open(
os.path.join(opt.results_dir, opt.name, 'test_latest',
'result.txt'), 'w') as f:
f.write('PSNR = %f\n' % avgPSNR)
f.write('SSIM = %f\n' % avgSSIM)
dataset = data_loader.load_data()
model = create_model(opt)
visualizer = Visualizer(opt)
# create website
web_dir = os.path.join(opt.results_dir, opt.name, '%s_%s' % (opt.phase, opt.which_epoch))
webpage = html.HTML(web_dir, 'Experiment = %s, Phase = %s, Epoch = %s' % (opt.name, opt.phase, opt.which_epoch))
# test
PSNR = 0.0
SSIM = 0.0
counter = 0
for i, data in enumerate(dataset):
if i >= opt.how_many:
break
counter = i
model.set_input(data)
model.test()
PSNR += PSNR(visuals['fake_B'],visuals['real_B'])
SSIM += SSIM(visuals['fake_B'],visuals['real_B'])
visuals = model.get_current_visuals()
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(webpage, visuals, img_path)
PSNR /= counter
SSIM /= counter
print('PSNR = %d, SSIM = %d' %
(PSNR, SSIM))
webpage.save()
break
w_s += step
if (h_s + img_s >= img_height):
break
h_s += step
img[:, :, 0] = img[:, :, 0] / area_count
img[:, :, 1] = img[:, :, 1] / area_count
img[:, :, 2] = img[:, :, 2] / area_count
fake_B = img.astype(np.uint8)
if opt.dataset_mode != 'single':
real_B = util.tensor2im(data['B'])
avgPSNR += PSNR(fake_B, real_B)
pilFake = Image.fromarray(fake_B)
pilReal = Image.fromarray(real_B)
avgSSIM += SSIM(pilFake).cw_ssim_value(pilReal)
img_path = model.get_image_paths()
print('process image... %s' % img_path)
visualizer.save_images(
webpage,
OrderedDict([('real_A', util.tensor2im(data['A'])),
('fake_B', fake_B)]), img_path)
if opt.dataset_mode != 'single':
avgPSNR /= counter
avgSSIM /= counter
with open(
net_G = net_G.cuda().eval()
imgpath = args.data / "images_256"
collpath = args.data / "collages"
respath = args.data / "results"
collpath.mkdir(exist_ok=True)
respath.mkdir(exist_ok=True)
files = [Path(f).stem for f in listdir(imgpath) if isfile(join(imgpath, f))][::50]
dataset = SCDataset(args.data, files)
psnr = 0.0
ssim = 0.0
l2 = 0.0
P = PSNR()
S = SSIM()
L = torch.nn.MSELoss()
n_elems = len(dataset)
for i, item in enumerate(tqdm(dataset)):
image, colormap, sketch, mask = (
item["image"].unsqueeze(0).cuda(),
item["colormap"].unsqueeze(0).cuda(),
item["sketch"].unsqueeze(0).cuda(),
item["mask"].unsqueeze(0).cuda(),
)
generator_input = torch.cat(
(image * mask, colormap * (1 - mask), sketch * (1 - mask), mask), dim=1
)
coarse_image, refined_image = net_G(generator_input)
# test
avgPSNR = 0.0
avgPSNR_b = 0.0
#avgSSIM = 0.0
#avgSSIM_b = 0.0
counter = 0
with torch.no_grad():
for i, data in enumerate(dataset):
if i >= opt.how_many:
break
counter = i
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
PSNR_b = PSNR(visuals['real_A'], visuals['real_B'])
PSNR_d = PSNR(visuals['fake_B'], visuals['real_B'])
avgPSNR += PSNR_d
avgPSNR_b += PSNR_b
#pilReala = Image.fromarray(visuals['real_A'])
#pilFake = Image.fromarray(visuals['fake_B'])
#pilReal = Image.fromarray(visuals['real_B'])
#SSIM_b = SSIM(pilReala,pilReal)
#SSIM_b = SSIM(pilReala).cw_ssim_value(pilReal)
#SSIM_d = SSIM(pilFake).cw_ssim_value(pilReal)
#avgSSIM += SSIM_d
#avgSSIM_b += SSIM_b
img_path = model.get_image_paths()
#print('process image... %s ... Deblurred PSNR ... %f' % (img_path, PSNR_d))
print(
'process image... %s ... Blurred PSNR ... %f ... Deblurred PSNR ... %f'
def train(opt, data_loader, model, visualizer):
dataset = data_loader.load_data()
dataset_size = len(data_loader)
print('#training images = %d' % dataset_size)
total_steps = 0
for epoch in range(opt.epoch_count, opt.niter + opt.niter_decay + 1):
epoch_start_time = time.time()
epoch_iter = 0
tot_errors = None
avg_errors = None
for i, data in enumerate(dataset):
iter_start_time = time.time()
total_steps += opt.batch_size
epoch_iter += opt.batch_size
model.set_input(data)
model.optimize_parameters(i)
errors = model.get_current_errors()
if tot_errors == None:
tot_errors = errors.copy()
avg_errors = errors.copy()
else:
for k in errors:
tot_errors[k] += errors[k]
# display on visdom
if total_steps % opt.display_freq == 0:
results = model.get_current_visuals()
# calc psnr
psnrMetric = PSNR(results['Restored_Train'],
results['Sharp_Train'])
print('PSNR on Train = %f' % (psnrMetric))
visualizer.display_current_results(results, epoch)
if total_steps % opt.print_freq == 0:
#errors = model.get_current_errors()
ttot_errors = tot_errors.copy()
for k in tot_errors:
avg_errors[k] = ttot_errors[k] / (i + 1)
t = (time.time() - iter_start_time) / opt.batch_size
visualizer.print_current_errors(epoch, epoch_iter, avg_errors,
t)
#if opt.display_id > 0 and total_steps % opt.show_freq == 0:
if opt.display_id > 0:
visualizer.plot_current_errors(
epoch,
float(epoch_iter) / dataset_size, opt, avg_errors)
if total_steps % opt.save_latest_freq == 0:
print('saving the latest model (epoch %d, total_steps %d)' %
(epoch, total_steps))
model.save('latest')
if epoch % opt.save_epoch_freq == 0:
print('saving the model at the end of epoch %d, iters %d' %
(epoch, total_steps))
model.save('latest')
model.save(epoch)
print('End of epoch %d / %d \t Time Taken: %d sec' %
(epoch, opt.niter + opt.niter_decay,
time.time() - epoch_start_time))
if epoch > opt.niter:
model.update_learning_rate()
def main():
# Load Data and optical flow net
dataDir = "../flowTestDataset/"
# Data loading code
# input_transform = transforms.Compose([
# flow_transforms.ArrayToTensor(),
# transforms.Normalize(mean=[0,0,0], std=[255,255,255]),
# transforms.Normalize(mean=[0.411,0.432,0.45], std=[1,1,1])
# ])
# print("=> fetching img pairs in '{}'".format(dataDir))
# test_set = optical_flow_dataset(
# dataDir,
# transform=input_transform
# )
test_set = reblurDataSet()
test_set.initialize("/scratch/user/jiangziyu/test/")
print('{} samples found'.format(len(test_set)))
val_loader = torch.utils.data.DataLoader(test_set,
batch_size=1,
num_workers=1,
pin_memory=True,
shuffle=False)
# create model
network_data = torch.load(
'/scratch/user/jiangziyu/flownets_EPE1.951.pth.tar')
model = flownets(network_data).cuda()
model = torch.nn.DataParallel(model).cuda()
cudnn.benchmark = True
transformBack = transforms.Normalize(mean=[-0.411, -0.432, -0.45],
std=[1, 1, 1])
model.eval()
flipKernel = Variable(torch.ones(2, 1, 1, 1), requires_grad=False).cuda()
flipKernel[1] = -flipKernel[1]
# Load Kernel Calculation Module
KernelModel = flowToKernel()
# Load blurWithKernel Module
BlurModel = reblurWithKernel()
avgPSNR = 0.0
counter = 0
for epoch in range(15):
for i, sample in enumerate(val_loader):
counter = counter + 1
input_var_1 = torch.autograd.Variable(torch.cat(
[sample['image0'], sample['image1']], 1).cuda(),
volatile=True)
input_var_2 = torch.autograd.Variable(torch.cat(
[sample['image2'], sample['image1']], 1).cuda(),
volatile=True)
# compute output
# print(input_var_1.data.size())
output_1 = model(input_var_1)
output_2 = model(input_var_2)
# filp the axis direction
output_1 = F.conv2d(output_1, flipKernel, groups=2)
output_2 = F.conv2d(output_2, flipKernel, groups=2)
output_1 = torch.transpose(output_1, 1, 2)
output_1 = torch.transpose(output_1, 2, 3)
output_2 = torch.transpose(output_2, 1, 2)
output_2 = torch.transpose(output_2, 2, 3)
ImageKernels = KernelModel.forward((20 / 16) * output_1,
(20 / 16) * output_2)
blurImg = BlurModel.forward(
torch.autograd.Variable(sample['image1']).cuda(), ImageKernels)
fake_B = (transformBack(blurImg.data[0]).cpu().float().numpy() *
255.0).astype(np.uint8)
real_B = (transformBack(sample['label'][0]).float().numpy() *
255.0).astype(np.uint8)
print(fake_B.shape)
avgPSNR += PSNR(fake_B, real_B)
print('process image... %s' % str(i))
# if(counter == 1):
# scipy.misc.imsave('blur1.png', np.transpose(transformBack(blurImg.data[0]).cpu().float().numpy(),(1,2,0)))
# scipy.misc.imsave('sharp1.png', np.transpose(transformBack(sample['image1'][0]).cpu().float().numpy(),(1,2,0)))
# scipy.misc.imsave('blurOrigin.png', np.transpose(transformBack(sample['label'][0]).float().numpy(),(1,2,0)))
# return
if (counter % 20 == 0):
print('PSNR = %f' % (avgPSNR / counter))
# scipy.misc.imsave('outfile{}.png'.format(i), flow2rgb(20 * output.data[0].cpu().numpy(), max_value=25))
print('PSNR = %f' % (avgPSNR / counter))
return
# test
avgPSNR = 0.0
avgSSIM = 0.0
counter = 0
for i, data in enumerate(dataset):
if i >= opt.how_many:
break
counter = i
model.set_input(data)
model.test()
visuals = model.get_current_visuals()
if opt.dataset_mode != 'single':
real_B = util.tensor2im(data['B1'])
avgPSNR += PSNR(visuals['Restored_Train'], real_B)
pilFake = Image.fromarray(visuals['Restored_Train'])
pilReal = Image.fromarray(real_B)
avgSSIM += SSIM(pilFake).cw_ssim_value(pilReal)
img_path = model.get_image_paths()
if opt.dataset_mode != 'single':
real_B = util.tensor2im(data['B1'])
psnr = PSNR(visuals['Restored_Train'], real_B)
avgPSNR += psnr
pilFake = Image.fromarray(visuals['Restored_Train'])
pilReal = Image.fromarray(real_B)
pilBlur = Image.fromarray(visuals['Blurred_Train'])
ssim = SSIM(pilFake).cw_ssim_value(pilReal)
avgSSIM += ssim
img_path = model.get_image_paths()
def forward(self):
self.fake_B = self.netG(self.real_A, self.label_channel)
self.loss_PSNR = PSNR(self.real_B, self.fake_B)
self.loss_SSIM = self.ssim_loss(self.real_B.repeat(1, 3, 1, 1), self.fake_B.repeat(1, 3, 1, 1))
