def bilinear_upsampling(opt, dataloader, scale):
for batch_no, data in enumerate(dataloader['test']):
high_img, _ = data
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize,
opt.imageSize)
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
outputs = F.upsample(inputs,
scale_factor=opt.upSampling,
mode='bilinear',
align_corners=True)
transform = transforms.Compose([
transforms.Normalize(mean=[-2.118, -2.036, -1.804],
std=[4.367, 4.464, 4.444]),
transforms.ToPILImage()
])
transform(outputs[0]).save('output/train/bilinear_fake/' +
str(batch_no) + '.png')
transform(high_img[0]).save('output/train/bilinear_real/' +
str(batch_no) + '.png')
# for output, himg in zip (outputs, high_img):
# psnr_val = psnr(output,himg)
#mssim = avg_msssim(himg, output)
print(psnr(un_normalize(outputs), un_normalize(high_img)))
def show_generated_images(dataset, net, device, show_n=5):
image_idx = np.random.choice(len(dataset), show_n)
image_idx
images = []
for idx in image_idx:
images.append(dataset[idx])
images = torch.stack(images).to(device)
original_images = un_normalize(images)
generated_images = un_normalize(net(images))
fig, axes = plt.subplots(2, len(original_images))
for i in range(len(original_images)):
axes[0, i].imshow(original_images[i])
axes[1, i].imshow(generated_images[i])
plt.show()
def test_multiple(generator, discriminator, opt, dataloader, scale):
generator.load_state_dict(torch.load(opt.generatorWeights))
discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
feature_extractor = FeatureExtractor(
torchvision.models.vgg19(pretrained=True))
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()
ones_const = Variable(torch.ones(opt.batchSize, 1))
if opt.cuda:
generator.cuda()
discriminator.cuda()
feature_extractor.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
curr_time = time.time()
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
mean_psnr = 0.0
mean_msssim = 0.0
high_img = torch.FloatTensor(opt.batchSize, 3, opt.imageSize,
opt.imageSize)
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)
high_res_fake = torch.FloatTensor(opt.batchSize, 3, opt.imageSize,
opt.imageSize)
max_psnr = 0.0
mean_psnr = 0.0
min_psnr = 999.0
mean_ssim = 0.0
for batch_no, data in enumerate(dataloader['test']):
high_img, _ = data
generator.train(False)
discriminator.train(False)
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
if opt.cuda:
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(
Variable(inputs[0][np.newaxis, :]).cuda(),
Variable(inputs[1][np.newaxis, :]).cuda(),
Variable(inputs[2][np.newaxis, :]).cuda(),
Variable(inputs[3][np.newaxis, :]).cuda())
target_real = Variable(torch.rand(opt.batchSize, 1) * 0.5 +
0.7).cuda()
target_fake = Variable(torch.rand(opt.batchSize, 1) * 0.3).cuda()
discriminator_loss = adversarial_criterion(
discriminator(Variable(inputs[0][np.newaxis, :]).cuda(), Variable(inputs[1][np.newaxis, :]).cuda(),
Variable(inputs[2][np.newaxis, :]).cuda(), Variable(inputs[3][np.newaxis, :]).cuda()),
target_real) + \
adversarial_criterion(
discriminator(high_res_fake[0][np.newaxis, :], high_res_fake[1][np.newaxis, :], high_res_fake[2][np.newaxis, :],
high_res_fake[3][np.newaxis, :]), target_fake)
mean_discriminator_loss += discriminator_loss.data.item()
#high_res_fake_cat = torch.cat([image for image in high_res_fake], 0)
fake_features = feature_extractor(high_res_fake)
real_features = Variable(feature_extractor(high_res_real).data)
generator_content_loss = content_criterion(
high_res_fake, high_res_real) + 0.006 * content_criterion(
fake_features, real_features)
mean_generator_content_loss += generator_content_loss.data.item()
generator_adversarial_loss = adversarial_criterion(
discriminator(high_res_fake[0][np.newaxis, :],
high_res_fake[1][np.newaxis, :],
high_res_fake[2][np.newaxis, :],
high_res_fake[3][np.newaxis, :]), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data.item(
)
generator_total_loss = generator_content_loss + 1e-3 * generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data.item()
imsave(high_res_fake.cpu().data,
train=False,
epoch=batch_no,
image_type='fake')
imsave(high_img, train=False, epoch=batch_no, image_type='real')
imsave(inputs, train=False, epoch=batch_no, image_type='low')
mssim = avg_msssim(high_res_real, high_res_fake)
ssim = pytorch_ssim.ssim(high_res_fake, high_res_real).data.item()
mean_ssim += ssim
psnr_val = psnr(un_normalize(high_res_fake),
un_normalize(high_res_real))
mean_psnr += psnr_val
max_psnr = psnr_val if psnr_val > max_psnr else max_psnr
min_psnr = psnr_val if psnr_val < min_psnr else min_psnr
sys.stdout.write(
'\rTesting batch no. [%d/%d] Generator_content_Loss: %.4f discriminator_loss %.4f psnr %.4f ssim %.4f'
% (batch_no, len(dataloader['test']), generator_content_loss,
discriminator_loss, psnr_val, ssim))
print("Min psnr is: ", min_psnr)
print("Mean psnr is: ", mean_psnr / 72)
print("Max psnr is: ", max_psnr)
print("Mean ssim is: ", mean_ssim / 72)
def train_single(generator, discriminator, opt, dataloader, writer, scale):
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()
ones_const = Variable(torch.ones(1, 1))
if opt.cuda:
generator.cuda()
discriminator.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
optimizer = optim.Adam(generator.parameters(), lr=opt.generatorLR)
optim_discriminator = optim.Adam(discriminator.parameters(),
lr=opt.discriminatorLR)
scheduler_gen = ReduceLROnPlateau(optimizer,
'min',
factor=0.5,
patience=3,
verbose=True)
scheduler_dis = ReduceLROnPlateau(optim_discriminator,
'min',
factor=0.5,
patience=3,
verbose=True)
curr_time = time.time()
for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
high_res_fake = 0
for phase in ['train', 'test']:
if phase == 'test':
generator.train(False)
discriminator.train(False)
else:
generator.train(True)
discriminator.train(True)
for batch_no, data in enumerate(dataloader[phase]):
high_img, _ = data
input1 = high_img[0, :, :, :]
input2 = high_img[1, :, :, :]
input3 = high_img[2, :, :, :]
input4 = high_img[3, :, :, :]
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize,
opt.imageSize)
# imshow(input3)
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
high_comb = torch.cat(
[high_img[0], high_img[1], high_img[2], high_img[3]], 0)
high_comb = Variable(high_comb[np.newaxis, :]).cuda()
# imshow(high_comb.cpu().data)
input_comb = torch.cat([
scale(input1),
scale(input2),
scale(input3),
scale(input4)
], 0)
input_comb = input_comb[np.newaxis, :]
if opt.cuda:
optimizer.zero_grad()
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(input_comb).cuda())
target_real = Variable(torch.rand(1, 1) * 0.5 + 0.7).cuda()
target_fake = Variable(torch.rand(1, 1) * 0.3).cuda()
outputs = torch.chunk(high_res_fake, 4, 1)
outputs = torch.cat(
[outputs[0], outputs[1], outputs[2], outputs[3]], 0)
discriminator.zero_grad()
discriminator_loss = adversarial_criterion(discriminator(high_comb), target_real) + \
adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
mean_discriminator_loss += discriminator_loss.data[0]
if phase == 'train':
discriminator_loss.backward()
optim_discriminator.step()
generator_content_loss = content_criterion(
high_res_fake, high_comb)
mean_generator_content_loss += generator_content_loss.data[
0]
generator_adversarial_loss = adversarial_criterion(
discriminator(high_res_fake), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data[
0]
generator_total_loss = generator_content_loss + 1e-3 * generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data[0]
if phase == 'train':
generator_total_loss.backward()
optimizer.step()
if (batch_no % 10 == 0):
sys.stdout.write(
'\rphase [%s] epoch [%d/%d] batch no. [%d/%d] Generator_content_Loss: %.4f Discriminator loss: %.4f'
% (phase, epoch, opt.nEpochs, batch_no,
len(dataloader[phase]), generator_content_loss,
discriminator_loss))
# imshow(high_res_fake.cpu().data)
scheduler_gen.step(mean_generator_total_loss)
scheduler_dis.step(mean_discriminator_loss)
psnr_val = psnr(un_normalize(high_res_real), un_normalize(outputs))
# imsave(outputs.cpu().data, train=True, epoch=epoch, image_type='fake')
# imsave(high_img, train=True, epoch=epoch, image_type='real')
# imsave(inputs, train=True, epoch=epoch, image_type='low')
writer.add_scalar(phase + " per epoch/generator lr",
optimizer.param_groups[0]['lr'], epoch)
writer.add_scalar(phase + " per epoch/discriminator lr",
optim_discriminator.param_groups[0]['lr'], epoch)
writer.add_scalar(phase + " per epoch/PSNR", psnr_val, epoch)
if phase == 'train':
writer.add_scalar(
phase + " per epoch/discriminator training loss",
mean_discriminator_loss, epoch)
writer.add_scalar(phase + " per epoch/generator training loss",
mean_generator_total_loss, epoch)
writer.add_scalar("per epoch/time taken",
time.time() - curr_time, epoch)
torch.save(generator.state_dict(),
'%s/generator_single.pth' % opt.out)
torch.save(discriminator.state_dict(),
'%s/discriminator_single.pth' % opt.out)
def train_firstmodel(generator, opt, dataloader, writer, scale):
content_criterion = nn.MSELoss()
ones_const = Variable(torch.ones(1, 1))
if opt.cuda:
generator.cuda()
content_criterion.cuda()
optimizer = optim.SGD(generator.parameters(), lr=opt.generatorLR)
scheduler_gen = ReduceLROnPlateau(optimizer,
'min',
factor=0.5,
patience=3,
verbose=True)
curr_time = time.time()
for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_total_loss = 0.0
high_res_fake = 0
for phase in ['train', 'test']:
if phase == 'test':
generator.train(False)
else:
generator.train(True)
for batch_no, data in enumerate(dataloader[phase]):
high_img, _ = data
input1 = high_img[0, :, :, :]
input2 = high_img[1, :, :, :]
input3 = high_img[2, :, :, :]
input4 = high_img[3, :, :, :]
# imshow(input3)
for j in range(opt.batchSize):
high_img[j] = normalize(high_img[j])
high_comb = torch.cat(
[high_img[0], high_img[1], high_img[2], high_img[3]], 0)
high_comb = Variable(high_comb[np.newaxis, :]).cuda()
# imshow(high_comb.cpu().data)
input_comb = torch.cat([
scale(input1),
scale(input2),
scale(input3),
scale(input4)
], 0)
input_comb = input_comb[np.newaxis, :]
if opt.cuda:
if phase == 'train':
optimizer.zero_grad()
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(input_comb).cuda())
outputs = torch.chunk(high_res_fake, 4, 1)
outputs = torch.cat(
[outputs[0], outputs[1], outputs[2], outputs[3]], 0)
# imshow(outputs[0])
generator_content_loss = content_criterion(
high_res_fake, high_comb)
mean_generator_content_loss += generator_content_loss.data[
0]
generator_total_loss = generator_content_loss
mean_generator_total_loss += generator_total_loss.data[0]
if phase == 'train':
generator_total_loss.backward()
optimizer.step()
if (batch_no % 10 == 0):
sys.stdout.write(
'\rphase [%s] epoch [%d/%d] batch no. [%d/%d] Generator_content_Loss: %.4f '
% (phase, epoch, opt.nEpochs, batch_no,
len(dataloader[phase]), generator_content_loss))
if phase == 'train':
# imsave(outputs,train=True,epoch=epoch,image_type='fake')
# imsave(high_img, train=True, epoch=epoch, image_type='real')
# imsave(input_comb, train=True, epoch=epoch, image_type='low')
writer.add_scalar(phase + " per epoch/generator lr",
optimizer.param_groups[0]['lr'], epoch + 1)
scheduler_gen.step(mean_generator_total_loss /
len(dataloader[phase]))
mssim = avg_msssim(high_res_real, outputs)
psnr_val = psnr(un_normalize(high_res_real), un_normalize(outputs))
writer.add_scalar(phase + " per epoch/PSNR", psnr_val, epoch + 1)
writer.add_scalar(
phase + " per epoch/generator loss",
mean_generator_total_loss / len(dataloader[phase]), epoch + 1)
writer.add_scalar("per epoch/total time taken",
time.time() - curr_time, epoch + 1)
writer.add_scalar(phase + " per epoch/avg_mssim", mssim, epoch + 1)
torch.save(generator.state_dict(),
'%s/generator_firstfinal.pth' % opt.out)
def train_multiple(generator, discriminator, opt, dataloader, writer, scale):
feature_extractor = FeatureExtractor(
torchvision.models.vgg19(pretrained=True))
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()
aesthetic_loss = AestheticLoss()
ones_const = Variable(torch.ones(opt.batchSize, 1))
if opt.cuda:
generator.cuda()
discriminator.cuda()
feature_extractor.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
optimizer = optim.Adam(generator.parameters(), lr=opt.generatorLR)
optim_discriminator = optim.Adam(
discriminator.parameters(), lr=opt.discriminatorLR)
scheduler_gen = ReduceLROnPlateau(
optimizer, 'min', factor=0.7, patience=10, verbose=True)
scheduler_dis = ReduceLROnPlateau(
optim_discriminator, 'min', factor=0.7, patience=10, verbose=True)
curr_time = time.time()
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize, opt.imageSize)
# pretraining
for epoch in range(2):
mean_generator_content_loss = 0.0
inputs = torch.FloatTensor(
opt.batchSize, 3, opt.imageSize, opt.imageSize)
for batch_no, data in enumerate(dataloader['train']):
high_img, _ = data
# save_image(high_img, "test.png")
# time.sleep(10)
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
# print(high_img[0].shape)
# print(inputs[0].shape)
# time.sleep(10)
if opt.cuda:
optimizer.zero_grad()
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(inputs[0][np.newaxis, :]).cuda(), Variable(inputs[1][np.newaxis, :]).cuda(
), Variable(inputs[2][np.newaxis, :]).cuda(), Variable(inputs[3][np.newaxis, :]).cuda())
generator_content_loss = content_criterion(
high_res_fake, high_res_real)
mean_generator_content_loss += generator_content_loss.data.item()
generator_content_loss.backward()
optimizer.step()
sys.stdout.write('\r[%d/%d][%d/%d] Generator_MSE_Loss: %.4f' % (
epoch, 2, batch_no, len(dataloader['train']), generator_content_loss.data.item()))
# training
for epoch in range(opt.nEpochs):
for phase in ['train', 'test']:
if phase == 'test':
generator.train(False)
discriminator.train(False)
else:
generator.train(True)
discriminator.train(True)
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
# mean_psnr = 0.0
# mean_msssim = 0.0
high_img = torch.FloatTensor(
opt.batchSize, 3, opt.imageSize, opt.imageSize)
inputs = torch.FloatTensor(
opt.batchSize, 3, opt.imageSize, opt.imageSize)
for batch_no, data in enumerate(dataloader[phase]):
high_img, _ = data
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
if opt.cuda:
optimizer.zero_grad()
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(inputs[0][np.newaxis, :]).cuda(), Variable(inputs[1][np.newaxis, :]).cuda(
), Variable(inputs[2][np.newaxis, :]).cuda(), Variable(inputs[3][np.newaxis, :]).cuda())
# save_image(high_res_real, "REAL.png")
# save_image(high_res_fake, "FAKE.png")
target_real = Variable(torch.rand(
opt.batchSize, 1) * 0.5 + 0.7).cuda()
target_fake = Variable(torch.rand(
opt.batchSize, 1) * 0.3).cuda()
discriminator.zero_grad()
discriminator_loss = adversarial_criterion(
discriminator(Variable(inputs[0][np.newaxis, :]).cuda(), Variable(inputs[1][np.newaxis, :]).cuda(),
Variable(inputs[2][np.newaxis, :]).cuda(), Variable(inputs[3][np.newaxis, :]).cuda()),
target_real) + \
adversarial_criterion(
discriminator(high_res_fake[0][np.newaxis, :], high_res_fake[1][np.newaxis, :], high_res_fake[2][np.newaxis, :],
high_res_fake[3][np.newaxis, :]), target_fake)
mean_discriminator_loss += discriminator_loss.data.item()
if phase == 'train':
discriminator_loss.backward(retain_graph=True)
optim_discriminator.step()
#high_res_fake_cat = torch.cat([ image for image in high_res_fake ], 0)
fake_features = feature_extractor(high_res_fake)
real_features = Variable(
feature_extractor(high_res_real).data)
# generator_content_loss = content_criterion(high_res_fake, high_res_real) + 0.006*content_criterion(fake_features, real_features)
generator_content_loss = content_criterion(high_res_fake,
high_res_real) + content_criterion(fake_features,
real_features)
mean_generator_content_loss += generator_content_loss.data.item()
generator_adversarial_loss = adversarial_criterion(discriminator(
high_res_fake[0][np.newaxis, :], high_res_fake[1][np.newaxis, :], high_res_fake[2][np.newaxis, :], high_res_fake[3][np.newaxis, :]), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data.item()
generator_total_loss = generator_content_loss + 1e-3 * generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data.item()
if phase == 'train':
generator_total_loss.backward()
optimizer.step()
if(batch_no % 10 == 0):
# print("phase {} batch no. {} generator_content_loss {} discriminator_loss {}".format(phase, batch_no, generator_content_loss, discriminator_loss))
sys.stdout.write('\rphase [%s] epoch [%d/%d] batch no. [%d/%d] Generator_content_Loss: %.4f discriminator_loss %.4f' % (
phase, epoch, opt.nEpochs, batch_no, len(dataloader[phase]), generator_content_loss, discriminator_loss))
if phase == 'train':
imsave(high_res_fake.cpu().data, train=True,
epoch=epoch, image_type='fake')
imsave(high_img, train=True, epoch=epoch, image_type='real')
imsave(inputs, train=True, epoch=epoch, image_type='low')
writer.add_scalar(phase + " per epoch/generator lr",
optimizer.param_groups[0]['lr'], epoch + 1)
writer.add_scalar(phase + " per epoch/discriminator lr", optim_discriminator.param_groups[0]['lr'],
epoch + 1)
scheduler_gen.step(
mean_generator_total_loss / len(dataloader[phase]))
scheduler_dis.step(mean_discriminator_loss /
len(dataloader[phase]))
else:
imsave(high_res_fake.cpu().data, train=False,
epoch=epoch, image_type='fake')
imsave(high_img, train=False, epoch=epoch, image_type='real')
imsave(inputs, train=False, epoch=epoch, image_type='low')
# import ipdb;
# ipdb.set_trace()
mssim = avg_msssim(high_res_real, high_res_fake)
psnr_val = psnr(un_normalize(high_res_real),
un_normalize(high_res_fake))
writer.add_scalar(phase + " per epoch/PSNR", psnr_val,
epoch + 1)
writer.add_scalar(phase+" per epoch/discriminator loss",
mean_discriminator_loss/len(dataloader[phase]), epoch+1)
writer.add_scalar(phase+" per epoch/generator loss",
mean_generator_total_loss/len(dataloader[phase]), epoch+1)
writer.add_scalar("per epoch/total time taken",
time.time()-curr_time, epoch+1)
writer.add_scalar(phase+" per epoch/avg_mssim", mssim, epoch+1)
# Do checkpointing
torch.save(generator.state_dict(), '%s/generator_final.pth' % opt.out)
torch.save(discriminator.state_dict(),
'%s/discriminator_final.pth' % opt.out)
def test_firstmodel(generator, opt, dataloader, writer, scale):
content_criterion = nn.MSELoss()
ones_const = Variable(torch.ones(1, 1))
if opt.cuda:
generator.cuda()
content_criterion.cuda()
curr_time = time.time()
for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_total_loss = 0.0
high_res_fake = 0
for batch_no, data in enumerate(dataloader['test']):
high_img, _ = data
generator.train(False)
input1 = high_img[0, :, :, :]
input2 = high_img[1, :, :, :]
input3 = high_img[2, :, :, :]
input4 = high_img[3, :, :, :]
# imshow(input3)
for j in range(opt.batchSize):
high_img[j] = normalize(high_img[j])
high_comb = torch.cat(
[high_img[0], high_img[1], high_img[2], high_img[3]], 0)
high_comb = Variable(high_comb[np.newaxis, :]).cuda()
# imshow(high_comb.cpu().data)
input_comb = torch.cat(
[scale(input1),
scale(input2),
scale(input3),
scale(input4)], 0)
input_comb = input_comb[np.newaxis, :]
if opt.cuda:
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(input_comb).cuda())
outputs = torch.chunk(high_res_fake, 4, 1)
outputs = torch.cat(
[outputs[0], outputs[1], outputs[2], outputs[3]], 0)
# imshow(outputs[0])
generator_content_loss = content_criterion(
high_res_fake, high_comb)
mean_generator_content_loss += generator_content_loss.data[0]
generator_total_loss = generator_content_loss
mean_generator_total_loss += generator_total_loss.data[0]
if (batch_no % 10 == 0):
# # print("phase {} batch no. {} generator_content_loss {} discriminator_loss {}".format(phase, batch_no, generator_content_loss, discriminator_loss))
sys.stdout.write(
'\r epoch [%d/%d] batch no. [%d/%d] Generator_content_Loss: %.4f '
% (epoch, opt.nEpochs, batch_no, len(
dataloader['test']), generator_content_loss))
mssim = avg_msssim(high_res_real, outputs)
psnr_val = psnr(un_normalize(high_res_real), un_normalize(outputs))
writer.add_scalar("test per epoch/PSNR", psnr_val, epoch + 1)
# writer.add_scalar(phase+" per epoch/discriminator loss", mean_discriminator_loss/len(dataloader[phase]), epoch+1)
writer.add_scalar("test per epoch/generator loss",
mean_generator_total_loss / len(dataloader[phase]),
epoch + 1)
writer.add_scalar("per epoch/total time taken",
time.time() - curr_time, epoch + 1)
writer.add_scalar("test per epoch/avg_mssim", mssim, epoch + 1)
torch.save(generator.state_dict(),
'%s/generator_firstfinal.pth' % opt.out)
def test_single(generator, discriminator, opt, dataloader, scale):
generator.load_state_dict(torch.load(opt.generatorWeights))
discriminator.load_state_dict(torch.load(opt.discriminatorWeights))
content_criterion = nn.MSELoss()
adversarial_criterion = nn.BCELoss()
ones_const = Variable(torch.ones(1, 1))
if opt.cuda:
generator.cuda()
discriminator.cuda()
content_criterion.cuda()
adversarial_criterion.cuda()
ones_const = ones_const.cuda()
curr_time = time.time()
# for epoch in range(opt.nEpochs):
mean_generator_content_loss = 0.0
mean_generator_adversarial_loss = 0.0
mean_generator_total_loss = 0.0
mean_discriminator_loss = 0.0
high_res_fake = 0
for batch_no, data in enumerate(dataloader['test']):
high_img, _ = data
generator.train(False)
discriminator.train(False)
print("batch no. {} shape of input {}".format(batch_no,
high_img.shape))
input1 = high_img[0, :, :, :]
input2 = high_img[1, :, :, :]
input3 = high_img[2, :, :, :]
input4 = high_img[3, :, :, :]
inputs = torch.FloatTensor(opt.batchSize, 3, opt.imageSize,
opt.imageSize)
# imshow(input3)
for j in range(opt.batchSize):
inputs[j] = scale(high_img[j])
high_img[j] = normalize(high_img[j])
high_comb = torch.cat(
[high_img[0], high_img[1], high_img[2], high_img[3]], 0)
high_comb = Variable(high_comb[np.newaxis, :]).cuda()
# imshow(high_comb.cpu().data)
input_comb = torch.cat(
[scale(input1),
scale(input2),
scale(input3),
scale(input4)], 0)
# inputs = [scale(input1), scale(input2), scale(input3), scale(input4)]
input_comb = input_comb[np.newaxis, :]
if opt.cuda:
# optimizer.zero_grad()
high_res_real = Variable(high_img.cuda())
high_res_fake = generator(Variable(input_comb).cuda())
target_real = Variable(torch.rand(1, 1) * 0.5 + 0.7).cuda()
target_fake = Variable(torch.rand(1, 1) * 0.3).cuda()
outputs = torch.chunk(high_res_fake, 4, 1)
outputs = torch.cat(
[outputs[0], outputs[1], outputs[2], outputs[3]], 0)
# imshow(outputs[0])
generator_content_loss = content_criterion(high_res_fake,
high_comb)
mean_generator_content_loss += generator_content_loss.data[0]
generator_adversarial_loss = adversarial_criterion(
discriminator(high_res_fake), ones_const)
mean_generator_adversarial_loss += generator_adversarial_loss.data[
0]
generator_total_loss = generator_content_loss + 1e-3 * generator_adversarial_loss
mean_generator_total_loss += generator_total_loss.data[0]
discriminator_loss = adversarial_criterion(discriminator(high_comb), target_real) + \
adversarial_criterion(discriminator(Variable(high_res_fake.data)), target_fake)
mean_discriminator_loss += discriminator_loss.data[0]
psnr_val = psnr(un_normalize(outputs), un_normalize(high_res_real))
print(psnr_val)
imsave(outputs.cpu().data,
train=False,
epoch=batch_no,
image_type='fake')
imsave(high_img, train=False, epoch=batch_no, image_type='real')
imsave(inputs, train=False, epoch=batch_no, image_type='low')
