def test_images(epoch):
def write_image(img, filename):
out_img = img.cpu().detach().numpy()
out_img *= 255.0
out_img = out_img.clip(0, 255)
out_img = np.uint8(out_img)
writer.add_image(filename, out_img, epoch)
with torch.no_grad():
num_minibatch = len(testing_full_data_loader)
pg = ProgressBar(num_minibatch,
'Test %d Images' % num_minibatch,
length=50)
model.eval()
if criterion.has_discriminator:
criterion.discr_eval()
for i, batch in enumerate(testing_full_data_loader):
pg.print_progress_bar(i)
input = batch[0].to(device)
B, _, Cin, H, W = input.shape
Hhigh = H * opt.upscale_factor
Whigh = W * opt.upscale_factor
Cout = output_channels
channel_mask = [0, 1, 2] #RGB
previous_output = None
for j in range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :], Cout,
opt.initialImage, False,
opt.upscale_factor)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, opt.upscale_factor)
single_input = torch.cat(
(input[:, j, :, :, :], previous_warped_flattened), dim=1)
# run generator and cost
prediction, residual = model(single_input)
# write prediction image
write_image(prediction[0, channel_mask],
'image%03d/frame%03d_prediction' % (i, j))
# write residual image
if residual is not None:
write_image(residual[0, channel_mask],
'image%03d/frame%03d_residual' % (i, j))
# save output for next frame
previous_output = prediction
pg.print_progress_bar(num_minibatch)
print("Test images sent to Tensorboard for visualization")
file_flow = os.path.join(folder, "flow_%05d.npy" % i)
# load them
sample_low = torch.from_numpy(np.load(file_low)).to(device)
sample_high = torch.from_numpy(np.load(file_high)).to(device)
sample_flow = torch.from_numpy(np.load(file_flow)).to(device)
# iterate over all models
for model_index in range(len(MODELS)):
statistics[model_index].reset()
# iterate over time
NF, C, H, W = sample_low.shape
for j in range(NF):
# SUPER-RES
# prepare input
if j == 0:
previous_warped = initialImage(
sample_low[0:1, :, :, :],
model_list[model_index].prev_input_channels,
'zero', False, UPSCALING)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
sample_flow[j - 1:j, :, :, :],
UPSCALING,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, UPSCALING)
# run model
pred_color, previous_output = model_list[model_index](
sample_low[j:j + 1, :, :, :],
previous_warped_flattened)
##DEBUG: save the images
file_flow = os.path.join(folder, "flow_%05d.npy" % i)
# load them
sample_low = torch.from_numpy(np.load(file_low)).to(device)
sample_high = torch.from_numpy(np.load(file_high)).to(device)
sample_flow = torch.from_numpy(np.load(file_flow)).to(device)
# iterate over all models
for model_index in range(len(MODELS)):
statistics[model_index].reset()
# iterate over time
NF, C, H, W = sample_low.shape
for j in range(NF):
# SUPER-RES
# prepare input
if j == 0:
previous_warped = initialImage(
sample_low[0:1, :, :, :], 6, 'zero', False,
UPSCALING)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
sample_flow[j - 1:j, :, :, :],
UPSCALING,
special_mask=True)
#previous_warped = previous_output
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, UPSCALING)
single_input = torch.cat((sample_low[j:j + 1, :, :, :],
previous_warped_flattened),
dim=1)
# run model
prediction, _ = model_list[model_index](single_input)
def trainAdv_v2(epoch):
"""
Second version of adverserial training,
for each batch, train both discriminator and generator.
Not full epoch for each seperately
"""
print("===> Epoch %d Training" % epoch)
discr_scheduler.step()
writer.add_scalar('train/lr_discr', discr_scheduler.get_lr()[0], epoch)
gen_scheduler.step()
writer.add_scalar('train/lr_gen', gen_scheduler.get_lr()[0], epoch)
disc_steps = opt.advDiscrInitialSteps if opt.advDiscrInitialSteps is not None and epoch == 1 else opt.advDiscrMaxSteps
gen_steps = opt.advGenMaxSteps
num_minibatch = len(training_data_loader)
model.train()
criterion.discr_train()
total_discr_loss = 0
total_gen_loss = 0
total_gt_score = 0
total_pred_score = 0
pg = ProgressBar(num_minibatch, 'Train', length=50)
for iteration, batch in enumerate(training_data_loader):
pg.print_progress_bar(iteration)
input, flow, target = batch[0].to(device), batch[1].to(
device), batch[2].to(device)
B, _, Cout, Hhigh, Whigh = target.shape
_, _, Cin, H, W = input.shape
# DISCRIMINATOR
for _ in range(disc_steps):
discr_optimizer.zero_grad()
gen_optimizer.zero_grad()
loss = 0
#iterate over all timesteps
for j in range(dataset_data.num_frames):
# prepare input for the generator
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :], Cout,
opt.initialImage, False,
opt.upscale_factor)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:, 0, :, :, :]
previous_input = F.interpolate(input[:, 0, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_loss = previous_warped
previous_input = F.interpolate(input[:, j - 1, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
previous_input = models.VideoTools.warp_upscale(
previous_input,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, opt.upscale_factor)
single_input = torch.cat(
(input[:, j, :, :, :], previous_warped_flattened), dim=1)
#evaluate generator
with torch.no_grad():
prediction, _ = model(single_input)
#prepare input for the discriminator
gt_prev_warped = models.VideoTools.warp_upscale(
target[:, j - 1, :, :, :],
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
#evaluate discriminator
input_high = F.interpolate(input[:, j, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
disc_loss, gt_score, pred_score = criterion.train_discriminator(
input_high, target[:, j, :, :, :], previous_input,
gt_prev_warped, prediction, previous_warped_loss)
loss += disc_loss
total_gt_score += float(gt_score)
total_pred_score += float(pred_score)
# save output
previous_output = torch.cat(
[
torch.clamp(prediction[:, 0:1, :, :], -1, +1), # mask
ScreenSpaceShading.normalize(prediction[:, 1:4, :, :],
dim=1),
torch.clamp(prediction[:, 4:5, :, :], 0, +1), # depth
torch.clamp(prediction[:, 5:6, :, :], 0, +1) # ao
],
dim=1)
loss.backward()
discr_optimizer.step()
total_discr_loss += loss.item()
# GENERATOR
for _ in range(disc_steps):
discr_optimizer.zero_grad()
gen_optimizer.zero_grad()
loss = 0
#iterate over all timesteps
for j in range(dataset_data.num_frames):
# prepare input for the generator
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :], Cout,
opt.initialImage, False,
opt.upscale_factor)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:, 0, :, :, :]
previous_input = F.interpolate(input[:, 0, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_loss = previous_warped
previous_input = F.interpolate(input[:, j - 1, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
previous_input = models.VideoTools.warp_upscale(
previous_input,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, opt.upscale_factor)
single_input = torch.cat(
(input[:, j, :, :, :], previous_warped_flattened), dim=1)
#evaluate generator
prediction, _ = model(single_input)
#evaluate loss
input_high = F.interpolate(input[:, j, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
loss0, map = criterion(target[:, j, :, :, :], prediction,
input_high, previous_input,
previous_warped_loss)
loss += loss0
# save output
previous_output = torch.cat(
[
torch.clamp(prediction[:, 0:1, :, :], -1, +1), # mask
ScreenSpaceShading.normalize(prediction[:, 1:4, :, :],
dim=1),
torch.clamp(prediction[:, 4:5, :, :], 0, +1), # depth
torch.clamp(prediction[:, 5:6, :, :], 0, +1) # ao
],
dim=1)
loss.backward()
gen_optimizer.step()
total_gen_loss += loss.item()
pg.print_progress_bar(num_minibatch)
total_discr_loss /= num_minibatch * dataset_data.num_frames
total_gen_loss /= num_minibatch * dataset_data.num_frames
total_gt_score /= num_minibatch * dataset_data.num_frames
total_pred_score /= num_minibatch * dataset_data.num_frames
writer.add_scalar('train/discr_loss', total_discr_loss, epoch)
writer.add_scalar('train/gen_loss', total_gen_loss, epoch)
writer.add_scalar('train/gt_score', total_gt_score, epoch)
writer.add_scalar('train/pred_score', total_pred_score, epoch)
print("===> Epoch {} Complete".format(epoch))
def test(epoch):
avg_psnr = 0
avg_losses = defaultdict(float)
with torch.no_grad():
num_minibatch = len(testing_data_loader)
pg = ProgressBar(num_minibatch, 'Testing', length=50)
model.eval()
if criterion.has_discriminator:
criterion.discr_eval()
for iteration, batch in enumerate(testing_data_loader, 0):
pg.print_progress_bar(iteration)
input, target = batch[0].to(device), batch[1].to(device)
B, _, Cout, Hhigh, Whigh = target.shape
_, _, Cin, H, W = input.shape
previous_output = None
for j in range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :], Cout,
opt.initialImage, False,
opt.upscale_factor)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:, 0, :, :, :]
previous_input = F.interpolate(input[:, 0, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_loss = previous_warped
previous_input = F.interpolate(input[:, j - 1, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
previous_input = models.VideoTools.warp_upscale(
previous_input,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, opt.upscale_factor)
single_input = torch.cat(
(input[:, j, :, :, :], previous_warped_flattened), dim=1)
# run generator
prediction, _ = model(single_input)
# evaluate cost
input_high = F.interpolate(input[:, j, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
loss0, loss_values = criterion(target[:, j, :, :, :],
prediction, input_high,
previous_warped_loss)
avg_losses['total_loss'] += loss0.item()
psnr = 10 * log10(1 / max(1e-10, loss_values['mse']))
avg_losses['psnr'] += psnr
for key, value in loss_values.items():
avg_losses[str(key)] += value
# save output for next frame
previous_output = prediction
pg.print_progress_bar(num_minibatch)
for key in avg_losses.keys():
avg_losses[key] /= num_minibatch * dataset_data.num_frames
print("===> Avg. PSNR: {:.4f} dB".format(avg_losses['psnr']))
print(" losses:", avg_losses)
for key, value in avg_losses.items():
writer.add_scalar('test/%s' % key, value, epoch)
def test_images(epoch):
def write_image(img, filename):
out_img = img.cpu().detach().numpy()
out_img *= 255.0
out_img = out_img.clip(0, 255)
out_img = np.uint8(out_img)
writer.add_image(filename, out_img, epoch)
with torch.no_grad():
num_minibatch = len(testing_full_data_loader)
pg = ProgressBar(num_minibatch,
'Test %d Images' % num_minibatch,
length=50)
model.eval()
if criterion.has_discriminator:
criterion.discr_eval()
for i, batch in enumerate(testing_full_data_loader):
pg.print_progress_bar(i)
input, flow, target = batch[0].to(device), batch[1].to(
device), batch[2].to(device)
B, _, Cin, H, W = input.shape
Hhigh = H * upscale_factor
Whigh = W * upscale_factor
Cout = output_channels
channel_mask = [1, 2, 3] #normal
previous_output = None
for j in range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :],
Cout, opt.initialImage,
False, upscale_factor)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
upscale_factor,
special_mask=True)
# TODO: enable temporal component again
#previous_warped_flattened = models.VideoTools.flatten_high(previous_warped, opt.upscale_factor)
#single_input = torch.cat((
# input[:,j,:,:,:],
# previous_warped_flattened),
# dim=1)
single_input = input[:, j, :, :, :]
# run generator
heatMap = model(single_input)
heatMap = postprocess(heatMap)
prediction = importance.adaptiveSmoothing(
target[:, j, :, :, :].contiguous(),
1 / heatMap.unsqueeze(1),
opt.distanceToStandardDeviation)
# write heatmap
write_image(heatMap[0].unsqueeze(0),
'image%03d/frame%03d_heatmap' % (i, j))
## write warped previous frame
#write_image(previous_warped[0, channel_mask], 'image%03d/frame%03d_warped' % (i, j))
# write predicted normals
prediction[:, 1:4, :, :] = ScreenSpaceShading.normalize(
prediction[:, 1:4, :, :], dim=1)
write_image(prediction[0, channel_mask],
'image%03d/frame%03d_prediction' % (i, j))
# write shaded image if network runs in deferredShading mode
shaded_image = shading(prediction)
write_image(shaded_image[0],
'image%03d/frame%03d_shaded' % (i, j))
# write mask
write_image(prediction[0, 0:1, :, :] * 0.5 + 0.5,
'image%03d/frame%03d_mask' % (i, j))
# write ambient occlusion
write_image(prediction[0, 5:6, :, :],
'image%03d/frame%03d_ao' % (i, j))
# save output for next frame
previous_output = prediction
pg.print_progress_bar(num_minibatch)
print("Test images sent to Tensorboard for visualization")
def trainNormal(epoch):
epoch_loss = 0
num_minibatch = len(training_data_loader)
pg = ProgressBar(num_minibatch, 'Training', length=50)
model.train()
for iteration, batch in enumerate(training_data_loader, 0):
pg.print_progress_bar(iteration)
input, target = batch[0].to(device), batch[1].to(device)
B, _, Cout, Hhigh, Whigh = target.shape
_, _, Cin, H, W = input.shape
assert (Cout == output_channels)
assert (Cin == input_channels)
assert (H == dataset_data.crop_size)
assert (W == dataset_data.crop_size)
assert (Hhigh == dataset_data.crop_size * opt.upscale_factor)
assert (Whigh == dataset_data.crop_size * opt.upscale_factor)
optimizer.zero_grad()
previous_output = None
loss = 0
for j in range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :], Cout,
opt.initialImage, False,
opt.upscale_factor)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:, 0, :, :, :]
previous_input = F.interpolate(input[:, 0, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_loss = previous_warped
previous_input = F.interpolate(input[:, j - 1, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
previous_input = models.VideoTools.warp_upscale(
previous_input,
flow[:, j - 1, :, :, :],
opt.upscale_factor,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, opt.upscale_factor)
single_input = torch.cat(
(input[:, j, :, :, :], previous_warped_flattened), dim=1)
# run generator
prediction, _ = model(single_input)
# evaluate cost
input_high = F.interpolate(input[:, j, :, :, :],
size=(Hhigh, Whigh),
mode=opt.upsample)
loss0, _ = criterion(target[:, j, :, :, :], prediction, input_high,
previous_warped_loss)
del _
loss += loss0
epoch_loss += loss0.item()
# save output
previous_output = prediction
loss.backward()
optimizer.step()
pg.print_progress_bar(num_minibatch)
epoch_loss /= num_minibatch * dataset_data.num_frames
print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(
epoch, epoch_loss))
writer.add_scalar('train/total_loss', epoch_loss, epoch)
writer.add_scalar('train/lr', scheduler.get_lr()[0], epoch)
scheduler.step()
def trainNormal(epoch):
epoch_loss = 0
num_minibatch = len(training_data_loader)
pg = ProgressBar(num_minibatch, 'Training', length=50)
model.train()
for iteration, batch in enumerate(training_data_loader, 0):
pg.print_progress_bar(iteration)
input, flow, target = batch[0].to(device), batch[1].to(
device), batch[2].to(device)
B, _, Cout, Hhigh, Whigh = target.shape
_, _, Cin, H, W = input.shape
assert (Cout == output_channels)
assert (Cin == input_channels)
assert (H == dataset_data.crop_size)
assert (W == dataset_data.crop_size)
assert (Hhigh == dataset_data.crop_size * upscale_factor)
assert (Whigh == dataset_data.crop_size * upscale_factor)
optimizer.zero_grad()
previous_output = None
loss = 0
for j in range(1): #range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :], Cout,
opt.initialImage, False,
upscale_factor)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:, 0, :, :, :]
previous_input = F.interpolate(input[:, 0, :, :, :],
size=(Hhigh, Whigh),
mode='bilinear')
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
upscale_factor,
special_mask=True)
previous_warped_loss = previous_warped
previous_input = F.interpolate(input[:, j - 1, :, :, :],
size=(Hhigh, Whigh),
mode='bilinear')
previous_input = models.VideoTools.warp_upscale(
previous_input,
flow[:, j - 1, :, :, :],
upscale_factor,
special_mask=True)
# TODO: enable temporal component again
#previous_warped_flattened = models.VideoTools.flatten_high(previous_warped, opt.upscale_factor)
#single_input = torch.cat((
# input[:,j,:,:,:],
# previous_warped_flattened),
# dim=1)
single_input = input[:, j, :, :, :]
# run generator
heatMap = model(single_input)
heatMapCrop = heatMap[:, opt.
lossBorderPadding:-opt.lossBorderPadding,
opt.
lossBorderPadding:-opt.lossBorderPadding]
heatMap = postprocess(heatMap)
prediction = importance.adaptiveSmoothing(
target[:, j, :, :, :].contiguous(),
1 / heatMap.unsqueeze(1), opt.distanceToStandardDeviation)
# evaluate cost
input_high = F.interpolate(input[:, j, :, :, :],
size=(Hhigh, Whigh),
mode='bilinear')
loss0, _ = criterion(target[:, j, :, :, :], prediction,
input_high, previous_input,
previous_warped_loss)
del _
loss0 += opt.lossHeatmapMean * (
(0.5 - torch.mean(heatMapCrop))**2)
#print("Mean:",torch.mean(heatMapCrop).item())
loss += loss0
epoch_loss += loss0.item()
# save output
previous_output = prediction
#loss.retain_grad()
loss.backward()
optimizer.step()
pg.print_progress_bar(num_minibatch)
epoch_loss /= num_minibatch * dataset_data.num_frames
print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(
epoch, epoch_loss))
writer.add_scalar('train/total_loss', epoch_loss, epoch)
writer.add_scalar('train/lr', scheduler.get_lr()[0], epoch)
scheduler.step()
def test(epoch):
avg_psnr = 0
avg_losses = defaultdict(float)
heatmap_min = 1e10
heatmap_max = -1e10
heatmap_avg = heatmap_count = 0
with torch.no_grad():
num_minibatch = len(testing_data_loader)
pg = ProgressBar(num_minibatch, 'Testing', length=50)
model.eval()
if criterion.has_discriminator:
criterion.discr_eval()
for iteration, batch in enumerate(testing_data_loader, 0):
pg.print_progress_bar(iteration)
input, flow, target = batch[0].to(device), batch[1].to(
device), batch[2].to(device)
B, _, Cout, Hhigh, Whigh = target.shape
_, _, Cin, H, W = input.shape
previous_output = None
for j in range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:, 0, :, :, :],
Cout, opt.initialImage,
False, upscale_factor)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:, 0, :, :, :]
previous_input = F.interpolate(input[:, 0, :, :, :],
size=(Hhigh, Whigh),
mode='bilinear')
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
upscale_factor,
special_mask=True)
previous_warped_loss = previous_warped
previous_input = F.interpolate(input[:,
j - 1, :, :, :],
size=(Hhigh, Whigh),
mode='bilinear')
previous_input = models.VideoTools.warp_upscale(
previous_input,
flow[:, j - 1, :, :, :],
upscale_factor,
special_mask=True)
# TODO: enable temporal component again
#previous_warped_flattened = models.VideoTools.flatten_high(previous_warped, opt.upscale_factor)
#single_input = torch.cat((
# input[:,j,:,:,:],
# previous_warped_flattened),
# dim=1)
single_input = input[:, j, :, :, :]
# run generator
heatMap = model(single_input)
heatMapCrop = heatMap[:, opt.lossBorderPadding:-opt.
lossBorderPadding,
opt.lossBorderPadding:-opt.
lossBorderPadding]
heatmap_min = min(heatmap_min,
torch.min(heatMapCrop).item())
heatmap_max = max(heatmap_max,
torch.max(heatMapCrop).item())
heatmap_avg += torch.mean(heatMapCrop).item()
heatmap_count += 1
heatMap = postprocess(heatMap)
prediction = importance.adaptiveSmoothing(
target[:, j, :, :, :].contiguous(),
1 / heatMap.unsqueeze(1),
opt.distanceToStandardDeviation)
# evaluate cost
input_high = F.interpolate(input[:, j, :, :, :],
size=(Hhigh, Whigh),
mode='bilinear')
loss0, loss_values = criterion(target[:, j, :, :, :],
prediction, input_high,
previous_input,
previous_warped_loss)
avg_losses['total_loss'] += loss0.item()
psnr = 10 * log10(
1 / max(1e-10, loss_values[('mse', 'color')]))
avg_losses['psnr'] += psnr
for key, value in loss_values.items():
avg_losses[str(key)] += value
# save output for next frame
previous_output = torch.cat(
[
torch.clamp(prediction[:, 0:1, :, :], -1,
+1), # mask
ScreenSpaceShading.normalize(
prediction[:, 1:4, :, :], dim=1),
torch.clamp(prediction[:, 4:5, :, :], 0,
+1), # depth
torch.clamp(prediction[:, 5:6, :, :], 0, +1) # ao
],
dim=1)
pg.print_progress_bar(num_minibatch)
for key in avg_losses.keys():
avg_losses[key] /= num_minibatch * dataset_data.num_frames
print("===> Avg. PSNR: {:.4f} dB".format(avg_losses['psnr']))
print(" losses:", avg_losses)
for key, value in avg_losses.items():
writer.add_scalar('test/%s' % key, value, epoch)
print(" heatmap: min=%f, max=%f, avg=%f" %
(heatmap_min, heatmap_max, heatmap_avg / heatmap_count))
writer.add_scalar('test/heatmap_min', heatmap_min, epoch)
writer.add_scalar('test/heatmap_max', heatmap_max, epoch)
writer.add_scalar('test/heatmap_avg', heatmap_avg / heatmap_count,
epoch)
def test_images(epoch):
def write_image(img, filename):
out_img = img.cpu().detach().numpy()
out_img *= 255.0
out_img = out_img.clip(0, 255)
out_img = np.uint8(out_img)
writer.add_image(filename, out_img, epoch)
with torch.no_grad():
num_minibatch = len(testing_full_data_loader)
pg = ProgressBar(num_minibatch, 'Test %d Images'%num_minibatch, length=50)
model.eval()
if criterion.has_discriminator:
criterion.discr_eval()
for i,batch in enumerate(testing_full_data_loader):
pg.print_progress_bar(i)
input, flow = batch[0].to(device), batch[1].to(device)
B, _, Cin, H, W = input.shape
Hhigh = H * opt.upscale_factor
Whigh = W * opt.upscale_factor
Cout = output_channels
channel_mask = [1, 2, 3] #normal
previous_output = None
for j in range(dataset_data.num_frames):
# prepare input
if j == 0 or opt.disableTemporal:
previous_warped = initialImage(input[:,0,:,:,:], Cout,
opt.initialImage, False, opt.upscale_factor)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j-1, :, :, :],
opt.upscale_factor,
special_mask = True)
previous_warped_flattened = models.VideoTools.flatten_high(previous_warped, opt.upscale_factor)
single_input = torch.cat((
input[:,j,:,:,:],
previous_warped_flattened),
dim=1)
# write warped previous frame
write_image(previous_warped[0, channel_mask], 'image%03d/frame%03d_warped' % (i, j))
# run generator and cost
prediction, residual = model(single_input)
# normalize normal
prediction[:,1:4,:,:] = ScreenSpaceShading.normalize(prediction[:,1:4,:,:], dim=1)
# write prediction image
write_image(prediction[0, channel_mask], 'image%03d/frame%03d_prediction' % (i, j))
# write residual image
if residual is not None:
write_image(residual[0, channel_mask], 'image%03d/frame%03d_residual' % (i, j))
# write shaded image if network runs in deferredShading mode
shaded_image = shading(prediction)
write_image(shaded_image[0], 'image%03d/frame%03d_shaded' % (i, j))
# write mask
write_image(prediction[0, 0:1, :, :]*0.5+0.5, 'image%03d/frame%03d_mask' % (i, j))
# write ambient occlusion
# write mask
write_image(prediction[0, 5:6, :, :], 'image%03d/frame%03d_ao' % (i, j))
# save output for next frame
previous_output = torch.cat([
torch.clamp(prediction[:,0:1,:,:], -1, +1), # mask
prediction[:,1:4,:,:], #already normalized
torch.clamp(prediction[:,4:5,:,:], 0, +1), # depth
torch.clamp(prediction[:,5:6,:,:], 0, +1) # ao
], dim=1)
pg.print_progress_bar(num_minibatch)
print("Test images sent to Tensorboard for visualization")
def inference(self, current_low, prev_high):
"""
Performs the superresolution.
current_low: low-resolution input from the renderer, 10 channels (RGB, mask, normal, depth, flow), GPU. Format: (B,C,H,W)
prev_high: RGB-image of the previous inference result
"""
with torch.no_grad():
current_low_cpu = current_low.cpu().numpy()[0]
# compute flow
flow_inpaint = np.stack(
(cv.inpaint(current_low_cpu[8, :, :],
np.uint8(current_low_cpu[3, :, :] == 0), 3,
cv.INPAINT_NS),
cv.inpaint(current_low_cpu[9, :, :],
np.uint8(current_low_cpu[3, :, :] == 0), 3,
cv.INPAINT_NS)),
axis=0).astype(np.float32)
flow = torch.unsqueeze(torch.from_numpy(flow_inpaint),
dim=0).to(self.device)
#input
if self.unshaded:
input = torch.cat((current_low[:, 3:4, :, :] * 2 - 1,
current_low[:, 4:8, :, :]),
dim=1)
if prev_high is None:
previous_warped = initialImage(
input, 6, self.initial_image_mode, self.inverse_ao,
self.upscale_factor).to(self.device)
else:
previous_warped = VideoTools.warp_upscale(
prev_high.to(self.device),
flow,
self.upscale_factor,
special_mask=True)
else:
if self.has_normal and self.has_depth:
input = torch.clamp(current_low[:, 0:8, :, :], 0, 1)
elif self.has_normal: #no depth
input = current_low[:, 0:7, :, :]
elif self.has_depth: #no normal
input = torch.cat(
(current_low[:, 0:4, :, :], current_low[:, 7:8, :, :]),
dim=1)
else: #only color+mask
input = current_low[:, 0:4, :, :]
if prev_high is None:
#prev_high = np.zeros(
# (3, input.shape[2]*self.upscale_factor, input.shape[3]*self.upscale_factor),
# dtype=current_low.dtype)
prev_high = initialImage(input, 3, self.initial_image_mode,
self.upscale_factor)
previous_warped = VideoTools.warp_upscale(prev_high.to(
self.device),
flow,
self.upscale_factor,
special_mask=False)
previous_warped_flattened = VideoTools.flatten_high(
previous_warped, self.upscale_factor)
# run the network
single_input = torch.cat((input, previous_warped_flattened), dim=1)
prediction, _ = self.model(single_input)
return prediction
def test(epoch, save_images):
def write_image(img, filename):
out_img = img.cpu().detach().numpy()
out_img *= 255.0
out_img = out_img.clip(0, 255)
out_img = np.uint8(out_img)
writer.add_image(filename, out_img, epoch)
avg_psnr = 0
avg_losses = defaultdict(float)
with torch.no_grad():
num_minibatch = len(test_set) // opt.testBatchSize
pg = ProgressBar(num_minibatch, 'Testing ', length=50)
model.eval()
for iteration, batch in enumerate(testing_data_loader, 0):
pg.print_progress_bar(iteration)
input, target = batch[0].to(device), batch[1].to(device)
B, T, Cout, H, W = target.shape
_, _, Cin, _, _ = input.shape
#assert(Cout == output_channels)
#assert(Cin == input_channels)
input_flow = input[:,:,6:8,:,:]
input, input_mask = preprocessInput(input)
previous_output = None
for j in range(T):
# prepare input
flow = input_flow[:,j-1,:,:,:]
if j == 0 or opt.disableTemporal:
previous_input = utils.initialImage(input[:,0,0:output_channels,:,:], output_channels,
opt.initialImage, False, 1)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:,0,:,:,:]
else:
previous_input = models.VideoTools.warp_upscale(
previous_output,
flow,
1,
special_mask = opt.warpSpecialMask)
previous_warped_loss = previous_input
single_input = torch.cat((
input[:,j,:,:,:],
previous_input),
dim=1)
if opt.externalFlow:
# remove flow from the input and output
single_input = torch.cat((
single_input[:,:6,:,:],
single_input[:,8:,:,:]),
dim=1)
# run generator
prediction, masks = model(single_input, input_mask[:,j,:,:,:])
# evaluate cost
loss0, loss_values = criterion(
target[:,j,:output_channels,:,:],
prediction,
previous_warped_loss,
no_temporal_loss = (j==0))
# accumulate average values
avg_losses['total_loss'] += loss0.item()
psnr = 10 * log10(1 / max(1e-10, loss_values[('mse','color')]))
avg_losses['psnr'] += psnr
for key, value in loss_values.items():
avg_losses[str(key)] += value
# save output
if opt.externalFlow:
previous_output = torch.cat([
torch.clamp(prediction[:,0:1,:,:], -1, +1), # mask
utils.ScreenSpaceShading.normalize(prediction[:,1:4,:,:], dim=1),
torch.clamp(prediction[:,4:5,:,:], 0, +1), # depth
torch.clamp(prediction[:,5:6,:,:], 0, +1) # ao
], dim=1)
else:
previous_output = torch.cat([
torch.clamp(prediction[:,0:1,:,:], -1, +1), # mask
utils.ScreenSpaceShading.normalize(prediction[:,1:4,:,:], dim=1),
torch.clamp(prediction[:,4:5,:,:], 0, +1), # depth
torch.clamp(prediction[:,5:6,:,:], 0, +1), # ao
torch.clamp(prediction[:,6:8,:,:], -1, +1) # flow
], dim=1)
# save images
imagesToSave = opt.numVisImages - iteration*opt.testBatchSize
if imagesToSave>0 and save_images:
# for each image in the batch
for b in range(min(B, imagesToSave)):
imgID = b + iteration * B
# mask
if j==0:
for layer,mask in enumerate(masks):
write_image(mask[b,:,:,:], 'image%03d/debug/mask%d'%(imgID, layer))
if opt.disableTemporal:
# images, two in a row: current prediction, ground truth
maskPredGT = torch.cat([previous_output[b,0:1,:,:], target[b,j,0:1,:,:]], dim=2)*0.5+0.5
write_image(maskPredGT, 'image%03d/mask/frame%03d' % (imgID, j))
normalPredGT = torch.cat([previous_output[b,1:4,:,:], target[b,j,1:4,:,:]], dim=2)*0.5+0.5
write_image(normalPredGT, 'image%03d/normal/frame%03d' % (imgID, j))
depthPredGT = torch.cat([previous_output[b,4:5,:,:], target[b,j,4:5,:,:]], dim=2)
write_image(depthPredGT, 'image%03d/depth/frame%03d' % (imgID, j))
aoPredGT = torch.cat([previous_output[b,5:6,:,:], target[b,j,5:6,:,:]], dim=2)
write_image(aoPredGT, 'image%03d/ao/frame%03d' % (imgID, j))
else:
# images, three in a row: previous-warped, current prediction, ground truth
maskPredGT = torch.cat([previous_input[b,0:1,:,:], previous_output[b,0:1,:,:], target[b,j,0:1,:,:]], dim=2)*0.5+0.5
write_image(maskPredGT, 'image%03d/mask/frame%03d' % (imgID, j))
normalPredGT = torch.cat([previous_input[b,1:4,:,:], previous_output[b,1:4,:,:], target[b,j,1:4,:,:]], dim=2)*0.5+0.5
write_image(normalPredGT, 'image%03d/normal/frame%03d' % (imgID, j))
depthPredGT = torch.cat([previous_input[b,4:5,:,:], previous_output[b,4:5,:,:], target[b,j,4:5,:,:]], dim=2)
write_image(depthPredGT, 'image%03d/depth/frame%03d' % (imgID, j))
aoPredGT = torch.cat([previous_input[b,5:6,:,:], previous_output[b,5:6,:,:], target[b,j,5:6,:,:]], dim=2)
write_image(aoPredGT, 'image%03d/ao/frame%03d' % (imgID, j))
# flow
if opt.externalFlow:
flowPredGT = torch.cat([
torch.cat([flow[b,:,:,:], torch.zeros_like(target[b,j,6:7,:,:])], dim=0),
torch.cat([target[b,j,6:8,:,:], torch.zeros_like(target[b,j,6:7,:,:]) ], dim=0)], dim=2)*20+0.5
write_image(flowPredGT, 'image%03d/flow/frame%03d' % (imgID, j))
else:
flowPredGT = torch.cat([
torch.cat([previous_output[b,6:8,:,:], torch.zeros_like(previous_output[b,6:7,:,:])], dim=0),
torch.cat([target[b,j,6:8,:,:], torch.zeros_like(target[b,j,6:7,:,:]) ], dim=0)], dim=2)*20+0.5
write_image(flowPredGT, 'image%03d/flow/frame%03d' % (imgID, j))
pg.print_progress_bar(num_minibatch)
for key in avg_losses.keys():
avg_losses[key] /= num_minibatch * T
print("===> Avg. PSNR: {:.4f} dB".format(avg_losses['psnr']))
print(" losses:",avg_losses)
for key, value in avg_losses.items():
writer.add_scalar('test/%s'%key, value, epoch)
writer.flush()
def train(epoch):
epoch_loss = 0
num_minibatch = len(train_set) // opt.trainBatchSize
pg = ProgressBar(num_minibatch, 'Training', length=50)
model.train()
for iteration, batch in enumerate(training_data_loader, 0):
pg.print_progress_bar(iteration)
input, target = batch[0].to(device), batch[1].to(device)
B, T, Cout, H, W = target.shape
_, _, Cin, _, _ = input.shape
#assert(Cout == output_channels)
#assert(Cin == input_channels)
input_flow = input[:,:,6:8,:,:]
input, input_mask = preprocessInput(input)
optimizer.zero_grad()
previous_output = None
loss = 0
for j in range(T):
# prepare input
flow = input_flow[:,j-1,:,:,:]
if j == 0 or opt.disableTemporal:
previous_input = utils.initialImage(input[:,0,0:output_channels,:,:], output_channels,
opt.initialImage, False, 1)
# loss takes the ground truth current image as warped previous image,
# to not introduce a bias and big loss for the first image
previous_warped_loss = target[:,0,:,:,:]
else:
previous_input = models.VideoTools.warp_upscale(
previous_output,
flow,
1,
special_mask = opt.warpSpecialMask)
previous_warped_loss = previous_input
single_input = torch.cat((
input[:,j,:,:,:],
previous_input),
dim=1)
if opt.externalFlow:
# remove flow from the input and output
single_input = torch.cat((
single_input[:,:6,:,:],
single_input[:,8:,:,:]),
dim=1)
# run generator
prediction, _ = model(single_input, input_mask[:,j,:,:,:])
# evaluate cost
loss0,_ = criterion(
target[:,j,:output_channels,:,:],
prediction,
previous_warped_loss,
no_temporal_loss = (j==0))
del _
loss += loss0
epoch_loss += loss0.item()
# save output
if opt.externalFlow:
previous_output = torch.cat([
torch.clamp(prediction[:,0:1,:,:], -1, +1), # mask
utils.ScreenSpaceShading.normalize(prediction[:,1:4,:,:], dim=1),
torch.clamp(prediction[:,4:5,:,:], 0, +1), # depth
torch.clamp(prediction[:,5:6,:,:], 0, +1) # ao
], dim=1)
else:
previous_output = torch.cat([
torch.clamp(prediction[:,0:1,:,:], -1, +1), # mask
utils.ScreenSpaceShading.normalize(prediction[:,1:4,:,:], dim=1),
torch.clamp(prediction[:,4:5,:,:], 0, +1), # depth
torch.clamp(prediction[:,5:6,:,:], 0, +1), # ao
torch.clamp(prediction[:,6:8,:,:], -1, +1) # flow
], dim=1)
loss.backward()
optimizer.step()
pg.print_progress_bar(num_minibatch)
epoch_loss /= num_minibatch * T
print("===> Epoch {} Complete: Avg. Loss: {:.4f}".format(epoch, epoch_loss))
writer.add_scalar('train/total_loss', epoch_loss, epoch)
writer.add_scalar('train/lr', scheduler.get_lr()[0], epoch)
scheduler.step()
pg = ProgressBar(num_minibatch,
'Test %d Images' % num_minibatch,
length=50)
for i, batch in enumerate(data_loader):
pg.print_progress_bar(i)
input, flow, high = batch[0].to(device), batch[1].to(
device), batch[2].to(device)
B, _, Cin, H, W = input.shape
Hhigh = H * 4
Whigh = W * 4
previous_output = None
# loop over frames
for j in range(dataset_data.num_frames):
# prepare input
if j == 0:
previous_warped = initialImage(input[:, 0, :, :, :], 6,
'zero', False, 4)
else:
previous_warped = models.VideoTools.warp_upscale(
previous_output,
flow[:, j - 1, :, :, :],
4,
special_mask=True)
previous_warped_flattened = models.VideoTools.flatten_high(
previous_warped, 4)
single_input = torch.cat(
(input[:, j, :, :, :], previous_warped_flattened), dim=1)
# run model
prediction, _ = modelList[model](single_input)
# shade output
prediction[:, 1:4, :, :] = ScreenSpaceShading.normalize(
prediction[:, 1:4, :, :], dim=1)