def test(args, loader_test, model_AttentionNet, epoch, root_dir) :
model_AttentionNet.eval()
for itr, data in enumerate(loader_test):
testImg, fileName = data[0], data[1]
if args.cuda:
testImg = testImg.cuda()
with torch.no_grad():
test_result = model_AttentionNet(testImg)
test_result_img = train_utils.tensor2im(test_result)
result_save_dir = root_dir + fileName[0].split('.')[0]+('_epoch_{}_itr_{}.png'.format(epoch, itr))
train_utils.save_images(test_result_img, result_save_dir)
def test(loader_test, VAN, EN, root_dir):
VAN.eval()
EN.eval()
for itr, data in enumerate(loader_test):
testImg, img_name = data[0], data[1]
testImg = testImg.cuda()
with torch.no_grad():
visual_attention_map = VAN(testImg)
enhance_result = EN(testImg, visual_attention_map)
enhance_result_img = train_utils.tensor2im(enhance_result)
result_save_dir = root_dir + 'enhance'+ img_name[0].split('.')[0]+('.png')
train_utils.save_images(enhance_result_img, result_save_dir)
def test(loader_test, visualAttentionNet, root_dir):
visualAttentionNet.eval()
for itr, data in enumerate(loader_test):
testImg, fileName = data[0], data[1]
testImg = testImg.cuda()
with torch.no_grad():
test_attention_result = visualAttentionNet(testImg)
test_recon_result_img = train_utils.tensor2im(
test_attention_result)
norm_input_img = train_utils.tensor2im(testImg +
test_attention_result)
recon_save_dir = root_dir + 'visual_attention_map_' + fileName[
0].split('.')[0] + ('.png')
recon_save_dir2 = root_dir + 'sum_' + fileName[0].split('.')[0] + (
'.png')
train_utils.save_images(test_recon_result_img, recon_save_dir)
train_utils.save_images(norm_input_img, recon_save_dir2)
def reconstruct(checkpoint_path, data_args, model_args):
training_loader = video_pipe(batch_size=data_args['batch_size'],
num_threads=data_args['num_threads'],
device_id=data_args['device_id'],
filenames=data_args['training_data_files'],
seed=data_args['seed'])
training_loader.build()
training_loader = DALIGenericIterator(training_loader, ['data'])
checkpoint = load_checkpoint(checkpoint_path, device_id=0)
model = VqVae(**model_args).to('cuda')
model.load_state_dict(checkpoint['state_dict'])
model.eval()
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
unnormalize = NormalizeInverse(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
images = next(training_loader)[0]['data']
b, d, _, _, c = images.size()
images = rearrange(images, 'b d h w c -> (b d) c h w')
images = normalize(images.float() / 255.)
images = rearrange(images, '(b d) c h w -> b (d c) h w', b=b, d=d, c=c)
vq_loss, images_recon, _ = model(images)
print('reconstruct error: %6.2f' % vq_loss)
images, images_recon = map(
lambda t: rearrange(t, 'b (d c) h w -> (b d) c h w', b=b, d=d, c=c),
[images, images_recon])
images_orig, images_recs = train_visualize(unnormalize=unnormalize,
images=images,
n_images=b * d,
image_recs=images_recon)
save_images(file_name='images_orig.png', image=images_orig)
save_images(file_name='images_recon.png', image=images_recs)
def train(self):
self.model.train()
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
meter_loss = AverageMeter('Loss', ':.4e')
meter_loss_constr = AverageMeter('Constr', ':6.2f')
meter_loss_perp = AverageMeter('Perplexity', ':6.2f')
progress = ProgressMeter(
self.training_loader.epoch_size()['__Video_0'], [
batch_time, data_time, meter_loss, meter_loss_constr,
meter_loss_perp
],
prefix="Steps: [{}]".format(self.num_steps))
data_iter = DALIGenericIterator(self.training_loader, ['data'],
auto_reset=True)
end = time.time()
for i in range(self.start_steps, self.num_steps):
# measure output loading time
data_time.update(time.time() - end)
try:
images = next(data_iter)[0]['data']
except StopIteration:
data_iter.reset()
images = next(data_iter)[0]['data']
images = images.to('cuda')
b, d, _, _, c = images.size()
images = rearrange(images, 'b d h w c -> (b d) c h w')
images = self.normalize(images.float() / 255.)
images = rearrange(images,
'(b d) c h w -> b (d c) h w',
b=b,
d=d,
c=c)
self.optimizer.zero_grad()
vq_loss, images_recon, perplexity = self.model(images)
recon_error = F.mse_loss(images_recon, images)
loss = recon_error + vq_loss
loss.backward()
self.optimizer.step()
meter_loss_constr.update(recon_error.item(), 1)
meter_loss_perp.update(perplexity.item(), 1)
meter_loss.update(loss.item(), 1)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % 20 == 0:
progress.display(i)
if i % 1000 == 0:
print('saving ...')
save_checkpoint(
self.folder_name, {
'steps': i,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict()
}, 'checkpoint%s.pth.tar' % i)
self.scheduler.step()
images, images_recon = map(
lambda t: rearrange(
t, 'b (d c) h w -> b d c h w', b=b, d=d, c=c),
[images, images_recon])
images_orig, images_recs = train_visualize(
unnormalize=self.unnormalize,
images=images[0, :self.n_images_save],
n_images=self.n_images_save,
image_recs=images_recon[0, :self.n_images_save])
save_images(file_name=os.path.join(self.path_img_orig,
f'image_{i}.png'),
image=images_orig)
save_images(file_name=os.path.join(self.path_img_recs,
f'image_{i}.png'),
image=images_recs)
if self.run_wandb:
logs = {
'iter': i,
'loss_recs': meter_loss_constr.val,
'loss': meter_loss.val,
'lr': self.scheduler.get_last_lr()[0]
}
self.run_wandb.log(logs)
print('saving ...')
save_checkpoint(
self.folder_name, {
'steps': self.num_steps,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
}, 'checkpoint%s.pth.tar' % self.num_steps)
def train(self):
self.model.train()
batch_time = AverageMeter('Time', ':6.3f')
data_time = AverageMeter('Data', ':6.3f')
meter_loss = AverageMeter('Loss', ':.4e')
meter_loss_constr = AverageMeter('Constr', ':6.2f')
meter_loss_perp = AverageMeter('Perplexity', ':6.2f')
progress = ProgressMeter(
len(self.training_loader),
[batch_time, data_time, meter_loss, meter_loss_constr, meter_loss_perp],
prefix="Steps: [{}]".format(self.num_steps))
data_iter = iter(self.training_loader)
end = time.time()
for i in range(self.start_steps, self.num_steps):
# measure output loading time
data_time.update(time.time() - end)
try:
images = next(data_iter)
except StopIteration:
data_iter = iter(self.training_loader)
images = next(data_iter)
images = images.to('cuda')
self.optimizer.zero_grad()
vq_loss, images_recon, perplexity = self.model(images)
recon_error = F.mse_loss(images_recon, images)
loss = recon_error + vq_loss
loss.backward()
self.optimizer.step()
meter_loss_constr.update(recon_error.item(), 1)
meter_loss_perp.update(perplexity.item(), 1)
meter_loss.update(loss.item(), 1)
# measure elapsed time
batch_time.update(time.time() - end)
end = time.time()
if i % 20 == 0:
progress.display(i)
if i % 1000 == 0:
print('saving ...')
save_checkpoint(self.folder_name, {
'steps': i,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict()
}, 'checkpoint%s.pth.tar' % i)
self.scheduler.step()
images_orig, images_recs = train_visualize(
unnormalize=self.unnormalize, images=images[:self.n_images_save], n_images=self.n_images_save,
image_recs=images_recon[:self.n_images_save])
save_images(file_name=os.path.join(self.path_img_orig, f'image_{i}.png'), image=images_orig)
save_images(file_name=os.path.join(self.path_img_recs, f'image_{i}.png'), image=images_recs)
if self.run_wandb:
logs = {
'iter': i,
'loss_recs': meter_loss_constr.val,
'loss': meter_loss.val,
'lr': self.scheduler.get_last_lr()[0]
}
self.run_wandb.log(logs)
print('saving ...')
save_checkpoint(self.folder_name, {
'steps': self.num_steps,
'state_dict': self.model.state_dict(),
'optimizer': self.optimizer.state_dict(),
'scheduler': self.scheduler.state_dict(),
}, 'checkpoint%s.pth.tar' % self.num_steps)