class DRCNTrainer(Trainer):
def __init__(self, config, training_loader, testing_loader):
super(DRCNTrainer, self).__init__()
self.config = config
self.GPU_IN_USE = torch.cuda.is_available()
self.device = torch.device('cuda' if self.GPU_IN_USE else 'cpu')
self.model = None
self.lr = config.lr
self.nEpochs = config.nEpochs
self.criterion = None
self.optimizer = None
self.scheduler = None
self.seed = config.seed
self.upscale_factor = config.upscale_factor
self.training_loader = training_loader
self.testing_loader = testing_loader
# DRCN setup
self.momentum = 0.9
self.weight_decay = 0.0001
self.loss_alpha = 1.0
self.loss_alpha_zero_epoch = 25
self.loss_alpha_decay = self.loss_alpha / self.loss_alpha_zero_epoch
self.loss_beta = 0.001
self.num_recursions = 16
def build_model(self):
self.model = Net(num_channels=1,
base_channel=64,
num_recursions=self.num_recursions,
device=self.device).to(self.device)
self.model.weight_init()
self.criterion = nn.MSELoss()
torch.manual_seed(self.seed)
if self.GPU_IN_USE:
torch.cuda.manual_seed(self.seed)
cudnn.benchmark = True
self.criterion.cuda()
# setup optimizer and scheduler
param_groups = [{'params': list(self.model.parameters())}]
param_groups += [{'params': [self.model.w]}]
self.set_optimizer()
def img_preprocess(self, data, interpolation='bicubic'):
if interpolation == 'bicubic':
interpolation = Image.BICUBIC
elif interpolation == 'bilinear':
interpolation = Image.BILINEAR
elif interpolation == 'nearest':
interpolation = Image.NEAREST
size = list(data.shape)
if len(size) == 4:
target_height = int(size[2] * self.upscale_factor)
target_width = int(size[3] * self.upscale_factor)
out_data = torch.FloatTensor(size[0], size[1], target_height,
target_width)
for i, img in enumerate(data):
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((target_width, target_height),
interpolation=interpolation),
transforms.ToTensor()
])
out_data[i, :, :, :] = transform(img)
return out_data
else:
target_height = int(size[1] * self.upscale_factor)
target_width = int(size[2] * self.upscale_factor)
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((target_width, target_height),
interpolation=interpolation),
transforms.ToTensor()
])
return transform(data)
def train(self):
"""
data: [torch.cuda.FloatTensor], 4 batches: [64, 64, 64, 8]
"""
self.model.train()
train_loss = 0
for batch_num, (data, target) in enumerate(self.training_loader):
data = self.img_preprocess(data) # resize input image size
data, target = data.to(self.device), target.to(self.device)
target_d, output = self.model(data)
# loss1
loss_1 = 0
for d in range(self.num_recursions):
loss_1 += (self.criterion(target_d[d], target) /
self.num_recursions)
# loss2
loss_2 = self.criterion(output, target)
# regularization
reg_term = 0
for theta in self.model.parameters():
reg_term += torch.mean(torch.sum(theta**2))
# total loss
loss = self.loss_alpha * loss_1 + (
1 - self.loss_alpha) * loss_2 + self.loss_beta * reg_term
loss.backward()
train_loss += loss.item()
self.optimizer.step()
total_time = progress_bar(
batch_num, len(self.training_loader),
'Loss: %.4f' % (train_loss / (batch_num + 1)))
avg_loss = train_loss / len(self.training_loader)
return [avg_loss, total_time]
def test(self):
"""
data: [torch.cuda.FloatTensor], 10 batches: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
"""
self.model.eval()
avg_psnr = 0
with torch.no_grad():
for batch_num, (data, target) in enumerate(self.testing_loader):
data = self.img_preprocess(data) # resize input image size
data, target = data.to(self.device), target.to(self.device)
_, prediction = self.model(data)
mse = self.criterion(prediction, target)
psnr = 10 * log10(1 / mse.item())
avg_psnr += psnr
total_time = progress_bar(
batch_num, len(self.testing_loader),
'PSNR: %.4f' % (avg_psnr / (batch_num + 1)))
avg_psnr = avg_psnr / len(self.testing_loader)
return [avg_psnr, total_time]
def run(self):
self.build_model()
for epoch in range(1, self.nEpochs + 1):
print("\n===> Epoch {} starts:".format(epoch))
self.loss_alpha = max(0.0, self.loss_alpha - self.loss_alpha_decay)
avg_loss = self.train()
avg_psnr = self.test()
self.scheduler.step(epoch)
self.save_model(epoch=epoch, avg_error=avg_loss, avg_psnr=avg_psnr)
class DRCNTrainer(object):
def __init__(self, config, training_loader, testing_loader):
self.model = None
self.lr = config.lr
self.nEpochs = config.nEpochs
self.criterion = None
self.optimizer = None
self.scheduler = None
self.GPU_IN_USE = torch.cuda.is_available()
self.seed = config.seed
self.upscale_factor = config.upscale_factor
self.training_loader = training_loader
self.testing_loader = testing_loader
# DRCN setup
self.momentum = 0.9
self.weight_decay = 0.0001
self.loss_alpha = 1.0
self.loss_alpha_zero_epoch = 25
self.loss_alpha_decay = self.loss_alpha / self.loss_alpha_zero_epoch
self.loss_beta = 0.001
self.num_recursions = 16
def build_model(self):
self.model = Net(num_channels=1,
base_channel=64,
num_recursions=self.num_recursions)
self.model.weight_init()
self.criterion = nn.MSELoss()
torch.manual_seed(self.seed)
if self.GPU_IN_USE:
torch.cuda.manual_seed(self.seed)
cudnn.benchmark = True
self.model.cuda()
self.criterion.cuda()
# setup optimizer and scheduler
param_groups = [{'params': list(self.model.parameters())}]
param_groups += [{'params': [self.model.w]}]
self.optimizer = optim.Adam(param_groups, lr=self.lr)
self.scheduler = optim.lr_scheduler.MultiStepLR(
self.optimizer, milestones=[50, 75, 100], gamma=0.5) # lr decay
def img_preprocess(self, data, interpolation='bicubic'):
if interpolation == 'bicubic':
interpolation = Image.BICUBIC
elif interpolation == 'bilinear':
interpolation = Image.BILINEAR
elif interpolation == 'nearest':
interpolation = Image.NEAREST
size = list(data.shape)
if len(size) == 4:
target_height = int(size[2] * self.upscale_factor)
target_width = int(size[3] * self.upscale_factor)
out_data = torch.FloatTensor(size[0], size[1], target_height,
target_width)
for i, img in enumerate(data):
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((target_width, target_height),
interpolation=interpolation),
transforms.ToTensor()
])
out_data[i, :, :, :] = transform(img)
return out_data
else:
target_height = int(size[1] * self.upscale_factor)
target_width = int(size[2] * self.upscale_factor)
transform = transforms.Compose([
transforms.ToPILImage(),
transforms.Resize((target_width, target_height),
interpolation=interpolation),
transforms.ToTensor()
])
return transform(data)
def save(self):
model_out_path = "DRCN_model_path.pth"
torch.save(self.model, model_out_path)
print("Checkpoint saved to {}".format(model_out_path))
def train(self):
"""
data: [torch.cuda.FloatTensor], 4 batches: [64, 64, 64, 8]
"""
self.model.train()
train_loss = 0
for batch_num, (data, target) in enumerate(self.training_loader):
data = self.img_preprocess(data) # resize input image size
if self.GPU_IN_USE:
data, target = Variable(data).cuda(), Variable(target).cuda()
self.optimizer.zero_grad()
target_d, output = self.model(data)
# loss1
loss_1 = 0
for d in range(self.num_recursions):
loss_1 += (self.criterion(target_d[d], target) /
self.num_recursions)
# loss2
loss_2 = self.criterion(output, target)
# regularization
reg_term = 0
for theta in self.model.parameters():
reg_term += torch.mean(torch.sum(theta**2))
# total loss
loss = self.loss_alpha * loss_1 + (
1 - self.loss_alpha) * loss_2 + self.loss_beta * reg_term
loss.backward()
train_loss += loss.data[0]
self.optimizer.step()
progress_bar(batch_num, len(self.training_loader),
'Loss: %.4f' % (train_loss / (batch_num + 1)))
print(" Average Loss: {:.4f}".format(train_loss /
len(self.training_loader)))
def test(self):
"""
data: [torch.cuda.FloatTensor], 10 batches: [10, 10, 10, 10, 10, 10, 10, 10, 10, 10]
"""
self.model.eval()
avg_psnr = 0
for batch_num, (data, target) in enumerate(self.testing_loader):
data = self.img_preprocess(data) # resize input image size
if self.GPU_IN_USE:
data, target = Variable(data).cuda(), Variable(target).cuda()
_, prediction = self.model(data)
mse = self.criterion(prediction, target)
psnr = 10 * log10(1 / mse.data[0])
avg_psnr += psnr
progress_bar(batch_num, len(self.testing_loader),
'PSNR: %.4f' % (avg_psnr / (batch_num + 1)))
print(" Average PSNR: {:.4f} dB".format(avg_psnr /
len(self.testing_loader)))
def validate(self):
self.build_model()
for epoch in range(1, self.nEpochs + 1):
print("\n===> Epoch {} starts:".format(epoch))
self.loss_alpha = max(0.0, self.loss_alpha - self.loss_alpha_decay)
self.train()
self.test()
self.scheduler.step(epoch)
if epoch == self.nEpochs:
self.save()