I need to normalize my train set in the following code:
network = Network()
network.cuda()
criterion = nn.MSELoss()
optimizer = optim.Adam(network.parameters(), lr=0.0001)
loss_min = np.inf
num_epochs = 1
start_time = time.time()
for epoch in range(1,num_epochs+1):
loss_train = 0
loss_test = 0
running_loss = 0
network.train()
print('size of train loader is: ', len(train_loader))
for step in range(1,len(train_loader)+1):
batch = next(iter(train_loader))
images, landmarks = batch['image'], batch['landmarks']
#RuntimeError: Given groups=1, weight of size [64, 3, 7, 7], expected input[64, 600, 800, 3] to have 3 channels, but got 600 channels instead
#using permute below to fix the above error
images = images.permute(0,3,1,2)
images = images.cuda()
landmarks = landmarks.view(landmarks.size(0),-1).cuda()
images = transforms.Normalize([97.13, 97.15, 97.15], [28.74, 28.79, 28.81]) #find the args later
##landmarks = torchvision.transforms.Normalize(landmarks) #Do I need to normalize the target?
predictions = network(images)
# clear all the gradients before calculating them
optimizer.zero_grad()
print('predictions are: ', predictions.float())
print('landmarks are: ', landmarks.float())
# find the loss for the current step
loss_train_step = criterion(predictions.float(), landmarks.float())
loss_train_step = loss_train_step.to(torch.float32)
print("loss_train_step before backward: ", loss_train_step)
# calculate the gradients
loss_train_step.backward()
# update the parameters
optimizer.step()
print("loss_train_step after backward: ", loss_train_step)
loss_train += loss_train_step.item()
print("loss_train: ", loss_train)
running_loss = loss_train/step
print('step: ', step)
print('running loss: ', running_loss)
print_overwrite(step, len(train_loader), running_loss, 'train')
network.eval()
with torch.no_grad():
for step in range(1,len(test_loader)+1):
batch = next(iter(train_loader))
images, landmarks = batch['image'], batch['landmarks']
images = images.permute(0,3,1,2)
images = images.cuda()
landmarks = landmarks.view(landmarks.size(0),-1).cuda()
predictions = network(images)
# find the loss for the current step
loss_test_step = criterion(predictions, landmarks)
loss_test += loss_test_step.item()
running_loss = loss_test/step
print_overwrite(step, len(test_loader), running_loss, 'Validation')
loss_train /= len(train_loader)
loss_test /= len(test_loader)
print('\n--------------------------------------------------')
print('Epoch: {} Train Loss: {:.4f} Valid Loss: {:.4f}'.format(epoch, loss_train, loss_test))
print('--------------------------------------------------')
if loss_test < loss_min:
loss_min = loss_test
torch.save(network.state_dict(), '../moth_landmarks.pth')
print("\nMinimum Valid Loss of {:.4f} at epoch {}/{}".format(loss_min, epoch, num_epochs))
print('Model Saved\n')
print('Training Complete')
print("Total Elapsed Time : {} s".format(time.time()-start_time))
and I get this error:
size of train loader is: 90
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-101-ea938d47ca19> in <module>
35 ##landmarks = torchvision.transforms.Normalize(landmarks) #Do I need to normalize the target?
36
---> 37 predictions = network(images)
38
39 # clear all the gradients before calculating them
~/anaconda3/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
720 result = self._slow_forward(*input, **kwargs)
721 else:
--> 722 result = self.forward(*input, **kwargs)
723 for hook in itertools.chain(
724 _global_forward_hooks.values(),
<ipython-input-19-46116d2a7101> in forward(self, x)
9
10 def forward(self, x):
---> 11 x = x.float()
12 out = self.model(x)
13 return out
AttributeError: 'Normalize' object has no attribute 'float'
How else should I normalize the data to avoid this error?