Styjun

I am trying to get the output of a neural network which I have already trained. The input is an image of the size 300x300. I am using a batch size of 1, but I still get a CUDA error: out of memory error after I have successfully got the output for 25 images.

I searched for some solutions online and came across torch.cuda.empty_cache(). But this still doesn't seem to solve the problem.

This is the code I am using.

device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")

train_x = torch.tensor(train_x, dtype=torch.float32).view(-1, 1, 300, 300)
train_x = train_x.to(device)
dataloader = torch.utils.data.DataLoader(train_x, batch_size=1, shuffle=False)

right = []
for i, left in enumerate(dataloader):
 print(i)
 temp = model(left).view(-1, 1, 300, 300)
 right.append(temp.to('cpu'))
 del temp
 torch.cuda.empty_cache()

This for loop runs for 25 times every time before giving the memory error.

Every time, I am sending a new image in the network for computation. So, I don't really need to store the previous computation results in the GPU after every iteration in the loop. Is there any way to achieve this?

Any help will be appreciated. Thanks.

I figured out where I was going wrong. I am posting the solution as an answer for others who might be struggling with the same problem.

Basically, what PyTorch does is that it creates a computational graph whenever I pass the data through my network and stores the computations on the GPU memory, in case I want to calculate the gradient during backpropagation. But since I only wanted to perform a forward propagation, I simply needed to specify torch.no_grad() for my model.

Thus, the for loop in my code could be rewritten as:

for i, left in enumerate(dataloader):
 print(i)
 with torch.no_grad():
 temp = model(left).view(-1, 1, 300, 300)
 right.append(temp.to('cpu'))
 del temp
 torch.cuda.empty_cache()

Specifying no_grad() to my model tells PyTorch that I don't want to store any previous computations, thus freeing my GPU space.