Memory estimation inconsistent with actual GPU memory utilization

Describe the bug Memory estimation inconsistent with actual GPU memory utilization

To Reproduce

• I am using a simple UNet with 2 layers (same as here).
• The input size is (1, 1, 4096, 3328)

Expected behavior When forwarding an image of size (1, 1, 4096, 3328) in testing mode, i.e., model.eval() on, the reported GPU memory is approximatly 15GB:

However, torchinfo.summary reports 50GB, even though eval is passed as argument:

summary(model, input_size=(1, 1, 4096, 3328), mode='eval', device=device)

[mert-kurttutan]

Actually, gradient is not calculated in any of the modes since torch.no_grad is used for both train and eval mode, see forward_pass function in torchinfo.py. I also checked. Gpu memory usage remains the same when changing the mode.

[rodrigovimieiro]

@devrimcavusoglu I don’t have enough GPU memory for the model. That’s why I was trying to estimate it