Hi there,
Good days everyone, I am trying on a network with input shape of 6x224x224 tensor, however, the torch.summary() report that the input size is around 86436.00 MB which is 84GB , so that it is killed.
I have google around on how is the input size calculated but it didn’t match with the output shows.
Is there any thing wrong in my code ?
Thanks ~~
Dick
Here is the output of torch.summary()

    Layer (type)               Output Shape         Param #

================================================================
Conv2d-1         [-1, 64, 224, 224]           3,520
ReLU-2         [-1, 64, 224, 224]               0
Conv2d-3         [-1, 64, 224, 224]          36,928
ReLU-4         [-1, 64, 224, 224]               0
MaxPool2d-5         [-1, 64, 112, 112]               0
Conv2d-6        [-1, 128, 112, 112]          73,856
ReLU-7        [-1, 128, 112, 112]               0
Conv2d-8        [-1, 128, 112, 112]         147,584
ReLU-9        [-1, 128, 112, 112]               0
MaxPool2d-10          [-1, 128, 56, 56]               0
Conv2d-11          [-1, 256, 56, 56]         295,168
ReLU-12          [-1, 256, 56, 56]               0
Conv2d-13          [-1, 256, 56, 56]         590,080
ReLU-14          [-1, 256, 56, 56]               0
MaxPool2d-15          [-1, 256, 28, 28]               0
Conv2d-16          [-1, 512, 28, 28]       1,180,160
ReLU-17          [-1, 512, 28, 28]               0
Conv2d-18          [-1, 512, 28, 28]       2,359,808
ReLU-19          [-1, 512, 28, 28]               0
Conv2d-20          [-1, 512, 28, 28]       2,359,808
ReLU-21          [-1, 512, 28, 28]               0
MaxPool2d-22          [-1, 512, 14, 14]               0
Conv2d-23          [-1, 512, 14, 14]       2,359,808
ReLU-24          [-1, 512, 14, 14]               0
Conv2d-25          [-1, 512, 14, 14]       2,359,808
ReLU-26          [-1, 512, 14, 14]               0
Conv2d-27          [-1, 512, 14, 14]       2,359,808
ReLU-28          [-1, 512, 14, 14]               0
MaxPool2d-29            [-1, 512, 7, 7]               0
Linear-30                 [-1, 4096]     102,764,544
ReLU-31                 [-1, 4096]               0
Linear-32                 [-1, 4096]      16,781,312
ReLU-33                 [-1, 4096]               0
Linear-34                    [-1, 1]           4,097
Total params: 133,676,289
Trainable params: 133,676,289
Non-trainable params: 0
Input size (MB): 86436.00
Forward/backward pass size (MB): 206.27
Params size (MB): 509.93
Estimated Total Size (MB): 87152.20
Here is the forward code, I have use cat to stack up 2 image to 6 channel
```
def forward(self,leftimage,rightimage):
combine=torch.cat((leftimage,rightimage),1)
combine=self.encoder(combine)
#combine=self.encoder(leftimage)
    #print(combine.shape)
    combine=torch.flatten(combine,1)
    #print(combine.shape)
    combine=self.classifier(combine)
    return combine