Hello,

Can we apply image transformation inside the forward function of our network?

I want to apply this transformation in the forward definition of my network.

```
self.transforms = transforms.Compose([transforms.ToPILImage().convert('RGB'),transforms.Resize(90),
transforms.CenterCrop(70),transforms.ToTensor()])
```

I am using a pre-trained model(resnet50) and changing the last fully connected to my needs.

In the case when I apply transformation outside the network(transform PIL ie resize, center-crop, then Tensor) my network works.

But, for the case when I covert my input image to tensor and then inside my network try to convert it back to PIL and apply the operations. It doesn’t work.

I receive this error, even if I have applied to unsqueeze operation.

```
Expected 4-dimensional input for 4-dimensional weight 64 3 7 7, but got 3-dimensional input of size [3, 640, 960] instead
```

If I try to unsqueeze my tensor before entering the network, I get “CUDA memory full” error.

My Network Definition :-

```
class Network(nn.Module):
def __init__(self):
super().__init__()
self.model = models.resnet50(pretrained=True)
for param in self.model.parameters():
param.requires_grad = False
self.model_in_fetures = self.model.fc.in_features
self.model.fc = nn.Sequential(nn.Linear(self.model_in_fetures,1000),nn.ReLU(),nn.Dropout(0.5),
nn.Linear(1000,250),nn.ReLU(),nn.Dropout(0.5),
nn.Linear(250,50),nn.ReLU(),nn.Dropout(0.5),
nn.Linear(50,8))
self.transforms = transforms.Compose([transforms.ToPILImage().convert('RGB'),transforms.Resize(90),
transforms.CenterCrop(70),transforms.ToTensor()])
def forward(self, x):
x = self.transforms(x)
x = x.unsqueeze(0)
return self.model(x)
```