As per the documentation for Conv3d, the input shall be passed as [batch_size=128, channels=3, depth=32, height=64, width=64].

  
      pytorch.org
  
  
    

Conv3d — PyTorch 1.7.0 documentation 1



  
  
    
    
  
  





 Hassan_Imani:

If I feed like second case, what will happen? The network will not learn or it will not learn efficiently?


It might learn something and the effect of this on performance is unknown.
The common understanding might be lost between the design of the network and how the input is organized. It all depends on how you formulate the kernel size I guess. (whether it is designed keeping [batch_size, channel, depth, height, width] or [batch_size, channel, height, width, depth] in mind).
Given that you are using pretrained resnet3D model, it would have been trained with [batch_size, channel, depth, height, width].