I am using the mse loss described here.
https://pytorch.org/docs/stable/nn.html?highlight=mse#torch.nn.functional.mse_loss 25

torch.nn.functional. mse_loss ( input ,  target ,  size_average=None ,  reduce=None ,  reduction=‘mean’ ) → Tensor

My input and target are of size [16, 2, 48, 120] i.e. a batch size of 16 where each item is a tensor of size [2, 48, 120].
Supplying the argument reduction="sum" returns tensor(3304.8472, grad_fn=<MseLossBackward>)
whereas the argument reduction="mean" returns tensor(0.0747, grad_fn=<MseLossBackward>)
This probably means that the method is averaging over the total number of pixel instead of averaging only by the batch size. What can I do to sum over every pixel then divide by the batch size ?