I’m trying to replicate a Keras model which starts out like this:
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
input_2 (InputLayer)         (None, 720, 1, 1)         0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 720, 1, 128)       1152      
_________________________________________________________________


The input is (batch_size, 720, 1, 1) and then a conv2d layer is applied on it using 128 filters and kernel size of 8.  In trying to replicate this in pytorch, I have:
import torch

a = torch.randn(32,720, 1, 1)
print('a:', a.size()) # a: torch.Size([32, 720, 1, 1])

torch.nn.Conv2d(720, 128, kernel_size=8, stride=1)(a)

But I’m getting the following error…

RuntimeError: Calculated padded input size per channel: (1 x 1). Kernel size: (8 x 8). Kernel size can’t greater than actual input size at /pytorch/aten/src/THNN/generic/SpatialConvolutionMM.c:48

Any ideas what I’m doing wrong and why this is working on keras and not on pytorch?