I’m trying to apply senet for classification problem. as you can see in the image, last conv2d takes in 128 channels and outputs 2048 channels. so I made last linear layer (2048, 1383)
I only changed last 1000 to 1383 and the following error comes out.
RuntimeError: size mismatch, m1: [32 x 4096], m2: [2048 x 1383] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:266
how can I solve the problem??
It looks like your spatial size is not 1x1 after the last pooling layer, but apparently 2x1 or 1x2.
Could you check that by adding a print statement showing the shape of your activation before flattening it it your forward method?
I printed out the shape after the avg_pool layer and I got (batch size, 2048, 1, 1). Isn’t this correct?
if I make in_features 4096 it works fine, but it makes me uncomfortable…
In that case, could you post your forward method?
Maybe the reshaping might be buggy.
I assume you are using @Cadene’s implementation of SeNet.
Could you add print(x.shape) before this line of code and use the complete model again?
I changed the following code and generated x = torch.randn(1, 3, 224, 224)
and got the following shape (1, 2048, 1, 1)
I do not see which part that I’m doing wrong 
I’m not sure, what’s going on, as I’ve just tested your approach and it’s working fine:
model = pretrainedmodels.senet154()
# Use vanilla model
x = torch.randn(1, 3, 224, 224)
output = model(x)
print(model.last_linear)
>Linear(in_features=2048, out_features=1000, bias=True)
# Change last_linear
model.last_linear = nn.Linear(2048, 1383)
output = model(x)
print(output.shape)
> torch.Size([1, 1383])
Could you check your code again for differences?
I’m so sorry… I checked the transform and I found that I resized the input images to (224, 244) not (224, 224)
again sorry for wasting your time 
No worries! Glad you figured it out! 