hi,
I have a 10241024 dimension feature vector to reduce dimension to 1,1024, so how can I get the final dimension using conv2d operation over 10241024. please explain the forward function for this or any other way
I don’t know which dimensions are referenced by the shape [1024, 1024]
, but assume the spatial ones.
If so, you could use a conv layer with kernel_size=(1024, 1)
to create an output of [batch_size, out_channels, 1, 1024]
:
conv = nn.Conv2d(3, 4, (1024, 1))
x = torch.randn(2, 3, 1024, 1024)
out = conv(x)
print(out.shape)
# > torch.Size([2, 4, 1, 1024])
can you please explain why we use x = torch.randn(2, 3, 1024, 1024)
instead of x = torch.randn(1024,1024)
nn.Conv2d
expects an input in the shape [batch_size, channels, height, width]
as described in the docs, so I’ve used random values for the missing dimensions.
ok. in my case its like x = torch.randn(1, 1, 1024, 1024)