Comments (4)
nn.conv2d:
def init(
self,
in_channels,
out_channels,
kernel_size,
stride,#<==
padding,#<==
dilation,
groups,
bias,
padding_mode
)
ffconv2d:
def init(
self,
in_channels,
out_channels,
kernel_size,
padding,#<==
stride,#<==
dilation,
groups
)
from fft-conv-pytorch.
Hi -- I'm not sure this is a bug? The weights/biases for nn.Conv2d
and FFTConv2d
are randomly initialized, so in your example they will be different for the two layers. Can you manually set the weights/biases to be equal, and see if the outputs are still different?
from fft-conv-pytorch.
Not a bug, the padding and the stride switch places
from fft-conv-pytorch.
Understood. I'll switch the positions of those arguments, to match the form of f.conv2d
.
By the way, you can format code in Markdown using backticks: https://www.freecodecamp.org/news/how-to-format-code-in-markdown/
Makes your code snippets much easier to read.
from fft-conv-pytorch.
Related Issues (13)
- can't work on GPU? HOT 1
- FFTConvTranspose
- Stride HOT 1
- CUDA out of memory with complex_matmul HOT 5
- Complex value support?
- Using fft-conv hurts convergence HOT 2
- License HOT 8
- How to achieve overlap and add/save
- Depth-wise separable convolution? HOT 11
- Propagation of error becomes large very fast HOT 1
- Autograd for complex matrix multiplication in Pytorch ? HOT 3
- in_channels must be divisible by groups
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from fft-conv-pytorch.