Error with 3D convolution on custom Kernels
Closed this issue · 2 comments
I wanted to add my own 3D kernel on every layer. I tried using this,
`
def conv3(in_channels, out_channels, kernel_size=3, stride=1,
padding=1, bias=True, planar=False, dim=3):
"""Returns an appropriate spatial convolution layer, depending on args.
- dim=2: Conv2d with 3x3 kernel
- dim=3 and planar=False: Conv3d with 3x3x3 kernel
- dim=3 and planar=True: Conv3d with 1x3x3 kernel
"""
if planar:
stride = planar_kernel(stride)
padding = planar_pad(padding)
kernel_size = planar_kernel(kernel_size)
weights = torch.tensor([[[4., 1., 4.],
[1., 1., 1.],
[4., 1., 4.]],
[[1., 1., 1.],
[1., 10., 1.],
[1., 1., 1.]],
[[4., 1., 4.],
[1., 1., 1.],
[4., 1., 4.]]])
# weights
weightsu = weights.view(3, 3, 3).repeat(1, 1, 1, 1, 1)
# weightsu
kernel = get_conv(dim)(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
bias=bias
)
print(kernel.weight.shape)
with torch.no_grad():
kernel.weight = nn.Parameter(weightsu)
return kernel
`
But I got an error like this,
Testing 3D U-Net with n_blocks = 1, planar_blocks = ()...
torch.Size([32, 1, 3, 3, 3])
torch.Size([32, 32, 3, 3, 3])
RuntimeError: Given weight of size [1, 1, 3, 3, 3], expected bias to be 1-dimensional with 1 elements, but got bias of size [32] instead
I kindly request you to Help me fix this!!!
With Random kernels and planar setting, I get output as
Testing 3D U-Net with n_blocks = 1, planar_blocks = ()...
torch.Size([32, 1, 3, 3, 3])
torch.Size([32, 32, 3, 3, 3])
Testing 3D U-Net with n_blocks = 1, planar_blocks = (0,)...
torch.Size([32, 1, 1, 3, 3])
torch.Size([32, 32, 1, 3, 3])
...
...
...
All tests sucessful!
This issue is not really related to elektronn3. If I understand correctly you want to change the initialization of your weights. Note that the get_conv(dim) call here evaluates to torch.nn.Conv3d Please refer to the PyTorch docs and https://discuss.pytorch.org/ if you need help with changing weights of torch.nn.Conv3d modules.