I am trying to do a 2D scattering transform on an input image. When I run the following code I get this error: "The filters are not compatible for multiplication."? Can anyone help please? Thanx!
import torch
from kymatio import Scattering2D
import numpy as np
import PIL
from PIL import Image
FILENAME = "add a png file path"
image = PIL.Image.open(FILENAME).convert("L")
a = np.array(image).astype(np.float32)
x = torch.from_numpy(a)
imageSize=x.shape
scattering = Scattering2D(J=2, shape=imageSize, L=8)
Sx = scattering.forward(x)
print(Sx.size())
Seems to be working OK for a small 1KB .png
with equal width and height (square, not rectangle):
import torch
from kymatio import Scattering2D
import numpy as np
import PIL
from PIL import Image
FILENAME = "/path/to/dir/small_size_1_KB.png"
image = PIL.Image.open(FILENAME).convert("L")
a = np.array(image).astype(np.float64)
x = torch.from_numpy(a)
imageSize = x.shape
scattering = Scattering2D(J=2, shape=imageSize, L=8)
Sx = scattering.forward(x)
print(Sx.size())
torch.Size([81, 19, 19])
The error that you're getting though is in this method ( backend_torch.py
), should have to do something with tensor sizes:
def cdgmm(A, B, inplace=False):
"""
Complex pointwise multiplication between (batched) tensor A and tensor B.
Parameters
----------
A : tensor
input tensor with size (B, C, M, N, 2)
B : tensor
B is a complex tensor of size (M, N, 2)
inplace : boolean, optional
if set to True, all the operations are performed inplace
Returns
-------
C : tensor
output tensor of size (B, C, M, N, 2) such that:
C[b, c, m, n, :] = A[b, c, m, n, :] * B[m, n, :]
"""
A, B = A.contiguous(), B.contiguous()
if A.size()[-3:] != B.size():
raise RuntimeError('The filters are not compatible for multiplication!')
if not iscomplex(A) or not iscomplex(B):
raise TypeError('The input, filter and output should be complex')
if B.ndimension() != 3:
raise RuntimeError('The filters must be simply a complex array!')
if type(A) is not type(B):
raise RuntimeError('A and B should be same type!')
C = A.new(A.size())
A_r = A[..., 0].contiguous().view(-1, A.size(-2)*A.size(-3))
A_i = A[..., 1].contiguous().view(-1, A.size(-2)*A.size(-3))
B_r = B[...,0].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_i)
B_i = B[..., 1].contiguous().view(B.size(-2)*B.size(-3)).unsqueeze(0).expand_as(A_r)
C[..., 0].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_r - A_i * B_i
C[..., 1].view(-1, C.size(-2)*C.size(-3))[:] = A_r * B_i + A_i * B_r
return C if not inplace else A.copy_(C)
https://github.com/edouardoyallon/pyscatwave/blob/master/scatwave/utils.py
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