assert (torch.abs(x - y)).sum() < 1e-10 # x == y within numerical tolerance
numerical tolerance error cannot satisfy 1e-10, far beyond this

When I import and use this package, it sometimes appears the segmentation fault (core dumped）problem. Has any one meet this kind of problem before?

When I try to run the example, I get a runtime error

>>> X = dct.dct(x)   # DCT-II done through the last dimension
Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/usr/local/lib/python3.8/dist-packages/torch_dct/_dct.py", line 48, in dct
    Vc = torch.rfft(v, 1, onesided=False)
AttributeError: module 'torch' has no attribute 'rfft'

my torch version is '1.10.2+cu113'. Did I miss something?

Also, I want to ask what is scaled DCT? Does the input of scaled DCT need to be in range [0,1]?

Thank you very much!

input image "im_gray" of uint8 and size of [256,256], then i made following operations with opencv and torch-dct, respectively:
a = cv2.dct(im_gray.astype("float"))
b = torch_dct.dct_2d(torch.from_numpy(im_gray.astype("float"))).
But a and b are greatly different.

Hi,
Is there any hope of adding a 4D version, as well? I want to run experiments in encoding 2D-convolution layers as a subset of 4D DCT coefficients.

assert (torch.abs(x - y)).sum() < 1e-10 # x == y within numerical tolerance
numerical tolerance error cannot satisfy 1e-10, far beyond this

Hi @zh217,

I just installed torch-dct and tried the test on the README page. I am getting an AssertionError. The value of (torch.abs(x - y)).sum() is small though: 3.0896e-05.

Any idea what maybe the issue? Happy to help. Thanks

x = torch.randn(200)
X = dct.dct(x) # DCT-II done through the last dimension
y = dct.idct(X) # scaled DCT-III done through the last dimension
assert (torch.abs(x - y)).sum() < 1e-10 # x == y within numerical tolerance

AssertionError Traceback (most recent call last)
in
X = dct.dct(x) # DCT-II done through the last dimension
y = dct.idct(X) # scaled DCT-III done through the last dimension
assert (torch.abs(x - y)).sum() < 1e-10 # x == y within numerical tolerance

X = dct.dct(x)
UserWarning: The function torch.rfft is deprecated and will be removed in a future PyTorch release. Use the new torch.fft module functions, instead, by importing torch.fft and calling torch.fft.fft or torch.fft.rfft. (Triggered internally at /pytorch/aten/src/ATen/native/SpectralOps.cpp:590.)
Vc = torch.rfft(v, 1, onesided=False)

def test_dct_3d():
for N1 in [2, 5, 32]:
x = np.random.normal(size=(1, N1, N1, N1))
ref = fftpack.dct(x, axis=3, type=2)
ref = fftpack.dct(ref, axis=2, type=2)
ref = fftpack.dct(ref, axis=1, type=2)
act = dct_3d(torch.tensor(x).float()).numpy()
assert np.abs(ref - act).max() < EPS, (ref, act)

def test_idct_3d():
for N1 in [2, 5, 32]:
x = np.random.normal(size=(1, N1, N1, N1))
X = dct_3d(torch.tensor(x).float())
y = idct_3d(X).numpy()
assert np.abs(x - y).max() < EPS, x

In the above code, the last three dims are the same. If they are different, met with a problem:
RuntimeError: size mismatch

刚刚使用dct_2d函数试了一下，结果和cv2的dct()函数结果出入较大。手动计算了DC系数，手算的结果和cv2.dct()是吻合的，但是与dct_2d()的DC系数相差较大，其它系数暂未验证。请问您遇到过这样的情况吗

Could please tell me how can i apply LinearDCT which is faster in gpu on a batch of images ?

I am a new learner of discrete cosine transform. Can you tell me what is the difference between DCT-I and DCT-II?

in https://docs.scipy.org/doc/scipy-0.14.0/reference/generated/scipy.fftpack.dct.html, it has parameter "n" which is described as optional Length of the transform. I want to use this parameter but this package's dct function seems to have no such parameter. Is there any possible way to use this parameter or should it be reimplemented?

Hi,

I read the docs of torch. rfft. It says that torch. rfft is implementation of FFT. But FFT is not dct. In your code you use torch. rfft to implement dct. Do you think your code is right?

Thanks.

I was working on using your DCT implementation to implement ACDC layers, and I found that the DCT implementation you have here is slower than using a Linear layer with the DCT matrix. The code in my fork doing the timing testing is here: https://github.com/BayesWatch/torch-dct/blob/master/torch_dct/_dct.py#L233-L259

I found it could be made a bit faster by using the 2N Makhoul method (and also storing the scaling, but that didn't make much difference): https://github.com/BayesWatch/torch-dct/blob/master/torch_dct/_dct.py#L64-L98

I could open a pull request with just the 2N Makhoul method if you like, otherwise I just wanted to let you know.

The current module is incompatible in my projects with torch.compile. I was able to resolve the error by changing the np.pi calls to torch.tensor(math.pi) and convert all numpy operations to torch operations.

The function dct is not equal with scipy.fftpack.dct.
I assume that scipy.fftpack.dct is correct, since scipy.fftpack.dct is equal with matlab's dct.

Hi, thanks for this great Pytorch version of DCT, it helps me and our team a lot.

I'm not quite familiar with autograd mechanism and not sure what the difference between require_grad and requires_grad is, are they the same thing but different variable name in different pytorch version? or is it a typo?