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- import numpy as np
- import torch
- def dct(x, norm=None):
- x_shape = x.shape
- N = x_shape[-1]
- x = x.contiguous().view(-1, N)
- v = torch.cat([x[:, ::2], x[:, 1::2].flip([1])], dim=1)
- Vc = torch.view_as_real(torch.fft.fft(v, dim=1)) # add this line
- k = - torch.arange(N, dtype=x.dtype, device=x.device)[None, :] * np.pi / (2 * N)
- W_r = torch.cos(k)
- W_i = torch.sin(k)
- V = Vc[:, :, 0] * W_r - Vc[:, :, 1] * W_i
- if norm == 'ortho':
- V[:, 0] /= np.sqrt(N) * 2
- V[:, 1:] /= np.sqrt(N / 2) * 2
- V = 2 * V.view(*x_shape)
- return V
- def idct(X, norm=None):
- x_shape = X.shape
- N = x_shape[-1]
- X_v = X.contiguous().view(-1, x_shape[-1]) / 2
- if norm == 'ortho':
- X_v[:, 0] *= np.sqrt(N) * 2
- X_v[:, 1:] *= np.sqrt(N / 2) * 2
- k = torch.arange(x_shape[-1], dtype=X.dtype, device=X.device)[None, :] * np.pi / (2 * N)
- W_r = torch.cos(k)
- W_i = torch.sin(k)
- V_t_r = X_v
- V_t_i = torch.cat([X_v[:, :1] * 0, -X_v.flip([1])[:, :-1]], dim=1)
- V_r = V_t_r * W_r - V_t_i * W_i
- V_i = V_t_r * W_i + V_t_i * W_r
- V = torch.cat([V_r.unsqueeze(2), V_i.unsqueeze(2)], dim=2)
- # v = torch.irfft(V, 1, onesided=False) # comment this line
- v = torch.fft.irfft(torch.view_as_complex(V), n=V.shape[1], dim=1) # add this line
- x = v.new_zeros(v.shape)
- x[:, ::2] += v[:, :N - (N // 2)]
- x[:, 1::2] += v.flip([1])[:, :N // 2]
- return x.view(*x_shape)
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