from pytorch_wavelets.dwt import DWTForward 2. 调用dwtforward函数进行小波变换 DWTForward类用于执行二维离散小波变换(DWT)的前向分解。你可以创建一个DWTForward实例,并传入相应的参数来执行小波变换。 以下是一个示例代码,展示了如何使用DWTForward函数对一个图像进行小波变换: python import torch from pytorch_...
2.2 小波包变换(WPT) 在小波变换的金字塔算法中,细节“分支”(detail coefficients)不再进一步计算,也就是说,在每一个分辨率的level上只对尺度系数(approximation coefficients)进一步分解,得到下一层分辨率level的近似参数和细节参数。 2.2.1 与Haar小波相关的小波包变换 同样长度的离散信号进行小波包变换(N=16),以Ha...
从pytorch_wavelets的源码https://github.com/fbcotter/pytorch_wavelets/blob/master/pytorch_wavelets/dwt/transform2d.py中可见其wave参数使用的是pywt.Wavelet: classDWTForward(nn.Module):"""Performs a 2d DWT Forward decomposition of an imageArgs: J (int): Number of levels of decomposition wave (st...
在使用pytorch_wavelets包的DWT1DInverse时,发现报错信息如下: Traceback (most recent call last): File "/work/GDN/test/test_DWT.py", line 24, in x_ = idwt((YL, YH)) File "/opt/conda/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, incall result = self.forward(*i...
1. 2. 3. 4. 5. 6. 7. 8. 9. In the code above, we first create a sample signal with random noise. We then use theDWTForwardclass from Pytorch Wavelets to perform a wavelet transform on the signal with 3 decomposition levels and using the Daubechies wavelet ‘db1’. The result is...
import torch from pytorch_wavelets import DWTForward, DWTInverse xfm = DWTForward(J=3, wave='db3', mode='zero') X = torch.randn(10,5,64,64) Yl, Yh = xfm(X) print(Yl.shape) >>> torch.Size([10, 5, 12, 12]) print(Yh[0].shape) >>> torch.Size([10, 5, 3, 34, 34]...
Module): """Performs a 1d DWT Forward decomposition of an 2d image Args: J (int): Number of levels of decomposition wave (str or pywt.Wavelet or tuple(ndarray)): Which wavelet to use. Can be: 1) a string to pass to pywt.Wavelet constructor 2) a pywt.Wavelet class 3) a tuple ...
import torch from pytorch_wavelets import DWTForward, DWTInverse xfm = DWTForward(J=3, wave='db3', mode='zero') X = torch.randn(10,5,64,64) Yl, Yh = xfm(X) print(Yl.shape) >>> torch.Size([10, 5, 12, 12]) print(Yh[0].shape) >>> torch.Size([10, 5, 3, 34, 34]...
import torch from pytorch_wavelets import DWTForward, DWTInverse xfm = DWTForward(J=3, wave='db3', mode='zero') X = torch.randn(10,5,64,64) Yl, Yh = xfm(X) print(Yl.shape) >>> torch.Size([10, 5, 12, 12]) print(Yh[0].shape) >>> torch.Size([10, 5, 3, 34, 34]...
The DWT and DTCWT transforms support cuda calling: import torch from pytorch_wavelets import DTCWTForward, DTCWTInverse xfm = DTCWTForward(J=3, biort='near_sym_b', qshift='qshift_b').cuda() X = torch.randn(10,5,64,64).cuda() Yl, Yh = xfm(X) ifm = DTCWTInverse(J=3, bio...