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(ECCV2024) 小波卷积.py
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(ECCV2024) 小波卷积.py
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import pywt
import pywt.data
import torch
from torch import nn
from functools import partial
import torch.nn.functional as F
# 论文地址 https://arxiv.org/pdf/2407.05848
def create_wavelet_filter(wave, in_size, out_size, type=torch.float):
w = pywt.Wavelet(wave)
dec_hi = torch.tensor(w.dec_hi[::-1], dtype=type)
dec_lo = torch.tensor(w.dec_lo[::-1], dtype=type)
dec_filters = torch.stack([dec_lo.unsqueeze(0) * dec_lo.unsqueeze(1),
dec_lo.unsqueeze(0) * dec_hi.unsqueeze(1),
dec_hi.unsqueeze(0) * dec_lo.unsqueeze(1),
dec_hi.unsqueeze(0) * dec_hi.unsqueeze(1)], dim=0)
dec_filters = dec_filters[:, None].repeat(in_size, 1, 1, 1)
rec_hi = torch.tensor(w.rec_hi[::-1], dtype=type).flip(dims=[0])
rec_lo = torch.tensor(w.rec_lo[::-1], dtype=type).flip(dims=[0])
rec_filters = torch.stack([rec_lo.unsqueeze(0) * rec_lo.unsqueeze(1),
rec_lo.unsqueeze(0) * rec_hi.unsqueeze(1),
rec_hi.unsqueeze(0) * rec_lo.unsqueeze(1),
rec_hi.unsqueeze(0) * rec_hi.unsqueeze(1)], dim=0)
rec_filters = rec_filters[:, None].repeat(out_size, 1, 1, 1)
return dec_filters, rec_filters
def wavelet_transform(x, filters):
b, c, h, w = x.shape
pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1)
x = F.conv2d(x, filters, stride=2, groups=c, padding=pad)
x = x.reshape(b, c, 4, h // 2, w // 2)
return x
def inverse_wavelet_transform(x, filters):
b, c, _, h_half, w_half = x.shape
pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1)
x = x.reshape(b, c * 4, h_half, w_half)
x = F.conv_transpose2d(x, filters, stride=2, groups=c, padding=pad)
return x
# Wavelet Transform Conv(WTConv2d)
class WTConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=5, stride=1, bias=True, wt_levels=1, wt_type='db1'):
super(WTConv2d, self).__init__()
assert in_channels == out_channels
self.in_channels = in_channels
self.wt_levels = wt_levels
self.stride = stride
self.dilation = 1
self.wt_filter, self.iwt_filter = create_wavelet_filter(wt_type, in_channels, in_channels, torch.float)
self.wt_filter = nn.Parameter(self.wt_filter, requires_grad=False)
self.iwt_filter = nn.Parameter(self.iwt_filter, requires_grad=False)
self.wt_function = partial(wavelet_transform, filters=self.wt_filter)
self.iwt_function = partial(inverse_wavelet_transform, filters=self.iwt_filter)
self.base_conv = nn.Conv2d(in_channels, in_channels, kernel_size, padding='same', stride=1, dilation=1,
groups=in_channels, bias=bias)
self.base_scale = _ScaleModule([1, in_channels, 1, 1])
self.wavelet_convs = nn.ModuleList(
[nn.Conv2d(in_channels * 4, in_channels * 4, kernel_size, padding='same', stride=1, dilation=1,
groups=in_channels * 4, bias=False) for _ in range(self.wt_levels)]
)
self.wavelet_scale = nn.ModuleList(
[_ScaleModule([1, in_channels * 4, 1, 1], init_scale=0.1) for _ in range(self.wt_levels)]
)
if self.stride > 1:
self.stride_filter = nn.Parameter(torch.ones(in_channels, 1, 1, 1), requires_grad=False)
self.do_stride = lambda x_in: F.conv2d(x_in, self.stride_filter, bias=None, stride=self.stride,
groups=in_channels)
else:
self.do_stride = None
def forward(self, x):
x_ll_in_levels = []
x_h_in_levels = []
shapes_in_levels = []
curr_x_ll = x
for i in range(self.wt_levels):
curr_shape = curr_x_ll.shape
shapes_in_levels.append(curr_shape)
if (curr_shape[2] % 2 > 0) or (curr_shape[3] % 2 > 0):
curr_pads = (0, curr_shape[3] % 2, 0, curr_shape[2] % 2)
curr_x_ll = F.pad(curr_x_ll, curr_pads)
curr_x = self.wt_function(curr_x_ll)
curr_x_ll = curr_x[:, :, 0, :, :]
shape_x = curr_x.shape
curr_x_tag = curr_x.reshape(shape_x[0], shape_x[1] * 4, shape_x[3], shape_x[4])
curr_x_tag = self.wavelet_scale[i](self.wavelet_convs[i](curr_x_tag))
curr_x_tag = curr_x_tag.reshape(shape_x)
x_ll_in_levels.append(curr_x_tag[:, :, 0, :, :])
x_h_in_levels.append(curr_x_tag[:, :, 1:4, :, :])
next_x_ll = 0
for i in range(self.wt_levels - 1, -1, -1):
curr_x_ll = x_ll_in_levels.pop()
curr_x_h = x_h_in_levels.pop()
curr_shape = shapes_in_levels.pop()
curr_x_ll = curr_x_ll + next_x_ll
curr_x = torch.cat([curr_x_ll.unsqueeze(2), curr_x_h], dim=2)
next_x_ll = self.iwt_function(curr_x)
next_x_ll = next_x_ll[:, :, :curr_shape[2], :curr_shape[3]]
x_tag = next_x_ll
assert len(x_ll_in_levels) == 0
x = self.base_scale(self.base_conv(x))
x = x + x_tag
if self.do_stride is not None:
x = self.do_stride(x)
return x
class _ScaleModule(nn.Module):
def __init__(self, dims, init_scale=1.0, init_bias=0):
super(_ScaleModule, self).__init__()
self.dims = dims
self.weight = nn.Parameter(torch.ones(*dims) * init_scale)
self.bias = None
def forward(self, x):
return torch.mul(self.weight, x)
class DepthwiseSeparableConvWithWTConv2d(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size=3):
super(DepthwiseSeparableConvWithWTConv2d, self).__init__()
# 深度卷积：使用 WTConv2d 替换 3x3 卷积
self.depthwise = WTConv2d(in_channels, in_channels, kernel_size=kernel_size)
# 逐点卷积：使用 1x1 卷积
self.pointwise = nn.Conv2d(in_channels, out_channels, kernel_size=1, stride=1, padding=0, bias=False)
def forward(self, x):
x = self.depthwise(x)
x = self.pointwise(x)
return x
if __name__ == '__main__':
input = torch.randn(3, 32, 64, 64) # b c h w输入
wtconv = DepthwiseSeparableConvWithWTConv2d(in_channels=32, out_channels=32)
output = wtconv(input)
print(output.size())