198 lines
7.3 KiB
Python
198 lines
7.3 KiB
Python
import pywt
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import pywt.data
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import torch
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from torch import nn
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from torch.autograd import Function
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import torch.nn.functional as F
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"""ECCV2024 https://arxiv.org/abs/2407.05848
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近年来，人们尝试增加卷积神经网络 (CNN) 的内核大小，以模拟 Vision Transformers (ViTs) 自注意力模块的全局接受场。
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然而，这种方法在实现全局接受场之前就很快达到上限并饱和。
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在这项研究中，我们证明了通过利用小波变换 (WT)，实际上可以获得非常大的接受场，而不会遭受过度参数化，所提出的层名为 WTConv，
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可用作现有架构中的直接替换，产生有效的多频响应，并可随接受场的大小优雅地扩展。
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我们证明了 ConvNeXt 和 MobileNetV2 架构中 WTConv 层对图像分类的有效性，以及下游任务的主干，并表明它具有其他属性，
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例如对图像损坏的鲁棒性和对纹理形状的响应增强。
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"""
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def create_wavelet_filter(wave, in_size, out_size, type=torch.float):
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w = pywt.Wavelet(wave)
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dec_hi = torch.tensor(w.dec_hi[::-1], dtype=type)
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dec_lo = torch.tensor(w.dec_lo[::-1], dtype=type)
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dec_filters = torch.stack([dec_lo.unsqueeze(0) * dec_lo.unsqueeze(1),
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dec_lo.unsqueeze(0) * dec_hi.unsqueeze(1),
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dec_hi.unsqueeze(0) * dec_lo.unsqueeze(1),
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dec_hi.unsqueeze(0) * dec_hi.unsqueeze(1)], dim=0)
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dec_filters = dec_filters[:, None].repeat(in_size, 1, 1, 1)
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rec_hi = torch.tensor(w.rec_hi[::-1], dtype=type).flip(dims=[0])
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rec_lo = torch.tensor(w.rec_lo[::-1], dtype=type).flip(dims=[0])
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rec_filters = torch.stack([rec_lo.unsqueeze(0) * rec_lo.unsqueeze(1),
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rec_lo.unsqueeze(0) * rec_hi.unsqueeze(1),
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rec_hi.unsqueeze(0) * rec_lo.unsqueeze(1),
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rec_hi.unsqueeze(0) * rec_hi.unsqueeze(1)], dim=0)
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rec_filters = rec_filters[:, None].repeat(out_size, 1, 1, 1)
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return dec_filters, rec_filters
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def wavelet_transform(x, filters):
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b, c, h, w = x.shape
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pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1)
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x = F.conv2d(x, filters, stride=2, groups=c, padding=pad)
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x = x.reshape(b, c, 4, h // 2, w // 2)
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return x
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def inverse_wavelet_transform(x, filters):
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b, c, _, h_half, w_half = x.shape
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pad = (filters.shape[2] // 2 - 1, filters.shape[3] // 2 - 1)
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x = x.reshape(b, c * 4, h_half, w_half)
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x = F.conv_transpose2d(x, filters, stride=2, groups=c, padding=pad)
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return x
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def wavelet_transform_init(filters):
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class WaveletTransform(Function):
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@staticmethod
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def forward(ctx, input):
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with torch.no_grad():
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x = wavelet_transform(input, filters)
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return x
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@staticmethod
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def backward(ctx, grad_output):
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grad = inverse_wavelet_transform(grad_output, filters)
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return grad, None
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return WaveletTransform().apply
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def inverse_wavelet_transform_init(filters):
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class InverseWaveletTransform(Function):
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@staticmethod
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def forward(ctx, input):
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with torch.no_grad():
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x = inverse_wavelet_transform(input, filters)
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return x
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@staticmethod
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def backward(ctx, grad_output):
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grad = wavelet_transform(grad_output, filters)
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return grad, None
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return InverseWaveletTransform().apply
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class WTConv2d(nn.Module):
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def __init__(self, in_channels, out_channels, kernel_size=5, stride=1, bias=True, wt_levels=1, wt_type='db1'):
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super(WTConv2d, self).__init__()
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assert in_channels == out_channels
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self.in_channels = in_channels
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self.wt_levels = wt_levels
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self.stride = stride
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self.dilation = 1
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self.wt_filter, self.iwt_filter = create_wavelet_filter(wt_type, in_channels, in_channels, torch.float)
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self.wt_filter = nn.Parameter(self.wt_filter, requires_grad=False)
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self.iwt_filter = nn.Parameter(self.iwt_filter, requires_grad=False)
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self.wt_function = wavelet_transform_init(self.wt_filter)
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self.iwt_function = inverse_wavelet_transform_init(self.iwt_filter)
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self.base_conv = nn.Conv2d(in_channels, in_channels, kernel_size, padding='same', stride=1, dilation=1,
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groups=in_channels, bias=bias)
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self.base_scale = _ScaleModule([1, in_channels, 1, 1])
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self.wavelet_convs = nn.ModuleList(
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[nn.Conv2d(in_channels * 4, in_channels * 4, kernel_size, padding='same', stride=1, dilation=1,
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groups=in_channels * 4, bias=False) for _ in range(self.wt_levels)]
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)
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self.wavelet_scale = nn.ModuleList(
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[_ScaleModule([1, in_channels * 4, 1, 1], init_scale=0.1) for _ in range(self.wt_levels)]
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)
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if self.stride > 1:
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self.stride_filter = nn.Parameter(torch.ones(in_channels, 1, 1, 1), requires_grad=False)
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self.do_stride = lambda x_in: F.conv2d(x_in, self.stride_filter, bias=None, stride=self.stride,
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groups=in_channels)
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else:
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self.do_stride = None
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def forward(self, x):
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x_ll_in_levels = []
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x_h_in_levels = []
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shapes_in_levels = []
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curr_x_ll = x
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for i in range(self.wt_levels):
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curr_shape = curr_x_ll.shape
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shapes_in_levels.append(curr_shape)
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if (curr_shape[2] % 2 > 0) or (curr_shape[3] % 2 > 0):
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curr_pads = (0, curr_shape[3] % 2, 0, curr_shape[2] % 2)
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curr_x_ll = F.pad(curr_x_ll, curr_pads)
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curr_x = self.wt_function(curr_x_ll)
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curr_x_ll = curr_x[:, :, 0, :, :]
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shape_x = curr_x.shape
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curr_x_tag = curr_x.reshape(shape_x[0], shape_x[1] * 4, shape_x[3], shape_x[4])
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curr_x_tag = self.wavelet_scale[i](self.wavelet_convs[i](curr_x_tag))
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curr_x_tag = curr_x_tag.reshape(shape_x)
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x_ll_in_levels.append(curr_x_tag[:, :, 0, :, :])
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x_h_in_levels.append(curr_x_tag[:, :, 1:4, :, :])
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next_x_ll = 0
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for i in range(self.wt_levels - 1, -1, -1):
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curr_x_ll = x_ll_in_levels.pop()
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curr_x_h = x_h_in_levels.pop()
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curr_shape = shapes_in_levels.pop()
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curr_x_ll = curr_x_ll + next_x_ll
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curr_x = torch.cat([curr_x_ll.unsqueeze(2), curr_x_h], dim=2)
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next_x_ll = self.iwt_function(curr_x)
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next_x_ll = next_x_ll[:, :, :curr_shape[2], :curr_shape[3]]
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x_tag = next_x_ll
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assert len(x_ll_in_levels) == 0
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x = self.base_scale(self.base_conv(x))
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x = x + x_tag
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if self.do_stride is not None:
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x = self.do_stride(x)
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return x
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class _ScaleModule(nn.Module):
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def __init__(self, dims, init_scale=1.0, init_bias=0):
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super(_ScaleModule, self).__init__()
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self.dims = dims
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self.weight = nn.Parameter(torch.ones(*dims) * init_scale)
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self.bias = None
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def forward(self, x):
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return torch.mul(self.weight, x)
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if __name__ == '__main__':
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in_channels = 3
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out_channels = 3
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block = WTConv2d(in_channels, out_channels)
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input = torch.rand(1, in_channels, 64, 64)
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output = block(input)
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print(input.size())
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print(output.size()) |