927d049f13
- 新增 DEConv、DynamicFilter、SCSA、SEBlock、SMFA、TIAM 和 UFFC 模块 - 这些模块提供了不同的特征增强功能，如卷积差分、频域滤波、注意力机制等 - 可以根据需求选择合适的模块来提升模型性能
111 lines
5.1 KiB
Python
111 lines
5.1 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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"""Elsevier2024
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变化检测 (CD) 是地球观测中一种重要的监测方法，尤其适用于土地利用分析、城市管理和灾害损失评估。然而，在星座互联和空天协作时代，感兴趣区域 (ROI) 的变化由于几何透视旋转和时间风格差异而导致许多错误检测。
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为了应对这些挑战，我们引入了 CDNeXt，该框架阐明了一种稳健而有效的方法，用于将基于预训练主干的 Siamese 网络与用于遥感图像的创新时空交互注意模块 (TIAM) 相结合。
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CDNeXt 可分为四个主要组件：编码器、交互器、解码器和检测器。值得注意的是，由 TIAM 提供支持的交互器从编码器提取的二进制时间特征中查询和重建空间透视依赖关系和时间风格相关性，以扩大 ROI 变化的差异。
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最后，检测器集成解码器生成的分层特征，随后生成二进制变化掩码。
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"""
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class SpatiotemporalAttentionFullNotWeightShared(nn.Module):
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def __init__(self, in_channels, inter_channels=None, dimension=2, sub_sample=False):
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super(SpatiotemporalAttentionFullNotWeightShared, self).__init__()
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assert dimension in [2, ]
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self.dimension = dimension
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self.sub_sample = sub_sample
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self.in_channels = in_channels
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self.inter_channels = inter_channels
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if self.inter_channels is None:
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self.inter_channels = in_channels // 2
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if self.inter_channels == 0:
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self.inter_channels = 1
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self.g1 = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0)
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)
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self.g2 = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0),
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)
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self.W1 = nn.Sequential(
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nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
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kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(self.in_channels)
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)
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self.W2 = nn.Sequential(
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nn.Conv2d(in_channels=self.inter_channels, out_channels=self.in_channels,
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kernel_size=1, stride=1, padding=0),
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nn.BatchNorm2d(self.in_channels)
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)
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self.theta = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0),
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)
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self.phi = nn.Sequential(
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nn.BatchNorm2d(self.in_channels),
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nn.Conv2d(in_channels=self.in_channels, out_channels=self.inter_channels,
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kernel_size=1, stride=1, padding=0),
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)
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def forward(self, x1, x2):
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"""
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:param x: (b, c, h, w)
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:param return_nl_map: if True return z, nl_map, else only return z.
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:return:
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"""
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batch_size = x1.size(0)
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g_x11 = self.g1(x1).reshape(batch_size, self.inter_channels, -1)
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g_x12 = g_x11.permute(0, 2, 1)
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g_x21 = self.g2(x2).reshape(batch_size, self.inter_channels, -1)
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g_x22 = g_x21.permute(0, 2, 1)
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theta_x1 = self.theta(x1).reshape(batch_size, self.inter_channels, -1)
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theta_x2 = theta_x1.permute(0, 2, 1)
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phi_x1 = self.phi(x2).reshape(batch_size, self.inter_channels, -1)
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phi_x2 = phi_x1.permute(0, 2, 1)
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energy_time_1 = torch.matmul(theta_x1, phi_x2)
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energy_time_2 = energy_time_1.permute(0, 2, 1)
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energy_space_1 = torch.matmul(theta_x2, phi_x1)
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energy_space_2 = energy_space_1.permute(0, 2, 1)
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energy_time_1s = F.softmax(energy_time_1, dim=-1)
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energy_time_2s = F.softmax(energy_time_2, dim=-1)
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energy_space_2s = F.softmax(energy_space_1, dim=-2)
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energy_space_1s = F.softmax(energy_space_2, dim=-2)
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# C1*S(C2) energy_time_1s * C1*H1W1 g_x12 * energy_space_1s S(H2W2)*H1W1 -> C1*H1W1
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y1 = torch.matmul(torch.matmul(energy_time_2s, g_x11), energy_space_2s).contiguous() # C2*H2W2
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# C2*S(C1) energy_time_2s * C2*H2W2 g_x21 * energy_space_2s S(H1W1)*H2W2 -> C2*H2W2
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y2 = torch.matmul(torch.matmul(energy_time_1s, g_x21), energy_space_1s).contiguous() # C1*H1W1
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y1 = y1.reshape(batch_size, self.inter_channels, *x2.size()[2:])
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y2 = y2.reshape(batch_size, self.inter_channels, *x1.size()[2:])
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return x1 + self.W1(y1), x2 + self.W2(y2)
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if __name__ == '__main__':
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in_channels = 64
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batch_size = 8
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height = 32
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width = 32
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block = SpatiotemporalAttentionFullNotWeightShared(in_channels=in_channels)
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input1 = torch.rand(batch_size, in_channels, height, width)
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input2 = torch.rand(batch_size, in_channels, height, width)
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output1, output2 = block(input1, input2)
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print(f"Input1 size: {input1.size()}")
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print(f"Input2 size: {input2.size()}")
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print(f"Output1 size: {output1.size()}")
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print(f"Output2 size: {output2.size()}")
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