590 lines
22 KiB
Python
590 lines
22 KiB
Python
import torch
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import torch.nn as nn
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import math
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import torch.nn.functional as F
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import torch.utils.checkpoint as checkpoint
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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from einops import rearrange
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from componets.TIAM import SpatiotemporalAttentionFullNotWeightShared
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from componets.WTConvCV2 import WTConv2d
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# 以一定概率随机丢弃输入张量中的路径，用于正则化模型
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def drop_path(x, drop_prob: float = 0., training: bool = False):
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if drop_prob == 0. or not training:
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return x
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keep_prob = 1 - drop_prob
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# work with diff dim tensors, not just 2D ConvNets
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shape = (x.shape[0],) + (1,) * (x.ndim - 1)
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random_tensor = keep_prob + \
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torch.rand(shape, dtype=x.dtype, device=x.device)
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random_tensor.floor_() # binarize
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output = x.div(keep_prob) * random_tensor
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return output
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class DropPath(nn.Module):
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"""
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Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
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"""
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def __init__(self, drop_prob=None):
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super(DropPath, self).__init__()
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self.drop_prob = drop_prob
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def forward(self, x):
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return drop_path(x, self.drop_prob, self.training)
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# 改点，使用Pooling替换AttentionBase
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class Pooling(nn.Module):
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def __init__(self, kernel_size=3):
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super().__init__()
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self.pool = nn.AvgPool2d(
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kernel_size, stride=1, padding=kernel_size // 2)
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def forward(self, x):
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return self.pool(x) - x
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class PoolMlp(nn.Module):
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"""
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实现基于1x1卷积的MLP模块。
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输入：形状为[B, C, H, W]的张量。
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"""
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def __init__(self,
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in_features,
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hidden_features=None,
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out_features=None,
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act_layer=nn.GELU,
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bias=False,
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drop=0.):
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"""
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初始化PoolMlp模块。
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参数:
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in_features (int): 输入特征的数量。
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hidden_features (int, 可选): 隐藏层特征的数量。默认为None，设置为与in_features相同。
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out_features (int, 可选): 输出特征的数量。默认为None，设置为与in_features相同。
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act_layer (nn.Module, 可选): 使用的激活层。默认为nn.GELU。
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bias (bool, 可选): 是否在卷积层中包含偏置项。默认为False。
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drop (float, 可选): Dropout比率。默认为0。
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"""
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super().__init__()
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out_features = out_features or in_features
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hidden_features = hidden_features or in_features
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self.fc1 = nn.Conv2d(in_features, hidden_features, 1, bias=bias)
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self.act = act_layer()
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self.fc2 = nn.Conv2d(hidden_features, out_features, 1, bias=bias)
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self.drop = nn.Dropout(drop)
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def forward(self, x):
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"""
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通过PoolMlp模块的前向传播。
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参数:
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x (torch.Tensor): 形状为[B, C, H, W]的输入张量。
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返回:
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torch.Tensor: 形状为[B, C, H, W]的输出张量。
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"""
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x = self.fc1(x) # (B, C, H, W) --> (B, C, H, W)
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x = self.act(x)
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x = self.drop(x)
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x = self.fc2(x) # (B, C, H, W) --> (B, C, H, W)
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x = self.drop(x)
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return x
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# class BaseFeatureExtraction1(nn.Module):
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# def __init__(self, dim, pool_size=3, mlp_ratio=4.,
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# act_layer=nn.GELU,
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# # norm_layer=nn.LayerNorm,
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# drop=0., drop_path=0.,
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# use_layer_scale=True, layer_scale_init_value=1e-5):
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#
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# super().__init__()
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#
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# self.norm1 = LayerNorm(dim, 'WithBias')
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# self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa，MLPs是mlp，这个用pool来替代
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# self.norm2 = LayerNorm(dim, 'WithBias')
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# mlp_hidden_dim = int(dim * mlp_ratio)
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# self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
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# act_layer=act_layer, drop=drop)
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#
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# # The following two techniques are useful to train deep PoolFormers.
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# self.drop_path = DropPath(drop_path) if drop_path > 0. \
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# else nn.Identity()
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# self.use_layer_scale = use_layer_scale
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#
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# if use_layer_scale:
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# self.layer_scale_1 = nn.Parameter(
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# torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
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#
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# self.layer_scale_2 = nn.Parameter(
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# torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
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#
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# def forward(self, x): # 1 64 128 128
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# if self.use_layer_scale:
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# # self.layer_scale_1(64,)
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# tmp1 = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) # 64 1 1
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# normal = self.norm1(x) # 1 64 128 128
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# token_mix = self.token_mixer(normal) # 1 64 128 128
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# x = (x +
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# self.drop_path(
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# tmp1 * token_mix
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# )
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# # 该表达式将 self.layer_scale_1 这个一维张量（或变量）在维度末尾添加两个新的维度，使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作，如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。
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# )
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# x = x + self.drop_path(
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# self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
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# * self.poolmlp(self.norm2(x)))
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# else:
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# x = x + self.drop_path(self.token_mixer(self.norm1(x))) # 匹配cddfuse
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# x = x + self.drop_path(self.poolmlp(self.norm2(x)))
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# return x
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class BaseFeatureExtraction(nn.Module):
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def __init__(self, dim, pool_size=3, mlp_ratio=4.,
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act_layer=nn.GELU,
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# norm_layer=nn.LayerNorm,
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drop=0., drop_path=0.,
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use_layer_scale=True, layer_scale_init_value=1e-5):
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super().__init__()
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self.WTConv2d = WTConv2d(dim, dim)
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self.norm1 = LayerNorm(dim, 'WithBias')
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self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa，MLPs是mlp，这个用pool来替代
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self.norm2 = LayerNorm(dim, 'WithBias')
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mlp_hidden_dim = int(dim * mlp_ratio)
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self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
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act_layer=act_layer, drop=drop)
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# The following two techniques are useful to train deep PoolFormers.
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self.drop_path = DropPath(drop_path) if drop_path > 0. \
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else nn.Identity()
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self.use_layer_scale = use_layer_scale
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if use_layer_scale:
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self.layer_scale_1 = nn.Parameter(
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torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
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self.layer_scale_2 = nn.Parameter(
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torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
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def forward(self, x): # 1 64 128 128
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if self.use_layer_scale:
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# self.layer_scale_1(64,)
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tmp1 = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) # 64 1 1
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normal = self.norm1(x) # 1 64 128 128
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token_mix = self.token_mixer(normal) # 1 64 128 128
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x = self.WTConv2d(x)
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x = (x +
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self.drop_path(
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tmp1 * token_mix
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)
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# 该表达式将 self.layer_scale_1 这个一维张量（或变量）在维度末尾添加两个新的维度，使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作，如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。
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)
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x = x + self.drop_path(
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self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
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* self.poolmlp(self.norm2(x)))
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else:
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x = x + self.drop_path(self.token_mixer(self.norm1(x))) # 匹配cddfuse
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x = x + self.drop_path(self.poolmlp(self.norm2(x)))
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return x
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class InvertedResidualBlock(nn.Module):
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def __init__(self, inp, oup, expand_ratio):
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super(InvertedResidualBlock, self).__init__()
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hidden_dim = int(inp * expand_ratio)
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self.bottleneckBlock = nn.Sequential(
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# pw
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nn.Conv2d(inp, hidden_dim, 1, bias=False),
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# nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(inplace=True),
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# dw
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nn.ReflectionPad2d(1),
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nn.Conv2d(hidden_dim, hidden_dim, 3, groups=hidden_dim, bias=False),
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# nn.BatchNorm2d(hidden_dim),
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nn.ReLU6(inplace=True),
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# pw-linear
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nn.Conv2d(hidden_dim, oup, 1, bias=False),
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# nn.BatchNorm2d(oup),
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)
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def forward(self, x):
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return self.bottleneckBlock(x)
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class DetailNode(nn.Module):
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# <img src = "http://42.192.130.83:9000/picgo/imgs/小绿鲸英文文献阅读器_ELTITYqm5G.png" / > '
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def __init__(self):
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super(DetailNode, self).__init__()
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self.theta_phi = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
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self.theta_rho = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
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self.theta_eta = InvertedResidualBlock(inp=32, oup=32, expand_ratio=2)
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self.shffleconv = nn.Conv2d(64, 64, kernel_size=1,
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stride=1, padding=0, bias=True)
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def separateFeature(self, x):
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z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]]
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return z1, z2
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def forward(self, z1, z2):
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z1, z2 = self.separateFeature(
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self.shffleconv(torch.cat((z1, z2), dim=1)))
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z2 = z2 + self.theta_phi(z1)
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z1 = z1 * torch.exp(self.theta_rho(z2)) + self.theta_eta(z2)
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return z1, z2
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class DetailFeatureExtraction(nn.Module):
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def __init__(self, num_layers=3):
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super(DetailFeatureExtraction, self).__init__()
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INNmodules = [DetailNode() for _ in range(num_layers)]
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self.net = nn.Sequential(*INNmodules)
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self.enhancement_module = WTConv2d(32, 32)
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def forward(self, x): # 1 64 128 128
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z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
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# 增强并添加残差连接
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enhanced_z1 = self.enhancement_module(z1)
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enhanced_z2 = self.enhancement_module(z2)
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# 残差连接
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z1 = z1 + enhanced_z1
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z2 = z2 + enhanced_z2
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for layer in self.net:
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z1, z2 = layer(z1, z2)
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return torch.cat((z1, z2), dim=1)
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# =============================================================================
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# =============================================================================
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import numbers
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##########################################################################
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## Layer Norm
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def to_3d(x):
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return rearrange(x, 'b c h w -> b (h w) c')
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def to_4d(x, h, w):
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return rearrange(x, 'b (h w) c -> b c h w', h=h, w=w)
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class BiasFree_LayerNorm(nn.Module):
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def __init__(self, normalized_shape):
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super(BiasFree_LayerNorm, self).__init__()
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if isinstance(normalized_shape, numbers.Integral):
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normalized_shape = (normalized_shape,)
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normalized_shape = torch.Size(normalized_shape)
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assert len(normalized_shape) == 1
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.normalized_shape = normalized_shape
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def forward(self, x):
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sigma = x.var(-1, keepdim=True, unbiased=False)
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return x / torch.sqrt(sigma + 1e-5) * self.weight
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class WithBias_LayerNorm(nn.Module):
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def __init__(self, normalized_shape):
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super(WithBias_LayerNorm, self).__init__()
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if isinstance(normalized_shape, numbers.Integral):
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normalized_shape = (normalized_shape,)
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normalized_shape = torch.Size(normalized_shape)
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assert len(normalized_shape) == 1
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self.weight = nn.Parameter(torch.ones(normalized_shape))
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self.bias = nn.Parameter(torch.zeros(normalized_shape))
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self.normalized_shape = normalized_shape
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def forward(self, x):
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mu = x.mean(-1, keepdim=True)
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sigma = x.var(-1, keepdim=True, unbiased=False)
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return (x - mu) / torch.sqrt(sigma + 1e-5) * self.weight + self.bias
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class LayerNorm(nn.Module):
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def __init__(self, dim, LayerNorm_type):
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super(LayerNorm, self).__init__()
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if LayerNorm_type == 'BiasFree':
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self.body = BiasFree_LayerNorm(dim)
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else:
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self.body = WithBias_LayerNorm(dim)
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def forward(self, x):
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h, w = x.shape[-2:]
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return to_4d(self.body(to_3d(x)), h, w)
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##########################################################################
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## Gated-Dconv Feed-Forward Network (GDFN)
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class FeedForward(nn.Module):
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def __init__(self, dim, ffn_expansion_factor, bias):
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super(FeedForward, self).__init__()
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hidden_features = int(dim * ffn_expansion_factor)
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self.project_in = nn.Conv2d(
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dim, hidden_features * 2, kernel_size=1, bias=bias)
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self.dwconv = nn.Conv2d(hidden_features * 2, hidden_features * 2, kernel_size=3,
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stride=1, padding=1, groups=hidden_features * 2, bias=bias)
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self.project_out = nn.Conv2d(
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hidden_features, dim, kernel_size=1, bias=bias)
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def forward(self, x):
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x = self.project_in(x)
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x1, x2 = self.dwconv(x).chunk(2, dim=1)
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x = F.gelu(x1) * x2
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x = self.project_out(x)
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return x
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##########################################################################
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## Multi-DConv Head Transposed Self-Attention (MDTA)
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class Attention(nn.Module):
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def __init__(self, dim, num_heads, bias):
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super(Attention, self).__init__()
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self.num_heads = num_heads
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self.temperature = nn.Parameter(torch.ones(num_heads, 1, 1))
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self.qkv = nn.Conv2d(dim, dim * 3, kernel_size=1, bias=bias)
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self.qkv_dwconv = nn.Conv2d(
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dim * 3, dim * 3, kernel_size=3, stride=1, padding=1, groups=dim * 3, bias=bias)
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self.project_out = nn.Conv2d(dim, dim, kernel_size=1, bias=bias)
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def forward(self, x):
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b, c, h, w = x.shape
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qkv = self.qkv_dwconv(self.qkv(x))
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q, k, v = qkv.chunk(3, dim=1)
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q = rearrange(q, 'b (head c) h w -> b head c (h w)',
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head=self.num_heads)
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k = rearrange(k, 'b (head c) h w -> b head c (h w)',
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head=self.num_heads)
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v = rearrange(v, 'b (head c) h w -> b head c (h w)',
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head=self.num_heads)
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q = torch.nn.functional.normalize(q, dim=-1)
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k = torch.nn.functional.normalize(k, dim=-1)
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attn = (q @ k.transpose(-2, -1)) * self.temperature
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attn = attn.softmax(dim=-1)
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out = (attn @ v)
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out = rearrange(out, 'b head c (h w) -> b (head c) h w',
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head=self.num_heads, h=h, w=w)
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out = self.project_out(out)
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return out
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##########################################################################
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class TransformerBlock(nn.Module):
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def __init__(self, dim, num_heads, ffn_expansion_factor, bias, LayerNorm_type):
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super(TransformerBlock, self).__init__()
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self.norm1 = LayerNorm(dim, LayerNorm_type)
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self.attn = Attention(dim, num_heads, bias)
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self.norm2 = LayerNorm(dim, LayerNorm_type)
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self.ffn = FeedForward(dim, ffn_expansion_factor, bias)
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def forward(self, x):
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x = x + self.attn(self.norm1(x))
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x = x + self.ffn(self.norm2(x))
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return x
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##########################################################################
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## Overlapped image patch embedding with 3x3 Conv
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class OverlapPatchEmbed(nn.Module):
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def __init__(self, in_c=3, embed_dim=48, bias=False):
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super(OverlapPatchEmbed, self).__init__()
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self.proj = nn.Conv2d(in_c, embed_dim, kernel_size=3,
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stride=1, padding=1, bias=bias)
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def forward(self, x):
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x = self.proj(x)
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return x
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class BaseFeatureExtractionSAR(nn.Module):
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def __init__(self, dim, pool_size=3, mlp_ratio=4.,
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act_layer=nn.GELU,
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# norm_layer=nn.LayerNorm,
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drop=0., drop_path=0.,
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use_layer_scale=True, layer_scale_init_value=1e-5):
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super().__init__()
|
||
|
||
self.WTConv2d = WTConv2d(dim, dim)
|
||
self.norm1 = LayerNorm(dim, 'WithBias')
|
||
self.token_mixer = Pooling(kernel_size=pool_size) # vits是msa，MLPs是mlp，这个用pool来替代
|
||
self.norm2 = LayerNorm(dim, 'WithBias')
|
||
mlp_hidden_dim = int(dim * mlp_ratio)
|
||
self.poolmlp = PoolMlp(in_features=dim, hidden_features=mlp_hidden_dim,
|
||
act_layer=act_layer, drop=drop)
|
||
|
||
# The following two techniques are useful to train deep PoolFormers.
|
||
self.drop_path = DropPath(drop_path) if drop_path > 0. \
|
||
else nn.Identity()
|
||
self.use_layer_scale = use_layer_scale
|
||
|
||
if use_layer_scale:
|
||
self.layer_scale_1 = nn.Parameter(
|
||
torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
|
||
|
||
self.layer_scale_2 = nn.Parameter(
|
||
torch.ones(dim, dtype=torch.float32) * layer_scale_init_value)
|
||
|
||
def forward(self, x): # 1 64 128 128
|
||
if self.use_layer_scale:
|
||
# self.layer_scale_1(64,)
|
||
tmp1 = self.layer_scale_1.unsqueeze(-1).unsqueeze(-1) # 64 1 1
|
||
normal = self.norm1(x) # 1 64 128 128
|
||
token_mix = self.token_mixer(normal) # 1 64 128 128
|
||
|
||
x = self.WTConv2d(x)
|
||
|
||
x = (x +
|
||
self.drop_path(
|
||
tmp1 * token_mix
|
||
)
|
||
# 该表达式将 self.layer_scale_1 这个一维张量（或变量）在维度末尾添加两个新的维度，使其从一维变为三维。这通常用于使其能够与三维的特征图进行广播操作，如元素相乘。具体用途可能包括调整卷积层或注意力机制中的权重。
|
||
)
|
||
x = x + self.drop_path(
|
||
self.layer_scale_2.unsqueeze(-1).unsqueeze(-1)
|
||
* self.poolmlp(self.norm2(x)))
|
||
else:
|
||
x = x + self.drop_path(self.token_mixer(self.norm1(x))) # 匹配cddfuse
|
||
x = x + self.drop_path(self.poolmlp(self.norm2(x)))
|
||
return x
|
||
|
||
|
||
|
||
class DetailFeatureExtractionSAR(nn.Module):
|
||
def __init__(self, num_layers=3):
|
||
super(DetailFeatureExtractionSAR, self).__init__()
|
||
INNmodules = [DetailNode() for _ in range(num_layers)]
|
||
self.net = nn.Sequential(*INNmodules)
|
||
self.enhancement_module = WTConv2d(32, 32)
|
||
|
||
def forward(self, x): # 1 64 128 128
|
||
z1, z2 = x[:, :x.shape[1] // 2], x[:, x.shape[1] // 2:x.shape[1]] # 1 32 128 128
|
||
# 增强并添加残差连接
|
||
enhanced_z1 = self.enhancement_module(z1)
|
||
enhanced_z2 = self.enhancement_module(z2)
|
||
# 残差连接
|
||
z1 = z1 + enhanced_z1
|
||
z2 = z2 + enhanced_z2
|
||
for layer in self.net:
|
||
z1, z2 = layer(z1, z2)
|
||
return torch.cat((z1, z2), dim=1)
|
||
|
||
|
||
|
||
class Restormer_Encoder(nn.Module):
|
||
def __init__(self,
|
||
inp_channels=1,
|
||
out_channels=1,
|
||
dim=64,
|
||
num_blocks=[4, 4],
|
||
heads=[8, 8, 8],
|
||
ffn_expansion_factor=2,
|
||
bias=False,
|
||
LayerNorm_type='WithBias',
|
||
):
|
||
super(Restormer_Encoder, self).__init__()
|
||
|
||
# 区分
|
||
|
||
self.patch_embed = OverlapPatchEmbed(inp_channels, dim)
|
||
self.encoder_level1 = nn.Sequential(
|
||
*[TransformerBlock(dim=dim, num_heads=heads[0], ffn_expansion_factor=ffn_expansion_factor,
|
||
bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[0])])
|
||
self.baseFeature = BaseFeatureExtraction(dim=dim)
|
||
self.detailFeature = DetailFeatureExtraction()
|
||
|
||
self.baseFeature_sar = BaseFeatureExtractionSAR(dim=dim)
|
||
self.detailFeature_sar = DetailFeatureExtractionSAR()
|
||
|
||
def forward(self, inp_img,is_sar = False):
|
||
inp_enc_level1 = self.patch_embed(inp_img)
|
||
out_enc_level1 = self.encoder_level1(inp_enc_level1)
|
||
if is_sar:
|
||
base_feature = self.baseFeature_sar(out_enc_level1) # 1 64 128 128
|
||
detail_feature = self.detailFeature_sar(out_enc_level1) # 1 64 128 128
|
||
return base_feature, detail_feature, out_enc_level1 # 1 64 128 128
|
||
|
||
else:
|
||
base_feature = self.baseFeature(out_enc_level1) # 1 64 128 128
|
||
detail_feature = self.detailFeature(out_enc_level1) # 1 64 128 128
|
||
return base_feature, detail_feature, out_enc_level1 # 1 64 128 128
|
||
|
||
|
||
|
||
class Restormer_Decoder(nn.Module):
|
||
def __init__(self,
|
||
inp_channels=1,
|
||
out_channels=1,
|
||
dim=64,
|
||
num_blocks=[4, 4],
|
||
heads=[8, 8, 8],
|
||
ffn_expansion_factor=2,
|
||
bias=False,
|
||
LayerNorm_type='WithBias',
|
||
):
|
||
|
||
super(Restormer_Decoder, self).__init__()
|
||
self.reduce_channel = nn.Conv2d(int(dim * 2), int(dim), kernel_size=1, bias=bias)
|
||
self.encoder_level2 = nn.Sequential(*[TransformerBlock(dim=dim, num_heads=heads[1], ffn_expansion_factor=ffn_expansion_factor,
|
||
bias=bias, LayerNorm_type=LayerNorm_type) for i in range(num_blocks[1])])
|
||
self.output = nn.Sequential(
|
||
nn.Conv2d(int(dim), int(dim) // 2, kernel_size=3,
|
||
stride=1, padding=1, bias=bias),
|
||
nn.LeakyReLU(),
|
||
nn.Conv2d(int(dim) // 2, out_channels, kernel_size=3,
|
||
stride=1, padding=1, bias=bias), )
|
||
self.sigmoid = nn.Sigmoid()
|
||
self.spatiotemporalAttentionFullNotWeightShared = SpatiotemporalAttentionFullNotWeightShared(dim=dim)
|
||
|
||
def forward(self, inp_img, base_feature, detail_feature):
|
||
|
||
base_feature, detail_feature =self.spatiotemporalAttentionFullNotWeightShared(base_feature, detail_feature)
|
||
|
||
out_enc_level0 = torch.cat((base_feature, detail_feature), dim=1)
|
||
out_enc_level0 = self.reduce_channel(out_enc_level0)
|
||
out_enc_level1 = self.encoder_level2(out_enc_level0)
|
||
if inp_img is not None:
|
||
out_enc_level1 = self.output(out_enc_level1) + inp_img
|
||
else:
|
||
out_enc_level1 = self.output(out_enc_level1)
|
||
return self.sigmoid(out_enc_level1), out_enc_level0
|
||
|
||
|
||
if __name__ == '__main__':
|
||
height = 128
|
||
width = 128
|
||
window_size = 8
|
||
modelE = Restormer_Encoder().cuda()
|
||
modelD = Restormer_Decoder().cuda()
|
||
print(modelE)
|
||
print(modelD)
|