239 lines
9.4 KiB
Python
239 lines
9.4 KiB
Python
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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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from einops import rearrange, repeat
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from timm.models.layers import DropPath, to_2tuple, trunc_normal_
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import timm
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class ConvBNReLU(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d, bias=False):
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super(ConvBNReLU, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
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norm_layer(out_channels),
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nn.ReLU6()
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)
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class ConvBN(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, norm_layer=nn.BatchNorm2d, bias=False):
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super(ConvBN, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2),
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norm_layer(out_channels)
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)
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class Conv(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, dilation=1, stride=1, bias=False):
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super(Conv, self).__init__(
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nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, bias=bias,
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dilation=dilation, stride=stride, padding=((stride - 1) + dilation * (kernel_size - 1)) // 2)
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)
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class SelfAttention(nn.Module):
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def __init__(self, dim, num_heads):
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super(SelfAttention, self).__init__()
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self.num_heads = num_heads
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head_dim = dim // self.num_heads
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self.scale = head_dim ** -0.5
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self.qkv = nn.Linear(dim, 3 * dim)
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self.o_proj = nn.Linear(dim, dim)
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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(x).view(B, -1, self.num_heads, 3, H * W).permute(3, 0, 2, 1, 4)
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q, k, v = qkv[0], qkv[1], qkv[2]
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dots = torch.matmul(q.transpose(-2, -1), k) * self.scale
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attn = dots.softmax(dim=-1)
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out = torch.matmul(attn, v).transpose(1, 2).reshape(B, C, H, W)
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return self.o_proj(out)
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class SeparableConvBNReLU(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1,
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norm_layer=nn.BatchNorm2d):
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super(SeparableConvBNReLU, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
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groups=in_channels, bias=False),
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norm_layer(out_channels),
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False),
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nn.ReLU6()
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)
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class SeparableConvBN(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1,
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norm_layer=nn.BatchNorm2d):
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super(SeparableConvBN, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
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groups=in_channels, bias=False),
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norm_layer(out_channels),
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
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)
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class SeparableConv(nn.Sequential):
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def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, dilation=1):
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super(SeparableConv, self).__init__(
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nn.Conv2d(in_channels, in_channels, kernel_size, stride=stride, dilation=dilation,
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padding=((stride - 1) + dilation * (kernel_size - 1)) // 2,
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groups=in_channels, bias=False),
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nn.Conv2d(in_channels, out_channels, kernel_size=1, bias=False)
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)
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# class SEBlock(nn.Module):
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# def __init__(self, in_channels, reduction=16):
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# super(SEBlock, self).__init__()
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# self.avg_pool = nn.AdaptiveAvgPool2d(1)
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# self.fc = nn.Sequential(
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# nn.Linear(in_channels, in_channels // reduction, bias=False),
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# nn.ReLU(inplace=True),
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# nn.Linear(in_channels // reduction, in_channels, bias=False),
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# nn.Sigmoid()
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# )
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#
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# def forward(self, x):
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# b, c, _, _ = x.size()
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# y = self.avg_pool(x).view(b, c)
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# y = self.fc(y).view(b, c, 1, 1)
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# return x * y.expand_as(x)
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class SEResidualBlock(nn.Module):
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def __init__(self, in_channels, reduction=16):
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super(SEResidualBlock, self).__init__()
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self.avg_pool = nn.AdaptiveAvgPool2d(1)
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self.fc = nn.Sequential(
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nn.Linear(in_channels, in_channels // reduction, bias=False),
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nn.ReLU(inplace=True),
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nn.Linear(in_channels // reduction, in_channels, bias=False),
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nn.Sigmoid()
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)
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def forward(self, x):
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b, c, _, _ = x.size()
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y = self.avg_pool(x).view(b, c)
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y = self.fc(y).view(b, c, 1, 1)
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y = x * y.expand_as(x)
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return x + y # 这里添加残差连接
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class ImprovedLocalAttention(nn.Module):
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def __init__(self, dim):
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super(ImprovedLocalAttention, self).__init__()
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self.conv1x1 = nn.Conv2d(dim, dim, kernel_size=1, bias=False)
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self.conv3x3 = nn.Conv2d(dim, dim, kernel_size=3, padding=1, bias=False)
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self.conv5x5 = nn.Conv2d(dim, dim, kernel_size=5, padding=2, bias=False)
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self.bn = nn.BatchNorm2d(dim)
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self.se = SEResidualBlock(dim)
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def forward(self, x):
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out1 = self.conv1x1(x)
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out2 = self.conv3x3(x)
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out3 = self.conv5x5(x)
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out = out1 + out2 + out3
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out = self.bn(out)
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out = self.se(out)
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return out
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class GlobalLocalAttention(nn.Module):
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def __init__(self,
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dim=256,
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num_heads=16,
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qkv_bias=False,
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window_size=8,
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relative_pos_embedding=True):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // self.num_heads
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self.scale = head_dim ** -0.5
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self.ws = window_size
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self.qkv = Conv(dim, 3*dim, kernel_size=1, bias=qkv_bias)
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# self.local1 = ImprovedLocalAttention(dim)
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self.local1 = ImprovedLocalAttention(dim)
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self.proj = SeparableConvBN(dim, dim, kernel_size=window_size)
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self.attn_x = nn.AvgPool2d(kernel_size=(window_size, 1), stride=1, padding=(window_size//2 - 1, 0))
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self.attn_y = nn.AvgPool2d(kernel_size=(1, window_size), stride=1, padding=(0, window_size//2 - 1))
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self.relative_pos_embedding = relative_pos_embedding
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if self.relative_pos_embedding:
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self.relative_position_bias_table = nn.Parameter(
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torch.zeros((2 * window_size - 1) * (2 * window_size - 1), num_heads))
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coords_h = torch.arange(self.ws)
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coords_w = torch.arange(self.ws)
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coords = torch.stack(torch.meshgrid([coords_h, coords_w]))
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coords_flatten = torch.flatten(coords, 1)
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relative_coords = coords_flatten[:, :, None] - coords_flatten[:, None, :]
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relative_coords = relative_coords.permute(1, 2, 0).contiguous()
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relative_coords[:, :, 0] += self.ws - 1
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relative_coords[:, :, 1] += self.ws - 1
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relative_coords[:, :, 0] *= 2 * self.ws - 1
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relative_position_index = relative_coords.sum(-1)
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self.register_buffer("relative_position_index", relative_position_index)
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trunc_normal_(self.relative_position_bias_table, std=.02)
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def pad(self, x, ps):
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_, _, H, W = x.size()
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if W % ps != 0:
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x = F.pad(x, (0, ps - W % ps), mode='reflect')
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if H % ps != 0:
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x = F.pad(x, (0, 0, 0, ps - H % ps), mode='reflect')
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return x
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def pad_out(self, x):
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x = F.pad(x, pad=(0, 1, 0, 1), mode='reflect')
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return x
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def forward(self, x):
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B, C, H, W = x.shape
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# local = self.local2(x) + self.local1(x)
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local = self.local1(x)
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x = self.pad(x, self.ws)
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B, C, Hp, Wp = x.shape
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qkv = self.qkv(x)
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q, k, v = rearrange(qkv, 'b (qkv h d) (hh ws1) (ww ws2) -> qkv (b hh ww) h (ws1 ws2) d', h=self.num_heads,
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d=C//self.num_heads, hh=Hp//self.ws, ww=Wp//self.ws, qkv=3, ws1=self.ws, ws2=self.ws)
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dots = (q @ k.transpose(-2, -1)) * self.scale
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if self.relative_pos_embedding:
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relative_position_bias = self.relative_position_bias_table[self.relative_position_index.view(-1)].view(
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self.ws * self.ws, self.ws * self.ws, -1)
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relative_position_bias = relative_position_bias.permute(2, 0, 1).contiguous()
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dots += relative_position_bias.unsqueeze(0)
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attn = dots.softmax(dim=-1)
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attn = attn @ v
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attn = rearrange(attn, '(b hh ww) h (ws1 ws2) d -> b (h d) (hh ws1) (ww ws2)', h=self.num_heads,
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d=C//self.num_heads, hh=Hp//self.ws, ww=Wp//self.ws, ws1=self.ws, ws2=self.ws)
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attn = attn[:, :, :H, :W]
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out = self.attn_x(F.pad(attn, pad=(0, 0, 0, 1), mode='reflect')) + \
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self.attn_y(F.pad(attn, pad=(0, 1, 0, 0), mode='reflect'))
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out = out + local
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out = self.pad_out(out)
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out = self.proj(out)
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out = out[:, :, :H, :W]
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return out
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gl_attention = GlobalLocalAttention(dim=256, num_heads=16, qkv_bias=False, window_size=8, relative_pos_embedding=True)
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x = torch.randn(1, 256, 64, 64)
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output = gl_attention(x)
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print(output.shape)
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