341 lines
13 KiB
Python
341 lines
13 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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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
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from torch.nn.init import trunc_normal_
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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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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 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 = SEBlock(dim)
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def forward(self, x):
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# Applying different convolutions and combining results
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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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out = out + out1 + out3
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return out
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# class MultiHeadGlobalAttention(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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#
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# self.qkv = nn.Conv2d(dim, 3 * dim, kernel_size=1, bias=qkv_bias)
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# self.proj = nn.Conv2d(dim, dim, kernel_size=1, bias=False)
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#
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# self.relative_pos_embedding = relative_pos_embedding
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#
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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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#
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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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#
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# nn.init.trunc_normal_(self.relative_position_bias_table, std=.02)
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#
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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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#
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# def forward(self, x):
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# B, C, H, W = x.shape
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#
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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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#
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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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#
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# dots = (q @ k.transpose(-2, -1)) * self.scale
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#
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# if self.relative_pos_embedding:
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# relative_position_bias = self.relative_position_bias_table[
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# 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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#
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# attn = dots.softmax(dim=-1)
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# attn = attn @ v
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#
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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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#
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# attn = attn[:, :, :H, :W]
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#
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# out = self.proj(attn)
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# out = out[:, :, :H, :W]
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#
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# return out
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# class EffcirntMutilSelfAttention(nn.Module):
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# def __init__(self,
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# dim,
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# num_heads=8,
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# sr_ratio=1):
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# super().__init__()
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# self.num_heads = num_heads
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# head_dim = dim // num_heads
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# self.scale = head_dim ** -0.5
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# self.dim = dim
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#
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# self.q = nn.Conv2d(dim, dim, kernel_size=1, bias=True)
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# self.kv = nn.Conv2d(dim, dim * 2, kernel_size=1, bias=True)
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#
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# self.sr_ratio = sr_ratio
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# if sr_ratio > 1:
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# self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio + 1, stride=sr_ratio, padding=sr_ratio // 2, groups=dim)
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# self.sr_norm = nn.LayerNorm(dim, eps=1e-6)
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#
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# self.up = nn.Sequential(
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# nn.Conv2d(dim, sr_ratio * sr_ratio * dim, kernel_size=3, stride=1, padding=1, groups=dim),
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# nn.PixelShuffle(upscale_factor=sr_ratio)
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# )
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# self.up_norm = nn.LayerNorm(dim, eps=1e-6)
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#
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# self.proj = nn.Conv2d(dim, dim, kernel_size=1)
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#
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# def forward(self, x):
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# B, C, H, W = x.shape
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# N = H * W
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#
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# q = self.q(x).reshape(B, self.num_heads, C // self.num_heads, N).permute(0, 1, 3, 2)
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#
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# if self.sr_ratio > 1:
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# x = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
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# x = self.sr_norm(x)
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# x = x.permute(0, 2, 1).reshape(B, C, H // self.sr_ratio, W // self.sr_ratio)
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# else:
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# x = x.reshape(B, C, N).permute(0, 2, 1)
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#
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# kv = self.kv(x).reshape(B, 2, self.num_heads, C // self.num_heads, -1).permute(1, 0, 2, 4, 3)
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# k, v = kv[0], kv[1]
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#
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# attn = (q @ k.transpose(-2, -1)) * self.scale
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# attn = attn.softmax(dim=-1)
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#
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# x = (attn @ v).transpose(1, 2).reshape(B, C, H, W)
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#
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# identity = v.transpose(-1, -2).reshape(B, C, H // self.sr_ratio, W // self.sr_ratio)
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# identity = self.up(identity)
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# identity = identity.flatten(2).transpose(1, 2).reshape(B, C, H, W)
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# x = self.proj(x + identity)
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# return x
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class EffcirntMutilSelfAttention(nn.Module):
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def __init__(self,
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dim,
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num_heads=8,
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qkv_bias=False,
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qk_scale=None,
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attn_drop=0.,
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proj_drop=0.,
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sr_ratio=1,
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apply_transform=False):
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super().__init__()
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self.num_heads = num_heads
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head_dim = dim // num_heads
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self.scale = qk_scale or head_dim ** -0.5
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self.q = nn.Conv2d(dim, dim, kernel_size=1, bias=qkv_bias)
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self.kv = nn.Conv2d(dim, dim * 2, kernel_size=1, bias=qkv_bias)
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self.attn_drop = nn.Dropout(attn_drop)
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self.proj = nn.Conv2d(dim, dim, kernel_size=1)
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self.proj_drop = nn.Dropout(proj_drop)
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self.sr_ratio = sr_ratio
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if sr_ratio > 1:
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self.sr = nn.Conv2d(dim, dim, kernel_size=sr_ratio + 1, stride=sr_ratio, padding=sr_ratio // 2, groups=dim)
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self.sr_norm = nn.LayerNorm(dim)
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self.apply_transform = apply_transform and num_heads > 1
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if self.apply_transform:
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self.transform_conv = nn.Conv2d(self.num_heads, self.num_heads, kernel_size=1, stride=1)
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self.transform_norm = nn.InstanceNorm2d(self.num_heads)
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def forward(self, x):
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B, C, H, W = x.shape
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q = self.q(x).reshape(B, self.num_heads, C // self.num_heads, H * W).permute(0, 1, 3, 2)
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if self.sr_ratio > 1:
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x_ = self.sr(x).reshape(B, C, -1).permute(0, 2, 1)
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x_ = self.sr_norm(x_)
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kv = self.kv(x_).reshape(B, -1, 2, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
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else:
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kv = self.kv(x).reshape(B, 2, self.num_heads, C // self.num_heads, H * W).permute(1, 0, 2, 4, 3)
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k, v = kv[0], kv[1]
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attn = (q @ k.transpose(-2, -1)) * self.scale
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if self.apply_transform:
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attn = self.transform_conv(attn)
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attn = attn.softmax(dim=-1)
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attn = self.transform_norm(attn)
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else:
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attn = attn.softmax(dim=-1)
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attn = self.attn_drop(attn)
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x = (attn @ v).transpose(2, 3).reshape(B, self.num_heads * (C // self.num_heads), H, W)
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x = self.proj(x)
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x = self.proj_drop(x)
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return x
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class GlobalLocalAttention(nn.Module):
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def __init__(self, dim=256, num_heads=16, qkv_bias=False, window_size=8, relative_pos_embedding=True):
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super(GlobalLocalAttention, self).__init__()
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|
self.local1 = ImprovedLocalAttention(dim)
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|
|
# self.local2 = ImprovedLocalAttention(dim)
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self.global_attention = EffcirntMutilSelfAttention(dim, num_heads, qkv_bias, window_size, relative_pos_embedding)
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||
|
|
|
||
|
|
def forward(self, x):
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|
|
# Combining local and global attention
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||
|
|
local = self.local1(x)
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||
|
|
global_attn = self.global_attention(x)
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||
|
|
return local + global_attn
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||
|
|
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||
|
|
|
||
|
|
# Testing the model with a random tensor
|
||
|
|
gl_attention = GlobalLocalAttention(dim=256, num_heads=16, qkv_bias=False, window_size=8, relative_pos_embedding=True)
|
||
|
|
x = torch.randn(1, 256, 64, 64)
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||
|
|
output = gl_attention(x)
|
||
|
|
print(output.shape) # Output should be (1, 256, 64, 64)
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