def __init__(self, dim, window_size, num_heads, qkv_bias=True, qk_scale=None, attn_drop=0., proj_drop=0.): super().__init__() self.dim = dim self.window_size = window_size # Wh, Ww self.num_heads = num_heads head_dim = dim // num_heads self.scale = qk_scale or head_dim ** -0.5 # define a parameter table of relative position bias self.relative_position_bias_table = nn.Parameter( torch.zeros((2 * window_size[0] - 1) * (2 * window_size[1] - 1), num_heads)) # 2*Wh-1 * 2*Ww-1, nH

这段代码是一个类的初始化方法，用于创建一个多头自注意力机制（multi-head self-attention）的模型。其中，dim表示输入特征的维度，window_size表示窗口大小，num_heads表示注意力头的数量。qkv_bias、qk_scale、attn_drop和proj_drop则是一些可选的超参数。具体来说，该初始化方法定义了一个相对位置偏差参数表，其大小为(2 * Wh - 1) * (2 * Ww - 1) * nH，其中Wh和Ww分别表示窗口的高度和宽度，nH表示注意力头的数量。

相关问题

yolov5添加transformer模型

要将Transformer模型添加到Yolov5中，需要进行以下步骤：

1. 在models文件夹中创建一个名为swintransformer.py的新文件，并将以下代码添加到该文件中：

import torch.nn as nn
from timm.models.layers import DropPath, to_2tuple, trunc_normal_
from timm.models.vision_transformer import Mlp, PatchEmbed

class SwinTransformerBlock(nn.Module):
    def __init__(self, dim, num_heads, window_size, shift_size, mlp_ratio=4., qkv_bias=False, qk_scale=None, drop=0., attn_drop=0., drop_path=0., act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.dim = dim
        self.num_heads = num_heads
        self.window_size = window_size
        self.shift_size = shift_size
        self.mlp_ratio = mlp_ratio
        self.qkv_bias = qkv_bias
        self.qk_scale = qk_scale
        self.drop = nn.Dropout(drop)
        self.attn_drop = nn.Dropout(attn_drop)
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()

        self.norm1 = norm_layer(dim)
        self.attn = nn.MultiheadAttention(dim, num_heads, qkv_bias=qkv_bias, dropout=attn_drop, kdim=dim, vdim=dim, batch_first=True)
        self.local_context = LocalContext(window_size, shift_size)
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

    def forward(self, x):
        H, W = x.shape[-2:]
        B, N, C = x.shape
        x = x.reshape(B, H // self.window_size, self.window_size, W // self.window_size, self.window_size, C)
        x = x.permute(0, 1, 3, 2, 4, 5).reshape(B, -1, self.window_size * self.window_size * C)

        x = self.norm1(x)
        qkv = self.attn(x, x, x)[0]
        qkv = self.drop(qkv)
        x = x 

swin transformer中如何加入cbam注意力机制

在 Swin Transformer 中加入 CBAM（Convolutional Block Attention Module）注意力机制，可以通过以下步骤实现：

1. 导入必要的库和模块：

import torch
import torch.nn as nn

2. 定义 CBAM 模块：

class CBAM(nn.Module):
    def __init__(self, channel, reduction=16):
        super(CBAM, self).__init__()
        self.channel = channel
        self.reduction = reduction

        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)

        self.fc1 = nn.Conv2d(self.channel, self.channel // self.reduction, kernel_size=1, stride=1, padding=0)
        self.relu = nn.ReLU(inplace=True)
        self.fc2 = nn.Conv2d(self.channel // self.reduction, self.channel, kernel_size=1, stride=1, padding=0)

        self.sigmoid = nn.Sigmoid()

    def forward(self, x):
        avg_out = self.avg_pool(x)
        max_out = self.max_pool(x)
        avg_out = self.fc1(avg_out)
        avg_out = self.relu(avg_out)
        avg_out = self.fc2(avg_out)
        max_out = self.fc1(max_out)
        max_out = self.relu(max_out)
        max_out = self.fc2(max_out)
        out = avg_out + max_out
        out = self.sigmoid(out)
        out = out.expand_as(x)
        return out * x

3. 在 Swin Transformer 的基础上，在每个阶段的每个块之后添加 CBAM 模块：

class SwinBlock(nn.Module):
    def __init__(self, dim, num_heads, window_size, shift_size, mlp_ratio=4.0, qkv_bias=False, qk_scale=None, drop=0.0, attn_drop=0.0, drop_path=0.0, act_layer=nn.GELU, norm_layer=nn.LayerNorm):
        super().__init__()
        self.norm1 = norm_layer(dim)
        self.attn = WindowAttention(
            dim, num_heads=num_heads, window_size=window_size, shift_size=shift_size,
            qkv_bias=qkv_bias, qk_scale=qk_scale, attn_drop=attn_drop, proj_drop=drop
        )
        self.drop_path = DropPath(drop_path) if drop_path > 0. else nn.Identity()
        self.norm2 = norm_layer(dim)
        mlp_hidden_dim = int(dim * mlp_ratio)
        self.mlp = Mlp(in_features=dim, hidden_features=mlp_hidden_dim, act_layer=act_layer, drop=drop)

        # 添加 CBAM 注意力机制
        self.cbam = CBAM(dim)

    def forward(self, x, mask_matrix):
        x = x + self.drop_path(self.attn(self.norm1(x), mask_matrix))
        x = x + self.drop_path(self.mlp(self.norm2(x)))
        x = self.cbam(x)  # 应用 CBAM 注意力机制
        return x

通过以上步骤，你就可以在 Swin Transformer 中成功加入 CBAM 注意力机制。当然，这只是一个示例实现，你可以根据具体需求进行修改和优化。