A247-多个yolo模型对比融合创新CBAM注意力集成GhostNet实验停车位线识别检测

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import torch
import torch.nn as nn
 
 
class ChannelAttention(nn.Module):
    """Channel-attention module https://github.com/open-mmlab/mmdetection/tree/v3.0.0rc1/configs/rtmdet."""
 
    def __init__(self, channels: int) -> None:
        """Initializes the class and sets the basic configurations and instance variables required."""
        super().__init__()
        self.pool = nn.AdaptiveAvgPool2d(1)
        self.fc = nn.Conv2d(channels, channels, 1, 1, 0, bias=True)
        self.act = nn.Sigmoid()
 
    def forward(self, x: torch.Tensor) -> torch.Tensor:
        """Applies forward pass using activation on convolutions of the input, optionally using batch normalization."""
        return x * self.act(self.fc(self.pool(x)))
 
 
class SpatialAttention(nn.Module):
    """Spatial-attention module."""
 
    def __init__(self, kernel_size=7):
        """Initialize Spatial-attention module with kernel size argument."""
        super().__init__()
        assert kernel_size in (3, 7), "kernel size must be 3 or 7"
        padding = 3 if kernel_size == 7 else 1
        self.cv1 = nn.Conv2d(2, 1, kernel_size, padding=padding, bias=False)
        self.act = nn.Sigmoid()
 
    def forward(self, x):
        """Apply channel and spatial attention on input for feature recalibration."""
        return x * self.act(self.cv1(torch.cat([torch.mean(x, 1, keepdim=True), torch.max(x, 1, keepdim=True)[0]], 1)))
 
 
class CBAM(nn.Module):
    """Convolutional Block Attention Module."""
 
    def __init__(self, c1, kernel_size=7):
        """Initialize CBAM with given input channel (c1) and kernel size."""
        super().__init__()
        self.channel_attention = ChannelAttention(c1)
        self.spatial_attention = SpatialAttention(kernel_size)
 
    def forward(self, x):
        """Applies the forward pass through C1 module."""
        return self.spatial_attention(self.channel_attention(x))

import torch
import torch.nn as nn
import torch.nn.functional as F
from ultralytics.nn.modules.conv import Conv
import math
 
 
class GhostModule(nn.Module):
    def __init__(self, c1, c2, kernel_size=1, ratio=2, dw_size=3, stride=1, relu=True):
        super(GhostModule, self).__init__()
        self.oup = c2
        init_channels = math.ceil(c2 / ratio)
        new_channels = init_channels*(ratio-1)
 
        self.primary_conv = nn.Sequential(
            nn.Conv2d(c1, init_channels, kernel_size, stride, kernel_size//2, bias=False),
            nn.BatchNorm2d(init_channels),
            nn.ReLU(inplace=True) if relu else nn.Sequential(),
        )
 
        self.cheap_operation = nn.Sequential(
            nn.Conv2d(init_channels, new_channels, dw_size, 1, dw_size//2, groups=init_channels, bias=False),
            nn.BatchNorm2d(new_channels),
            nn.ReLU(inplace=True) if relu else nn.Sequential(),
        )
 
    def forward(self, x):
        x1 = self.primary_conv(x)
        x2 = self.cheap_operation(x1)
        out = torch.cat([x1,x2], dim=1)
        return out[:,:self.oup,:,:]