一、空间金字塔池化

SPP

#   SPP结构，利用不同大小的池化核进行池化 5*5 9*9 13*13
#   先构建kernel_size=5, stride=1, padding=2的最大池化层
#   再构建kernel_size=9, stride=1, padding=4的最大池化层
#   再构建kernel_size=13, stride=1, padding=6的最大池化层
#   池化后堆叠
#---------------------------------------------------#
class SpatialPyramidPooling(nn.Module):
    def __init__(self, pool_sizes=[5, 9, 13]):
        super(SpatialPyramidPooling, self).__init__()
 
        self.maxpools = nn.ModuleList([nn.MaxPool2d(kernel_size=pool_size, stride=1, padding=pool_size//2) for pool_size in pool_sizes])
 
    def forward(self, x):
        features = [maxpool(x) for maxpool in self.maxpools[::-1]]
        features = torch.cat(features + [x], dim=1) # x指的是未经过最大池化的层
 
        return features

SPPF

class SPPF(nn.Module):
    # Spatial Pyramid Pooling - Fast (SPPF) layer for YOLOv5 by Glenn Jocher
    def __init__(self, c1, c2, k=5):  # equivalent to SPP(k=(5, 9, 13))
        super().__init__()
        c_ = c1 // 2  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c_ * 4, c2, 1, 1)
        self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2)
 
    def forward(self, x):
        x = self.cv1(x)
        with warnings.catch_warnings():
            warnings.simplefilter('ignore')  # suppress torch 1.9.0 max_pool2d() warning
            y1 = self.m(x)
            y2 = self.m(y1)
            return self.cv2(torch.cat([x, y1, y2, self.m(y2)], 1))

SPPCSPC

class SPPCSPC(nn.Module):
    # CSP https://github.com/WongKinYiu/CrossStagePartialNetworks
    def __init__(self, c1, c2, n=1, shortcut=False, g=1, e=0.5, k=(5, 9, 13)):
        super(SPPCSPC, self).__init__()
        c_ = int(2 * c2 * e)  # hidden channels
        self.cv1 = Conv(c1, c_, 1, 1)
        self.cv2 = Conv(c1, c_, 1, 1)
        self.cv3 = Conv(c_, c_, 3, 1)
        self.cv4 = Conv(c_, c_, 1, 1)
        self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k])
        self.cv5 = Conv(4 * c_, c_, 1, 1)
        self.cv6 = Conv(c_, c_, 3, 1)
        self.cv7 = Conv(2 * c_, c2, 1, 1)

    def forward(self, x):
        x1 = self.cv4(self.cv3(self.cv1(x)))
        y1 = self.cv6(self.cv5(torch.cat([x1] + [m(x1) for m in self.m], 1)))
        y2 = self.cv2(x)
        return self.cv7(torch.cat((y1, y2), dim=1))

使用方式

第一步 各个代码放入common.py中

第二步 找到yolo.py文件里的parse_model函数，将类名加入进去

第三步 修改配置文件

在我自己的数据集上跑了一下，发现 SPPCSPC的效果是最好的~~~

二、上采样方式

1. 最近邻插值(Nearest neighbor interpolation)

YOLOV5中默认使用的是最近邻插值‘nearest’

 2. 双线性插值(Bi-Linear interpolation)

 若要改为双线性插值只需在yaml文件中将nearest改为bilinear，然后在后面加上True即可

reference

空间金字塔池化改进 SPP / SPPF / ASPP / RFB / SPPCSPC_迪菲赫尔曼的博客-CSDN博客