yolov5s核心代码及核心代码解释

以下是 YOLOv5s 的核心代码及其解释：

class Detect(nn.Module):
    stride = None  # strides computed during build
    export = False  # onnx export

    def __init__(self, nc=80, anchors=(), ch=()):
        super(Detect, self).__init__()
        self.nc = nc  # number of classes
        self.no = nc + 5  # number of outputs per anchor
        self.nl = len(anchors)  # number of detection layers
        self.na = len(anchors[0]) // 2  # number of anchors
        self.grid = [torch.zeros(1)] * self.nl  # init grid
        a = torch.tensor(anchors).float().view(self.nl, -1, 2)
        self.register_buffer('anchors', a)
        self.register_buffer('anchor_grid', a.clone().view(self.nl, 1, -1, 1, 1, 2))
        self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch)  # output conv

    def forward(self, x):
        # x(bs,channels,width,height) -> y(bs,anchors,nc+5,width,height)
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = x[i].shape  # x(bs,anchors*(nc+5),width,height) to x(bs,anchors,nc+5,width,height)
            x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

            if not self.training:
                if self.grid[i].shape[2:4] != x[i].shape[2:4]:
                    self.grid[i] = self._make_grid(nx, ny).to(x[i].device)

                y = x[i].sigmoid()
                y[..., 0:2] = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
                z.append(y.view(bs, -1, self.no))

        return x if self.training else (torch.cat(z, 1), x)

    def _make_grid(self, nx=20, ny=20):
        xv, yv = torch.meshgrid([torch.arange(ny), torch.arange(nx)])
        return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float()

    def _initialize_biases(self, cf):
        # cf is class frequency
        pi = 3.141592653589793
        b = [-4.0, -3.0, -2.0, -1.0, 0, 1.0, 2.0, 3.0]
        w = [2.0 ** i for i in b]
        x = [w[i] * pi / 2 for i in range(len(w))]
        y = [(1 - cf[i] / sum(cf)) / (self.nc - 1) for i in range(self.nc)]
        for j in range(self.nl):
            bias = self.m[j].bias.view(self.na, -1)  # conv.bias(255) to (3,85)
            if bias.shape[1] == self.no:
                # yolov3-tiny
                # https://github.com/pjreddie/darknet/blob/master/cfg/yolov3-tiny.cfg
                bias[:, 4] += math.log(8. / (640. / 13) ** 2)  # obj (8 objects per 13x13 image)
                bias[:, 5:] += math.log(0.6 / (self.nc - 0.99))  # cls
            elif bias.shape[1] == self.no + 1:
                # yolov3 and yolov5
                # https://github.com/pjreddie/darknet/blob/master/cfg/yolov3.cfg
                # https://github.com/ultralytics/yolov5/blob/master/models/yolo.py
                bias[:, 4] += math.log(8. / (640. / 32) ** 2)  # obj (8 objects per 32x32 feature map)
                bias[:, 5:-1] += math.log(0.6 / (self.nc - 0.99))  # cls
                bias[:, -1] += -math.log(1. / (self.na * 0.05))  # ratio

            for i in range(self.na):
                # if self.na == 1:
                #     b = bias
                # else:
                #     b = bias[i:i+1]
                b = bias[i:i+1]
                b[:, 0] += x  # x
                b[:, 1] += y * 2*pi  # y
                b[:, 2] += x  # w
                b[:, 3] += y * 2*pi  # h
                bias[i:i+1] = b
            self.m[j].bias = torch.nn.Parameter(bias.view(-1), requires_grad=True)

这个代码实现了 YOLOv5s 中的检测模块，它的主要作用是将输入的特征图进行处理，输出包含物体类别、位置、置信度等信息的检测结果。

- `nc`: 表示物体的类别数；
- `anchors`: 表示锚框的大小和位置；
- `ch`: 表示特征图的通道数；
- `self.grid`: 表示特征图上的网格；
- `self.na`: 表示锚框的数量；
- `y`: 表示检测结果。

在 forward 函数中，首先将输入的特征图通过卷积层进行卷积操作，然后对卷积结果进行变形，得到检测结果。在训练时，会输出特征图，而在测试时，会输出检测结果。

_make_grid 函数将特征图上的每个点映射到原图上，用于计算物体的位置。

_initialize_biases 函数用于初始化偏置，以便更好地检测不同类别的物体。