YoloV5 的部署【详解】_yolov5的部署

YoloV5的源码： github链接
本博客使用的是branch版本，也可以使用tag5.0的版本。
首先在命令行进行下载，

wget https://github.com/ultralytics/yolov5/archive/refs/heads/master.zip
1

再进行解压，

unzip mater.zip
1

删除master压缩文件

rm mater.zip
1

进入到文件夹里面去

code yolov5-master/
1

激活环境，这里需要python版本＞3.6的

conda activate torch1.8
1

执行export.py文件，导出ONNX，

python export.py
1

我们只需要输出ONNX，所以只需指定ONNX即可，

python export.py --include=onnx
1

1、避免直接使用shape、size的返回值

int代替shape、size的返回值，这里使用了map函数，避免ONNX导出生成gather、shape等节点，

    def forward(self, x):
        z = []  # inference output
        for i in range(self.nl):
            x[i] = self.m[i](x[i])  # conv
            bs, _, ny, nx = map(int, x[i].shape)  # 原代码：x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
1
2
3
4
5

2、batch维度需要指定为-1

请看代码的最后两句，batch维度指定为-1，

def forward(self, x):
    z = []  # inference output
    for i in range(self.nl):
        x[i] = self.m[i](x[i])  # conv
        bs, _, ny, nx = map(int, x[i].shape)  # 原代码：x[i].shape  # x(bs,255,20,20) to x(bs,3,20,20,85)
        x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()

        if not self.training:  # inference
            if self.onnx_dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]:
                self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i)

            y = x[i].sigmoid()
            if self.inplace:
                y[..., 0:2] = (y[..., 0:2] * 2 + self.grid[i]) * self.stride[i]  # xy
                y[..., 2:4] = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i]  # wh
            else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
                xy, wh, conf = y.split((2, 2, self.nc + 1), 4)  # y.tensor_split((2, 4, 5), 4)  # torch 1.8.0
                xy = (xy * 2 + self.grid[i]) * self.stride[i]  # xy
                wh = (wh * 2) ** 2 * self.anchor_grid[i]  # wh
                y = torch.cat((xy, wh, conf), 4)
            # z.append(y.view(bs, -1, self.no))
            z.append(y.view(-1, int(y.size(1) * y.size(2) * y.size(3), self.no)))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22