【已解决】pt文件转onnx后再转rknn时得到推理图片出现大量锚框变花屏

前言

环境介绍：

1.编译环境

Ubuntu 18.04.5 LTS

2.RKNN版本

py3.8-rknn2-1.4.0

3.单板

迅为itop-3568开发板

一、现象

采用yolov5训练并将pt转换为onnx，再将onnx采用py3.8-rknn2-1.4.0推理转换为rknn，rknn模型能正常转换，并且推理显示正常。但将rknn文件放到开发板，使用rknn_toolkit_lite2进行推理时，得到的推理图片出现大量锚框变花屏，如下。
在这里插入图片描述

二、解决

经过排查发现是前面为了解决rknn置信度大于1，图像出现乱框问题在将pt导出为onnx文件时，对yolo.py文件做了修改，引入了sigmoid函数。
models/yolo.py

def forward(self, x):z = []  # inference outputfor i in range(self.nl):if os.getenv('RKNN_model_hack', '0') != '0':x[i] = torch.sigmoid(self.m[i](x[i]))  # convreturn x
# def forward(self, x):
#     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,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.grid[i].shape[2:4] != x[i].shape[2:4] or self.onnx_dynamic:
#                 self.grid[i] = self._make_grid(nx, ny).to(x[i].device)
#
#             y = x[i].sigmoid()
#             if self.inplace:
#                 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
#             else:  # for YOLOv5 on AWS Inferentia https://github.com/ultralytics/yolov5/pull/2953
#                 xy = (y[..., 0:2] * 2. - 0.5 + self.grid[i]) * self.stride[i]  # xy
#                 wh = (y[..., 2:4] * 2) ** 2 * self.anchor_grid[i].view(1, self.na, 1, 1, 2)  # wh
#                 y = torch.cat((xy, wh, y[..., 4:]), -1)
#             z.append(y.view(bs, -1, self.no))
#
#     return x if self.training else (torch.cat(z, 1), x)