苏州建设网站多少钱,线上外贸平台有哪些,百度打广告收费表,福建省网站备案注销官方预训练模型转换
下载yolov5-v6.0分支源码解压到本地#xff0c;并配置基础运行环境。下载官方预训练模型
yolov5n.ptyolov5s.ptyolov5m.pt…
进入yolov5-6.0目录下#xff0c;新建文件夹weights#xff0c;并将步骤2中下载的权重文件放进去。修改models/yolo.py文件 …官方预训练模型转换
下载yolov5-v6.0分支源码解压到本地并配置基础运行环境。下载官方预训练模型
yolov5n.ptyolov5s.ptyolov5m.pt…
进入yolov5-6.0目录下新建文件夹weights并将步骤2中下载的权重文件放进去。修改models/yolo.py文件 def forward(self, x):z [] # inference outputfor i in range(self.nl):x[i] self.m[i](x[i]).sigmoid() # 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.anchor_grid[i] self._make_grid(nx, ny, i)# 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] # 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)return x[0], x[1], x[2]新建export_rknn.py文件
import os
import torch
import onnx
from onnxsim import simplify
import onnxoptimizer
import argparse
from models.yolo import Detect, Modelif __name__ __main__:parser argparse.ArgumentParser()parser.add_argument(--weights, typestr, default./weights/yolov5n.pt, helpinitial weights path) #opt parser.parse_args()print(opt)#Save Only weightsckpt torch.load(opt.weights, map_locationtorch.device(cpu))torch.save(ckpt[model].state_dict(), opt.weights.replace(.pt, -model.pt))#Load model without postprocessingnew_model Model(./models/{}.yaml.format(os.path.basename(opt.weights).strip(.pt)))new_model.load_state_dict(torch.load(opt.weights.replace(.pt, -model.pt), map_locationtorch.device(cpu)), False)new_model.eval()#save to JIT scriptexample torch.rand(1, 3, 640, 640)traced_script_module torch.jit.trace(new_model, example)traced_script_module.save(opt.weights.replace(.pt, -jit.pt))#save to onnxf opt.weights.replace(.pt, .onnx)torch.onnx.export(new_model, example, f, verboseFalse, opset_version12,trainingtorch.onnx.TrainingMode.EVAL,do_constant_foldingTrue,input_names[data],output_names[out0,out1,out2])#onnxsimmodel_simp, check simplify(f)assert check, Simplified ONNX model could not be validatedonnx.save(model_simp, opt.weights.replace(.pt, -sim.onnx))#optimize onnxpasses [extract_constant_to_initializer, eliminate_unused_initializer]optimized_model onnxoptimizer.optimize(model_simp, passes)onnx.checker.check_model(optimized_model)onnx.save(optimized_model, opt.weights.replace(.pt, -op.onnx))print(finished exporting onnx)命令行执行python3 export_rknn.py脚本(默认为yolov5n.pt, 加–weights参数可指定权重)转换成功会输出一下信息, 转换后的模型存于权重同级目录(*-op.onnx后缀模型)
Namespace(weights./weights/yolov5n.pt)
finished exporting onnxRKNN开发板植入-模型转换篇
前期准备
RKNN开发环境pythonrknn-toolkits2
详细流程
进入rknn-toolkits2/examples/onnx/yolov5示例目录下修改test.py内容按需修改ONNX_MODEL、RKNN_MODEL、IMG_PATH、DATASET等等超参数
def sigmoid(x):# return 1 / (1 np.exp(-x))return x命令行执行python3 test.py即可获取推理结果 RKNN开发板植入-NPU加载推理篇(C)
后续放出代码
