在pc上使用rknn-toolkit 1.3.2 调用rknn.inference(inputs=[img])需要5分钟

root@843ead96d6af:/shares/PyTorch-YOLOv3-train# python rknn_yolo.py
--> config model
done
--> Loading model
/usr/local/lib/python3.5/dist-packages/onnx_tf/common/__init__.py:87: UserWarning: FrontendHandler.get_outputs_names is deprecated. It will be removed in future release.. Use node.outputs instead.
  warnings.warn(message)
./yoloV3.pt ********************
WARNING: Token 'COMMENT' defined, but not used
WARNING: There is 1 unused token
done
--> Building model
W The target_platform is not set in config, using default target platform rk1808.
done
--> Export RKNN model
done
--> Init runtime environment
W [set_chip_platform_env:187]evaluate model on RK1808
done
--> Running model
W [rknn_inputs_set:1288] warning: inputs[0] expected input len is 602112, but actual len is 502500!
done
--> Begin evaluate model performance
W When performing performance evaluation, inputs can be set to None to use fake inputs.
W [set_chip_platform_env:187]evaluate model on RK1808
W [set_chip_platform_env:187]evaluate model on RK1808

在开始评估之后，需要大约5分钟才有结果。

1.     model = Darknet(opt.model_def, img_size=opt.img_size)
   
2.     model.load_state_dict(torch.load("./checkpoints/custom_yolov3_ckpt_900.pth", map_location=torch.device('cpu')))
   
3.     model.eval()
   
4.     trace_model = torch.jit.trace(model, torch.Tensor(1,3,448,448))
   
5.     trace_model.save('./yoloV3.pt')
   
6.     np.set_printoptions(formatter={'float': '{: 0.3f}'.format})
   
7.     model_path = './yoloV3.pt'
   
8.     input_size_list = [[3, 448, 448]]
   
9. 
   
10.     # Create RKNN object
    
11.     rknn = RKNN()
    
12.     print('--> config model')
    
13.     rknn.config(channel_mean_value='0. 0. 0. 255.', reorder_channel='0 1 2')
    
14.     print('done')
    
15. 
    
16.     # Load pytorch model
    
17.     print('--> Loading model')
    
18.     ret = rknn.load_pytorch(model=model_path, input_size_list=input_size_list)
    
19.     if ret != 0:
    
20.         print('Load pytorch model failed!')
    
21.         exit(ret)
    
22.     print('done')
    
23. 
    
24.     # Build model
    
25.     print('--> Building model')
    
26.     # 不量化，结果基本一致
    
27.     ret = rknn.build(do_quantization=False, dataset='./valid.txt')
    
28.     if ret != 0:
    
29.         print('Build pytorch failed!')
    
30.         exit(ret)
    
31.     print('done')
    
32. 
    
33.     # Export rknn model
    
34.     print('--> Export RKNN model')
    
35.     ret = rknn.export_rknn('./yoloV3.rknn')
    
36.     if ret != 0:
    
37.         print('Export yoloV3.rknn failed!')
    
38.         exit(ret)
    
39.     print('done')
    
40. 
    
41.     ret = rknn.load_rknn('./yoloV3.rknn')
    
42. 
    
43.     # Set inputs
    
44.     img = cv2.imread('./data/custom/images/train.jpg')
    
45.     img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
    
46.     # 归一化
    
47.     img = np.array(img, dtype=np.float32)
    
48.     img = np.array((img-img.min()) / img.ptp())
    
49.     # print("img: ", img)
    
50. 
    
51.     # init runtime environment
    
52.     print('--> Init runtime environment')
    
53.     ret = rknn.init_runtime()
    
54.     if ret != 0:
    
55.         print('Init runtime environment failed')
    
56.         exit(ret)
    
57.     print('done')
    
58. 
    
59.     # Inference
    
60.     print('--> Running model')
    
61.     outputs = rknn.inference(inputs=[img])
    
62.     print('done')
    
63. 
    
64.     # # perf
    
65.     print('--> Begin evaluate model performance')
    
66.     perf_results = rknn.eval_perf(inputs=[img])
    
67.     print('done')
    
68.     rknn.release()

复制代码

1. 先保证drv和api是匹配的，否则运行不出结果，5分钟可能是错误超时时间。
2. 需要量化后再来看，不量化的时间是没意义的。
3. yolov3看我们教学贴都有，自己对比下和我们有什么区别。