yolov5s在RKNN3588上以FP16推理时的精度问题

[blueWatermelonFri]

通过本地的ubuntu服务器对yolov5s模型进行连板调试时，fp16的精度下降了4个点，请问这个现象正常吗？

rknntoolkit2的版本如下：
rknn-toolkit2 version: 2.0.0b0+9bab5682
rk3588的驱动版本如下：

D RKNNAPI:   API: 2.0.0b0 (18eacd0 build@2024-03-22T06:07:59)
D RKNNAPI:   DRV: rknn_server: 2.0.0b0 (18eacd0 build@2024-03-22T14:07:19)
D RKNNAPI:   DRV: rknnrt: 2.0.0b0 (35a6907d79@2024-03-24T10:31:14)

rknn.config的参数如下：

rknn.config(mean_values=[[0, 0, 0]], 
                std_values=[[255, 255, 255]], 
                target_platform="rk3588")

精度分析结果如下：

rknn convert start!
I rknn-toolkit2 version: 2.0.0b0+9bab5682
--> Config model
done
--> Loading model
I It is recommended onnx opset 19, but your onnx model opset is 13!
I Model converted from pytorch, 'opset_version' should be set 19 in torch.onnx.export for successful convert!
I Loading : 100%|██████████████████████████████████████████████| 121/121 [00:00<00:00, 39667.87it/s]
done
--> Building model
W build: The dataset='calibrate_dataset.txt' is ignored because do_quantization = False!
I rknn building ...
I rknn buiding done.
done
--> Export rknn model
done
--> Accuracy analysis
adb: unable to connect for root: closed
I target set by user is: rk3588
I Get hardware info: target_platform = rk3588, os = Linux, aarch = aarch64
I Check RK3588 board npu runtime version
I Starting ntp or adb, target is RK3588
I Start adb...
I Connect to Device success!
I NPUTransfer: Starting NPU Transfer Client, Transfer version 2.1.0 (b5861e7@2020-11-23T11:50:36)
D RKNNAPI: ==============================================
D RKNNAPI: RKNN VERSION:
D RKNNAPI:   API: 2.0.0b0 (18eacd0 build@2024-03-22T06:07:59)
D RKNNAPI:   DRV: rknn_server: 2.0.0b0 (18eacd0 build@2024-03-22T14:07:19)
D RKNNAPI:   DRV: rknnrt: 2.0.0b0 (35a6907d79@2024-03-24T10:31:14)
D RKNNAPI: ==============================================
D RKNNAPI: Input tensors:
D RKNNAPI:   index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=2457600, w_stride = 0, size_with_stride = 0, fmt=NHWC, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
D RKNNAPI: Output tensors:
D RKNNAPI:   index=0, name=output1, n_dims=4, dims=[1, 18, 20, 20], n_elems=7200, size=14400, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
D RKNNAPI:   index=1, name=output2, n_dims=4, dims=[1, 18, 40, 40], n_elems=28800, size=57600, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
D RKNNAPI:   index=2, name=output3, n_dims=4, dims=[1, 18, 80, 80], n_elems=115200, size=230400, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
adb: unable to connect for root: closed
/userdata/dumps/: 89 files pulled. 4.2 MB/s (149901680 bytes in 33.662s)
I Save Tensors to txt: 100%|████████████████████████████████████████| 89/89 [00:03<00:00, 27.86it/s]
I GraphPreparing : 100%|███████████████████████████████████████| 145/145 [00:00<00:00, 15836.22it/s]
I AccuracyAnalysing : 100%|███████████████████████████████████████| 145/145 [00:16<00:00,  8.98it/s]

# simulator_error: calculate the output error of each layer of the simulator (compared to the 'golden' value).
#              entire: output error of each layer between 'golden' and 'simulator', these errors will accumulate layer by layer.
#              single: single-layer output error between 'golden' and 'simulator', can better reflect the single-layer accuracy of the simulator.
# runtime_error: calculate the output error of each layer of the runtime.
#              entire: output error of each layer between 'golden' and 'runtime', these errors will accumulate layer by layer.
#              single_sim: single-layer output error between 'simulator' and 'runtime', can better reflect the single-layer accuracy of runtime.

layer_name                                                  simulator_error                             runtime_error                    
                                                        entire              single                  entire           single_sim          
                                                     cos      euc        cos      euc            cos      euc        cos      euc        
-----------------------------------------------------------------------------------------------------------------------------------
[Input] images                                     1.00000 | 0.0       1.00000 | 0.0           1.00000 | 0.1180    1.00000 | 0.1180      
[Conv] /model.0/conv/Conv_output_0                 1.00000 | 2.4982    1.00000 | 2.4982
[exSwish] /model.0/act/Mul_output_0                1.00000 | 1.9841    1.00000 | 1.3346        1.00000 | 10.165    1.00000 | 10.132      
[Conv] /model.1/conv/Conv_output_0                 1.00000 | 2.4134    1.00000 | 1.8541
[exSwish] /model.1/act/Mul_output_0                1.00000 | 1.9273    1.00000 | 1.0880        1.00000 | 13.101    1.00000 | 8.1516      
[Conv] /model.2/cv1/conv/Conv_output_0             1.00000 | 0.8336    1.00000 | 0.5864
[exSwish] /model.2/cv1/act/Mul_output_0            1.00000 | 0.4872    1.00000 | 0.3146        1.00000 | 3.0559    1.00000 | 1.9833      
[Conv] /model.2/m/m.0/cv1/conv/Conv_output_0       1.00000 | 1.3587    1.00000 | 0.8088
[exSwish] /model.2/m/m.0/cv1/act/Mul_output_0      1.00000 | 0.8585    1.00000 | 0.3834        1.00000 | 4.1053    1.00000 | 1.7504      
[Conv] /model.2/m/m.0/cv2/conv/Conv_output_0       1.00000 | 2.4838    1.00000 | 1.0474
[exSwish] /model.2/m/m.0/cv2/act/Mul_output_0      1.00000 | 2.1420    1.00000 | 0.5897        1.00000 | 6.9511    1.00000 | 2.0777      
[Add] /model.2/m/m.0/Add_output_0                  1.00000 | 2.2869    1.00000 | 0.7157        1.00000 | 7.7536    1.00000 | 0.0         
[Conv] /model.2/cv2/conv/Conv_output_0             1.00000 | 1.5840    1.00000 | 1.0400
[exSwish] /model.2/cv2/act/Mul_output_0            1.00000 | 1.0642    1.00000 | 0.5301        1.00000 | 6.9092    1.00000 | 3.4345      
[Concat] /model.2/Concat_output_0                  1.00000 | 2.5224    1.00000 | 0.6349        1.00000 | 10.385    1.00000 | 0.0         
[Conv] /model.2/cv3/conv/Conv_output_0             1.00000 | 3.0151    1.00000 | 1.0589
[exSwish] /model.2/cv3/act/Mul_output_0            1.00000 | 1.7501    1.00000 | 0.4406        0.99999 | 6.0663    1.00000 | 1.6735      
[Conv] /model.3/conv/Conv_output_0                 1.00000 | 1.1325    1.00000 | 0.3195
[exSwish] /model.3/act/Mul_output_0                1.00000 | 0.5785    1.00000 | 0.1652        0.99999 | 2.2395    1.00000 | 0.3982      
[Conv] /model.4/cv1/conv/Conv_output_0             1.00000 | 0.3326    1.00000 | 0.1160
[exSwish] /model.4/cv1/act/Mul_output_0            1.00000 | 0.1531    1.00000 | 0.0573        1.00000 | 0.5484    1.00000 | 0.1548      
[Conv] /model.4/m/m.0/cv1/conv/Conv_output_0       1.00000 | 0.7294    1.00000 | 0.2583
[exSwish] /model.4/m/m.0/cv1/act/Mul_output_0      1.00000 | 0.4092    1.00000 | 0.1448        1.00000 | 1.4311    1.00000 | 0.3293      
[Conv] /model.4/m/m.0/cv2/conv/Conv_output_0       1.00000 | 0.5763    1.00000 | 0.1975
[exSwish] /model.4/m/m.0/cv2/act/Mul_output_0      1.00000 | 0.2526    1.00000 | 0.0820        1.00000 | 0.8956    1.00000 | 0.2005      
[Add] /model.4/m/m.0/Add_output_0                  1.00000 | 0.3054    1.00000 | 0.0972        1.00000 | 1.0543    1.00000 | 0.0         
[Conv] /model.4/m/m.1/cv1/conv/Conv_output_0       1.00000 | 0.8030    1.00000 | 0.2504
[exSwish] /model.4/m/m.1/cv1/act/Mul_output_0      1.00000 | 0.3734    1.00000 | 0.0868        0.99999 | 1.2211    1.00000 | 0.2090      
[Conv] /model.4/m/m.1/cv2/conv/Conv_output_0       1.00000 | 0.9418    1.00000 | 0.2373
[exSwish] /model.4/m/m.1/cv2/act/Mul_output_0      1.00000 | 0.6107    1.00000 | 0.1805        0.99999 | 1.9182    1.00000 | 0.4279      
[Add] /model.4/m/m.1/Add_output_0                  1.00000 | 0.7068    1.00000 | 0.2072        1.00000 | 2.2366    1.00000 | 0.0         
[Conv] /model.4/cv2/conv/Conv_output_0             1.00000 | 0.9380    1.00000 | 0.2877
[exSwish] /model.4/cv2/act/Mul_output_0            1.00000 | 0.6277    1.00000 | 0.1712        0.99999 | 2.7682    1.00000 | 0.4022      
[Concat] /model.4/Concat_output_0                  1.00000 | 0.9453    1.00000 | 0.1837        0.99999 | 3.5589    1.00000 | 0.0         
[Conv] /model.4/cv3/conv/Conv_output_0             1.00000 | 1.0833    1.00000 | 0.3435
[exSwish] /model.4/cv3/act/Mul_output_0            1.00000 | 0.4307    1.00000 | 0.1062        0.99999 | 1.6902    1.00000 | 0.2590      
[Conv] /model.5/conv/Conv_output_0                 1.00000 | 0.7330    1.00000 | 0.1894
[exSwish] /model.5/act/Mul_output_0                1.00000 | 0.3233    1.00000 | 0.0738        0.99998 | 1.6541    1.00000 | 0.2039      
[Conv] /model.6/cv1/conv/Conv_output_0             1.00000 | 0.2583    1.00000 | 0.0809
[exSwish] /model.6/cv1/act/Mul_output_0            1.00000 | 0.1132    1.00000 | 0.0361        0.99999 | 0.5547    1.00000 | 0.0952      
[Conv] /model.6/m/m.0/cv1/conv/Conv_output_0       1.00000 | 0.5540    1.00000 | 0.1694
[exSwish] /model.6/m/m.0/cv1/act/Mul_output_0      1.00000 | 0.2677    1.00000 | 0.0821        0.99999 | 1.2722    1.00000 | 0.1834      
[Conv] /model.6/m/m.0/cv2/conv/Conv_output_0       1.00000 | 0.3577    1.00000 | 0.1134
[exSwish] /model.6/m/m.0/cv2/act/Mul_output_0      1.00000 | 0.1338    1.00000 | 0.0329        0.99998 | 0.6488    1.00000 | 0.0815      
[Add] /model.6/m/m.0/Add_output_0                  1.00000 | 0.1792    1.00000 | 0.0528        0.99999 | 0.8114    1.00000 | 0.0         
[Conv] /model.6/m/m.1/cv1/conv/Conv_output_0       1.00000 | 0.5771    1.00000 | 0.1766
[exSwish] /model.6/m/m.1/cv1/act/Mul_output_0      1.00000 | 0.2299    1.00000 | 0.0546        0.99998 | 1.0036    1.00000 | 0.1380      
[Conv] /model.6/m/m.1/cv2/conv/Conv_output_0       1.00000 | 0.4603    1.00000 | 0.1238
[exSwish] /model.6/m/m.1/cv2/act/Mul_output_0      1.00000 | 0.2284    1.00000 | 0.0528        0.99998 | 1.0113    1.00000 | 0.1356      
[Add] /model.6/m/m.1/Add_output_0                  1.00000 | 0.2980    1.00000 | 0.0734        0.99999 | 1.3001    1.00000 | 0.0         
[Conv] /model.6/m/m.2/cv1/conv/Conv_output_0       1.00000 | 0.5523    1.00000 | 0.1619
[exSwish] /model.6/m/m.2/cv1/act/Mul_output_0      1.00000 | 0.2044    1.00000 | 0.0524        0.99999 | 0.8724    1.00000 | 0.1243      
[Conv] /model.6/m/m.2/cv2/conv/Conv_output_0       1.00000 | 0.6084    1.00000 | 0.1569
[exSwish] /model.6/m/m.2/cv2/act/Mul_output_0      1.00000 | 0.3599    1.00000 | 0.0859        0.99998 | 1.5912    1.00000 | 0.2535      
[Add] /model.6/m/m.2/Add_output_0                  1.00000 | 0.4789    1.00000 | 0.1097        0.99998 | 2.1075    1.00000 | 0.0         
[Conv] /model.6/cv2/conv/Conv_output_0             1.00000 | 0.6856    1.00000 | 0.1651
[exSwish] /model.6/cv2/act/Mul_output_0            1.00000 | 0.4227    1.00000 | 0.0984        0.99997 | 2.6074    1.00000 | 0.2393      
[Concat] /model.6/Concat_output_0                  1.00000 | 0.6388    1.00000 | 0.0984        0.99998 | 3.3526    1.00000 | 0.0         
[Conv] /model.6/cv3/conv/Conv_output_0             1.00000 | 0.8111    1.00000 | 0.2263
[exSwish] /model.6/cv3/act/Mul_output_0            1.00000 | 0.3316    1.00000 | 0.0756        0.99997 | 1.9800    1.00000 | 0.1875      
[Conv] /model.7/conv/Conv_output_0                 1.00000 | 0.5560    1.00000 | 0.1404
[exSwish] /model.7/act/Mul_output_0                1.00000 | 0.2298    1.00000 | 0.0507        0.99994 | 1.8461    1.00000 | 0.1214      
[Conv] /model.8/cv1/conv/Conv_output_0             1.00000 | 0.2673    1.00000 | 0.0892
[exSwish] /model.8/cv1/act/Mul_output_0            1.00000 | 0.0746    1.00000 | 0.0210        0.99997 | 0.6137    1.00000 | 0.0458      
[Conv] /model.8/m/m.0/cv1/conv/Conv_output_0       1.00000 | 0.4261    1.00000 | 0.1309
[exSwish] /model.8/m/m.0/cv1/act/Mul_output_0      1.00000 | 0.2042    1.00000 | 0.0467        0.99995 | 1.5758    1.00000 | 0.1170      
[Conv] /model.8/m/m.0/cv2/conv/Conv_output_0       1.00000 | 0.5180    1.00000 | 0.1237
[exSwish] /model.8/m/m.0/cv2/act/Mul_output_0      1.00000 | 0.2913    1.00000 | 0.0669        0.99994 | 2.5182    1.00000 | 0.1759      
[Add] /model.8/m/m.0/Add_output_0                  1.00000 | 0.3065    1.00000 | 0.0670        0.99993 | 2.6031    1.00000 | 0.0         
[Conv] /model.8/cv2/conv/Conv_output_0             1.00000 | 0.4653    1.00000 | 0.1089
[exSwish] /model.8/cv2/act/Mul_output_0            1.00000 | 0.2483    1.00000 | 0.0540        0.99993 | 2.1787    1.00000 | 0.1265      
[Concat] /model.8/Concat_output_0                  1.00000 | 0.3945    1.00000 | 0.0571        0.99993 | 3.3945    1.00000 | 0.0         
[Conv] /model.8/cv3/conv/Conv_output_0             1.00000 | 0.6968    1.00000 | 0.1682
[exSwish] /model.8/cv3/act/Mul_output_0            1.00000 | 0.3261    1.00000 | 0.0670        0.99991 | 2.8786    1.00000 | 0.1646      
[Conv] /model.9/cv1/conv/Conv_output_0             1.00000 | 0.3497    1.00000 | 0.0960
[exSwish] /model.9/cv1/act/Mul_output_0            1.00000 | 0.3225    1.00000 | 0.1103        0.99997 | 2.6514    1.00000 | 0.2577      
[MaxPool] /model.9/m/MaxPool_output_0              1.00000 | 0.4414    1.00000 | 0.1297        0.99999 | 3.4128    1.00000 | 0.0         
[MaxPool] /model.9/m_1/MaxPool_output_0            1.00000 | 0.4784    1.00000 | 0.1560        0.99999 | 3.6140    1.00000 | 0.0         
[MaxPool] /model.9/m_2/MaxPool_output_0            1.00000 | 0.4955    1.00000 | 0.1723        0.99999 | 3.6602    1.00000 | 0.0         
[Concat] /model.9/Concat_output_0                  1.00000 | 0.8794    1.00000 | 0.2754        0.99999 | 6.7183    1.00000 | 0.0         
[Conv] /model.9/cv2/conv/Conv_output_0             1.00000 | 0.6059    1.00000 | 0.2436
[exSwish] /model.9/cv2/act/Mul_output_0            1.00000 | 0.1914    1.00000 | 0.0398        0.99992 | 1.6811    1.00000 | 0.0925      
[Conv] /model.10/conv/Conv_output_0                1.00000 | 0.4219    1.00000 | 0.1043
[exSwish] /model.10/act/Mul_output_0               1.00000 | 0.1706    1.00000 | 0.0341        0.99991 | 1.4851    1.00000 | 0.0814      
[Resize] /model.11/Resize_output_0                 1.00000 | 0.3412    1.00000 | 0.0475        0.99991 | 2.9703    1.00000 | 0.0         
[Concat] /model.12/Concat_output_0                 1.00000 | 0.4758    1.00000 | 0.0701        0.99994 | 3.5698    1.00000 | 0.0         
[Conv] /model.13/cv1/conv/Conv_output_0            1.00000 | 0.4899    1.00000 | 0.1309
[exSwish] /model.13/cv1/act/Mul_output_0           1.00000 | 0.1896    1.00000 | 0.0595        0.99997 | 1.3748    1.00000 | 0.1441      
[Conv] /model.13/m/m.0/cv1/conv/Conv_output_0      1.00000 | 0.5131    1.00000 | 0.1695
[exSwish] /model.13/m/m.0/cv1/act/Mul_output_0     1.00000 | 0.2320    1.00000 | 0.0631        0.99996 | 1.6936    1.00000 | 0.1499      
[Conv] /model.13/m/m.0/cv2/conv/Conv_output_0      1.00000 | 0.5625    1.00000 | 0.1369
[exSwish] /model.13/m/m.0/cv2/act/Mul_output_0     1.00000 | 0.2800    1.00000 | 0.0610        0.99993 | 2.2030    1.00000 | 0.1513      
[Conv] /model.13/cv2/conv/Conv_output_0            1.00000 | 0.5349    1.00000 | 0.1256
[exSwish] /model.13/cv2/act/Mul_output_0           1.00000 | 0.2531    1.00000 | 0.0567        0.99994 | 1.9512    1.00000 | 0.1389      
[Concat] /model.13/Concat_output_0                 1.00000 | 0.3775    1.00000 | 0.0559        0.99994 | 2.9429    1.00000 | 0.0         
[Conv] /model.13/cv3/conv/Conv_output_0            1.00000 | 0.8536    1.00000 | 0.1991
[exSwish] /model.13/cv3/act/Mul_output_0           1.00000 | 0.4066    1.00000 | 0.0833        0.99992 | 3.2888    1.00000 | 0.2034      
[Conv] /model.14/conv/Conv_output_0                1.00000 | 0.4692    1.00000 | 0.1020
[exSwish] /model.14/act/Mul_output_0               1.00000 | 0.2757    1.00000 | 0.0680        0.99994 | 2.3128    1.00000 | 0.1682      
[Resize] /model.15/Resize_output_0                 1.00000 | 0.5514    1.00000 | 0.0886        0.99994 | 4.6257    1.00000 | 0.0         
[Concat] /model.16/Concat_output_0                 1.00000 | 0.6997    1.00000 | 0.1145        0.99996 | 4.9248    1.00000 | 0.0         
[Conv] /model.17/cv1/conv/Conv_output_0            1.00000 | 0.5299    1.00000 | 0.1643
[exSwish] /model.17/cv1/act/Mul_output_0           1.00000 | 0.2125    1.00000 | 0.0811        0.99998 | 1.4724    1.00000 | 0.1977      
[Conv] /model.17/m/m.0/cv1/conv/Conv_output_0      1.00000 | 0.5593    1.00000 | 0.2251
[exSwish] /model.17/m/m.0/cv1/act/Mul_output_0     1.00000 | 0.2300    1.00000 | 0.0934        0.99998 | 1.5433    1.00000 | 0.2135      
[Conv] /model.17/m/m.0/cv2/conv/Conv_output_0      1.00000 | 0.8545    1.00000 | 0.2604
[exSwish] /model.17/m/m.0/cv2/act/Mul_output_0     1.00000 | 0.5550    1.00000 | 0.1444        0.99996 | 3.8579    1.00000 | 0.3635      
[Conv] /model.17/cv2/conv/Conv_output_0            1.00000 | 0.5023    1.00000 | 0.1378
[exSwish] /model.17/cv2/act/Mul_output_0           1.00000 | 0.3705    1.00000 | 0.1101        0.99995 | 3.3247    1.00000 | 0.2729      
[Concat] /model.17/Concat_output_0                 1.00000 | 0.6673    1.00000 | 0.1171        0.99996 | 5.0929    1.00000 | 0.0         
[Conv] /model.17/cv3/conv/Conv_output_0            1.00000 | 2.2759    1.00000 | 0.5792
[exSwish] /model.17/cv3/act/Mul_output_0           1.00000 | 1.4949    1.00000 | 0.3410        0.99994 | 11.339    1.00000 | 1.0927      
[Conv] /model.18/conv/Conv_output_0                1.00000 | 0.7697    1.00000 | 0.1473
[exSwish] /model.18/act/Mul_output_0               1.00000 | 0.4751    1.00000 | 0.0723        0.99986 | 3.8054    1.00000 | 0.1805      
[Concat] /model.19/Concat_output_0                 1.00000 | 0.5493    1.00000 | 0.0650        0.99990 | 4.4532    1.00000 | 0.0         
[Conv] /model.20/cv1/conv/Conv_output_0            1.00000 | 0.7051    1.00000 | 0.1141
[exSwish] /model.20/cv1/act/Mul_output_0           1.00000 | 0.3838    1.00000 | 0.0557        0.99984 | 3.1648    1.00000 | 0.1376      
[Conv] /model.20/m/m.0/cv1/conv/Conv_output_0      1.00000 | 0.6564    1.00000 | 0.1113
[exSwish] /model.20/m/m.0/cv1/act/Mul_output_0     1.00000 | 0.3043    1.00000 | 0.0461        0.99986 | 2.5376    1.00000 | 0.1101      
[Conv] /model.20/m/m.0/cv2/conv/Conv_output_0      1.00000 | 0.9716    1.00000 | 0.1299
[exSwish] /model.20/m/m.0/cv2/act/Mul_output_0     1.00000 | 0.6093    1.00000 | 0.0851        0.99981 | 5.2704    1.00000 | 0.2502      
[Conv] /model.20/cv2/conv/Conv_output_0            1.00000 | 0.5102    1.00000 | 0.0828
[exSwish] /model.20/cv2/act/Mul_output_0           1.00000 | 0.3326    1.00000 | 0.0576        0.99985 | 3.2475    1.00000 | 0.1576      
[Concat] /model.20/Concat_output_0                 1.00000 | 0.6942    1.00000 | 0.0676        0.99982 | 6.1906    1.00000 | 0.0         
[Conv] /model.20/cv3/conv/Conv_output_0            1.00000 | 1.5858    1.00000 | 0.2464
[exSwish] /model.20/cv3/act/Mul_output_0           1.00000 | 0.9009    1.00000 | 0.1510        0.99986 | 8.1236    1.00000 | 0.5544      
[Conv] /model.21/conv/Conv_output_0                1.00000 | 0.6364    1.00000 | 0.0815
[exSwish] /model.21/act/Mul_output_0               1.00000 | 0.3965    1.00000 | 0.0494        0.99974 | 3.5894    1.00000 | 0.1256      
[Concat] /model.22/Concat_output_0                 1.00000 | 0.4317    1.00000 | 0.0406        0.99980 | 3.8845    1.00000 | 0.0         
[Conv] /model.23/cv1/conv/Conv_output_0            1.00000 | 0.6592    1.00000 | 0.0848
[exSwish] /model.23/cv1/act/Mul_output_0           1.00000 | 0.3573    1.00000 | 0.0428        0.99974 | 3.1069    1.00000 | 0.1259      
[Conv] /model.23/m/m.0/cv1/conv/Conv_output_0      1.00000 | 0.7364    1.00000 | 0.0879
[exSwish] /model.23/m/m.0/cv1/act/Mul_output_0     0.99999 | 0.4095    1.00000 | 0.0404        0.99964 | 3.4908    1.00000 | 0.1387      
[Conv] /model.23/m/m.0/cv2/conv/Conv_output_0      1.00000 | 0.8693    1.00000 | 0.0855
[exSwish] /model.23/m/m.0/cv2/act/Mul_output_0     1.00000 | 0.5731    1.00000 | 0.0634        0.99970 | 4.9156    1.00000 | 0.1682      
[Conv] /model.23/cv2/conv/Conv_output_0            1.00000 | 0.5111    1.00000 | 0.0647
[exSwish] /model.23/cv2/act/Mul_output_0           1.00000 | 0.3930    1.00000 | 0.0593        0.99980 | 3.8416    1.00000 | 0.1424      
[Concat] /model.23/Concat_output_0                 1.00000 | 0.6949    1.00000 | 0.0571        0.99975 | 6.2387    1.00000 | 0.0         
[Conv] /model.23/cv3/conv/Conv_output_0            1.00000 | 0.9849    1.00000 | 0.1291
[exSwish] /model.23/cv3/act/Mul_output_0           1.00000 | 0.4929    1.00000 | 0.0801        0.99983 | 4.6763    1.00000 | 0.3220      
[Conv] /model.24/m.2/Conv_output_0                 1.00000 | 0.2135    1.00000 | 0.1117
[Sigmoid] output1                                  1.00000 | 0.0289    1.00000 | 0.0074        0.99999 | 0.2542    1.00000 | 0.0921      
[Conv] /model.24/m.1/Conv_output_0                 1.00000 | 0.4433    1.00000 | 0.2245
[Sigmoid] output2                                  1.00000 | 0.0470    1.00000 | 0.0160        0.99999 | 0.4188    1.00000 | 0.1851      
[Conv] /model.24/m.0/Conv_output_0                 1.00000 | 0.9307    1.00000 | 0.5451
[Sigmoid] output3                                  1.00000 | 0.0790    1.00000 | 0.0359        1.00000 | 0.6391    1.00000 | 0.3722      
I The error analysis results save to: ./snapshot/error_analysis.txt
W accuracy_analysis: The mapping of layer_name & file_name save to: ./snapshot/map_name_to_file.txt
done
--> Init runtime environment
adb: unable to connect for root: closed
I target set by user is: rk3588
I Get hardware info: target_platform = rk3588, os = Linux, aarch = aarch64
I Check RK3588 board npu runtime version
I Starting ntp or adb, target is RK3588
I Start adb...
I Connect to Device success!
I NPUTransfer: Starting NPU Transfer Client, Transfer version 2.1.0 (b5861e7@2020-11-23T11:50:36)
D RKNNAPI: ==============================================
D RKNNAPI: RKNN VERSION:
D RKNNAPI:   API: 2.0.0b0 (18eacd0 build@2024-03-22T06:07:59)
D RKNNAPI:   DRV: rknn_server: 2.0.0b0 (18eacd0 build@2024-03-22T14:07:19)
D RKNNAPI:   DRV: rknnrt: 2.0.0b0 (35a6907d79@2024-03-24T10:31:14)
D RKNNAPI: ==============================================
D RKNNAPI: Input tensors:
D RKNNAPI:   index=0, name=images, n_dims=4, dims=[1, 640, 640, 3], n_elems=1228800, size=2457600, w_stride = 0, size_with_stride = 0, fmt=NHWC, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
D RKNNAPI: Output tensors:
D RKNNAPI:   index=0, name=output1, n_dims=4, dims=[1, 18, 20, 20], n_elems=7200, size=14400, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
D RKNNAPI:   index=1, name=output2, n_dims=4, dims=[1, 18, 40, 40], n_elems=28800, size=57600, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
D RKNNAPI:   index=2, name=output3, n_dims=4, dims=[1, 18, 80, 80], n_elems=115200, size=230400, w_stride = 0, size_with_stride = 0, fmt=NCHW, type=FP16, qnt_type=NONE, zp=0, scale=1.000000
done

我的yolov5s模型是在自制训练集上训练得到的，在pytorch的fp32推理精度是81.8，fp16的推理精度是77.5，我翻阅了官方文档，没有找到解释这个现象的原因。所以fp16相较于fp32在rk3588上通过adb连板推理掉了4个点这个现象正常吗？