C:\Users\Administrator>for /L %i IN (1,1,254) DO ping -w 2 -n 1 192.168.10.%i

C:\Users\Administrator>arp -aInterface: 192.168.10.155 --- 0x9Internet Address      Physical Address      Type192.168.10.1          ff-ff-ff-ff-ff-ff     dynamic192.168.10.117        ff-ff-ff-ff-ff-ff     dynamic192.168.10.134        ff-ff-ff-ff-ff-ff     dynamic192.168.10.210        ff-ff-ff-ff-ff-ff     dynamic192.168.10.255        ff-ff-ff-ff-ff-ff     static224.0.0.2             01-00-5e-00-00-02     static224.0.0.22            01-00-5e-00-00-16     static224.0.0.251           01-00-5e-00-00-fb     static224.0.0.252           01-00-5e-00-00-fc     static239.255.255.250       ff-ff-ff-ff-ff-ff     static255.255.255.255       ff-ff-ff-ff-ff-ff     static

ubuntu@sxj731533730:~$ ssh firefly@192.168.10.210
firefly@192.168.10.210's password:
Linux firefly 4.19.111 #1 SMP PREEMPT Thu Jul 8 17:38:26 CST 2021 armv7lThe programs included with the Debian GNU/Linux system are free software;
the exact distribution terms for each program are described in the
individual files in /usr/share/doc/*/copyright.Debian GNU/Linux comes with ABSOLUTELY NO WARRANTY, to the extent
permitted by applicable law.
firefly@firefly:~$

firefly@firefly:~$ sudo apt-get update && sudo apt-get install python3-pip
firefly@firefly:~$ pip3 install numpy==1.16.3  psutil==5.6.2 ruamel.yaml==0.15.81 -i https://pypi.tuna.tsinghua.edu.cn/simple
firefly@firefly:~$ sudo apt-get install wget git cmake
firefly@firefly:~$ sudo apt-get install multiarch-support
firefly@firefly:~$ wget http://security.debian.org/debian-security/pool/updates/main/j/jasper/libjasper1_1.900.1-debian1-2.4+deb8u6_armhf.deb
firefly@firefly:~$ sudo dpkg -i libjasper1_1.900.1-debian1-2.4+deb8u6_armhf.deb
firefly@firefly:~$ wget http://security.debian.org/debian-security/pool/updates/main/j/jasper/libjasper-dev_1.900.1-debian1-2.4+deb8u6_armhf.deb
firefly@firefly:~$ sudo dpkg -i libjasper-dev_1.900.1-debian1-2.4+deb8u6_armhf.deb
firefly@firefly:~$ sudo apt-get install libhdf5-dev
firefly@firefly:~$ sudo apt-get install libatlas-base-dev
firefly@firefly:~$ sudo apt-get install libqtgui4
firefly@firefly:~$ sudo apt-get install libqt4-test
firefly@firefly:~$ git clone https://github.com/rockchip-linux/rknn-toolkit.git
firefly@firefly:~$ wget https://www.piwheels.org/simple/opencv-python/opencv_python-4.0.1.24-cp37-cp37m-linux_armv7l.whl
firefly@firefly:~$ pip3 install opencv_python-4.0.1.24-cp37-cp37m-linux_armv7l.whl
Processing ./opencv_python-4.0.1.24-cp37-cp37m-linux_armv7l.whl
Requirement already satisfied: numpy>=1.16.2 in ./.local/lib/python3.7/site-packages (from opencv-python==4.0.1.24) (1.16.3)
Installing collected packages: opencv-python
Successfully installed opencv-python-4.0.1.24
firefly@firefly:~/rknn-toolkit/rknn-toolkit-lite/packages$ pip3 install rknn_toolkit_lite-1.7.1-cp37-cp37m-linux_armv7l.whl
Processing ./rknn_toolkit_lite-1.7.1-cp37-cp37m-linux_armv7l.whl
Requirement already satisfied: ruamel.yaml==0.15.81 in /home/firefly/.local/lib/python3.7/site-packages (from rknn-toolkit-lite==1.7.1) (0.15.81)
Requirement already satisfied: numpy==1.16.3 in /home/firefly/.local/lib/python3.7/site-packages (from rknn-toolkit-lite==1.7.1) (1.16.3)
Requirement already satisfied: psutil==5.6.2 in /home/firefly/.local/lib/python3.7/site-packages (from rknn-toolkit-lite==1.7.1) (5.6.2)
Installing collected packages: rknn-toolkit-lite
Successfully installed rknn-toolkit-lite-1.7.1
firefly@firefly:~$ python3
Python 3.7.3 (default, Jan 22 2021, 20:04:44)
[GCC 8.3.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from rknnlite.api import RKNNLite
>>>

ubuntu@ubuntu:~$ wget https://mirrors.tuna.tsinghua.edu.cn/anaconda/miniconda/Miniconda3-latest-Linux-x86_64.sh
ubuntu@ubuntu:~$ bash Miniconda3-latest-Linux-x86_64.sh
ubuntu@ubuntu:~$ source ~/.bashrc
ubuntu@ubuntu:~$ conda config --set auto_activate_base false
ubuntu@ubuntu:~$ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/free/
ubuntu@ubuntu:~$ conda config --add channels https://mirrors.tuna.tsinghua.edu.cn/anaconda/pkgs/main/
ubuntu@ubuntu:~$ conda config --set show_channel_urls yes
ubuntu@ubuntu:~$ conda create -n rknnpy36 python=3.6
ubuntu@ubuntu:~$ conda activate rknnpy36
(rknnpy36) ubuntu@ubuntu:~$ git clone https://github.com/rockchip-linux/rknn-toolkit.git
(rknnpy36) ubuntu@ubuntu:~/Downloads/rknn-toolkit-master/packages$ pip3 install -r requirements-cpu.txt  -i https://pypi.tuna.tsinghua.edu.cn/simple
(rknnpy36) ubuntu@ubuntu:~/Downloads/rknn-toolkit-master/packages$ pip3 install -r requirements-cpu.txt
(rknnpy36) ubuntu@ubuntu:~/Downloads/rknn-toolkit-master/rknn-toolkit-lite/packages$ (rknnpy36) ubuntu@ubuntu:~/rknn-toolkit/rknn-toolkit-lite/packages$ pip3 install rknn_toolkit_lite-1.6.1-cp36-cp36m-linux_x86_64.whl  -i https://pypi.tuna.tsinghua.edu.cn/simple
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simpleProcessing ./rknn_toolkit_lite-1.7.1-cp36-cp36m-linux_x86_64.whl
Requirement already satisfied: ruamel.yaml==0.15.81 in /home/ubuntu/miniconda3/envs/rknnpy36/lib/python3.6/site-packages (from rknn-toolkit-lite==1.7.1) (0.15.81)
Requirement already satisfied: numpy==1.16.3 in /home/ubuntu/miniconda3/envs/rknnpy36/lib/python3.6/site-packages (from rknn-toolkit-lite==1.7.1) (1.16.3)
Requirement already satisfied: psutil==5.6.2 in /home/ubuntu/miniconda3/envs/rknnpy36/lib/python3.6/site-packages (from rknn-toolkit-lite==1.7.1) (5.6.2)
Installing collected packages: rknn-toolkit-lite
Successfully installed rknn-toolkit-lite-1.7.1(rknnpy36) ubuntu@ubuntu:~/Downloads$ wget http://repo.rock-chips.com/pypi/simple/rknn-toolkit/rknn_toolkit-1.7.1-cp36-cp36m-linux_x86_64.whl(rknnpy36) ubuntu@ubuntu:~/rknn-toolkit/rknn-toolkit-lite/packages$ pip3 install rknn_toolkit-1.7.1-cp36-cp36m-linux_x86_64.whl  -i https://pypi.tuna.tsinghua.edu.cn/simple(rknnpy36) ubuntu@ubuntu:~/Downloads$ python3
Python 3.6.13 |Anaconda, Inc.| (default, Jun  4 2021, 14:25:59)
[GCC 7.5.0] on linux
Type "help", "copyright", "credits" or "license" for more information.
>>> from rknn.api import RKNN
>>> from rknnlite.api import RKNNLite
>>>

ubuntu@ubuntu:~$ source ~/.bashrc
ubuntu@ubuntu:~$ conda activate rknnpy36
(rknnpy36) ubuntu@ubuntu:~$ cd rknn-toolkit/examples/onnx/yolov5
(rknnpy36) ubuntu@ubuntu:~/rknn-toolkit/examples/onnx/yolov5$ python3 test.py
--> Config model
done
--> Loading model
W Call onnx.optimizer.optimize fail, skip optimize
W Please confirm that your onnx opset_version <= 11 (current opset_verison = 12)!!!

firefly@firefly:~/rknn-toolkit/rknn-toolkit-lite/examples/inference_with_lite$ python3 test.py
--> Load RKNN model
done
--> Init runtime environment
E [vnn_CreateRKNN:5512]rknn model target platform[0] is RK1808, while current platform is RK1109
Create RKNN model fail, error=-13
E Catch exception when init runtime!
E Traceback (most recent call last):File "/home/firefly/.local/lib/python3.7/site-packages/rknnlite/api/rknn_lite.py", line 157, in init_runtimeself.rknn_runtime.build_graph(self.rknn_data, self.load_model_in_npu)File "rknnlite/api/rknn_runtime.py", line 733, in rknnlite.api.rknn_runtime.RKNNRuntime.build_graph
Exception: RKNN init failed. error code: RKNN_ERR_TARGET_PLATFORM_UNMATCHInit runtime environment failed

    rknn.config(reorder_channel='0 1 2',mean_values=[[0, 0, 0]],std_values=[[255, 255, 255]],optimization_level=3,target_platform = 'rk1808',output_optimize=1,quantize_input_node=QUANTIZE_ON)

    rknn.config(reorder_channel='0 1 2',mean_values=[[0, 0, 0]],std_values=[[255, 255, 255]],optimization_level=3,target_platform = 'rv1126',output_optimize=1,quantize_input_node=QUANTIZE_ON)

firefly@firefly:~/yolov5$ dpkg -l | grep npu
ii  libxi6:armhf                         2:1.7.9-1                               armhf        X11 Input extension library
ii  rknpu                                1.6.0                                   armhf        <rockchip rknpu package>

import os
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
from rknnlite.api import RKNNLite
import timeONNX_MODEL = 'yolov5s.onnx'
RKNN_MODEL = 'yolov5s.rknn'
IMG_PATH = 'bus.jpg'
DATASET = 'dataset.txt'QUANTIZE_ON = TrueBOX_THRESH = 0.5
NMS_THRESH = 0.6
IMG_SIZE = 640CLASSES = ("person", "bicycle", "car","motorbike ","aeroplane ","bus ","train","truck ","boat","traffic light","fire hydrant","stop sign ","parking meter","bench","bird","cat","dog ","horse ","sheep","cow","elephant","bear","zebra ","giraffe","backpack","umbrella","handbag","tie","suitcase","frisbee","skis","snowboard","sports ball","kite","baseball bat","baseball glove","skateboard","surfboard","tennis racket","bottle","wine glass","cup","fork","knife ","spoon","bowl","banana","apple","sandwich","orange","broccoli","carrot","hot dog","pizza ","donut","cake","chair","sofa","pottedplant","bed","diningtable","toilet ","tvmonitor","laptop  ","mouse  ","remote ","keyboard ","cell phone","microwave ","oven ","toaster","sink","refrigerator ","book","clock","vase","scissors ","teddy bear ","hair drier", "toothbrush ")def sigmoid(x):return 1 / (1 + np.exp(-x))def xywh2xyxy(x):# Convert [x, y, w, h] to [x1, y1, x2, y2]y = np.copy(x)y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left xy[:, 1] = x[:, 1] - x[:, 3] / 2  # top left yy[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right xy[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right yreturn ydef process(input, mask, anchors):anchors = [anchors[i] for i in mask]grid_h, grid_w = map(int, input.shape[0:2])box_confidence = sigmoid(input[..., 4])box_confidence = np.expand_dims(box_confidence, axis=-1)box_class_probs = sigmoid(input[..., 5:])box_xy = sigmoid(input[..., :2])*2 - 0.5col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)grid = np.concatenate((col, row), axis=-1)box_xy += gridbox_xy *= int(IMG_SIZE/grid_h)box_wh = pow(sigmoid(input[..., 2:4])*2, 2)box_wh = box_wh * anchorsbox = np.concatenate((box_xy, box_wh), axis=-1)return box, box_confidence, box_class_probsdef filter_boxes(boxes, box_confidences, box_class_probs):"""Filter boxes with box threshold. It's a bit different with origin yolov5 post process!# Argumentsboxes: ndarray, boxes of objects.box_confidences: ndarray, confidences of objects.box_class_probs: ndarray, class_probs of objects.# Returnsboxes: ndarray, filtered boxes.classes: ndarray, classes for boxes.scores: ndarray, scores for boxes."""box_classes = np.argmax(box_class_probs, axis=-1)box_class_scores = np.max(box_class_probs, axis=-1)pos = np.where(box_confidences[...,0] >= BOX_THRESH)boxes = boxes[pos]classes = box_classes[pos]scores = box_class_scores[pos]return boxes, classes, scoresdef nms_boxes(boxes, scores):"""Suppress non-maximal boxes.# Argumentsboxes: ndarray, boxes of objects.scores: ndarray, scores of objects.# Returnskeep: ndarray, index of effective boxes."""x = boxes[:, 0]y = boxes[:, 1]w = boxes[:, 2] - boxes[:, 0]h = boxes[:, 3] - boxes[:, 1]areas = w * horder = scores.argsort()[::-1]keep = []while order.size > 0:i = order[0]keep.append(i)xx1 = np.maximum(x[i], x[order[1:]])yy1 = np.maximum(y[i], y[order[1:]])xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)inter = w1 * h1ovr = inter / (areas[i] + areas[order[1:]] - inter)inds = np.where(ovr <= NMS_THRESH)[0]order = order[inds + 1]keep = np.array(keep)return keepdef yolov5_post_process(input_data):masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],[59, 119], [116, 90], [156, 198], [373, 326]]boxes, classes, scores = [], [], []for input,mask in zip(input_data, masks):b, c, s = process(input, mask, anchors)b, c, s = filter_boxes(b, c, s)boxes.append(b)classes.append(c)scores.append(s)boxes = np.concatenate(boxes)boxes = xywh2xyxy(boxes)classes = np.concatenate(classes)scores = np.concatenate(scores)nboxes, nclasses, nscores = [], [], []for c in set(classes):inds = np.where(classes == c)b = boxes[inds]c = classes[inds]s = scores[inds]keep = nms_boxes(b, s)nboxes.append(b[keep])nclasses.append(c[keep])nscores.append(s[keep])if not nclasses and not nscores:return None, None, Noneboxes = np.concatenate(nboxes)classes = np.concatenate(nclasses)scores = np.concatenate(nscores)return boxes, classes, scoresdef draw(image, boxes, scores, classes):"""Draw the boxes on the image.# Argument:image: original image.boxes: ndarray, boxes of objects.classes: ndarray, classes of objects.scores: ndarray, scores of objects.all_classes: all classes name."""for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom = boxprint('class: {}, score: {}'.format(CLASSES[cl], score))print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))top = int(top)left = int(left)right = int(right)bottom = int(bottom)cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),(top, left - 6),cv2.FONT_HERSHEY_SIMPLEX,0.6, (0, 0, 255), 2)def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]if isinstance(new_shape, int):new_shape = (new_shape, new_shape)# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingratio = r, r  # width, height ratiosnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh paddingdw /= 2  # divide padding into 2 sidesdh /= 2if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn im, ratio, (dw, dh)if __name__ == '__main__':# Create RKNN objectrknn = RKNNLite()# load RKNN modelprint('--> Load RKNN model')ret = rknn.load_rknn('./yolov5s.rknn')if ret != 0:print('Load RKNN model failed')exit(ret)print('done')# init runtime environmentprint('--> Init runtime environment')#ret = rknn.init_runtime()ret = rknn.init_runtime('rv1126', device_id='rv1126')if ret != 0:print('Init runtime environment failed')exit(ret)print('done')start = time.time()# Set inputsimg = cv2.imread(IMG_PATH)# img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)img = cv2.resize(img,(IMG_SIZE, IMG_SIZE))# Inferenceprint('--> Running model')outputs = rknn.inference(inputs=[img])# post processinput0_data = outputs[0]input1_data = outputs[1]input2_data = outputs[2]input0_data = input0_data.reshape([3,-1]+list(input0_data.shape[-2:]))input1_data = input1_data.reshape([3,-1]+list(input1_data.shape[-2:]))input2_data = input2_data.reshape([3,-1]+list(input2_data.shape[-2:]))input_data = list()input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))boxes, classes, scores = yolov5_post_process(input_data)img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)if boxes is not None:draw(img_1, boxes, scores, classes)end = time.time()print (end-start,"s")cv2.imwrite("result.jpg", img_1)rknn.release()

firefly@firefly:~/yolov5$ sudo python3 test.py
--> Load RKNN model
done
--> Init runtime environment
done
--> Running model
class: person, score: 0.9981504082679749
box coordinate left,top,right,down: [479.5712496638298, 257.1674613952637, 560.4287503361702, 516.2188911437988]
class: person, score: 0.9968554973602295
box coordinate left,top,right,down: [110.34747083485126, 229.37330585718155, 220.6373619288206, 531.1496433615685]
class: person, score: 0.9822962880134583
box coordinate left,top,right,down: [209.4318208694458, 244.13265788555145, 287.1548795700073, 506.1707478761673]
class: bus , score: 0.9902657866477966
box coordinate left,top,right,down: [90.29679995775223, 138.5124049782753, 552.5043475031853, 440.2887424826622]
0.619182825088501 s

ubuntu@ubuntu:~/yolov5-5.0$ python3 train.py --img 640 --batch 16 --epoch 10 --data data/trainData.yaml  --cfg models/yolov5s.yaml --weights weights/yolov5s.pt --device "0"
ubuntu@ubuntu:~/yolov5-5.0$ python3 export.py --weights runs/train/exp3/weights/best.pt  --img 640 --batch 1

from rknn.api import RKNNONNX_MODEL = '/home/ubuntu/yolov5-5.0/runs/train/exp3/weights/best.onnx'
RKNN_MODEL = 'yolov5s.rknn'if __name__ == '__main__':# Create RKNN objectrknn = RKNN(verbose=True)# pre-process configprint('--> config model')rknn.config(mean_values=[[0, 0, 0]], std_values=[[255, 255, 255]], reorder_channel='0 1 2',target_platform='rv1126',quantized_dtype='asymmetric_affine-u8', optimization_level=3, output_optimize=1)print('done')print('--> Loading model')ret = rknn.load_onnx(model=ONNX_MODEL)if ret != 0:print('Load model  failed!')exit(ret)print('done')# Build modelprint('--> Building model')ret = rknn.build(do_quantization=True, dataset='/home/ubuntu/yolov5-5.0/dataset.txt')  # ,pre_compile=Trueif ret != 0:print('Build yolov5s failed!')exit(ret)print('done')# Export rknn modelprint('--> Export RKNN model')ret = rknn.export_rknn(RKNN_MODEL)if ret != 0:print('Export yolov5s.rknn failed!')exit(ret)print('done')rknn.release()

x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
修改为
x[i] = x[i].view(bs, self.na, self.no, ny, nx)

import os
import urllib
import traceback
import time
import sys
import numpy as np
import cv2
from rknn.api import RKNNRKNN_MODEL = '/home/ubuntu/yolov5-5.0/yolov5s.rknn'
IMG_PATH = 'dog.jpg'QUANTIZE_ON = TrueBOX_THRESH = 0.5
NMS_THRESH = 0.6
IMG_SIZE = 640CLASSES = ("drug",
"battery",
"apple",
"bottle",
"banana",
"smoke",
"ylg")def sigmoid(x):return 1 / (1 + np.exp(-x))def xywh2xyxy(x):# Convert [x, y, w, h] to [x1, y1, x2, y2]y = np.copy(x)y[:, 0] = x[:, 0] - x[:, 2] / 2  # top left xy[:, 1] = x[:, 1] - x[:, 3] / 2  # top left yy[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right xy[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right yreturn ydef resize_postprocess(x, offset_x, offset_y):# Convert [x1, y1, x2, y2] to [x1, y1, x2, y2]y = np.copy(x)y[:, 0] = x[:, 0] / offset_x  # top left xy[:, 1] = x[:, 1] / offset_y  # top left yy[:, 2] = x[:, 2] / offset_x  # bottom right xy[:, 3] = x[:, 3] / offset_y  # bottom right yreturn ydef process(input, mask, anchors):anchors = [anchors[i] for i in mask]grid_h, grid_w = map(int, input.shape[0:2])box_confidence = sigmoid(input[..., 4])box_confidence = np.expand_dims(box_confidence, axis=-1)box_class_probs = sigmoid(input[..., 5:])box_xy = sigmoid(input[..., :2]) * 2 - 0.5col = np.tile(np.arange(0, grid_w), grid_w).reshape(-1, grid_w)row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_h)col = col.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)row = row.reshape(grid_h, grid_w, 1, 1).repeat(3, axis=-2)grid = np.concatenate((col, row), axis=-1)box_xy += gridbox_xy *= int(IMG_SIZE / grid_h)box_wh = pow(sigmoid(input[..., 2:4]) * 2, 2)box_wh = box_wh * anchorsbox = np.concatenate((box_xy, box_wh), axis=-1)return box, box_confidence, box_class_probsdef filter_boxes(boxes, box_confidences, box_class_probs):"""Filter boxes with box threshold. It's a bit different with origin yolov5 post process!# Argumentsboxes: ndarray, boxes of objects.box_confidences: ndarray, confidences of objects.box_class_probs: ndarray, class_probs of objects.# Returnsboxes: ndarray, filtered boxes.classes: ndarray, classes for boxes.scores: ndarray, scores for boxes."""box_classes = np.argmax(box_class_probs, axis=-1)box_class_scores = np.max(box_class_probs, axis=-1)pos = np.where(box_confidences[..., 0] >= BOX_THRESH)boxes = boxes[pos]classes = box_classes[pos]scores = box_class_scores[pos]return boxes, classes, scoresdef nms_boxes(boxes, scores):"""Suppress non-maximal boxes.# Argumentsboxes: ndarray, boxes of objects.scores: ndarray, scores of objects.# Returnskeep: ndarray, index of effective boxes."""x = boxes[:, 0]y = boxes[:, 1]w = boxes[:, 2] - boxes[:, 0]h = boxes[:, 3] - boxes[:, 1]areas = w * horder = scores.argsort()[::-1]keep = []while order.size > 0:i = order[0]keep.append(i)xx1 = np.maximum(x[i], x[order[1:]])yy1 = np.maximum(y[i], y[order[1:]])xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)inter = w1 * h1ovr = inter / (areas[i] + areas[order[1:]] - inter)inds = np.where(ovr <= NMS_THRESH)[0]order = order[inds + 1]keep = np.array(keep)return keepdef yolov5_post_process(input_data):masks = [[0, 1, 2], [3, 4, 5], [6, 7, 8]]anchors = [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45],[59, 119], [116, 90], [156, 198], [373, 326]]boxes, classes, scores = [], [], []for input, mask in zip(input_data, masks):b, c, s = process(input, mask, anchors)b, c, s = filter_boxes(b, c, s)boxes.append(b)classes.append(c)scores.append(s)boxes = np.concatenate(boxes)boxes = xywh2xyxy(boxes)classes = np.concatenate(classes)scores = np.concatenate(scores)nboxes, nclasses, nscores = [], [], []for c in set(classes):inds = np.where(classes == c)b = boxes[inds]c = classes[inds]s = scores[inds]keep = nms_boxes(b, s)nboxes.append(b[keep])nclasses.append(c[keep])nscores.append(s[keep])if not nclasses and not nscores:return None, None, Noneboxes = np.concatenate(nboxes)classes = np.concatenate(nclasses)scores = np.concatenate(nscores)return boxes, classes, scoresdef draw(image, boxes, scores, classes):"""Draw the boxes on the image.# Argument:image: original image.boxes: ndarray, boxes of objects.classes: ndarray, classes of objects.scores: ndarray, scores of objects.all_classes: all classes name."""for box, score, cl in zip(boxes, scores, classes):top, left, right, bottom = boxprint('class: {}, score: {}'.format(CLASSES[cl], score))print('box coordinate left,top,right,down: [{}, {}, {}, {}]'.format(top, left, right, bottom))top = int(top)left = int(left)right = int(right)bottom = int(bottom)cv2.rectangle(image, (top, left), (right, bottom), (255, 0, 0), 2)cv2.putText(image, '{0} {1:.2f}'.format(CLASSES[cl], score),(top, left - 6),cv2.FONT_HERSHEY_SIMPLEX,0.6, (0, 0, 255), 2)def letterbox(im, new_shape=(640, 640), color=(0, 0, 0)):# Resize and pad image while meeting stride-multiple constraintsshape = im.shape[:2]  # current shape [height, width]if isinstance(new_shape, int):new_shape = (new_shape, new_shape)# Scale ratio (new / old)r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])# Compute paddingratio = r, r  # width, height ratiosnew_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1]  # wh paddingdw /= 2  # divide padding into 2 sidesdh /= 2if shape[::-1] != new_unpad:  # resizeim = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))left, right = int(round(dw - 0.1)), int(round(dw + 0.1))im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add borderreturn im, ratio, (dw, dh)def letter_box_postprocess(x, scalingfactor, xy_correction):y = np.copy(x)y[:, 0] = (x[:, 0] - xy_correction[0]) / scalingfactor  # top left xy[:, 1] = (x[:, 1] - xy_correction[1]) / scalingfactor  # top left yy[:, 2] = (x[:, 2] - xy_correction[0]) / scalingfactor  # bottom right xy[:, 3] = (x[:, 3] - xy_correction[1]) / scalingfactor  # bottom right yreturn ydef get_file(filepath):templist = []with open(filepath, "r") as f:for item in f.readlines():templist.append(item.strip())return templistif __name__ == '__main__':# Create RKNN objectrknn = RKNN()image_process_mode = "letter_box"print("image_process_mode = ", image_process_mode)if not os.path.exists(RKNN_MODEL):print('model not exist')exit(-1)# Load ONNX modelprint('--> Loading model')ret = rknn.load_rknn(RKNN_MODEL)if ret != 0:print('Load rknn model failed!')exit(ret)print('done')# init runtime environmentprint('--> Init runtime environment')ret = rknn.init_runtime()# ret = rknn.init_runtime('rk180_8', device_id='1808')if ret != 0:print('Init runtime environment failed')exit(ret)print('done')image = cv2.imread("/home/ubuntu/yolov5-5.0/trainData/images/dc1_02.jpg")img_height = image.shape[0]img_width = image.shape[1]# img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))img = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)if image_process_mode == "resize":img = cv2.resize(img, (IMG_SIZE, IMG_SIZE))elif image_process_mode == "letter_box":img, scale_factor, correction = letterbox(img)# Inferenceprint('--> Running model')outputs = rknn.inference(inputs=[img])# post processinput0_data = outputs[0]input1_data = outputs[1]input2_data = outputs[2]input0_data = input0_data.reshape([3, -1] + list(input0_data.shape[-2:]))input1_data = input1_data.reshape([3, -1] + list(input1_data.shape[-2:]))input2_data = input2_data.reshape([3, -1] + list(input2_data.shape[-2:]))input_data = list()input_data.append(np.transpose(input0_data, (2, 3, 0, 1)))input_data.append(np.transpose(input1_data, (2, 3, 0, 1)))input_data.append(np.transpose(input2_data, (2, 3, 0, 1)))boxes, classes, scores = yolov5_post_process(input_data)if image_process_mode == "resize":scale_h = IMG_SIZE / img_heightscale_w = IMG_SIZE / img_widthboxes = resize_postprocess(boxes, scale_w, scale_h)elif image_process_mode == "letter_box":boxes = letter_box_postprocess(boxes, scale_factor[0], correction)# img_1 = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)if boxes is not None:draw(image, boxes, scores, classes)cv2.imwrite("image.jpg", image)rknn.release()

/home/ubuntu/miniconda3/envs/rknnpy36/bin/python /home/ubuntu/yolov5-5.0/rknn_detect.py
image_process_mode =  letter_box
--> Loading model
done
--> Init runtime environment
librknn_runtime version 1.7.1 (bd41dbc build: 2021-10-28 16:15:23 base: 1131)
done
--> Running model
class: battery, score: 0.9596196413040161
box coordinate left,top,right,down: [100.91769161224364, 57.873418962955476, 129.09340457916258, 105.65749076604843]Process finished with exit code 0

ubuntu@ubuntu:~$ git clone https://github.com/littledeep/YOLOv5-RK3399Pro
ubuntu@ubuntu:~/YOLOv5-RK3399Pro$ python3 train.py --img 640 --batch 16 --epoch 10 --data data/trainData.yaml --hyp data/hyp.scratch.yaml --cfg models/yolov5s.yaml --weights weights/yolov5s.pt --device "0"

https://github.com/ultralytics/yolov5/blob/v4.0/data/hyp.scratch.yaml

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size199/199     2.33G   0.01391  0.006809 0.0007568   0.02147        23       640: 100%|█| 106/106 [00:17<00:00, Class      Images      Labels           P           R      mAP@.5  mAP@.5:.95: 100%|█| 53/53 [00:all        1692        1692       0.999       0.999       0.996        0.92drug        1692         359           1           1       0.997       0.937battery        1692         157       0.999           1       0.995       0.889apple        1692         116       0.998           1       0.996       0.959bottle        1692         358           1           1       0.996       0.898banana        1692         225           1       0.996       0.996       0.918smoke        1692         107           1           1       0.996       0.894ylg        1692         370           1           1       0.996       0.942
200 epochs completed in 1.518 hours.Optimizer stripped from runs/train/exp6/weights/last.pt, 14.4MB
Optimizer stripped from runs/train/exp6/weights/best.pt, 14.4MB

parser.add_argument('--grid', default=False, action='store_true', help='export Detect() layer grid')

ubuntu@ubuntu:~/YOLOv5-RK3399Pro$ cp models/export.py .
ubuntu@ubuntu:~/YOLOv5-RK3399Pro$ python3 export.py --weights /home/ubuntu/YOLOv5-RK3399Pro/runs/train/exp6/weights/best.pt --rknn_mode --img 640 --batch 1

import yaml
from rknn.api import RKNN
import cv2_model_load_dict = {'caffe': 'load_caffe','tensorflow': 'load_tensorflow','tflite': 'load_tflite','onnx': 'load_onnx','darknet': 'load_darknet','pytorch': 'load_pytorch','mxnet': 'load_mxnet','rknn': 'load_rknn',}yaml_file = './config.yaml'def main():with open(yaml_file, 'r') as F:config = yaml.safe_load(F)# print('config is:')# print(config)model_type = config['running']['model_type']print('model_type is {}'.format(model_type))#检查模型的类型rknn = RKNN(verbose=True)#配置文件print('--> config model')rknn.config(**config['config'])print('done')print('--> Loading model')load_function = getattr(rknn, _model_load_dict[model_type])ret = load_function(**config['parameters'][model_type])if ret != 0:print('Load yolo failed! Ret = {}'.format(ret))exit(ret)print('done')#####print('hybrid_quantization')#ret = rknn.hybrid_quantization_step1(dataset=config['build']['dataset'])if model_type != 'rknn':print('--> Building model')ret = rknn.build(**config['build'])print('acc_eval')rknn.accuracy_analysis(inputs='/home/ubuntu/YOLOv5-RK3399Pro/convert/dataset 1.txt', target='rk1126')#根据个人开发板设置print('acc_eval done!')if ret != 0:print('Build yolo failed!')exit(ret)else:print('--> skip Building model step, cause the model is already rknn')#导出RKNN模型if config['running']['export'] is True:print('--> Export RKNN model')ret = rknn.export_rknn(**config['export_rknn'])if ret != 0:print('Init runtime environment failed1')exit(ret)else:print('--> skip Export model')#初始化print('--> Init runtime environment2')ret = rknn.init_runtime()if ret != 0:print('Init runtime environment failed2')exit(ret)print('done')print('--> load img')img = cv2.imread(config['img']['path'])print('img shape is {}'.format(img.shape))img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)inputs = [img]print(inputs[0][0:10,0,0])
#推理if config['running']['inference'] is True:print('--> Running model')config['inference']['inputs'] = inputs#print(config['inference'])outputs = rknn.inference(inputs)#outputs = rknn.inference(config['inference'])print('len of output {}'.format(len(outputs)))print('outputs[0] shape is {}'.format(outputs[0].shape))print(outputs[0][0][0:2])else:print('--> skip inference')
#评价if config['running']['eval_perf'] is True:print('--> Begin evaluate model performance')config['inference']['inputs'] = inputsperf_results = rknn.eval_perf(inputs=[img])else:print('--> skip eval_perf')if __name__ == '__main__':main()

  onnx:model: '/home/ubuntu/YOLOv5-RK3399Pro/runs/train/exp6/weights/best.onnx'

build:do_quantization: Truedataset: '/home/ubuntu/YOLOv5-RK3399Pro/convert/dataset 1.txt' # '/home/zen/rknn_convert/quant_data/hand_dataset/pic_path_less.txt'pre_compile: False

export_rknn:export_path: './best_noop1.rknn'

img: &imgpath: '/home/ubuntu/YOLOv5-RK3399Pro/trainData/images/yp4_09.jpg'

config:#mean_value: [[0,0,0]]#std_value: [[58.82,58.82,58.82]]channel_mean_value: '0 0 0 255' # 123.675 116.28 103.53 58.395 # 0 0 0 255reorder_channel: '0 1 2' # '2 1 0'need_horizontal_merge: Falsebatch_size: 20epochs: -1target_platform: ['rk1126']quantized_dtype: 'asymmetric_quantized-u8'
#asymmetric_quantized-u8,dynamic_fixed_point-8,dynamic_fixed_point-16optimization_level: 1

ubuntu@ubuntu:~/YOLOv5-RK3399Pro$ conda activate rknnpy36
(rknnpy36) ubuntu@ubuntu:~/YOLOv5-RK3399Pro$ cd convert/
(rknnpy36) ubuntu@ubuntu:~/YOLOv5-RK3399Pro/convert$ python3 rknn_convert.pyubuntu@ubuntu:~/YOLOv5-RK3399Pro$ conda activate rknnpy36
(rknnpy36) ubuntu@ubuntu:~/YOLOv5-RK3399Pro$ cd convert/
(rknnpy36) ubuntu@ubuntu:~/YOLOv5-RK3399Pro/convert$ python3 rknn_convert.py
.....
D 546(36.76%), Queue size 1
D 547(36.82%), Queue size 1
D 548(36.89%), Queue size 1
D 549(36.96%), Queue size 1
D 550(37.02%), Queue size 1
D 551(37.09%), Queue size 1
D 552(37.16%), Queue size 1
D 553(37.23%), Queue size 1
D 554(37.29%), Queue size 1
D 555(37.36%), Queue size 1
D 556(37.43%), Queue size 1
D 557(37.50%), Queue size 1
D 558(37.56%), Queue size 1
D 559(37.63%), Queue size 1
D 560(37.70%), Queue size 1
....
10          convolution.relu.pooling.layer2_2            143
4           openvx.tensor_transpose_3                    16
40          convolution.relu.pooling.layer2_2            1016
22          convolution.relu.pooling.layer2_2            325
48          convolution.relu.pooling.layer2_2            325
33          convolution.relu.pooling.layer2_2            82
21          convolution.relu.pooling.layer2_2            360
12          convolution.relu.pooling.layer2_2            208
9           convolution.relu.pooling.layer2_2            98
3           openvx.tensor_transpose_3                    7
Total Time(us): 31431
FPS(600MHz): 23.86
FPS(800MHz): 31.82
Note: Time of each layer is converted according to 800MHz!
========================================================================

class: rknn_detect_yolov5.Detector
opt:model: "/home/ubuntu/YOLOv5-RK3399Pro/convert/best_noop1.rknn"size: [ 640, 640 ]masks: [ [ 0, 1, 2 ], [ 3, 4, 5 ], [ 6, 7, 8 ] ]anchors: [ [ 10,13 ], [ 16,30 ], [ 33,23 ], [ 30,61 ], [ 62,45 ], [ 59,119 ], [ 116,90 ], [ 156,198 ], [ 373,326 ] ]names: [ "drug","battery","apple","bottle","banana","smoke","ylg" ]conf_thres: 0.3iou_thres: 0.5platform: 0

import cv2
import time
import random
import numpy as np
from rknn.api import RKNN"""
yolov5 官方原版 预测脚本 for rknn
"""def get_max_scale(img, max_w, max_h):h, w = img.shape[:2]scale = min(max_w / w, max_h / h, 1)return scaledef get_new_size(img, scale):return tuple(map(int, np.array(img.shape[:2][::-1]) * scale))def sigmoid(x):return 1 / (1 + np.exp(-x))def filter_boxes(boxes, box_confidences, box_class_probs, conf_thres):box_scores = box_confidences * box_class_probs  # 条件概率， 在该cell存在物体的概率的基础上是某个类别的概率box_classes = np.argmax(box_scores, axis=-1)  # 找出概率最大的类别索引box_class_scores = np.max(box_scores, axis=-1)  # 最大类别对应的概率值pos = np.where(box_class_scores >= conf_thres)  # 找出概率大于阈值的item# pos = box_class_scores >= OBJ_THRESH  # 找出概率大于阈值的itemboxes = boxes[pos]classes = box_classes[pos]scores = box_class_scores[pos]return boxes, classes, scoresdef nms_boxes(boxes, scores, iou_thres):x = boxes[:, 0]y = boxes[:, 1]w = boxes[:, 2]h = boxes[:, 3]areas = w * horder = scores.argsort()[::-1]keep = []while order.size > 0:i = order[0]keep.append(i)xx1 = np.maximum(x[i], x[order[1:]])yy1 = np.maximum(y[i], y[order[1:]])xx2 = np.minimum(x[i] + w[i], x[order[1:]] + w[order[1:]])yy2 = np.minimum(y[i] + h[i], y[order[1:]] + h[order[1:]])w1 = np.maximum(0.0, xx2 - xx1 + 0.00001)h1 = np.maximum(0.0, yy2 - yy1 + 0.00001)inter = w1 * h1ovr = inter / (areas[i] + areas[order[1:]] - inter)inds = np.where(ovr <= iou_thres)[0]order = order[inds + 1]keep = np.array(keep)return keepdef plot_one_box(x, img, color=None, label=None, line_thickness=None):tl = line_thickness or round(0.002 * (img.shape[0] + img.shape[1]) / 2) + 1  # line/font thicknesscolor = color or [random.randint(0, 255) for _ in range(3)]c1, c2 = (int(x[0]), int(x[1])), (int(x[2]), int(x[3]))cv2.rectangle(img, c1, c2, color, thickness=tl, lineType=cv2.LINE_AA)if label:tf = max(tl - 1, 1)  # font thicknesst_size = cv2.getTextSize(label, 0, fontScale=tl / 3, thickness=tf)[0]c2 = c1[0] + t_size[0], c1[1] - t_size[1] - 3cv2.rectangle(img, c1, c2, color, -1, cv2.LINE_AA)  # filledcv2.putText(img, label, (c1[0], c1[1] - 2), 0, tl / 3, [225, 255, 255], thickness=tf, lineType=cv2.LINE_AA)print(label)def auto_resize(img, max_w, max_h):h, w = img.shape[:2]scale = min(max_w / w, max_h / h, 1)new_size = tuple(map(int, np.array(img.shape[:2][::-1]) * scale))return cv2.resize(img, new_size), scaledef letterbox(img, new_wh=(640, 640), color=(114, 114, 114)):new_img, scale = auto_resize(img, *new_wh)shape = new_img.shapenew_img = cv2.copyMakeBorder(new_img, 0, new_wh[1] - shape[0], 0, new_wh[0] - shape[1], cv2.BORDER_CONSTANT,value=color)return new_img, (new_wh[0] / scale, new_wh[1] / scale)def load_model(model_path, npu_id):rknn = RKNN()print('-->loading model : ' + model_path)rknn.load_rknn(model_path)ret = rknn.init_runtime()if ret != 0:print('Init runtime environment failed')exit(ret)print('done')return rknnclass Detector:def __init__(self, opt):self.opt = optmodel = opt['model']wh = opt['size']masks = opt['masks']anchors = opt['anchors']names = opt['names']conf_thres = opt['conf_thres']iou_thres = opt['iou_thres']platform = opt['platform']self.wh = whself.size = whself._masks = masksself._anchors = anchorsself.names = list(filter(lambda a: len(a) > 0, map(lambda x: x.strip(), open(names, "r").read().split()))) if isinstance(names, str) else namesself.conf_thres = conf_thresself.iou_thres = iou_thresif isinstance(model, str):model = load_model(model, platform)self._rknn = modelself.draw_box = Falsedef _predict(self, img_src, img, gain):src_h, src_w = img_src.shape[:2]# _img = cv2.cvtColor(_img, cv2.COLOR_BGR2RGB)img = img[..., ::-1]  #img = np.concatenate([img[::2, ::2], img[1::2, ::2], img[::2, 1::2], img[1::2, 1::2]], 2)t0 = time.time()pred_onx = self._rknn.inference(inputs=[img])print("inference time:\t", time.time() - t0)boxes, classes, scores = [], [], []for t in range(3):input0_data = sigmoid(pred_onx[t][0])input0_data = np.transpose(input0_data, (1, 2, 0, 3))grid_h, grid_w, channel_n, predict_n = input0_data.shapeanchors = [self._anchors[i] for i in self._masks[t]]box_confidence = input0_data[..., 4]box_confidence = np.expand_dims(box_confidence, axis=-1)box_class_probs = input0_data[..., 5:]box_xy = input0_data[..., :2]box_wh = input0_data[..., 2:4]col = np.tile(np.arange(0, grid_w), grid_h).reshape(-1, grid_w)row = np.tile(np.arange(0, grid_h).reshape(-1, 1), grid_w)col = col.reshape((grid_h, grid_w, 1, 1)).repeat(3, axis=-2)row = row.reshape((grid_h, grid_w, 1, 1)).repeat(3, axis=-2)grid = np.concatenate((col, row), axis=-1)box_xy = box_xy * 2 - 0.5 + gridbox_wh = (box_wh * 2) ** 2 * anchorsbox_xy /= (grid_w, grid_h)  # 计算原尺寸的中心box_wh /= self.wh  # 计算原尺寸的宽高box_xy -= (box_wh / 2.)  # 计算原尺寸的中心box = np.concatenate((box_xy, box_wh), axis=-1)res = filter_boxes(box, box_confidence, box_class_probs, self.conf_thres)boxes.append(res[0])classes.append(res[1])scores.append(res[2])boxes, classes, scores = np.concatenate(boxes), np.concatenate(classes), np.concatenate(scores)nboxes, nclasses, nscores = [], [], []for c in set(classes):inds = np.where(classes == c)b = boxes[inds]c = classes[inds]s = scores[inds]keep = nms_boxes(b, s, self.iou_thres)nboxes.append(b[keep])nclasses.append(c[keep])nscores.append(s[keep])if len(nboxes) < 1:return [], []boxes = np.concatenate(nboxes)classes = np.concatenate(nclasses)scores = np.concatenate(nscores)label_list = []box_list = []for (x, y, w, h), score, cl in zip(boxes, scores, classes):print(score)x *= gain[0]y *= gain[1]w *= gain[0]h *= gain[1]x1 = max(0, np.floor(x).astype(int))y1 = max(0, np.floor(y).astype(int))x2 = min(src_w, np.floor(x + w + 0.5).astype(int))y2 = min(src_h, np.floor(y + h + 0.5).astype(int))# label_list.append(self.names[cl])label_list.append(cl)box_list.append((x1, y1, x2, y2))if self.draw_box:plot_one_box((x1, y1, x2, y2), img_src, label=self.names[cl])return label_list, np.array(box_list)def detect_resize(self, img_src):"""预测一张图片，预处理使用resizereturn: labels,boxes"""_img = cv2.resize(img_src, self.wh)gain = img_src.shape[:2][::-1]return self._predict(img_src, _img, gain)def detect(self, img_src):"""预测一张图片，预处理保持宽高比return: labels,boxes"""_img, gain = letterbox(img_src, self.wh)return self._predict(img_src, _img, gain)def close(self):self._rknn.release()def __enter__(self):return selfdef __exit__(self, exc_type, exc_val, exc_tb):self.close()def __del__(self):self.close()def test_video(det, video_path):reader = cv2.VideoCapture()reader.open(video_path)while True:ret, frame = reader.read()if not ret:breakt0 = time.time()det.detect(frame)print("total time", time.time() - t0)cv2.imshow("res", auto_resize(frame, 1200, 600)[0])cv2.waitKey(1)if __name__ == '__main__':import yamlimport cv2image = cv2.imread("/home/ubuntu/YOLOv5-RK3399Pro/trainData/images/ylg2_098.jpg")with open("/home/ubuntu/YOLOv5-RK3399Pro/rknn_detect/models/yolov5_rknn_640x640.yaml", "rb") as f:cfg = yaml.load(f, yaml.FullLoader)d = Detector(cfg["opt"])d.draw_box = Trued.detect(image)cv2.imshow("res", image)cv2.imwrite("image.jpg",image)cv2.waitKey()cv2.destroyAllWindows()

(rknnpy36) ubuntu@ubuntu:~/YOLOv5-RK3399Pro/rknn_detect$ python3 rknn_detect_for_yolov5_original.py
-->loading model : /home/ubuntu/YOLOv5-RK3399Pro/convert/best_noop1.rknn
librknn_runtime version 1.7.1 (bd41dbc build: 2021-10-28 16:15:23 base: 1131)
done
inference time:  62.44455170631409
0.85787344
ylg #易拉罐-->loading model : /home/ubuntu/YOLOv5-RK3399Pro/convert/best_noop1.rknn
librknn_runtime version 1.7.1 (bd41dbc build: 2021-10-28 16:15:23 base: 1131)
done
inference time:  64.14252042770386
banana
ylg

asymmetric_quantized-u8,dynamic_fixed_point-8,dynamic_fixed_point-16