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标题: RV1126推理时间异常 [打印本页]

作者: blue 时间: 2022-5-21 11:44
标题: RV1126推理时间异常
我现在是在rv1126上运行YOLOv5的demo，有时候会遇到推理时间过长达到20S的问题，有时候就正常运行，时间是70ms，我的rknn-toolkit-lite是1.7.1，npu也对应是最新的，这个问题是怎么回事呢，万分感谢！

作者: blue 时间: 2022-5-21 11:45

"""将RKNN模型部署在RV1126平台并运行测试，目前只是简单推理"""
#环境:rv1126（Python3.7）
import time
import cv2
import numpy as np
from rknnlite.api import RKNNLite

RKNN_MODEL = 'best1.rknn'
IMG_PATH = 'bus.jpg'  # 修改推理图片

QUANTIZE_ON = True

BOX_THRESH = 0.25
NMS_THRESH = 0.45
IMG_SIZE = 640
# CLASSES = ("1")
# CLASSES = ("pedestrians","crowd","partially","ignore","riders")
# CLASSES = ("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 ")
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog',
      'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
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 x
y[:, 1] = x[:, 1] - x[:, 3] / 2  # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2  # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2  # bottom right y
return y

def 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 - 0.5

col = 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 += grid
box_xy *= int(IMG_SIZE / grid_h)

box_wh = pow(sigmoid(input[..., 2:4]) * 2, 2)
box_wh = box_wh * anchors

box = np.concatenate((box_xy, box_wh), axis=-1)

return box, box_confidence, box_class_probs

def filter_boxes(boxes, box_confidences, box_class_probs):
"""Filter boxes with box threshold. It's a bit different with origin yolov5 post process!

# Arguments
      boxes: ndarray, boxes of objects.
      box_confidences: ndarray, confidences of objects.
      box_class_probs: ndarray, class_probs of objects.

# Returns
      boxes: 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, scores

def nms_boxes(boxes, scores):
"""Suppress non-maximal boxes.

# Arguments
      boxes: ndarray, boxes of objects.
      scores: ndarray, scores of objects.

# Returns
      keep: ndarray, index of effective boxes.
"""
x = boxes[:, 0]
y = boxes[:, 1]
w = boxes[:, 2] - boxes[:, 0]
h = boxes[:, 3] - boxes[:, 1]

areas = w * h
order = 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 * h1

      ovr = inter / (areas[i] + areas[order[1:]] - inter)
      inds = np.where(ovr <= NMS_THRESH)[0]
      order = order[inds + 1]
keep = np.array(keep)
return keep

def 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, None

boxes = np.concatenate(nboxes)
classes = np.concatenate(nclasses)
scores = np.concatenate(nscores)

return boxes, classes, scores

def 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 = box
      print('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 constraints
shape = 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 padding
ratio = r, r  # width, height ratios
new_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 padding

dw /= 2  # divide padding into 2 sides
dh /= 2

if shape[::-1] != new_unpad:  # resize
      im = 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 border
return im, ratio, (dw, dh)

def load_model():
"""载入model"""

# Create RKNN object
rknn_lite = RKNNLite()

print('--> list devices:')
rknn_lite.list_devices()
print('done')

print('--> query support target platform')
rknn_lite.list_support_target_platform(rknn_model=RKNN_MODEL)
print('done')

print('--> Load RKNN model')
ret = rknn_lite.load_rknn(RKNN_MODEL)
if ret != 0:
      print('Load RKNN model failed')
      exit(ret)
print('done')

# init runtime environment
print('--> Init runtime environment')
# 指定平台
# ret = rknn_lite.init_runtime(target='rv1126')
ret = rknn_lite.init_runtime()
print(ret)
if ret != 0:
      print('Init runtime environment failed')
      exit(ret)
print('done')
return rknn_lite

if __name__ == '__main__':

# rknn导入
rknn = load_model()

# Set inputs
img = cv2.imread(IMG_PATH)  #  改变输入，可以获取摄像头数据源
# img = cv2.resize(img, (640,640))
img, ratio, (dw, dh) = letterbox(img, new_shape=(IMG_SIZE, IMG_SIZE))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)

# print("--> get sdk version:")
# sdk_version = rknn_lite.get_sdk_version()
# print(sdk_version)
# print("done")

# Inference
print('--> Running model')
_force_builtin_perm = False
t0 = time.time()
outputs = rknn.inference(inputs=[img], inputs_pass_through=[0 if not _force_builtin_perm else 1])
print("inference time:\t", time.time() - t0)
# post process
input0_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)

cv2.imwrite('result.jpg', img_1)
# cv2.imshow("post process result", img_1)
# cv2.waitKeyEx(0)

rknn.release()

作者: blue 时间: 2022-5-21 11:46

blue 发表于 2022-5-21 11:45
"""将RKNN模型部署在RV1126平台并运行测试，目前只是简单推理"""
#环境:rv1126（Python3.7）
import time

这是运行在1126上的代码

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