服务器配置如下：
CPU/NPU ：鲲鹏 CPU（ARM64）+A300I pro推理卡
系统 ：Kylin V10 SP1【下载链接】【安装链接】
驱动与固件版本版本 ：
Ascend-hdk-310p-npu-driver_23.0.1_linux-aarch64.run【下载链接】
Ascend-hdk-310p-npu-firmware_7.1.0.4.220.run【下载链接】
MCU版本 ：Ascend-hdk-310p-mcu_23.2.3【下载链接】
CANN 开发套件：版本7.0.1【Toolkit下载链接】【Kernels下载链接】
测试om模型环境如下：
Python ：版本3.8.11
推理工具 ：ais_bench
测试YOLO系列：v5/6/7/8/9/10/11
专栏其他文章 ：
Atlas800昇腾服务器（型号：3000）---驱动与固件安装（一）
Atlas800昇腾服务器（型号：3000）---CANN安装（二）
Atlas800昇腾服务器（型号：3000）---YOLO全系列om模型转换测试（三）
Atlas800昇腾服务器（型号：3000）---AIPP加速前处理（四）
Atlas800昇腾服务器（型号：3000）---YOLO全系列NPU推理【检测】（五）
Atlas800昇腾服务器（型号：3000）---YOLO全系列NPU推理【实例分割】（六）
Atlas800昇腾服务器（型号：3000）---YOLO全系列NPU推理【关键点】（七）
Atlas800昇腾服务器（型号：3000）---YOLO全系列NPU推理【跟踪】（八）
全部代码github ：https://github.com/Bigtuo/NPU-ais_bench
1 基础环境安装
详情见第（三）章环境安装 ：https://blog.csdn.net/weixin_45679938/article/details/142966255
2 ais_bench编译安装
注意 ：目前ais_bench工具只支持单个input的带有动态AIPP配置的模型，只支持静态shape、动态batch、动态宽高三种场景，不支持动态shape场景。
参考链接：https://gitee.com/ascend/tools/tree/master/ais-bench_workload/tool/ais_bench
2.1 安装aclruntime包
在安装环境执行如下命令安装aclruntime包：
说明：若为覆盖安装，请增加"--force-reinstall"参数强制安装.
python
pip3 install -v 'git+https://gitee.com/ascend/tools.git#egg=aclruntime&subdirectory=ais-bench_workload/tool/ais_bench/backend' -i https://pypi.tuna.tsinghua.edu.cn/simple
2.2 安装ais_bench推理程序包
在安装环境执行如下命令安装ais_bench推理程序包：
python
pip3 install -v 'git+https://gitee.com/ascend/tools.git#egg=ais_bench&subdirectory=ais-bench_workload/tool/ais_bench' -i https://pypi.tuna.tsinghua.edu.cn/simple
卸载和更新【忽略】：
python
# 卸载aclruntime
pip3 uninstall aclruntime
# 卸载ais_bench推理程序
pip3 uninstall ais_bench3 裸代码推理测试
python
# 1.进入运行环境yolo【普通用户】
conda activate yolo
# 2.激活atc【atc --help测试是否可行】
source ~/bashrc注意 ：ais_bench调用和使用方式与onnx-runtime几乎一致，因此可参考进行撰写脚本！
新建YOLO_ais_bench_bytetrack_aipp.py，内容如下：
python
import argparse
import time
import cv2
import numpy as np
import os
import copy
from ais_bench.infer.interface import InferSession
from bytetrack.byte_tracker import BYTETracker
class YOLO:
"""YOLO object detection model class for handling inference and visualization."""
def __init__(self, om_model, imgsz=(640, 640), device_id=0, model_ndtype=np.single, mode="static", postprocess_type="v8", aipp=False):
"""
Initialization.
Args:
om_model (str): Path to the om model.
"""
# 构建ais_bench推理引擎
self.session = InferSession(device_id=device_id, model_path=om_model)
# Numpy dtype: support both FP32(np.single) and FP16(np.half) om model
self.ndtype = model_ndtype
self.mode = mode
self.postprocess_type = postprocess_type
self.aipp = aipp
self.model_height, self.model_width = imgsz[0], imgsz[1] # 图像resize大小
def __call__(self, im0, conf_threshold=0.4, iou_threshold=0.45):
"""
The whole pipeline: pre-process -> inference -> post-process.
Args:
im0 (Numpy.ndarray): original input image.
conf_threshold (float): confidence threshold for filtering predictions.
iou_threshold (float): iou threshold for NMS.
Returns:
boxes (List): list of bounding boxes.
"""
# 前处理Pre-process
t1 = time.time()
im, ratio, (pad_w, pad_h) = self.preprocess(im0)
pre_time = round(time.time() - t1, 3)
# 推理 inference
t2 = time.time()
preds = self.session.infer([im], mode=self.mode)[0] # mode有动态"dymshape"和静态"static"等
det_time = round(time.time() - t2, 3)
# 后处理Post-process
t3 = time.time()
if self.postprocess_type == "v5":
boxes = self.postprocess_v5(preds,
im0=im0,
ratio=ratio,
pad_w=pad_w,
pad_h=pad_h,
conf_threshold=conf_threshold,
iou_threshold=iou_threshold,
)
elif self.postprocess_type == "v8":
boxes = self.postprocess_v8(preds,
im0=im0,
ratio=ratio,
pad_w=pad_w,
pad_h=pad_h,
conf_threshold=conf_threshold,
iou_threshold=iou_threshold,
)
elif self.postprocess_type == "v10":
boxes = self.postprocess_v10(preds,
im0=im0,
ratio=ratio,
pad_w=pad_w,
pad_h=pad_h,
conf_threshold=conf_threshold
)
else:
boxes = []
post_time = round(time.time() - t3, 3)
return boxes, (pre_time, det_time, post_time)
# 前处理，包括：resize, pad, 其中HWC to CHW，BGR to RGB，归一化，增加维度CHW -> BCHW可选择是否开启AIPP加速处理
def preprocess(self, img):
"""
Pre-processes the input image.
Args:
img (Numpy.ndarray): image about to be processed.
Returns:
img_process (Numpy.ndarray): image preprocessed for inference.
ratio (tuple): width, height ratios in letterbox.
pad_w (float): width padding in letterbox.
pad_h (float): height padding in letterbox.
"""
# Resize and pad input image using letterbox() (Borrowed from Ultralytics)
shape = img.shape[:2] # original image shape
new_shape = (self.model_height, self.model_width)
r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
ratio = r, r
new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
pad_w, pad_h = (new_shape[1] - new_unpad[0]) / 2, (new_shape[0] - new_unpad[1]) / 2 # wh padding
if shape[::-1] != new_unpad: # resize
img = cv2.resize(img, new_unpad, interpolation=cv2.INTER_LINEAR)
top, bottom = int(round(pad_h - 0.1)), int(round(pad_h + 0.1))
left, right = int(round(pad_w - 0.1)), int(round(pad_w + 0.1))
img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=(114, 114, 114)) # 填充
# 是否开启aipp加速预处理，需atc中完成
if self.aipp:
return img, ratio, (pad_w, pad_h)
# Transforms: HWC to CHW -> BGR to RGB -> div(255) -> contiguous -> add axis(optional)
img = np.ascontiguousarray(np.einsum('HWC->CHW', img)[::-1], dtype=self.ndtype) / 255.0
img_process = img[None] if len(img.shape) == 3 else img
return img_process, ratio, (pad_w, pad_h)
# YOLOv5/6/7通用后处理，包括：阈值过滤与NMS
def postprocess_v5(self, preds, im0, ratio, pad_w, pad_h, conf_threshold, iou_threshold):
"""
Post-process the prediction.
Args:
preds (Numpy.ndarray): predictions come from ort.session.run().
im0 (Numpy.ndarray): [h, w, c] original input image.
ratio (tuple): width, height ratios in letterbox.
pad_w (float): width padding in letterbox.
pad_h (float): height padding in letterbox.
conf_threshold (float): conf threshold.
iou_threshold (float): iou threshold.
Returns:
boxes (List): list of bounding boxes.
"""
# (Batch_size, Num_anchors, xywh_score_conf_cls), v5和v6的[..., 4]是置信度分数，v8v9采用类别里面最大的概率作为置信度score
x = preds # outputs: predictions (1, 8400*3, 85)
# Predictions filtering by conf-threshold
x = x[x[..., 4] > conf_threshold]
# Create a new matrix which merge these(box, score, cls) into one
# For more details about `numpy.c_()`: https://numpy.org/doc/1.26/reference/generated/numpy.c_.html
x = np.c_[x[..., :4], x[..., 4], np.argmax(x[..., 5:], axis=-1)]
# NMS filtering
# 经过NMS后的值, np.array([[x, y, w, h, conf, cls], ...]), shape=(-1, 4 + 1 + 1)
x = x[cv2.dnn.NMSBoxes(x[:, :4], x[:, 4], conf_threshold, iou_threshold)]
# 重新缩放边界框，为画图做准备
if len(x) > 0:
# Bounding boxes format change: cxcywh -> xyxy
x[..., [0, 1]] -= x[..., [2, 3]] / 2
x[..., [2, 3]] += x[..., [0, 1]]
# Rescales bounding boxes from model shape(model_height, model_width) to the shape of original image
x[..., :4] -= [pad_w, pad_h, pad_w, pad_h]
x[..., :4] /= min(ratio)
# Bounding boxes boundary clamp
x[..., [0, 2]] = x[:, [0, 2]].clip(0, im0.shape[1])
x[..., [1, 3]] = x[:, [1, 3]].clip(0, im0.shape[0])
return x[..., :6] # boxes
else:
return []
# YOLOv8/9/11通用后处理，包括：阈值过滤与NMS
def postprocess_v8(self, preds, im0, ratio, pad_w, pad_h, conf_threshold, iou_threshold):
"""
Post-process the prediction.
Args:
preds (Numpy.ndarray): predictions come from ort.session.run().
im0 (Numpy.ndarray): [h, w, c] original input image.
ratio (tuple): width, height ratios in letterbox.
pad_w (float): width padding in letterbox.
pad_h (float): height padding in letterbox.
conf_threshold (float): conf threshold.
iou_threshold (float): iou threshold.
Returns:
boxes (List): list of bounding boxes.
"""
x = preds # outputs: predictions (1, 84, 8400)
# Transpose the first output: (Batch_size, xywh_conf_cls, Num_anchors) -> (Batch_size, Num_anchors, xywh_conf_cls)
x = np.einsum('bcn->bnc', x) # (1, 8400, 84)
# Predictions filtering by conf-threshold
x = x[np.amax(x[..., 4:], axis=-1) > conf_threshold]
# Create a new matrix which merge these(box, score, cls) into one
# For more details about `numpy.c_()`: https://numpy.org/doc/1.26/reference/generated/numpy.c_.html
x = np.c_[x[..., :4], np.amax(x[..., 4:], axis=-1), np.argmax(x[..., 4:], axis=-1)]
# NMS filtering
# 经过NMS后的值, np.array([[x, y, w, h, conf, cls], ...]), shape=(-1, 4 + 1 + 1)
x = x[cv2.dnn.NMSBoxes(x[:, :4], x[:, 4], conf_threshold, iou_threshold)]
# 重新缩放边界框，为画图做准备
if len(x) > 0:
# Bounding boxes format change: cxcywh -> xyxy
x[..., [0, 1]] -= x[..., [2, 3]] / 2
x[..., [2, 3]] += x[..., [0, 1]]
# Rescales bounding boxes from model shape(model_height, model_width) to the shape of original image
x[..., :4] -= [pad_w, pad_h, pad_w, pad_h]
x[..., :4] /= min(ratio)
# Bounding boxes boundary clamp
x[..., [0, 2]] = x[:, [0, 2]].clip(0, im0.shape[1])
x[..., [1, 3]] = x[:, [1, 3]].clip(0, im0.shape[0])
return x[..., :6] # boxes
else:
return []
# YOLOv10后处理，包括：阈值过滤-无NMS
def postprocess_v10(self, preds, im0, ratio, pad_w, pad_h, conf_threshold):
x = preds # outputs: predictions (1, 300, 6) -> (xyxy_conf_cls)
# Predictions filtering by conf-threshold
x = x[x[..., 4] > conf_threshold]
# 重新缩放边界框，为画图做准备
if len(x) > 0:
# Rescales bounding boxes from model shape(model_height, model_width) to the shape of original image
x[..., :4] -= [pad_w, pad_h, pad_w, pad_h]
x[..., :4] /= min(ratio)
# Bounding boxes boundary clamp
x[..., [0, 2]] = x[:, [0, 2]].clip(0, im0.shape[1])
x[..., [1, 3]] = x[:, [1, 3]].clip(0, im0.shape[0])
return x # boxes
else:
return []
# 绘框
def draw_and_visualize(self, im, bboxes, video_writer, classes, color_palette, vis=False, save=False, is_track=False):
"""
Draw and visualize results.
Args:
im (np.ndarray): original image, shape [h, w, c].
bboxes (numpy.ndarray): [n, 6], n is number of bboxes.
vis (bool): imshow using OpenCV.
save (bool): save image annotated.
Returns:
None
"""
# Draw rectangles
if not is_track:
for (*box, conf, cls_) in bboxes:
# draw bbox rectangle
cv2.rectangle(im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
color_palette[int(cls_)], 1, cv2.LINE_AA)
cv2.putText(im, f'{classes[int(cls_)]}: {conf:.3f}', (int(box[0]), int(box[1] - 9)),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, color_palette[int(cls_)], 2, cv2.LINE_AA)
else:
for (*box, conf, id_) in bboxes:
# draw bbox rectangle
cv2.rectangle(im, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])),
(0, 0, 255), 1, cv2.LINE_AA)
cv2.putText(im, f'{id_}: {conf:.3f}', (int(box[0]), int(box[1] - 9)),
cv2.FONT_HERSHEY_SIMPLEX, 0.7, (0, 0, 255), 2, cv2.LINE_AA)
# Show image
if vis:
cv2.imshow('demo', im)
cv2.waitKey(1)
# Save video
if save:
video_writer.write(im)
class ByteTrackerONNX(object):
def __init__(self, args):
self.args = args
self.tracker = BYTETracker(args, frame_rate=30)
def _tracker_update(self, dets, image):
online_targets = []
if dets is not None:
online_targets = self.tracker.update(
dets[:, :5],
[image.shape[0], image.shape[1]],
[image.shape[0], image.shape[1]],
)
online_tlwhs = []
online_ids = []
online_scores = []
for online_target in online_targets:
tlwh = online_target.tlwh
track_id = online_target.track_id
vertical = tlwh[2] / tlwh[3] > 1.6
if tlwh[2] * tlwh[3] > self.args.min_box_area and not vertical:
online_tlwhs.append(tlwh)
online_ids.append(track_id)
online_scores.append(online_target.score)
return online_tlwhs, online_ids, online_scores
def inference(self, image, dets):
"""
Args: dets: 检测结果, [x1, y1, x2, y2, conf, cls]
Returns: np.array([[x1, y1, x2, y2, conf, ids], ...])
"""
bboxes, ids, scores = self._tracker_update(dets, image)
if len(bboxes) == 0:
return []
# Bounding boxes format change: tlwh -> xyxy
bboxes = np.array(bboxes)
bboxes[..., [2, 3]] += bboxes[..., [0, 1]]
bboxes = np.c_[bboxes, np.array(scores), np.array(ids)]
return bboxes
if __name__ == '__main__':
# Create an argument parser to handle command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument('--det_model', type=str, default=r"yolov8s.om", help='Path to OM model')
parser.add_argument('--source', type=str, default=r'test.mp4', help='Path to input video')
parser.add_argument('--out_path', type=str, default=r'res.mp4', help='结果保存文件')
parser.add_argument('--imgsz_det', type=tuple, default=(640, 640), help='Image input size')
parser.add_argument('--classes', type=list, default=['person', 'bicycle', 'car', 'motorcycle', 'airplane', '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', 'couch', 'potted plant', 'bed',
'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone', 'microwave', 'oven',
'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear', 'hair drier', 'toothbrush'], help='类别')
parser.add_argument('--conf', type=float, default=0.25, help='Confidence threshold')
parser.add_argument('--iou', type=float, default=0.6, help='NMS IoU threshold')
parser.add_argument('--device_id', type=int, default=0, help='device id')
parser.add_argument('--mode', default='static', help='om是动态dymshape或静态static')
parser.add_argument('--model_ndtype', default=np.single, help='om是fp32或fp16')
parser.add_argument('--postprocess_type', type=str, default='v8', help='后处理方式, 对应v5/v8/v10三种后处理')
parser.add_argument('--aipp', default=False, action='store_true', help='是否开启aipp加速YOLO预处理, 需atc中完成om集成')
parser.add_argument('--is_track', default=False, action='store_true', help='是否启用跟踪')
parser.add_argument('--track_thresh', type=float, default=0.5, help='tracking confidence threshold')
parser.add_argument('--track_buffer', type=int, default=30, help='the frames for keep lost tracks, usually as same with FPS')
parser.add_argument('--match_thresh', type=float, default=0.8, help='matching threshold for tracking')
parser.add_argument('--min_box_area', type=float, default=10, help='filter out tiny boxes',)
parser.add_argument('--mot20', dest='mot20', default=False, action='store_true', help='test mot20.',)
args = parser.parse_args()
print('开始运行：')
# Build model
det_model = YOLO(args.det_model, args.imgsz_det, args.device_id, args.model_ndtype, args.mode, args.postprocess_type, args.aipp)
bytetrack = ByteTrackerONNX(args)
color_palette = np.random.uniform(0, 255, size=(len(args.classes), 3)) # 为每个类别生成调色板
# 读取视频,解析帧数宽高,保存视频
cap = cv2.VideoCapture(args.source)
width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)
height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)
fps = cap.get(cv2.CAP_PROP_FPS)
frame_count = cap.get(cv2.CAP_PROP_FRAME_COUNT)
video_writer = cv2.VideoWriter(str(args.out_path), cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height)))
frame_id = 1
while True:
start_time = time.time()
ret, img = cap.read()
if not ret:
break
# Inference
boxes, (pre_time, det_time, post_time) = det_model(img, conf_threshold=args.conf, iou_threshold=args.iou)
print('预处理: {:.3f}s, 推理: {:.3f}s, 后处理: {:.3f}s, 识别{}个目标'.format(pre_time, det_time, post_time, len(boxes)))
# track
if args.is_track:
track_time = time.time()
boxes = bytetrack.inference(img, boxes)
print('跟踪耗时: {:.3f}s'.format(time.time() - track_time))
# Visualize
if len(boxes) > 0:
det_model.draw_and_visualize(copy.deepcopy(img), boxes, video_writer, args.classes, color_palette, vis=False, save=True, is_track=args.is_track)
end_time = time.time() - start_time
print('frame {}/{} (Total time: {:.2f} ms)'.format(frame_id, int(frame_count), end_time * 1000))
frame_id += 1
可视化如下：
4 推理耗时
YOLOv8s未使用AIPP进行前处理加速，前处理+推理大约20ms左右；加速后前处理+推理大约10ms左右，bytetrack跟踪耗时2-3ms。
5 其他依赖
注意 ：Bytetrack需要安装lap和cython-bbox两个安装包。
有时候，直接pip安装即可，报错的话，去python库官网下载压缩包安装。
如安装lap报错：
python
cd lap-0.4.0
python setup.py install
# 若出现ModuleNotFoundError: No module named 'distutils.msvccompiler'，升级setuptools和whel
pip install setuptools==58.0.0 wheel==0.36.2