允泉
2022-03-03 20:30
采纳率: 100%
浏览 182

树莓派+openVINO+二代神经计算棒加速推理YOLOv5模型出错,如何解决?(语言-python)

错误代码描述:

pi@raspberrypi:~ $ python3 SDK.py -m last.xml -i cam
[ INFO ] Creating Inference Engine...
[ INFO ] Loading network files:
    last.xml
    last.bin
SDK.py:218: DeprecationWarning: Reading network using constructor is deprecated. Please, use IECore.read_network() method instead
  net = IENetwork(model=model_xml, weights=model_bin)
[ INFO ] Preparing inputs
[ INFO ] Loading model to the plugin
[ INFO ] Starting inference...
To close the application, press 'CTRL+C' here or switch to the output window and press ESC key
To switch between sync/async modes, press TAB key in the output window
(480, 640)
[ INFO ] Layer Transpose_258 parameters: 
[ INFO ]          classes : 63
[ INFO ]          num     : 3
[ INFO ]          coords  : 4
[ INFO ]          anchors : [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0, 198.0, 373.0, 326.0]
Traceback (most recent call last):
  File "SDK.py", line 399, in <module>
    sys.exit(main() or 0)
  File "SDK.py", line 319, in main
    args.prob_threshold)
  File "SDK.py", line 146, in parse_yolo_region
    out_blob_n, out_blob_c, out_blob_h, out_blob_w = blob.shape
ValueError: too many values to unpack (expected 4)

运行过程中,弹出摄像头窗口但没有图像,像是卡了一样,之后消失,伴随着终端提示上述错误,程序运行停止。
出错部分代码(完整代码位于问题描述后面)

def parse_yolo_region(blob, resized_image_shape, original_im_shape, params, threshold):
    # ------------------------------------------ Validating output parameters ------------------------------------------
    out_blob_n, out_blob_c, out_blob_h, out_blob_w = blob.shape  #问题出现地!!!!!!!!!!!!!(我好苦恼)
    predictions = 1.0 / (1.0 + np.exp(-blob))

    assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
                                     "be equal to width. Current height = {}, current width = {}" \
                                     "".format(out_blob_h, out_blob_w)

    # ------------------------------------------ Extracting layer parameters -------------------------------------------
    orig_im_h, orig_im_w = original_im_shape
    resized_image_h, resized_image_w = resized_image_shape
    objects = list()

    side_square = params.side * params.side

    # ------------------------------------------- Parsing YOLO Region output -------------------------------------------
    bbox_size = int(out_blob_c / params.num)  # 4+1+num_classes

    for row, col, n in np.ndindex(params.side, params.side, params.num):
        bbox = predictions[0, n * bbox_size:(n + 1) * bbox_size, row, col]

        x, y, width, height, object_probability = bbox[:5]
        class_probabilities = bbox[5:]
        if object_probability < threshold:
            continue
        x = (2 * x - 0.5 + col) * (resized_image_w / out_blob_w)
        y = (2 * y - 0.5 + row) * (resized_image_h / out_blob_h)
        if int(resized_image_w / out_blob_w) == 8 & int(resized_image_h / out_blob_h) == 8:  # 80x80,
            idx = 0
        elif int(resized_image_w / out_blob_w) == 16 & int(resized_image_h / out_blob_h) == 16:  # 40x40
            idx = 1
        elif int(resized_image_w / out_blob_w) == 32 & int(resized_image_h / out_blob_h) == 32:  # 20x20
            idx = 2

        width = (2 * width) ** 2 * params.anchors[idx * 6 + 2 * n]
        height = (2 * height) ** 2 * params.anchors[idx * 6 + 2 * n + 1]
        class_id = np.argmax(class_probabilities)
        confidence = object_probability
        objects.append(scale_bbox(x=x, y=y, height=height, width=width, class_id=class_id, confidence=confidence,
                                  im_h=orig_im_h, im_w=orig_im_w, resized_im_h=resized_image_h,
                                  resized_im_w=resized_image_w))
    return objects

全部过程简介:YOLOv5迁移训练采用yolov5s.pt预训练模型,训练生成的.pt模型采用YOLOv5自带export.py文件转换成ONNX模型,转换之前更改了export.py中opset版本为10(附上export.py部分代码)


        torch.onnx.export(model, im, f, verbose=False, opset_version=10,
                          training=torch.onnx.TrainingMode.TRAINING if train else torch.onnx.TrainingMode.EVAL,
                          do_constant_folding=not train,
                          input_names=['images'],
                          output_names=['output'],
                          dynamic_axes={'images': {0: 'batch', 2: 'height', 3: 'width'},  # shape(1,3,640,640)
                                        'output': {0: 'batch', 1: 'anchors'}  # shape(1,25200,85)
                                        } if dynamic else None)

之后采用openVINO模型优化器中mo.py,将ONNX模型转换成 IR 格式文件。将树莓派搭建好openVINO,搭配openVINO官方YOLOv5推理程序与二代神经计算棒在树莓派上加速推理模型(出错)
附上完整openVINO官方提供推理程序

from __future__ import print_function, division

import logging
import os
import sys
from argparse import ArgumentParser, SUPPRESS
from math import exp as exp
from time import time
import numpy as np

import cv2
from openvino.inference_engine import IENetwork, IECore

logging.basicConfig(format="[ %(levelname)s ] %(message)s", level=logging.INFO, stream=sys.stdout)
log = logging.getLogger()


def build_argparser():
    parser = ArgumentParser(add_help=False)
    args = parser.add_argument_group('Options')
    args.add_argument('-h', '--help', action='help', default=SUPPRESS, help='Show this help message and exit.')
    args.add_argument("-m", "--model", help="Required. Path to an .xml file with a trained model.",
                      required=True, type=str)
    args.add_argument("-i", "--input", help="Required. Path to an image/video file. (Specify 'cam' to work with "
                                            "camera)", required=True, type=str)
    args.add_argument("-l", "--cpu_extension",
                      help="Optional. Required for CPU custom layers. Absolute path to a shared library with "
                           "the kernels implementations.", type=str, default=None)
    args.add_argument("-d", "--device",
                      help="Optional. Specify the target device to infer on; CPU, GPU, FPGA, HDDL or MYRIAD is"
                           " acceptable. The sample will look for a suitable plugin for device specified. "
                           "Default value is CPU", default="CPU", type=str)
    args.add_argument("--labels", help="Optional. Labels mapping file", default=None, type=str)
    args.add_argument("-t", "--prob_threshold", help="Optional. Probability threshold for detections filtering",
                      default=0.5, type=float)
    args.add_argument("-iout", "--iou_threshold", help="Optional. Intersection over union threshold for overlapping "
                                                       "detections filtering", default=0.4, type=float)
    args.add_argument("-ni", "--number_iter", help="Optional. Number of inference iterations", default=1, type=int)
    args.add_argument("-pc", "--perf_counts", help="Optional. Report performance counters", default=False,
                      action="store_true")
    args.add_argument("-r", "--raw_output_message", help="Optional. Output inference results raw values showing",
                      default=False, action="store_true")
    args.add_argument("--no_show", help="Optional. Don't show output", action='store_true')
    return parser


class YoloParams:
    # ------------------------------------------- Extracting layer parameters ------------------------------------------
    # Magic numbers are copied from yolo samples
    def __init__(self, param, side):
        self.num = 3 if 'num' not in param else int(param['num'])
        self.coords = 4 if 'coords' not in param else int(param['coords'])
        self.classes = 80 if 'classes' not in param else int(param['classes'])
        self.side = side
        self.anchors = [10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0, 45.0, 59.0, 119.0, 116.0, 90.0, 156.0,
                        198.0,
                        373.0, 326.0] if 'anchors' not in param else [float(a) for a in param['anchors'].split(',')]

        self.isYoloV3 = False

        if param.get('mask'):
            mask = [int(idx) for idx in param['mask'].split(',')]
            self.num = len(mask)

            maskedAnchors = []
            for idx in mask:
                maskedAnchors += [self.anchors[idx * 2], self.anchors[idx * 2 + 1]]
            self.anchors = maskedAnchors

            self.isYoloV3 = True  # Weak way to determine but the only one.

    def log_params(self):
        params_to_print = {'classes': self.classes, 'num': self.num, 'coords': self.coords, 'anchors': self.anchors}
        [log.info("         {:8}: {}".format(param_name, param)) for param_name, param in params_to_print.items()]


def letterbox(img, size=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True):
    # Resize image to a 32-pixel-multiple rectangle https://github.com/ultralytics/yolov3/issues/232
    shape = img.shape[:2]  # current shape [height, width]
    w, h = size

    # Scale ratio (new / old)
    r = min(h / shape[0], w / shape[1])
    if not scaleup:  # only scale down, do not scale up (for better test mAP)
        r = min(r, 1.0)

    # Compute padding
    ratio = r, r  # width, height ratios
    new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
    dw, dh = w - new_unpad[0], h - new_unpad[1]  # wh padding
    if auto:  # minimum rectangle
        dw, dh = np.mod(dw, 64), np.mod(dh, 64)  # wh padding
    elif scaleFill:  # stretch
        dw, dh = 0.0, 0.0
        new_unpad = (w, h)
        ratio = w / shape[1], h / shape[0]  # width, height ratios

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

    if shape[::-1] != new_unpad:  # resize
        img = cv2.resize(img, 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))
    img = cv2.copyMakeBorder(img, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color)  # add border

    top2, bottom2, left2, right2 = 0, 0, 0, 0
    if img.shape[0] != h:
        top2 = (h - img.shape[0]) // 2
        bottom2 = top2
        img = cv2.copyMakeBorder(img, top2, bottom2, left2, right2, cv2.BORDER_CONSTANT, value=color)  # add border
    elif img.shape[1] != w:
        left2 = (w - img.shape[1]) // 2
        right2 = left2
        img = cv2.copyMakeBorder(img, top2, bottom2, left2, right2, cv2.BORDER_CONSTANT, value=color)  # add border
    return img


def scale_bbox(x, y, height, width, class_id, confidence, im_h, im_w, resized_im_h=640, resized_im_w=640):
    gain = min(resized_im_w / im_w, resized_im_h / im_h)  # gain  = old / new
    pad = (resized_im_w - im_w * gain) / 2, (resized_im_h - im_h * gain) / 2  # wh padding
    x = int((x - pad[0]) / gain)
    y = int((y - pad[1]) / gain)

    w = int(width / gain)
    h = int(height / gain)

    xmin = max(0, int(x - w / 2))
    ymin = max(0, int(y - h / 2))
    xmax = min(im_w, int(xmin + w))
    ymax = min(im_h, int(ymin + h))
    # Method item() used here to convert NumPy types to native types for compatibility with functions, which don't
    # support Numpy types (e.g., cv2.rectangle doesn't support int64 in color parameter)
    return dict(xmin=xmin, xmax=xmax, ymin=ymin, ymax=ymax, class_id=class_id.item(), confidence=confidence.item())


def entry_index(side, coord, classes, location, entry):
    side_power_2 = side ** 2
    n = location // side_power_2
    loc = location % side_power_2
    return int(side_power_2 * (n * (coord + classes + 1) + entry) + loc)


def parse_yolo_region(blob, resized_image_shape, original_im_shape, params, threshold):
    # ------------------------------------------ Validating output parameters ------------------------------------------
    out_blob_n, out_blob_c, out_blob_h, out_blob_w = blob.shape
    predictions = 1.0 / (1.0 + np.exp(-blob))

    assert out_blob_w == out_blob_h, "Invalid size of output blob. It sould be in NCHW layout and height should " \
                                     "be equal to width. Current height = {}, current width = {}" \
                                     "".format(out_blob_h, out_blob_w)

    # ------------------------------------------ Extracting layer parameters -------------------------------------------
    orig_im_h, orig_im_w = original_im_shape
    resized_image_h, resized_image_w = resized_image_shape
    objects = list()

    side_square = params.side * params.side

    # ------------------------------------------- Parsing YOLO Region output -------------------------------------------
    bbox_size = int(out_blob_c / params.num)  # 4+1+num_classes

    for row, col, n in np.ndindex(params.side, params.side, params.num):
        bbox = predictions[0, n * bbox_size:(n + 1) * bbox_size, row, col]

        x, y, width, height, object_probability = bbox[:5]
        class_probabilities = bbox[5:]
        if object_probability < threshold:
            continue
        x = (2 * x - 0.5 + col) * (resized_image_w / out_blob_w)
        y = (2 * y - 0.5 + row) * (resized_image_h / out_blob_h)
        if int(resized_image_w / out_blob_w) == 8 & int(resized_image_h / out_blob_h) == 8:  # 80x80,
            idx = 0
        elif int(resized_image_w / out_blob_w) == 16 & int(resized_image_h / out_blob_h) == 16:  # 40x40
            idx = 1
        elif int(resized_image_w / out_blob_w) == 32 & int(resized_image_h / out_blob_h) == 32:  # 20x20
            idx = 2

        width = (2 * width) ** 2 * params.anchors[idx * 6 + 2 * n]
        height = (2 * height) ** 2 * params.anchors[idx * 6 + 2 * n + 1]
        class_id = np.argmax(class_probabilities)
        confidence = object_probability
        objects.append(scale_bbox(x=x, y=y, height=height, width=width, class_id=class_id, confidence=confidence,
                                  im_h=orig_im_h, im_w=orig_im_w, resized_im_h=resized_image_h,
                                  resized_im_w=resized_image_w))
    return objects


def intersection_over_union(box_1, box_2):
    width_of_overlap_area = min(box_1['xmax'], box_2['xmax']) - max(box_1['xmin'], box_2['xmin'])
    height_of_overlap_area = min(box_1['ymax'], box_2['ymax']) - max(box_1['ymin'], box_2['ymin'])
    if width_of_overlap_area < 0 or height_of_overlap_area < 0:
        area_of_overlap = 0
    else:
        area_of_overlap = width_of_overlap_area * height_of_overlap_area
    box_1_area = (box_1['ymax'] - box_1['ymin']) * (box_1['xmax'] - box_1['xmin'])
    box_2_area = (box_2['ymax'] - box_2['ymin']) * (box_2['xmax'] - box_2['xmin'])
    area_of_union = box_1_area + box_2_area - area_of_overlap
    if area_of_union == 0:
        return 0
    return area_of_overlap / area_of_union


def main():
    args = build_argparser().parse_args()

    model_xml = args.model
    model_bin = os.path.splitext(model_xml)[0] + ".bin"

    # ------------- 1. Plugin initialization for specified device and load extensions library if specified -------------
    log.info("Creating Inference Engine...")
    ie = IECore()
    if args.cpu_extension and 'CPU' in args.device:
        ie.add_extension(args.cpu_extension, "CPU")

    # -------------------- 2. Reading the IR generated by the Model Optimizer (.xml and .bin files) --------------------
    log.info("Loading network files:\n\t{}\n\t{}".format(model_xml, model_bin))
    net = IENetwork(model=model_xml, weights=model_bin)

    # ---------------------------------- 3. Load CPU extension for support specific layer ------------------------------
    if "CPU" in args.device:
        supported_layers = ie.query_network(net, "CPU")
        not_supported_layers = [l for l in net.layers.keys() if l not in supported_layers]
        if len(not_supported_layers) != 0:
            log.error("Following layers are not supported by the plugin for specified device {}:\n {}".
                      format(args.device, ', '.join(not_supported_layers)))
            log.error("Please try to specify cpu extensions library path in sample's command line parameters using -l "
                      "or --cpu_extension command line argument")
            sys.exit(1)

    assert len(net.inputs.keys()) == 1, "Sample supports only YOLO V3 based single input topologies"

    # ---------------------------------------------- 4. Preparing inputs -----------------------------------------------
    log.info("Preparing inputs")
    input_blob = next(iter(net.inputs))

    #  Defaulf batch_size is 1
    net.batch_size = 1

    # Read and pre-process input images
    n, c, h, w = net.inputs[input_blob].shape

    if args.labels:
        with open(args.labels, 'r') as f:
            labels_map = [x.strip() for x in f]
    else:
        labels_map = None

    input_stream = 0 if args.input == "cam" else args.input

    is_async_mode = True
    cap = cv2.VideoCapture(input_stream)
    number_input_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
    number_input_frames = 1 if number_input_frames != -1 and number_input_frames < 0 else number_input_frames

    wait_key_code = 1

    # Number of frames in picture is 1 and this will be read in cycle. Sync mode is default value for this case
    if number_input_frames != 1:
        ret, frame = cap.read()
    else:
        is_async_mode = False
        wait_key_code = 0

    # ----------------------------------------- 5. Loading model to the plugin -----------------------------------------
    log.info("Loading model to the plugin")
    exec_net = ie.load_network(network=net, num_requests=2, device_name=args.device)

    cur_request_id = 0
    next_request_id = 1
    render_time = 0
    parsing_time = 0

    # ----------------------------------------------- 6. Doing inference -----------------------------------------------
    log.info("Starting inference...")
    print("To close the application, press 'CTRL+C' here or switch to the output window and press ESC key")
    print("To switch between sync/async modes, press TAB key in the output window")
    while cap.isOpened():
        # Here is the first asynchronous point: in the Async mode, we capture frame to populate the NEXT infer request
        # in the regular mode, we capture frame to the CURRENT infer request
        if is_async_mode:
            ret, next_frame = cap.read()
        else:
            ret, frame = cap.read()

        if not ret:
            break

        if is_async_mode:
            request_id = next_request_id
            in_frame = letterbox(frame, (w, h))
        else:
            request_id = cur_request_id
            in_frame = letterbox(frame, (w, h))

        in_frame0 = in_frame
        # resize input_frame to network size
        in_frame = in_frame.transpose((2, 0, 1))  # Change data layout from HWC to CHW
        in_frame = in_frame.reshape((n, c, h, w))

        # Start inference
        start_time = time()
        exec_net.start_async(request_id=request_id, inputs={input_blob: in_frame})
        det_time = time() - start_time

        # Collecting object detection results
        objects = list()
        if exec_net.requests[cur_request_id].wait(-1) == 0:
            output = exec_net.requests[cur_request_id].outputs
            start_time = time()
            for layer_name, out_blob in output.items():
                out_blob = out_blob.reshape(net.layers[layer_name].out_data[0].shape)
                layer_params = YoloParams(net.layers[layer_name].params, out_blob.shape[2])
                log.info("Layer {} parameters: ".format(layer_name))
                layer_params.log_params()
                objects += parse_yolo_region(out_blob, in_frame.shape[2:],
                                             # in_frame.shape[2:], layer_params,
                                             frame.shape[:-1], layer_params,
                                             args.prob_threshold)
            parsing_time = time() - start_time

        # Filtering overlapping boxes with respect to the --iou_threshold CLI parameter
        objects = sorted(objects, key=lambda obj: obj['confidence'], reverse=True)
        for i in range(len(objects)):
            if objects[i]['confidence'] == 0:
                continue
            for j in range(i + 1, len(objects)):
                if intersection_over_union(objects[i], objects[j]) > args.iou_threshold:
                    objects[j]['confidence'] = 0

        # Drawing objects with respect to the --prob_threshold CLI parameter
        objects = [obj for obj in objects if obj['confidence'] >= args.prob_threshold]

        if len(objects) and args.raw_output_message:
            log.info("\nDetected boxes for batch {}:".format(1))
            log.info(" Class ID | Confidence | XMIN | YMIN | XMAX | YMAX | COLOR ")

        origin_im_size = frame.shape[:-1]
        print(origin_im_size)
        for obj in objects:
            # Validation bbox of detected object
            if obj['xmax'] > origin_im_size[1] or obj['ymax'] > origin_im_size[0] or obj['xmin'] < 0 or obj['ymin'] < 0:
                continue
            color = (int(min(obj['class_id'] * 12.5, 255)),
                     min(obj['class_id'] * 7, 255), min(obj['class_id'] * 5, 255))
            det_label = labels_map[obj['class_id']] if labels_map and len(labels_map) >= obj['class_id'] else \
                str(obj['class_id'])

            if args.raw_output_message:
                log.info(
                    "{:^9} | {:10f} | {:4} | {:4} | {:4} | {:4} | {} ".format(det_label, obj['confidence'], obj['xmin'],
                                                                              obj['ymin'], obj['xmax'], obj['ymax'],
                                                                              color))

            cv2.rectangle(frame, (obj['xmin'], obj['ymin']), (obj['xmax'], obj['ymax']), color, 2)
            cv2.putText(frame,
                        "#" + det_label + ' ' + str(round(obj['confidence'] * 100, 1)) + ' %',
                        (obj['xmin'], obj['ymin'] - 7), cv2.FONT_HERSHEY_COMPLEX, 0.6, color, 1)

        # Draw performance stats over frame
        inf_time_message = "Inference time: N\A for async mode" if is_async_mode else \
            "Inference time: {:.3f} ms".format(det_time * 1e3)
        render_time_message = "OpenCV rendering time: {:.3f} ms".format(render_time * 1e3)
        async_mode_message = "Async mode is on. Processing request {}".format(cur_request_id) if is_async_mode else \
            "Async mode is off. Processing request {}".format(cur_request_id)
        parsing_message = "YOLO parsing time is {:.3f} ms".format(parsing_time * 1e3)

        cv2.putText(frame, inf_time_message, (15, 15), cv2.FONT_HERSHEY_COMPLEX, 0.5, (200, 10, 10), 1)
        cv2.putText(frame, render_time_message, (15, 45), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)
        cv2.putText(frame, async_mode_message, (10, int(origin_im_size[0] - 20)), cv2.FONT_HERSHEY_COMPLEX, 0.5,
                    (10, 10, 200), 1)
        cv2.putText(frame, parsing_message, (15, 30), cv2.FONT_HERSHEY_COMPLEX, 0.5, (10, 10, 200), 1)

        start_time = time()
        if not args.no_show:
            cv2.imshow("DetectionResults", frame)
        render_time = time() - start_time

        if is_async_mode:
            cur_request_id, next_request_id = next_request_id, cur_request_id
            frame = next_frame

        if not args.no_show:
            key = cv2.waitKey(wait_key_code)

            # ESC key
            if key == 27:
                break
            # Tab key
            if key == 9:
                exec_net.requests[cur_request_id].wait()
                is_async_mode = not is_async_mode
                log.info("Switched to {} mode".format("async" if is_async_mode else "sync"))

    cv2.destroyAllWindows()


if __name__ == '__main__':
    sys.exit(main() or 0)

2条回答 默认 最新

相关推荐 更多相似问题