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Not Implemented Error: Some layers are not supported on the device

I am trying running the face detection sample on two different computer using CPU.

I've verified notebook can run the sample code .


Notebook hardware:

CPU i7-9750

Desktop computer hardware:

CPU i3-6100


I changed to desktop computer, an error had shown up.


"Not Implemented Error: Some layers are not supported on the device"


I am not sure is the new version of openvino is not supported to the old generation core? 

And I found the parser in the sample code.


infer.add_argument('-l', '--cpu_lib', metavar="PATH", default="", help="(optional) For MKLDNN (CPU)-targeted custom layers, if any. " "Path to a shared library with custom layers implementations")


 What kind of library should I use or any other similar library?


Best regards,


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4 Replies
Community Manager

Hi Eddie,


Could you please verify the OpenVINO Toolkit installation by running the verification application on the CPU? There are two samples which are demo_squeezenet_download_convert_run.bat and demo_security_barrier_camera.bat. Check out the following link for more information.

Please try and come back to us with the result.





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Dear Aznie,

I've verified the verification applications on the CPU.

Two sample applications are running well.



I just want to use two different versions of Openvino, to verify my own code.

In Openvino version 2021.R1, this error message was shown on the terminal.


Not Implemented Error: Some layers are not supported on the device


But, on the Openvino version 2020.R4 running well.

I've tried to use different ".xml" and ".bin" file for different Openvino version.

My test result, use my own code:




Application version :

Python = 3.7.9

CMake = 3.14

Visual studio = 2019

Hardware info:

CPU = Intel i3-6100


Best regards,


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Community Manager

Hi Eddie,

Could you please share your application or models you are using for us to duplicate this case?




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Dear Aznie,

Application show as below:

#!/usr/bin/env python
# coding: utf-8

import logging as log
import os
import os.path as osp
import sys
import cv2
import numpy as np
import time

from openvino.inference_engine import IECore, IENetwork
from face_detector import FaceDetector
from argparse import ArgumentParser
from ie_module import InferenceContext

#from video import creat_capture
#import monitors

model_xml = r"C:\Users\test\Desktop\Thermal_python\face-detection-0200\FP32\face-detection-0200.xml"


def build_argparser():
    parser = ArgumentParser()

    general = parser.add_argument_group('General')
    general.add_argument('-i', '--input', metavar="PATH", default='0', help="(optional) Path to the input video " "('0' for the camera, default)")
    general.add_argument('-o', '--output', metavar="PATH", default="", help="(optional) Path to save the output video to")
    general.add_argument('-tl', '--timelapse', action='store_true', help="(optional) Auto-pause after each frame")
    general.add_argument('--no_show', action='store_true', help="(optional) Do not display output")
    general.add_argument('-cw', '--crop_width', default=0, type=int, help="(optional) Crop the input stream to this width " "(default: no crop). Both -cw and -ch parameters " "should be specified to use crop.")
    #gallery.add_argument('--run_detector', action='store_true', help="(optional) Use Face Detection model to find faces" " on the face images, otherwise use full images.")

    models = parser.add_argument_group('Models')
    #models.add_argument('-m', metavar="PATH", default="", required=True, help="Path to the Face Detection model XML file")
    models.add_argument('-fd_iw', '--fd_input_width', default=0, type=int, help="(optional) specify the input width of detection model " "(default: use default input width of model). Both -fd_iw and -fd_ih parameters " "should be specified for reshape.")
    models.add_argument('-fd_ih', '--fd_input_height', default=0, type=int, help="(optional) specify the input height of detection model " "(default: use default input height of model). Both -fd_iw and -fd_ih parameters " "should be specified for reshape.")
    infer = parser.add_argument_group('Inference options')
    infer.add_argument('-d', default='CPU', choices=DEVICE_KINDS, help="(optional) Target device for the " "Face Detection model (default: %(default)s)")
    infer.add_argument('-d_fd', default='CPU', choices=DEVICE_KINDS, help="(optional) Target device for the " "Face Detection model (default: %(default)s)")
    infer.add_argument('-l', '--cpu_lib', metavar="PATH", default="", help="(optional) For MKLDNN (CPU)-targeted custom layers, if any. " "Path to a shared library with custom layers implementations")
    infer.add_argument('-c', '--gpu_lib', metavar="PATH", default="", help="(optional) For clDNN (GPU)-targeted custom layers, if any. " "Path to the XML file with descriptions of the kernels")
    infer.add_argument('-v', '--verbose', action='store_true', help="(optional) Be more verbose")
    infer.add_argument('-pc', '--perf_stats', action='store_true', help="(optional) Output detailed per-layer performance stats")
    infer.add_argument('-t', metavar='[0..1]', type=float, default=0.4, help="(optional) Probability threshold for face detections" "(default: %(default)s)")
    infer.add_argument('--allow_grow', action='store_true', help="(optional) Allow to grow faces gallery and to dump on disk. " "Available only if --no_show option is off.")

    return parser

class FrameProcessor:
    QUEUE_SIZE = 16

    def __init__(self, args):
        used_devices = set([args.d])
        self.context = InferenceContext(used_devices, args.cpu_lib, args.gpu_lib, args.perf_stats)
        context = self.context"Loading models")
        face_detector_net = self.load_model(model_xml)
        assert (args.fd_input_height and args.fd_input_width) or (args.fd_input_height==0 and args.fd_input_width==0), \
            "Both -fd_iw and -fd_ih parameters should be specified for reshape"
        if args.fd_input_height and args.fd_input_width :
            face_detector_net.reshape({"data": [1, 3, args.fd_input_height,args.fd_input_width]})

        self.face_detector = FaceDetector(face_detector_net,confidence_threshold = args.t)

        self.face_detector.deploy(args.d_fd, context)"Models are loaded")

        self.allow_grow = args.allow_grow and not args.no_show

    def load_model(self, model_path):
        model_path = osp.abspath(model_path)

        model_weights_path = osp.splitext(model_path)[0] + ".bin""Loading the model from '%s'" % (model_path))
        assert osp.isfile(model_path), "Model description is not found at '%s'" % (model_path)
        assert osp.isfile(model_weights_path), "Model weights are not found at '%s'" % (model_weights_path)
        model = self.context.ie_core.read_network(model_path, model_weights_path)"Model is loaded")
        return model

    def process(self, frame):
        assert len(frame.shape) == 3, "Expected input frame in (H, W, C) format"
        assert frame.shape[2] in [3, 4], "Expected BGR or BGRA input"

        orig_image = frame.copy()
        frame = frame.transpose((2, 0, 1)) # HWC to CHW
        frame = np.expand_dims(frame, axis=0)


        rois = self.face_detector.get_roi_proposals(frame)
        if self.QUEUE_SIZE < len(rois):
            log.warning("Too many faces for processing." " Will be processed only %s of %s." % (self.QUEUE_SIZE, len(rois)))
            rois = rois[:self.QUEUE_SIZE]

        outputs = [rois]

        return outputs

    def get_performance_stats(self):
        stats = {
            'face_detector': self.face_detector.get_performance_stats(),
        return stats

class Visualizer:
    BREAK_KEY_LABELS = "q(Q) or Escape"
    BREAK_KEYS = {ord('q'), ord('Q'), 27}

    def __init__(self, args):
        self.frame_processor = FrameProcessor(args)
        self.display = not args.no_show
        self.print_perf_stats = args.perf_stats

        self.frame_time = 0
        self.frame_start_time = time.perf_counter()
        self.fps = 0
        self.frame_num = 0
        self.frame_count = -1

        self.input_crop = None
        if args.crop_width and args.crop_height:
            self.input_crop = np.array((args.crop_width, args.crop_height))

        self.frame_timeout = 0 if args.timelapse else 1

    def update_fps(self):
        now = time.perf_counter()
        self.frame_time = max(now - self.frame_start_time, sys.float_info.epsilon)
        self.fps = 1.0 / self.frame_time
        self.frame_start_time = now

    def draw_text_with_background(self, frame, text, origin, font=cv2.FONT_HERSHEY_SIMPLEX, scale=1.0, color=(0, 0, 0), thickness=1, bgcolor=(255, 255, 255)):
        text_size, baseline = cv2.getTextSize(text, font, scale, thickness)
        #cv2.rectangle(frame, tuple((origin + (0, baseline)).astype(int)), tuple((origin + (text_size[0], -text_size[1])).astype(int)), bgcolor, cv2.FILLED)
        cv2.putText(frame, text, tuple(origin.astype(int)), font, scale, color, thickness)
        return text_size, baseline

    def draw_detection_roi(self, frame, roi):
        for ovale in roi:
            sx,sy = ovale.position
            sw,sh = ovale.size
            if sx != 0 and sy != 0 and sw != 0 and sh != 0:
            cv2.rectangle(frame, tuple(ovale.position), tuple(ovale.position + ovale.size), (0, 220, 0), 2)

    def draw_detections(self, frame, detections):
        for roi in zip(*detections):
            self.draw_detection_roi(frame, roi)

    def draw_status(self, frame, detections):
        origin = np.array([15, 15])
        color = (0, 0, 255)
        font = cv2.FONT_HERSHEY_SIMPLEX
        text_scale = 0.5
        text_size, _ = self.draw_text_with_background(frame, "Frame time: %.3fs" % (self.frame_time), origin, font, text_scale, color)
        self.draw_text_with_background(frame, "FPS: %.1f" % (self.fps), (origin + (0, text_size[1] * 1.5)), font, text_scale, color)

        log.debug('Frame: %s/%s, detections: %s, ' 'frame time: %.3fs, fps: %.1f' % (self.frame_num, self.frame_count, len(detections[-1]), self.frame_time, self.fps))

        if self.print_perf_stats: 
  'Performance stats:')

    #def combine_frame(self):

    def display_interactive_window(self, frame):
        color = (255, 255, 255)
        font = cv2.FONT_HERSHEY_SIMPLEX
        text_scale = 0.5
        text = "Press '%s' key to exit" % (self.BREAK_KEY_LABELS)
        thickness = 2
        text_size = cv2.getTextSize(text, font, text_scale, thickness)
        origin = np.array([frame.shape[-2] - text_size[0][0] - 10, 10])
        line_height = np.array([0, text_size[0][1]]) * 1.5
        cv2.putText(frame, text, tuple(origin.astype(int)), font, text_scale, color, thickness)
        frame = cv2.resize(frame, (640, 480))
        cv2.imshow('Face recognition demo', frame)

    def should_stop_display(self):
        key = cv2.waitKey(self.frame_timeout) & 0xFF
        return key in self.BREAK_KEYS

    def process(self, input_stream, output_stream):
        self.input_stream = input_stream
        self.output_stream = output_stream

        while input_stream.isOpened():
            has_frame, frame =
            if not has_frame:

            if self.input_crop is not None:
                frame = Visualizer.center_crop(frame, self.input_crop)
            detections = self.frame_processor.process(frame)

            self.draw_detections(frame, detections)
            self.draw_status(frame, detections)

            if output_stream:
            if self.display:
                if self.should_stop_display():

            self.frame_num += 1

    def center_crop(frame, crop_size):
        fh, fw, fc = frame.shape
        crop_size[0] = min(fw, crop_size[0])
        crop_size[1] = min(fh, crop_size[1])
        return frame[(fh - crop_size[1]) // 2 : (fh + crop_size[1]) // 2,
                     (fw - crop_size[0]) // 2 : (fw + crop_size[0]) // 2, :]

    def run(self, args):
        input_stream = Visualizer.open_input_stream(args.input)
        if input_stream is None or not input_stream.isOpened():
            log.error("Cannot open input stream: %s" % args.input)
        fps = input_stream.get(cv2.CAP_PROP_FPS)
        frame_size = (int(input_stream.get(cv2.CAP_PROP_FRAME_WIDTH)), int(input_stream.get(cv2.CAP_PROP_FRAME_HEIGHT)))
        self.frame_count = int(input_stream.get(cv2.CAP_PROP_FRAME_COUNT))
        if args.crop_width and args.crop_height:
            crop_size = (args.crop_width, args.crop_height)
            frame_size = tuple(np.minimum(frame_size, crop_size))"Input stream info: %d x %d" % (frame_size[0], frame_size[1]))
        output_stream = Visualizer.open_output_stream(args.output, frame_size)

        self.process(input_stream, output_stream)

        # Release resources
        if output_stream:
        if input_stream:


    def open_input_stream(path):"Reading input data from '%s'" % (path))
        stream = path
            stream = int(path)
        except ValueError:
        return cv2.VideoCapture(stream)

    def open_output_stream(path, frame_size):
        output_stream = None
        if path != "":
            if not path.endswith('.avi'):
                log.warning("Output file extension is not 'avi'. " "Some issues with output can occur, check logs.")
  "Writing output to '%s'" % (path))
            output_stream = cv2.VideoWriter(path, cv2.VideoWriter.fourcc(*'MJPG'), 30, frame_size)
        return output_stream

def main():
    args = build_argparser().parse_args()
    log.basicConfig(format="[ %(levelname)s ] %(asctime)-15s %(message)s", level=log.INFO if not args.verbose else log.DEBUG, stream=sys.stdout)

    visualizer = Visualizer(args)

if __name__ == '__main__':


By the way, I've tried to use two different models, face_detection_0100 and face_detection_0200.


Best regards,


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