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Hi @Tome_at_Intel !
I plan to run two different graph on a stick for face detection and face recognition respectively.
Each graph runs in its own different sub processes. However, it is always blocked at write_elem ().
Debug information as follows:
D : [ 0] ncFifoWriteElem:2630 No layout conversion is needed 0
D : [ 0] convertDataTypeAndLayout:170 src data type 1 dst data type 0
D : [ 0] convertDataTypeAndLayout:172 SRC: w 300 h 300 c 3 w_s 12 h_s 3600 c_s 4
D : [ 0] convertDataTypeAndLayout:174 DST: w 300 h 300 c 3 w_s 6 h_s 1800 c_s 2
When I use the mp.set_start_method ('forkserver')
or mp.set_start_method ('spawn')
, will throw the following exception:
Traceback (most recent call last):
File "/home/sdu/PycharmProjects/test/multi_process.py", line 253, in <module>
main()
File "/home/sdu/PycharmProjects/test/multi_process.py", line 233, in main
detect_process.start()
File "/usr/lib/python3.5/multiprocessing/process.py", line 105, in start
self._popen = self._Popen(self)
File "/usr/lib/python3.5/multiprocessing/context.py", line 212, in _Popen
return _default_context.get_context().Process._Popen(process_obj)
File "/usr/lib/python3.5/multiprocessing/context.py", line 281, in _Popen
return Popen(process_obj)
File "/usr/lib/python3.5/multiprocessing/popen_forkserver.py", line 36, in __init__
super().__init__(process_obj)
File "/usr/lib/python3.5/multiprocessing/popen_fork.py", line 20, in __init__
self._launch(process_obj)
File "/usr/lib/python3.5/multiprocessing/popen_forkserver.py", line 48, in _launch
reduction.dump(process_obj, buf)
File "/usr/lib/python3.5/multiprocessing/reduction.py", line 59, in dump
ForkingPickler(file, protocol).dump(obj)
ValueError: ctypes objects containing pointers cannot be pickled
The following is my code, and I hope to get some advice.
Looking forward to your reply.
Zachary
_CODE_
!
! def detect_camera_func(data_queue, device, graph1, in_fifo1, out_fifo1):
! cap = cv2.VideoCapture(0)
! cv2.namedWindow('capture', cv2.WINDOW_AUTOSIZE)
! while True:
! ret, frame = cap.read()
! img_resized = cv2.resize(frame, (300, 300))
! image1 = image_preprocess.detect_img_preprocess(img_resized)
! in_fifo1.write_elem(image1.astype(np.float32), 'input_detect')
! graph1.queue_inference(in_fifo1, out_fifo1)
! out2, uob2 = out_fifo1.read_elem()
! print(uob2)
! print(out2.shape)
! face_boxes, face_images = ssd_detect_output_parse(frame, out2)
! print('face_images', len(face_images))
! # for img in face_images:
! for (x1, y1, x2, y2), image_array in zip(face_boxes, face_images):
! print('have face :', len(face_boxes))
! cv2.rectangle(frame, (x1, y1), (x2, y2), (0, 255, 0), 2)
! img = cv2.resize(image_array, (160, 160))
! img1_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
! image1 = image_preprocess.prewhiten(img1_rgb)
! data_queue.put((frame, image1.astype(np.float32)))
! cv2.imshow('capture', frame)
! cv2.waitKey(1)
!
!
! def reco_func(data_queue, device, graph2, in_fifo2, out_fifo2):
! sleep(5)
! while True:
! frame, pred_data = data_queue.get()
! in_fifo2.write_elem(pred_data, 'input_recognition')
! graph2.queue_inference(in_fifo2, out_fifo2)
! out1, uob1 = out_fifo2.read_elem()
! print(out1.shape)
!
!
! def main(graph_path_detect, graph_path_recognition):
! # mp.set_start_method('forkserver')
! q = Queue(20)
!
! mvncapi.global_set_option(mvncapi.GlobalOption.RW_LOG_LEVEL, mvncapi.LogLevel.DEBUG)
! devices = mvncapi.enumerate_devices()
! print(devices)
!
! if len(devices) == 0:
! print('no have device')
! exit(1)
!
! device_ = mvncapi.Device(devices[0])
! device_.open()
!
! graph1 = mvncapi.Graph('graph1')
! with open(graph_path1, 'rb') as f1:
! graph_buffer1 = f1.read()
! graph1.allocate(device_, graph_buffer1)
!
! graph2 = mvncapi.Graph('graph2')
! with open(graph_path2, 'rb') as f2:
! graph_buffer2 = f2.read()
! graph2.allocate(device_, graph_buffer2)
!
! in_fifo1 = mvncapi.Fifo('input_detect', mvncapi.FifoType.HOST_WO)
! out_fifo1 = mvncapi.Fifo('output_detect', mvncapi.FifoType.HOST_RO)
! input_descriptor_1 = graph1.get_option(mvncapi.GraphOption.RO_INPUT_TENSOR_DESCRIPTORS)
! output_descriptor_1 = graph1.get_option(mvncapi.GraphOption.RO_OUTPUT_TENSOR_DESCRIPTORS)
! in_fifo1.allocate(device_, input_descriptor_1[0], 5)
! out_fifo1.allocate(device_, output_descriptor_1[0], 5)
!
! in_fifo2 = mvncapi.Fifo('input_recognition', mvncapi.FifoType.HOST_WO)
! out_fifo2 = mvncapi.Fifo('output_recognition', mvncapi.FifoType.HOST_RO)
! input_descriptor_2 = graph2.get_option(mvncapi.GraphOption.RO_INPUT_TENSOR_DESCRIPTORS)
! output_descriptor_2 = graph2.get_option(mvncapi.GraphOption.RO_OUTPUT_TENSOR_DESCRIPTORS)
! in_fifo2.allocate(device_, input_descriptor_2[0], 30)
! out_fifo2.allocate(device_, output_descriptor_2[0], 30)
!
! detect_process = Process(target=detect_camera_func, args=(q, device_, graph1, in_fifo1, out_fifo1), name='detect')
! reco_process = Process(target=reco_func, args=(q, device_, graph2, in_fifo2, out_fifo2), name='face')
!
! detect_process.start()
! reco_process.start()
! detect_process.join()
! reco_process.join()
!
!
! if __name__ == '__main__':
! graph_path_detect = 'datas/facenet_celeb_ncs/face2.08.graph'
! graph_path_recognition = 'datas/detection_model/detect.2.08.graph'
! main(graph_path_detect, graph_path_recognition)
!
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@Zachary Hi I'm trying to reproduce your issue. What models were you using with your application?
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@Zachary Also, which write_elem() is causing the error, detect_camera_func() or reco_func()? Thanks.
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@Tome_at_Intel
Thank you for your reply.
I'm so sorry for not being able to describe the problem clearly!
I used Caffa MobileNet-ssd for face detection and the inception-resnet-v1 for face recognition.
The write_elem () will be blocked in detect_camera_func().
The ssd_detect_output_parse() is to get the boxes of faces and face images.
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@Zachary I think the NCS device cannot be shared across processes. Additionally it looks like there is an issue involving SSD_MobileNet that needs to be addressed.
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