我正在尝试使用 MYRIAD、Python API 在 NCS2 上运行SSD ResNet50 FPN COCO( ) 模型，但在将 IR 加载到插件时它会卡住并出现以下错误。ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03
E: [xLink] [ 80143] handleIncomingEvent:240 handleIncomingEvent() Read failed -4
E: [xLink] [ 80143] dispatcherEventReceive:308 dispatcherEventReceive() Read failed -4 | event 0x7f35137fde80 USB_WRITE_REQ
E: [xLink] [ 80143] eventReader:256 eventReader stopped
E: [xLink] [ 80144] dispatcherEventSend:908 Write failed event -4
E: [watchdog] [ 81144] sendPingMessage:164 Failed send ping message: X_LINK_ERROR
E: [watchdog] [ 82144] sendPingMessage:164 Failed send ping message: X_LINK_ERROR
E: [watchdog] [ 83144] sendPingMessage:164 Failed send ping message: X_LINK_ERROR
E: [watchdog] [ 84145] sendPingMessage:164 Failed send ping message: X_LINK_ERROR
...
一直显示，Failed send ping message: X_LINK_ERROR直到我按 ctrl+C 杀死脚本。我注意到USB_WRITE_REQ错误，所以我认为它与 USB3 端口有关，但是当我尝试更轻的型号ssd_mobilenet_v2_coco时，它就像一个魅力。
这是生成IR的脚本（IR生成成功）
python mo_tf.py --input_model ~/workspace/pi/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03/frozen_inference_graph.pb --output_dir ~/workspace/pi/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03/openvino_model/FP16 --tensorflow_use_custom_operations_config ~/intel/computer_vision_sdk/deployment_tools/model_optimizer/extensions/front/tf/ssd_v2_support.json --tensorflow_object_detection_api_pipeline_config ~/workspace/pi/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03/pipeline.config --data_type FP16
这是我用来测试的脚本
python test.py -m ~/workspace/pi/ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03/openvino_model/FP16/frozen_inference_graph.xml -i ~/workspace/object-detection/test_images/image.jpg -d MYRIAD
这是Python脚本的片段
plugin = IEPlugin(device=args.device, plugin_dirs=args.plugin_dir)
if args.cpu_extension and 'CPU' in args.device:
plugin.add_cpu_extension(args.cpu_extension)
# Read IR
log.info("Reading IR...")
net = IENetwork(model=model_xml, weights=model_bin)
if plugin.device == "CPU":
supported_layers = plugin.get_supported_layers(net)
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(plugin.device, ', '.join(not_supported_layers)))
log.error("Please try to specify cpu extensions library path in demo's command line parameters using -l "
"or --cpu_extension command line argument")
sys.exit(1)
assert len(net.inputs.keys()) == 1, "Demo supports only single input topologies"
assert len(net.outputs) == 1, "Demo supports only single output topologies"
input_blob = next(iter(net.inputs))
out_blob = next(iter(net.outputs))
n, c, h, w = net.inputs[input_blob].shape
log.info("Loading IR to the plugin...")
exec_net = plugin.load(network=net) # <== stuck at this line
我能想到为什么ssd_mobilenet_v2_coco_2018_03_29有效而ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03不是无效的唯一原因是前者的大小为 33MB，而后者的大小约为 100MB。我认为 SSD Resnet50 型号可能已经达到了我的笔记本电脑资源限制。如果这是原因，我该如何解决？我l_openvino_toolkit_p_2018.5.455在 Ubuntu 18.04 上使用。
该SSD ResNet50 FPN COCO模型来自 TensorFlow Object Detection Models Zoo，并由 Openvino 工具包 ( https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow ) 提供支持。