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openvino ssd_resnet50 model stuck when loading IR to the plugin

Hi,

I'm trying to run `SSD ResNet50 FPN COCO` (`ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03`) model on NCS2 using MYRIAD, Python API but it stucks when loading IR to the plugin with the following error.

    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
...

the `Failed send ping message: X_LINK_ERROR` keeps showing until I pressed ctrl+c to kill the script. I noticed the `USB_WRITE_REQ` in the error so I thought it has something to do with USB3 port but when I tried a lighter model `ssd_mobilenet_v2_coco`, it worked like a charm. 

This is the script to generate IR (IR generated successfully)

    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

This is the script I used to test

    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

Here's the snippet of Python script

    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

The only reason I could think of why `ssd_mobilenet_v2_coco_2018_03_29` works and `ssd_resnet50_v1_fpn_shared_box_predictor_640x640_coco14_sync_2018_07_03` not is the size which is 33MB for the former and about 100MB for the latter. I think the SSD Resnet50 model may have reached my laptop resource limitation. If this is the cause, how can I get around it? I'm using `l_openvino_toolkit_p_2018.5.455` on Ubuntu 18.04.
The `SSD ResNet50 FPN COCO` model is from TensorFlow Object Detection Models Zoo and supported by Openvino toolkit (https://software.intel.com/en-us/articles/OpenVINO-Using-TensorFlow).

thanks

Peeranat F.

0 Kudos
4 Replies
Shubha_R_Intel
Employee
180 Views

Dear Peeranat, does it work with -d CPU ? -d GPU ? Please report the results. 

Thanks kindly,

Shubha

180 Views

Hi Shubha,

 

Thanks for your reply. I tested with -d CPU but had the following error

[ ERROR ] Following layers are not supported by the plugin for specified device CPU:
 PriorBoxClustered_2, Resample_6859, PriorBoxClustered_3, PriorBoxClustered_4, Resample_, PriorBoxClustered_1, PriorBoxClustered_0, DetectionOutput
[ ERROR ] Please try to specify cpu extensions library path in demo's command line parameters using -l or --cpu_extension command line argument

It's clear CPU is not supported for this model. I don't have GPU. I'm sorry. Finally I plan to run this model on raspberry pi.

Thanks

Peeranat F. 

Shubha_R_Intel
Employee
180 Views

Dear Peeranat:

I think your issue is that you're reaching a memory limit on your computer at this line:   exec_net = plugin.load(network=net) # <== stuck at this line

and you want to know if there's a workaround ? OK got it. Let me check and get back to you.

180 Views

Hi Shubha,

Any update on this?

Thanks
Peeranat F.

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