When I run deeplab_mvn2 on NCS2, I get the following error.
In Openvino2020.2 I get an error. But with Openvino 2019 R3.1 it can run successfully
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segmentation_demo.exe -d MYRIAD -m deeplabv3_mnv2.xml -i test.mp4
[ INFO ] InferenceEngine: 00007FFB3F734020
[ INFO ] Parsing input parameters
[ INFO ] Device info
MYRIAD
myriadPlugin version ......... 2.1
Build ........... 42025
[35mE: [global] [ 0] [] XLinkResetRemote:249 can't wait dispatcherClosedSem
[0m
[ ERROR ] ArgMax/Squeeze of type Reshape: Stage node ArgMax/Squeeze (Reshape) types check error: output #0 has type FP16, but one of [S32] is expected
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It occurs on Linux, Windows, HDDL, NCS2.
Where to get the model
https://github.com/tensorflow/models/blob/master/research/deeplab/g3doc/model_zoo.md
mobilenetv2_coco_voc_trainaug
I hope it will be fixed in the next version.
連結已複製
Hi taka,
Thanks for reaching out.
Could you provide the MO command used, and confirm if it is our segmentation demo?
Regards,
Randall B.
Hi! I am having the same issue with a custom trained faster rcnnn model that i converted from pytorch and running on ncs2
"11101/Split of type Split: Check (actualType == expectedType) failed: Stage node 11101/Split types check error: output #1 has type FP16, but S32 is expected"
any ideas why this is happening? though i've set precision of all of my output nodes to FP32 in my c++ app, but this till keeps happening
hi Randall
mo_tf.py ^
--input_shape=[1,513,513,3]
--input=1:mul_1
--output=ArgMax
--input_model=deeplabv3_mnv2_pascal_train_aug/frozen_inference_graph.pb
--data_type=FP16
ran with segmanetation demo
# segmentation_demo.exe -d MYRIAD -m deeplabv3_mnv2.xml -i test.mp4
Best Regards,
Hi taka, Raza,
This error is due to an unsupported demo on the VPU devices, and may or may not work. The README, \deployment_tools\open_model_zoo\demos\segmentation_demo, for this demo says:
**NOTE**: On VPU devices (Intel® Movidius™ Neural Compute Stick, Intel® Neural Compute Stick 2, and Intel® Vision Accelerator Design with Intel® Movidius™ VPUs) this demo is not supported with any of the Model Downloader available topologies. Other models may produce unexpected results on these devices as well.
Take a look at the README here:
- https://docs.openvinotoolkit.org/latest/_demos_segmentation_demo_README.html
- https://docs.openvinotoolkit.org/latest/_demos_object_detection_demo_faster_rcnn_README.html
Regards,
Randall.
