Hi
I have converted my models to FP32 IR successfully and was able to do inference on CPU device. However, The program will be block in MYRIAD device.
environment info:
Neural Compute Stick2
MAC OS 10.13
openvino_2021.4.582
I have check the environment by run official mobile net successfully on CPU or MARIAD device. There probably exist some bugs in my model defination, which is attacher here (baseline.xml).
My test program:
from openvino.inference_engine import IECore
ie = IECore()
xml_path = './baseline.xml'
net = ie.read_network(model=xml_path)
exec_net = ie.load_network(network=net, device_name=MYRIAD) # !!! blocked here
I guess there may be some operator behave abnormally, but i don't know what happen here on MYRIAD. hope for your help.
Thanks
链接已复制
update after long time loading, it outputs log:
E: [ncAPI] [ 246602] [] ncGraphAllocate:2153 Not enough memory to allocate intermediate tensors on remote device
Traceback (most recent call last):
File "stereo_inference_openvino.py", line 88, in <module>
sys.exit(main())
File "stereo_inference_openvino.py", line 52, in main
exec_net = ie.load_network(network=net, device_name=args.device)
File "ie_api.pyx", line 367, in openvino.inference_engine.ie_api.IECore.load_network
File "ie_api.pyx", line 379, in openvino.inference_engine.ie_api.IECore.load_network
RuntimeError: Failed to allocate graph: NC_OUT_OF_MEMORY
Hi fariver,
Thanks for reaching out to us.
You’re getting this error because your model is too large and it exceeded the memory capacity of Intel® Neural Compute Stick 2 (NCS2) for intermediate processing.
For those large models, they can be supported by CPU plugin but not MYRIAD plugin. Check out this page for reference.
On a separate note, I noticed that you used FP32 IR for the inferencing on NCS2. Perhaps you may have a try with FP16 IR.
Regards,
Peh
Thanks for your replying. I have got through the block by cutting off some operators in network. However, I fall into two more confusion: 1) my new network run slower on MYRIAD than CPU; 2). the FP16 network runs comparable speed with its FP32 version on MYRIAD device without any speedup.
| FP16 | FP32 | |
|---|---|---|
| MYRIAD | 1.0966s | 1.0958s |
| CPU | 0.7722s | 0.7883s |
All the time consuming result is averaged by 10 iterations. Attachment is my new network definition. Hoping for further help.
Hi fariver,
Regarding the performance between CPU and Intel® Neural Compute Stick 2 (NCS2), you can have a look at this Benchmark Results. By looking into all the chosen models, the performance of CPU is always better than NCS2. Referring to this Supported Model Formats, the VPU plugins only support FP16 models.
Regards,
Peh
