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Hi,
Try to use implement ovc your_model_file.pb instead.
I managed to convert it this way.
You may refer to this documentation (click on CLI instead of Python)
Cordially,
Iffa
You will see that if you click on CLI section instead of Python
Converted your model using ovc command in cmd/terminal.
Inferencing result (the model has dynamic shape, need to provide shape during inferencing)
Cordially,
Iffa
Here is a sample code extracted from my openvino CPU inference thread (I use yolo8 with iGPU in a separate thread);
#! /usr/bin/python3
'''
28JUL2024wbk -- OpenVINO_SSD_Thread.py
Run MobilenetSSD_v2 inferences on CPU using OpenVINO 2024
For use with AI2.py
Setup and inference code largely lifted from the openvino python example:
hello_reshape_ssd.py
That was installed by the apt install of openvino 2024.2, the apt install is broken
so I had to do a pip install to run the code :(
'''
import numpy as np
import cv2
import datetime
import logging as log
import sys
from imutils.video import FPS
import openvino as ov
if True: # cut and paste from SSD_Thread avoid removing indents
###model_path = 'ssd_mobilenet_v2_coco.xml' # converted with openvino 2024
###model_path = '../ssd_mobilenet_v2_coco_2018_03_29/frozen_inference_graph.xml' # converted with openvion 2024
model_path = 'frozen_inference_graph.xml' # converted with openvion 2024 using frozen_inference_graph from 2021 conversion
### model_path = 'mobilenet_ssd_v2/MobilenetSSDv2cocoIR10.xml' # my IR10 conversion done with openvino 2021.3
'''
# simple python code to convert model, too bad it produces a multi output model.
import openvino as ov
ov_model = ov.convert_model('../ssd_mobilenet_v2_coco_2018_03_29/saved_model')
ov.save_model(ov_model,'ssd_mobilenet_v2_coco.xml')
'''
device_name = 'CPU'
log.basicConfig(format='[ %(levelname)s ] %(message)s', level=log.INFO, stream=sys.stdout)
## basically lifted from hello_reshape_ssd.py sample code installed with apt install of openvino 2024, which is broken
# --------------------------- Step 1. Initialize OpenVINO Runtime Core ------------------------------------------------
log.info('Creating OpenVINO Runtime Core')
core = ov.Core()
print('[INFO] Using OpenVINO: ' + ov.__version__)
devices = core.available_devices
log.info('Available devices:')
for device in devices:
deviceName = core.get_property(device, "FULL_DEVICE_NAME")
print(f" {device}: {deviceName}")
# --------------------------- Step 2. Read a model --------------------------------------------------------------------
log.info(f'Reading the model: {model_path}')
# (.xml and .bin files) or (.onnx file)
model = core.read_model(model_path)
## print(len(model.outputs), model.outputs)
## print('')
if len(model.inputs) != 1:
log.error('Supports only single input topologies.')
##return -1
'''
if len(model.outputs) != 1:
log.error('Supports only single output topologies')
print(len(model.outputs), model.outputs)
print('')
##return -1
'''
# --------------------------- Step 3. Set up input --------------------------------------------------------------------
## create image to set model size
'''
This was very confusing, sample code says:
'Reshaping the model to the height and width of the input image'
which makes no sence to me. If I feed in larger images it sort of works
but boxes are wrong and detections are poor. I know my MobilenetSSD_v2
model was for images sized 300x300 so I create a dummy image of this size
and use it to "reshape" the model.
'''
imageM = np.zeros(( 300, 300, 3), np.uint8)
imageM[:,:] = (127,127,127)
input_tensor = np.expand_dims(imageM, 0) # Add N dimension
log.info('Reshaping the model to the height and width of the input image')
n, h, w, c = input_tensor.shape
model.reshape({model.input().get_any_name(): ov.PartialShape((n, c, h, w))})
#print(n, c, w, h)
# --------------------------- Step 4. Apply preprocessing -------------------------------------------------------------
## I've made zero effort to understand this, but it seems to work!
ppp = ov.preprocess.PrePostProcessor(model)
# 1) Set input tensor information:
# - input() provides information about a single model input
# - precision of tensor is supposed to be 'u8'
# - layout of data is 'NHWC'
ppp.input().tensor() \
.set_element_type(ov.Type.u8) \
.set_layout(ov.Layout('NHWC')) # noqa: N400
# 2) Here we suppose model has 'NCHW' layout for input
ppp.input().model().set_layout(ov.Layout('NCHW'))
# 3) Set output tensor information:
# - precision of tensor is supposed to be 'f32'
### ppp.output().tensor().set_element_type(ov.Type.f32)
# 4) Apply preprocessing modifing the original 'model'
model = ppp.build()
# ---------------------------Step 4. Loading model to the device-------------------------------------------------------
log.info('Loading the model to the plugin')
compiled_model = core.compile_model(model, device_name)
### input_layer_ir = compiled_model.input(0)
### output_layer_ir = compiled_model.output("boxes")
image = cv2.imread('TestDetection.jpg')
### N, C, H, W = input_layer_ir.shape
resized_image = cv2.resize(image, (w, h))
input_tensor = np.expand_dims(resized_image, 0) # Add N dimension
cv2.imshow('SSD input', resized_image)
cv2.waitKey(0)
results = compiled_model.infer_new_request({0: input_tensor})
print(results)Running it as it gives this error:
[ INFO ] Creating OpenVINO Runtime Core
[INFO] Using OpenVINO: 2024.3.0-16041-1e3b88e4e3f-releases/2024/3
[ INFO ] Available devices:
CPU: 12th Gen Intel(R) Core(TM) i9-12900K
GPU: Intel(R) UHD Graphics 770 [0x4680] (iGPU)
[ INFO ] Reading the model: frozen_inference_graph.xml
[ INFO ] Reshaping the model to the height and width of the input image
Traceback (most recent call last):
File "/home/wally/AI_code/AI2/ssdv2.py", line 83, in <module>
model.reshape({model.input().get_any_name(): ov.PartialShape((n, c, h, w))})
RuntimeError: Check 'TRShape::broadcast_merge_into(output_shape, input_shapes[1], autob)' failed at src/core/shape_inference/include/eltwise_shape_inference.hpp:26:
While validating node 'opset1::Multiply Postprocessor/Decode/mul_2 (opset1::Multiply Postprocessor/Decode/div[0]:f32[191700], opset1::Subtract Postprocessor/Decode/get_center_coordinates_and_sizes/sub_1[0]:f32[1917]) -> (f32[?])' with friendly_name 'Postprocessor/Decode/mul_2':
Argument shapes are inconsistent.This is using the 2024 converted model in line 25 of the code. If I comment out line 25 and remove the comment from line 26 it uses the 2021 converted model and works fine.
Putting back the use of the 2024 converted model and commenting out line 83 it gets further but still fails:
[ INFO ] Loading the model to the plugin
Traceback (most recent call last):
File "/home/wally/AI_code/AI2/ssdv2.py", line 121, in <module>
results = compiled_model.infer_new_request({0: input_tensor})
File "/home/wally/VENV/y8ovv/lib/python3.10/site-packages/openvino/runtime/ie_api.py", line 298, in infer_new_request
return self.create_infer_request().infer(inputs)
File "/home/wally/VENV/y8ovv/lib/python3.10/site-packages/openvino/runtime/ie_api.py", line 132, in infer
return OVDict(super().infer(_data_dispatch(
RuntimeError: Exception from src/inference/src/cpp/infer_request.cpp:223:
Exception from src/plugins/intel_cpu/src/memory_desc/cpu_memory_desc.h:89:
ParameterMismatch: Can not clone with new dims. Descriptor's shape: {0 - ?, 0 - ?, 3, 0 - ?} is incompatible with provided dimensions: {1, 300, 300, 3}.
You can download my 2021 converted *.xml & *.bin files from this link as a *.tar.bz2 archive, ~29 MB:
https://1drv.ms/u/s!AnWizTQQ52Yzg1cAt2vo3tgzVGHn?e=EQgags
The model I'm trying to convert with 2024.3 is from:
http://download.tensorflow.org/models/object_detection/ssd_mobilenet_v2_coco_2018_03_29.tar.gz
Here is the test image that I load in the sample code, edit line 114 to load a different image, it is zoomed-in detection image from the 2021 version of my system that I use to test the Email and/or MMS notifications:
Hi,
I managed to run your model with Object Detection Python Demo and it seems your model can detect one of the objects (bottle) and at certain time, it could detect the other two bottles.
Perhaps you could take this demo code and modify it to fit your use case.
Cordially,
Iffa
If you are using the newer version of OpenVINO (2024.3) you should be able to use the Python demos directly.
Otherwise, (especially OV 2022.1 release) you need to install the Python* Model API Package before running the Demos or Model Tools: pip install <omz_dir>/demos/common/python
For more information and guide you can refer to this documentation.
Cordially,
Iffa
