Help convert person-detection-0302 from openvino to onnx

Issue Type

Feature Request

OS

Ubuntu

OS architecture

x86_64

Programming Language

Python

Framework

OpenVINO, ONNX

Download URL for ONNX / OpenVINO IR

https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/3/person-detection-0302/FP32/person-detection-0302.xml

https://storage.openvinotoolkit.org/repositories/open_model_zoo/2022.1/models_bin/3/person-detection-0302/FP32/person-detection-0302.bin

Convert Script

H=544 W=920 MODEL=person-detection-0302

sudo openvino2tensorflow --model_path ${MODEL}.xml --output_saved_model --output_pb --output_no_quant_float32_tflite --output_dynamic_range_quant_tflite --output_weight_quant_tflite --output_float16_quant_tflite --output_integer_quant_tflite --string_formulas_for_normalization 'data / 255.0' --output_tfjs --output_coreml

Description

I want to convert person-detection-0302 from openvino model to onnx, but I get error message and I truely do not know how to fit these error by reading 6-7 part in the README.

I have read 6-7 part in the README several times. But I'm still confused about how to do fix then. There are many error type information. For example , "dims must represent a vector, got shape [160,1] [Op:Fill]", "ValueError: Dimensions must be equal, but are 80 and 3", etc.

Can you elaborate on how you can fininsh replace.json file step by step for a new model?

It is better to teach a man to fish than to give him a fish

Thank you

Relevant Log Output

input_layer1_shape: layer_id=419: Const(ndarray).shape (2,)
input_layer1_value: layer_id=419: [160   1]
tf_layers_dict_shape: (160, 1)
tf_layers_dict: [[   0]
 [   8]
 [  16]
 [  24]
 [  32]
 [  40]
 [  48]
 [  56]
 [  64]
 [  72]
 [  80]
 [  88]
 [  96]
 [ 104]
 [ 112]
 [ 120]
 [ 128]
 [ 136]
 [ 144]
 [ 152]
 [ 160]
 [ 168]
 [ 176]
 [ 184]
 [ 192]
 [ 200]
 [ 208]
 [ 216]
 [ 224]
 [ 232]
 [ 240]
 [ 248]
 [ 256]
 [ 264]
 [ 272]
 [ 280]
 [ 288]
 [ 296]
 [ 304]
 [ 312]
 [ 320]
 [ 328]
 [ 336]
 [ 344]
 [ 352]
 [ 360]
 [ 368]
 [ 376]
 [ 384]
 [ 392]
 [ 400]
 [ 408]
 [ 416]
 [ 424]
 [ 432]
 [ 440]
 [ 448]
 [ 456]
 [ 464]
 [ 472]
 [ 480]
 [ 488]
 [ 496]
 [ 504]
 [ 512]
 [ 520]
 [ 528]
 [ 536]
 [ 544]
 [ 552]
 [ 560]
 [ 568]
 [ 576]
 [ 584]
 [ 592]
 [ 600]
 [ 608]
 [ 616]
 [ 624]
 [ 632]
 [ 640]
 [ 648]
 [ 656]
 [ 664]
 [ 672]
 [ 680]
 [ 688]
 [ 696]
 [ 704]
 [ 712]
 [ 720]
 [ 728]
 [ 736]
 [ 744]
 [ 752]
 [ 760]
 [ 768]
 [ 776]
 [ 784]
 [ 792]
 [ 800]
 [ 808]
 [ 816]
 [ 824]
 [ 832]
 [ 840]
 [ 848]
 [ 856]
 [ 864]
 [ 872]
 [ 880]
 [ 888]
 [ 896]
 [ 904]
 [ 912]
 [ 920]
 [ 928]
 [ 936]
 [ 944]
 [ 952]
 [ 960]
 [ 968]
 [ 976]
 [ 984]
 [ 992]
 [1000]
 [1008]
 [1016]
 [1024]
 [1032]
 [1040]
 [1048]
 [1056]
 [1064]
 [1072]
 [1080]
 [1088]
 [1096]
 [1104]
 [1112]
 [1120]
 [1128]
 [1136]
 [1144]
 [1152]
 [1160]
 [1168]
 [1176]
 [1184]
 [1192]
 [1200]
 [1208]
 [1216]
 [1224]
 [1232]
 [1240]
 [1248]
 [1256]
 [1264]
 [1272]]
====================================================================================
ERROR: dims must represent a vector, got shape [160,1] [Op:Fill]
ERROR: model_path  : person-detection-0302.xml
ERROR: weights_path: person-detection-0302.bin
ERROR: layer_id    : 421
ERROR: input_layer0 layer_id=414: tf.Tensor([160   2], shape=(2,), dtype=int64)
ERROR: input_layer1 layer_id=420: tf.Tensor(
[[   0]
 [   8]
 [  16]
 [  24]
 [  32]
 [  40]
 [  48]
 [  56]
 [  64]
 [  72]
 [  80]
 [  88]
 [  96]
 [ 104]
 [ 112]
 [ 120]
 [ 128]
 [ 136]
 [ 144]
 [ 152]
 [ 160]
 [ 168]
 [ 176]
 [ 184]
 [ 192]
 [ 200]
 [ 208]
 [ 216]
 [ 224]
 [ 232]
 [ 240]
 [ 248]
 [ 256]
 [ 264]
 [ 272]
 [ 280]
 [ 288]
 [ 296]
 [ 304]
 [ 312]
 [ 320]
 [ 328]
 [ 336]
 [ 344]
 [ 352]
 [ 360]
 [ 368]
 [ 376]
 [ 384]
 [ 392]
 [ 400]
 [ 408]
 [ 416]
 [ 424]
 [ 432]
 [ 440]
 [ 448]
 [ 456]
 [ 464]
 [ 472]
 [ 480]
 [ 488]
 [ 496]
 [ 504]
 [ 512]
 [ 520]
 [ 528]
 [ 536]
 [ 544]
 [ 552]
 [ 560]
 [ 568]
 [ 576]
 [ 584]
 [ 592]
 [ 600]
 [ 608]
 [ 616]
 [ 624]
 [ 632]
 [ 640]
 [ 648]
 [ 656]
 [ 664]
 [ 672]
 [ 680]
 [ 688]
 [ 696]
 [ 704]
 [ 712]
 [ 720]
 [ 728]
 [ 736]
 [ 744]
 [ 752]
 [ 760]
 [ 768]
 [ 776]
 [ 784]
 [ 792]
 [ 800]
 [ 808]
 [ 816]
 [ 824]
 [ 832]
 [ 840]
 [ 848]
 [ 856]
 [ 864]
 [ 872]
 [ 880]
 [ 888]
 [ 896]
 [ 904]
 [ 912]
 [ 920]
 [ 928]
 [ 936]
 [ 944]
 [ 952]
 [ 960]
 [ 968]
 [ 976]
 [ 984]
 [ 992]
 [1000]
 [1008]
 [1016]
 [1024]
 [1032]
 [1040]
 [1048]
 [1056]
 [1064]
 [1072]
 [1080]
 [1088]
 [1096]
 [1104]
 [1112]
 [1120]
 [1128]
 [1136]
 [1144]
 [1152]
 [1160]
 [1168]
 [1176]
 [1184]
 [1192]
 [1200]
 [1208]
 [1216]
 [1224]
 [1232]
 [1240]
 [1248]
 [1256]
 [1264]
 [1272]], shape=(160, 1), dtype=int64)
ERROR: The trace log is below.
Traceback (most recent call last):
  File "/usr/local/bin/openvino2tensorflow", line 5310, in convert
    tf.ones(
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/util/traceback_utils.py", line 153, in error_handler
    raise e.with_traceback(filtered_tb) from None
  File "/usr/local/lib/python3.8/dist-packages/tensorflow/python/framework/ops.py", line 7164, in raise_from_not_ok_status
    raise core._status_to_exception(e) from None  # pylint: disable=protected-access
tensorflow.python.framework.errors_impl.InvalidArgumentError: dims must represent a vector, got shape [160,1] [Op:Fill]
ERROR: Please refer to 6-7 in the README first. https://github.com/PINTO0309/openvino2tensorflow

Source code for simple inference testing code

No response

PINTO0309 / openvino2tensorflow