openvino2tensorflow 1.25.1

This script converts the OpenVINO IR model to Tensorflow's saved_model, tflite, h5 and pb. in (NCHW) format

openvino2tensorflow

This script converts the ONNX/OpenVINO IR model to Tensorflow's saved_model, tflite, h5, tfjs, tftrt(TensorRT), CoreML, EdgeTPU, ONNX and pb. PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW). And the conversion from .pb to saved_model and from saved_model to .pb and from .pb to .tflite and saved_model to .tflite and saved_model to onnx. Support for building environments with Docker. It is possible to directly access the host PC GUI and the camera to verify the operation. NVIDIA GPU (dGPU) support. Intel iHD GPU (iGPU) support.

Special custom TensorFlow binaries and special custom TensorFLow Lite binaries are used.

Work in progress now.

I'm continuing to add more layers of support and bug fixes on a daily basis. If you have a model that you are having trouble converting, please share the .bin and .xml with the issue. I will try to convert as much as possible.

1. Environment

• Python 3.6+
• TensorFlow v2.7.0+
• PyTorch v1.10.0+
• TorchVision
• TorchAudio
• OpenVINO 2021.4.582+
• TensorRT 8.2+
• trtexec
• pycuda 2021.1
• tensorflowjs
• coremltools
• paddle2onnx
• onnx
• onnxruntime
• onnx_graphsurgeon
• onnx-simplifier
• onnxconverter-common
• onnx-tensorrt
• onnx2json
• json2onnx
• tf2onnx
• torch2trt
• onnx-tf
• tensorflow-datasets
• tf_slim
• edgetpu_compiler
• tflite2tensorflow
• openvino2tensorflow
• gdown
• pandas
• matplotlib
• paddlepaddle
• paddle2onnx
• pycocotools
• scipy
• Intel-Media-SDK
• Intel iHD GPU (iGPU) support
• OpenCL
• Docker

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2. Use case

• PyTorch (NCHW) -> ONNX (NCHW) -> OpenVINO (NCHW) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFJS (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TF-TRT (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> CoreML (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> ONNX (NHWC/NCHW)
  • -> openvino2tensorflow -> Myriad Inference Engine Blob (NCHW)

• Caffe (NCHW) -> OpenVINO (NCHW) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFJS (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TF-TRT (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> CoreML (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> ONNX (NHWC/NCHW)
  • -> openvino2tensorflow -> Myriad Inference Engine Blob (NCHW)

• MXNet (NCHW) -> OpenVINO (NCHW) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFJS (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TF-TRT (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> CoreML (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> ONNX (NHWC/NCHW)
  • -> openvino2tensorflow -> Myriad Inference Engine Blob (NCHW)

• Keras (NHWC) -> OpenVINO (NCHW・Optimized) ->

  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFLite (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TFJS (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> TF-TRT (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC) -> EdgeTPU (NHWC)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> CoreML (NHWC/NCHW)
  • -> openvino2tensorflow -> Tensorflow/Keras (NHWC/NCHW) -> ONNX (NHWC/NCHW)
  • -> openvino2tensorflow -> Myriad Inference Engine Blob (NCHW)

• saved_model -> saved_model_to_pb -> pb

• saved_model ->

  • -> saved_model_to_tflite -> TFLite
  • -> saved_model_to_tflite -> TFJS
  • -> saved_model_to_tflite -> TF-TRT
  • -> saved_model_to_tflite -> EdgeTPU
  • -> saved_model_to_tflite -> CoreML
  • -> saved_model_to_tflite -> ONNX

• pb -> pb_to_tflite -> TFLite

• pb -> pb_to_saved_model -> saved_model

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3. Supported Layers

• Currently, there are problems with the Reshape and Transpose operation of 2D,3D,5D Tensor. Since it is difficult to accurately predict the shape of a simple shape change, I have added support for forced replacement of transposition parameters using JSON files. #6-7-replace-weights-or-constant-values-in-const-op-and-add-transpose-or-reshape-or-cast-just-beforeafter-the-operation-specified-by-layer_id

No.	OpenVINO Layer	TF Layer	Remarks
1	Parameter	Input	Convert to NHWC (Default) or NCHW
2	Const	Constant, Bias
3	Convolution	Conv1D, Conv2D, Conv3D	Conv3D has limited support
4	Add	Add
5	ReLU	ReLU
6	PReLU	PReLU	Maximum(0.0,x)+Minimum(0.0,alpha*x)
7	MaxPool	MaxPool2D
8	AvgPool	AveragePooling2D
9	GroupConvolution	DepthwiseConv2D, Conv2D/Split/Concat
10	ConvolutionBackpropData	Conv2DTranspose, Conv3DTranspose	Conv3DTranspose has limited support
11	Concat	Concat
12	Multiply	Multiply
13	Tan	Tan
14	Tanh	Tanh
15	Elu	Elu
16	Sigmoid	Sigmoid
17	HardSigmoid	hard_sigmoid
18	SoftPlus	SoftPlus
19	Swish	Swish	You can replace swish and hard-swish with each other by using the "--replace_swish_and_hardswish" option
20	Interpolate	ResizeNearestNeighbor, ResizeBilinear	4D [N,H,W,C] or 5D [N,D,H,W,C]
21	ShapeOf	Shape
22	Convert	Cast
23	StridedSlice	Strided_Slice
24	Pad	Pad, MirrorPad
25	Clamp	ReLU6, Clip
26	TopK	ArgMax, top_k
27	Transpose	Transpose
28	Squeeze	Squeeze
29	Unsqueeze	Identity, expand_dims	WIP
30	ReduceMean	reduce_mean
31	ReduceMax	reduce_max
32	ReduceMin	reduce_min
33	ReduceSum	reduce_sum
34	ReduceProd	reduce_prod
35	Subtract	Subtract
36	MatMul	MatMul
37	Reshape	Reshape
38	Range	Range	WIP
39	Exp	Exp
40	Abs	Abs
41	SoftMax	SoftMax
42	Negative	Negative
43	Maximum	Maximum	No broadcast
44	Minimum	Minimum	No broadcast
45	Acos	Acos
46	Acosh	Acosh
47	Asin	Asin
48	Asinh	Asinh
49	Atan	Atan
50	Atanh	Atanh
51	Ceiling	Ceil
52	Cos	Cos
53	Cosh	Cosh
54	Sin	Sin
55	Sinh	Sinh
56	Gather	Gather
57	Divide	Divide, FloorDiv
58	Erf	Erf
59	Floor	Floor
60	FloorMod	FloorMod
61	HSwish	HardSwish	x*ReLU6(x+3)*0.16666667, You can replace swish and hard-swish with each other by using the "--replace_swish_and_hardswish" option
62	Log	Log
63	Power	Pow	No broadcast
64	Mish	Mish	x*Tanh(softplus(x))
65	Selu	Selu
66	Equal	equal
67	NotEqual	not_equal
68	Greater	greater
69	GreaterEqual	greater_equal
70	Less	less
71	LessEqual	less_equal
72	Select	Select	No broadcast
73	LogicalAnd	logical_and
74	LogicalNot	logical_not
75	LogicalOr	logical_or
76	LogicalXor	logical_xor
77	Broadcast	broadcast_to, ones, Multiply	numpy / bidirectional mode, WIP
78	Split	Split
79	VariadicSplit	Split, Slice, SplitV
80	MVN	reduce_mean, sqrt, reduce_variance	(x - reduce_mean(x)) / sqrt(reduce_variance(x) + eps)
81	NonZero	not_equal, boolean_mask
82	ReduceL2	square, reduce_sum, sqrt
83	SpaceToDepth	SpaceToDepth
84	DepthToSpace	DepthToSpace
85	Sqrt	sqrt
86	SquaredDifference	squared_difference
87	FakeQuantize	subtract, multiply, round, greater, where, less_equal, add
88	Tile	tile
89	GatherND	gather_nd, reshape, cumprod, multiply, reduce_sum, gather, concat
90	NonMaxSuppression	non_max_suppression	WIP. Only available for batch size 1.
91	Gelu	gelu
92	NormalizeL2	tf.math.add, tf.math.l2_normalize	x/sqrt(max(sum(x**2), eps)) or x/sqrt(add(sum(x**2), eps))
93	ScatterElementsUpdate	shape, rank, floormod, add, cast, range, expand_dims, meshgrid, concat, reshape, tensor_scatter_nd_update
94	ROIAlign	crop_and_resize, avg_pool, max_pool
95	ScatterNDUpdate	tensor_scatter_nd_update
96	GatherElements	rank, add, shape, cast, floormod, range, tensor_scatter_nd_update, constant, transpose, meshgrid, expand_dims, concat, gather_nd	WIP
97	ConvertLike	Cast
98	ReduceL1	Abs, ReduceSum
99	ShuffleChannels	reshape, transpose
100	PriorBoxClustered	Constant	WIP
101	Result	Identity	Output

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4. Setup

4-1. [Environment construction pattern 1] Execution by Docker (strongly recommended)

You do not need to install any packages other than Docker. It consumes 24GB of storage.

$ docker pull ghcr.io/pinto0309/openvino2tensorflow:latest
or
$ docker build -t ghcr.io/pinto0309/openvino2tensorflow:latest .

# If you don't need to access the GUI of the HostPC and the USB camera.
$ docker run -it --rm \
  -v `pwd`:/home/user/workdir \
  ghcr.io/pinto0309/openvino2tensorflow:latest

# If conversion to TF-TRT is not required. And if you need to access the HostPC GUI and USB camera.
$ xhost +local: && \
  docker run -it --rm \
  -v `pwd`:/home/user/workdir \
  -v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
  --device /dev/video0:/dev/video0:mwr \
  --net=host \
  -e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
  -e DISPLAY=$DISPLAY \
  --privileged \
  ghcr.io/pinto0309/openvino2tensorflow:latest

# If you need to convert to TF-TRT. And if you need to access the HostPC GUI and USB camera.
$ xhost +local: && \
  docker run --gpus all -it --rm \
  -v `pwd`:/home/user/workdir \
  -v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
  --device /dev/video0:/dev/video0:mwr \
  --net=host \
  -e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
  -e DISPLAY=$DISPLAY \
  --privileged \
  ghcr.io/pinto0309/openvino2tensorflow:latest

# If you are using iGPU (OpenCL). And if you need to access the HostPC GUI and USB camera.
$ xhost +local: && \
  docker run -it --rm \
  -v `pwd`:/home/user/workdir \
  -v /tmp/.X11-unix/:/tmp/.X11-unix:rw \
  --device /dev/video0:/dev/video0:mwr \
  --net=host \
  -e LIBVA_DRIVER_NAME=iHD \
  -e XDG_RUNTIME_DIR=$XDG_RUNTIME_DIR \
  -e DISPLAY=$DISPLAY \
  --privileged \
  ghcr.io/pinto0309/openvino2tensorflow:latest

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4-2. [Environment construction pattern 2] Execution by Host machine

To install using the Python Package Index (PyPI), use the following command.

$ pip3 install --user --upgrade openvino2tensorflow

To install with the latest source code of the main branch, use the following command.

$ pip3 install --user --upgrade git+https://github.com/PINTO0309/openvino2tensorflow

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5. Usage

5-1. openvino to tensorflow convert

usage: openvino2tensorflow
  [-h]
  --model_path MODEL_PATH
  [--model_output_path MODEL_OUTPUT_PATH]
  [--output_saved_model]
  [--output_h5]
  [--output_weight_and_json]
  [--output_pb]
  [--output_no_quant_float32_tflite]
  [--output_dynamic_range_quant_tflite]
  [--output_weight_quant_tflite]
  [--output_float16_quant_tflite]
  [--output_integer_quant_tflite]
  [--output_full_integer_quant_tflite]
  [--output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE]
  [--string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION]
  [--calib_ds_type CALIB_DS_TYPE]
  [--ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION]
  [--split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION]
  [--download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS]
  [--tfds_download_flg]
  [--load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY]
  [--output_tfjs]
  [--output_tftrt_float32]
  [--output_tftrt_float16]
  [--tftrt_maximum_cached_engines TFTRT_MAXIMUM_CACHED_ENGINES]
  [--output_coreml]
  [--output_edgetpu]
  [--edgetpu_compiler_timeout EDGETPU_COMPILER_TIMEOUT]
  [--edgetpu_num_segments EDGETPU_NUM_SEGMENTS]
  [--output_onnx]
  [--onnx_opset ONNX_OPSET]
  [--output_myriad]
  [--vpu_number_of_shaves VPU_NUMBER_OF_SHAVES]
  [--vpu_number_of_cmx_slices VPU_NUMBER_OF_CMX_SLICES]
  [--replace_swish_and_hardswish]
  [--optimizing_hardswish_for_edgetpu]
  [--replace_prelu_and_minmax]
  [--yolact]
  [--restricted_resize_image_mode]
  [--weight_replacement_config WEIGHT_REPLACEMENT_CONFIG]
  [--disable_experimental_new_quantizer]
  [--optimizing_barracuda]
  [--layerids_of_the_terminating_output LAYERIDS_OF_THE_TERMINATING_OUTPUT]
  [--keep_input_tensor_in_nchw]

optional arguments:
  -h, --help
                        show this help message and exit
  --model_path MODEL_PATH
                        input IR model path (.xml)
  --model_output_path MODEL_OUTPUT_PATH
                        The output folder path of the converted model file
  --output_saved_model
                        saved_model output switch
  --output_h5
                        .h5 output switch
  --output_weight_and_json
                        weight of h5 and json output switch
  --output_pb
                        .pb output switch
  --output_no_quant_float32_tflite
                        float32 tflite output switch
  --output_dynamic_range_quant_tflite
                        dynamic range quant tflite output switch
  --output_weight_quant_tflite
                        weight quant tflite output switch
  --output_float16_quant_tflite
                        float16 quant tflite output switch
  --output_integer_quant_tflite
                        integer quant tflite output switch
  --output_full_integer_quant_tflite
                        full integer quant tflite output switch
  --output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE
                        Input and output types when doing Integer Quantization
                        ('int8 (default)' or 'uint8')
  --string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION
                        String formulas for normalization. It is evaluated by
                        Pythons eval() function.
                        Default: '(data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]'
  --calib_ds_type CALIB_DS_TYPE
                        Types of data sets for calibration. tfds or numpy
                        Default: numpy
  --ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION
                        Dataset name for TensorFlow Datasets for calibration.
                        https://www.tensorflow.org/datasets/catalog/overview
  --split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION
                        Split name for TensorFlow Datasets for calibration.
                        https://www.tensorflow.org/datasets/catalog/overview
  --download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS
                        Download destination folder path for the calibration
                        dataset. Default: $HOME/TFDS
  --tfds_download_flg
                        True to automatically download datasets from
                        TensorFlow Datasets. True or False
  --load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY
                        The path from which to load the .npy file containing
                        the numpy binary version of the calibration data.
                        Default: sample_npy/calibration_data_img_sample.npy
  --output_tfjs
                        tfjs model output switch
  --output_tftrt_float32
                        tftrt float32 model output switch
  --output_tftrt_float16
                        tftrt float16 model output switch
  --tftrt_maximum_cached_engines
                        Specifies the quantity of tftrt_maximum_cached_engines for TFTRT.
                        Default: 10000
  --output_coreml
                        coreml model output switch
  --output_edgetpu
                        edgetpu model output switch
  --edgetpu_compiler_timeout
                        edgetpu_compiler timeout for one compilation process in seconds.
                        Default: 3600
  --edgetpu_num_segments
                        Partition the model into 'num_segments' segments.
                        Default: 1 (no partition)
  --output_onnx
                        onnx model output switch
  --onnx_opset ONNX_OPSET
                        onnx opset version number
  --output_myriad
                        myriad inference engine blob output switch
  --vpu_number_of_shaves VPU_NUMBER_OF_SHAVES
                        vpu number of shaves. Default: 4
  --vpu_number_of_cmx_slices VPU_NUMBER_OF_CMX_SLICES
                        vpu number of cmx slices. Default: 4
  --replace_swish_and_hardswish
                        Replace swish and hard-swish with each other
  --optimizing_hardswish_for_edgetpu
                        Optimizing hardswish for edgetpu
  --replace_prelu_and_minmax
                        Replace prelu and minimum/maximum with each other
  --yolact
                        Specify when converting the Yolact model
  --restricted_resize_image_mode
                        Specify this if the upsampling contains OPs that are
                        not scaled by integer multiples. Optimization for
                        EdgeTPU will be disabled.
  --weight_replacement_config WEIGHT_REPLACEMENT_CONFIG
                        Replaces the value of Const for each layer_id defined
                        in json. Specify the path to the json file.
                        'weight_replacement_config.json'
  --disable_experimental_new_quantizer
                        Disable MLIRs new quantization feature during INT8 quantization
                        in TensorFlowLite.
  --optimizing_barracuda
                        Generates ONNX by replacing Barracuda unsupported layers
                        with standard layers. For example, GatherND.
  --layerids_of_the_terminating_output LAYERIDS_OF_THE_TERMINATING_OUTPUT
                        A comma-separated list of layerIDs to be used as output layers.
                        e.g. --layerids_of_the_terminating_output 100,201,560
                        Default: ''
  --keep_input_tensor_in_nchw
                        Does not convert the input to NHWC, but keeps the NCHW format.
                        Transpose is inserted right after the input layer, and
                        the model internals are handled by NHWC. Only 4D input is supported.

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5-2. saved_model to tflite convert

usage: saved_model_to_tflite
  [-h]
  --saved_model_dir_path SAVED_MODEL_DIR_PATH
  [--signature_def SIGNATURE_DEF]
  [--input_shapes INPUT_SHAPES]
  [--model_output_dir_path MODEL_OUTPUT_DIR_PATH]
  [--output_no_quant_float32_tflite]
  [--output_dynamic_range_quant_tflite]
  [--output_weight_quant_tflite]
  [--output_float16_quant_tflite]
  [--output_integer_quant_tflite]
  [--output_full_integer_quant_tflite]
  [--output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE]
  [--string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION]
  [--calib_ds_type CALIB_DS_TYPE]
  [--ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION]
  [--split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION]
  [--download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS]
  [--tfds_download_flg]
  [--load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY]
  [--output_tfjs]
  [--output_tftrt_float32]
  [--output_tftrt_float16]
  [--tftrt_maximum_cached_engines TFTRT_MAXIMUM_CACHED_ENGINES]
  [--output_coreml]
  [--output_edgetpu]
  [--edgetpu_compiler_timeout EDGETPU_COMPILER_TIMEOUT]
  [--edgetpu_num_segments EDGETPU_NUM_SEGMENTS]
  [--output_onnx]
  [--onnx_opset ONNX_OPSET]
  [--disable_experimental_new_quantizer]

optional arguments:
  -h, --help
                        show this help message and exit
  --saved_model_dir_path SAVED_MODEL_DIR_PATH
                        Input saved_model dir path
  --signature_def SIGNATURE_DEF
                        Specifies the signature name to load from saved_model
  --input_shapes INPUT_SHAPES
                        Overwrites an undefined input dimension (None or -1).
                        Specify the input shape in [n,h,w,c] format.
                        For non-4D tensors, specify [a,b,c,d,e], [a,b], etc.
                        A comma-separated list if there are multiple inputs.
                        (e.g.) --input_shapes [1,256,256,3],[1,64,64,3],[1,2,16,16,3]
  --model_output_dir_path MODEL_OUTPUT_DIR_PATH
                        The output folder path of the converted model file
  --output_no_quant_float32_tflite
                        float32 tflite output switch
  --output_dynamic_range_quant_tflite
                        dynamic range quant tflite output switch
  --output_weight_quant_tflite
                        weight quant tflite output switch
  --output_float16_quant_tflite
                        float16 quant tflite output switch
  --output_integer_quant_tflite
                        integer quant tflite output switch
  --output_full_integer_quant_tflite
                        full integer quant tflite output switch
  --output_integer_quant_type OUTPUT_INTEGER_QUANT_TYPE
                        Input and output types when doing Integer Quantization
                        ('int8 (default)' or 'uint8')
  --string_formulas_for_normalization STRING_FORMULAS_FOR_NORMALIZATION
                        String formulas for normalization. It is evaluated by
                        Pythons eval() function.
                        Default: '(data - [127.5,127.5,127.5]) / [127.5,127.5,127.5]'
  --calib_ds_type CALIB_DS_TYPE
                        Types of data sets for calibration. tfds or numpy
                        Default: numpy
  --ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION
                        Dataset name for TensorFlow Datasets for calibration.
                        https://www.tensorflow.org/datasets/catalog/overview
  --split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION
                        Split name for TensorFlow Datasets for calibration.
                        https://www.tensorflow.org/datasets/catalog/overview
  --download_dest_folder_path_for_the_calib_tfds DOWNLOAD_DEST_FOLDER_PATH_FOR_THE_CALIB_TFDS
                        Download destination folder path for the calibration
                        dataset. Default: $HOME/TFDS
  --tfds_download_flg
                        True to automatically download datasets from
                        TensorFlow Datasets. True or False
  --load_dest_file_path_for_the_calib_npy LOAD_DEST_FILE_PATH_FOR_THE_CALIB_NPY
                        The path from which to load the .npy file containing
                        the numpy binary version of the calibration data.
                        Default: sample_npy/calibration_data_img_sample.npy
  --output_tfjs
                        tfjs model output switch
  --output_tftrt_float32
                        tftrt float32 model output switch
  --output_tftrt_float16
                        tftrt float16 model output switch
  --tftrt_maximum_cached_engines
                        Specifies the quantity of tftrt_maximum_cached_engines for TFTRT.
                        Default: 10000
  --output_coreml
                        coreml model output switch
  --output_edgetpu
                        edgetpu model output switch
  --edgetpu_compiler_timeout
                        edgetpu_compiler timeout for one compilation process in seconds.
                        Default: 3600
  --edgetpu_num_segments
                        Partition the model into 'num_segments' segments.
                        Default: 1 (no partition)
  --output_onnx
                        onnx model output switch
  --onnx_opset ONNX_OPSET
                        onnx opset version number
  --disable_experimental_new_quantizer
                        Disable MLIRs new quantization feature during INT8 quantization
                        in TensorFlowLite.

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5-3. pb to saved_model convert

usage: pb_to_saved_model
  [-h]
  --pb_file_path PB_FILE_PATH
  --inputs INPUTS
  --outputs OUTPUTS
  [--model_output_path MODEL_OUTPUT_PATH]

optional arguments:
  -h, --help
                        show this help message and exit
  --pb_file_path PB_FILE_PATH
                        Input .pb file path (.pb)
  --inputs INPUTS
                        (e.g.1) input:0,input:1,input:2
                        (e.g.2) images:0,input:0,param:0
  --outputs OUTPUTS
                        (e.g.1) output:0,output:1,output:2
                        (e.g.2) Identity:0,Identity:1,output:0
  --model_output_path MODEL_OUTPUT_PATH
                        The output folder path of the converted model file

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5-4. pb to tflite convert

usage: pb_to_tflite
  [-h]
  --pb_file_path PB_FILE_PATH
  --inputs INPUTS
  --outputs OUTPUTS
  [--model_output_path MODEL_OUTPUT_PATH]

optional arguments:
  -h, --help
                        show this help message and exit
  --pb_file_path PB_FILE_PATH
                        Input .pb file path (.pb)
  --inputs INPUTS
                        (e.g.1) input,input_1,input_2
                        (e.g.2) images,input,param
  --outputs OUTPUTS
                        (e.g.1) output,output_1,output_2
                        (e.g.2) Identity,Identity_1,output
  --model_output_path MODEL_OUTPUT_PATH
                        The output folder path of the converted model file

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5-5. saved_model to pb convert

usage: saved_model_to_pb
  [-h]
  --saved_model_dir_path SAVED_MODEL_DIR_PATH
  [--model_output_dir_path MODEL_OUTPUT_DIR_PATH]
  [--signature_name SIGNATURE_NAME]

optional arguments:
  -h, --help
                        show this help message and exit
  --saved_model_dir_path SAVED_MODEL_DIR_PATH
                        Input saved_model dir path
  --model_output_dir_path MODEL_OUTPUT_DIR_PATH
                        The output folder path of the converted model file (.pb)
  --signature_name SIGNATURE_NAME
                        Signature name to be extracted from saved_model

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5-6. Extraction of IR weight

usage: ir_weight_extractor
  [-h]
  -m MODEL
  -o OUTPUT_PATH

optional arguments:
  -h, --help
                        show this help message and exit
  -m MODEL, --model MODEL
                        input IR model path
  -o OUTPUT_PATH, --output_path OUTPUT_PATH
                        weights output folder path

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6. Execution sample

6-1. Conversion of OpenVINO IR to Tensorflow models

OutOfMemory may occur when converting to saved_model or h5 when the file size of the original model is large, please try the conversion to a pb file alone.

$ openvino2tensorflow \
  --model_path openvino/448x448/FP32/Resnet34_3inputs_448x448_20200609.xml \
  --output_saved_model \
  --output_pb \
  --output_weight_quant_tflite \
  --output_float16_quant_tflite \
  --output_no_quant_float32_tflite

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6-2. Convert Protocol Buffer (.pb) to saved_model

This tool is useful if you want to check the internal structure of pb files, tflite files, .h5 files, coreml files and IR (.xml) files. https://lutzroeder.github.io/netron/

$ pb_to_saved_model \
  --pb_file_path model_float32.pb \
  --inputs inputs:0 \
  --outputs Identity:0

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6-3. Convert Protocol Buffer (.pb) to tflite

$ pb_to_tflite \
  --pb_file_path model_float32.pb \
  --inputs inputs \
  --outputs Identity,Identity_1,Identity_2

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6-4. Convert saved_model to Protocol Buffer (.pb)

$ saved_model_to_pb \
  --saved_model_dir_path saved_model \
  --model_output_dir_path pb_from_saved_model \
  --signature_name serving_default

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6-5. Converts saved_model to OpenVINO IR

$ python3 ${INTEL_OPENVINO_DIR}/deployment_tools/model_optimizer/mo_tf.py \
  --saved_model_dir saved_model \
  --output_dir openvino/reverse

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6-6. Checking the structure of saved_model

$ saved_model_cli show \
  --dir saved_model \
  --tag_set serve \
  --signature_def serving_default

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6-7. Replace weights or constant values in Const OP, and add Transpose or Reshape or Cast just before/after the operation specified by layer_id

6-7-1. Overview

If the transformation behavior of Reshape, Transpose, etc. does not go as expected, you can force the Const content to change by defining weights and constant values in a JSON file and having it read in. Alternatively, Transpose or Reshape or Cast can be added just before the operation specified by layer_id. After changing the structure, you need to carefully check the consistency of Reshape, Transpose and Interpolate before and after. Even if the model is successfully transformed, there is a possibility that the dimension that should be changed is transformed incorrectly. In particular, Reshape and Interpolate are often able to transform the model even if the number of elements in the dimension is messed up.

$ openvino2tensorflow \
  --model_path xxx.xml \
  --output_saved_model \
  --output_pb \
  --output_weight_quant_tflite \
  --output_float16_quant_tflite \
  --output_no_quant_float32_tflite \
  --weight_replacement_config weight_replacement_config_sample.json

Structure of JSON sample

{
    "format_version": 2,
    "layers": [
        {
            "layer_id": "659",
            "type": "Const",
            "replace_mode": "direct",
            "values": [
                0,
                1,
                2
            ]
        },
        {
            "layer_id": "660",
            "type": "Reshape",
            "replace_mode": "insert_after",
            "values": [
                2100,
                85
            ]
        },
        {
            "layer_id": "680",
            "type": "Cast",
            "replace_mode": "insert_after",
            "values": "i64"
        },
        {
            "layer_id": "442",
            "type": "Concat",
            "replace_mode": "change_axis",
            "values": 4
        },
        {
            "layer_id": "450",
            "type": "SoftMax",
            "replace_mode": "change_axis",
            "values": 2
        }
    ]
}

No.	Elements	Description
1	format_version	Format version of weight_replacement_config. Values less than or equal to 2.
2	layers	A list of layers. Enclose it with "[ ]" to define multiple layers to child elements.
2-1	layer_id	ID of the Const layer whose weight/constant parameter is to be swapped. For example, specify "1123" for layer id="1123" for type="Const" in .xml.
2-2	type	Fixed value replacement or type of operation to be added. "Const" or "Transpose" or "Reshape" or "Cast" or "Concat" or "SoftMax"
2-3	replace_mode	"direct" or "npy" or "insert_before" or "insert_after" or "change_axis". "direct": Specify the values of the Numpy matrix directly in the "values" attribute. Ignores the values recorded in the .bin file and replaces them with the values specified in "values". "npy": Load a Numpy binary file with the matrix output by np.save('xyz', a). The "values" attribute specifies the path to the Numpy binary file. "insert_before": Add Transpose or Reshape or Cast just before the operation specified by layer_id. "insert_after": Add Transpose or Reshape or Cast just after the operation specified by layer_id. "change_axis": Changes the axis of the Concat or SoftMax attribute value.
2-4	values	Specify the value or the path to the Numpy binary file to replace the weight/constant value recorded in .bin. The way to specify is as described in the description of 'replace_mode'. The table below shows the correspondence between the strings that can be specified for the "Cast" operation and the TensorFlow types. In most cases, you will probably only use "i32", "i64", "f32", and "f16".

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6-7-2. Example

• YOLOX Nano 320x320 (NCHW format)
• yolox_nano_320x320.xml
• yolox_nano_320x320.bin

1. Let's assume that you don't need Transpose in the final layer of the model. Here you have [1, 85, 2100] as input, and the original OpenVINO model transposes [0, 2, 1] in that order to obtain the tensor [1, 2100, 85]. This figure shows the visualization of a yolox_nano_320x320.xml file using Netron. The number shown in the OUTPUTS - output - name: is the layer ID of Transpose. The layer ID 660 is the number in the part before the colon. The number in the part after the colon is called the port number 2. However, what you are trying to change is the transposition parameter of the INPUTS - custom - name: part. The name of the parameter you are trying to change is 625. Note that 625 is not a layer ID, just a name.
2. Check the model structure as recorded in .xml. First, open yolox_nano_320x320.xml in your favorite IDE.
3. Search for to-layer="660" (Transpose) in the IDE. In the figure below, Layer ID 658 and Layer ID 659 are represented as input values connected to Layer ID 660.

In the figure below, one of them is 658 and one of them is 659. It is difficult to determine exactly what it is from the image alone. You must again note that 658:3 in the image is only a name, not a layer ID. It is worth noting here that the type of value you want to replace is Const.

4. Now you will search for layer ID "658" in the IDE. The type is "Concat", so the desired layer was not this one. What you are looking for is "Const".
5. Now, search for layer ID 659 in the IDE. The type is "Const". Now you can finally identify that the layer ID of the layer you want to replace is 659.
6. Create a JSON file to replace the constants [0, 2, 1] with [0, 1, 2], and you can use any name for the JSON file. Suppose you save the file with the name replace.json. If you want to replace it with a numpy matrix, specify "npy" for "replace_mode": and the path to the .npy file for "values":.

{
  "format_version": 2,
  "layers": [
      {
          "layer_id": "659",
          "type": "Const",
          "replace_mode": "direct",
          "values": [
              0,
              1,
              2
          ]
      }
  ]
}

{
  "format_version": 2,
  "layers": [
      {
          "layer_id": "659",
          "type": "Const",
          "replace_mode": "npy",
          "values": "path/to/your/xxx.npy"
      }
  ]
}

7. Specify the created JSON file as the argument of the --weight_replacement_config parameter of the conversion command and execute it. This is the end of the explanation of how to replace weights and constants.

$ openvino2tensorflow \
--model_path yolox_nano_320x320.xml \
--output_saved_model \
--output_pb \
--output_no_quant_float32_tflite \
--weight_replacement_config replace.json

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6-8. Check the contents of the .npy file, which is a binary version of the image file

$ view_npy --npy_file_path sample_npy/calibration_data_img_sample.npy

Press the Q button to display the next image. calibration_data_img_sample.npy contains 20 images extracted from the MS-COCO data set.

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6-9. Sample image of a conversion error message

Since it is very difficult to mechanically predict the correct behavior of Transpose and Reshape, errors like the one shown below may occur. Using the information in the figure below, try several times to force the replacement of constants and weights using the --weight_replacement_config option #6-7-replace-weights-or-constant-values-in-const-op-and-add-transpose-or-reshape-or-cast-just-beforeafter-the-operation-specified-by-layer_id. This is a very patient process, but if you take the time, you should be able to convert it correctly.

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6-10. Ability to specify an output layer for debugging the output values of the model

If you want to debug the output values of each layer, specify multiple layer IDs separated by commas in the --layerids_of_the_terminating_output option to check the output values. For example, if you want to debug the output values of two layers, LayerID=1007 (Add) and LayerID=1214 (Sigmoid), as shown in the figure below, specify as --layerids_of_the_terminating_output 1007,1214. When you convert a model, the output will be censored at the two specified layer IDs, and the model will be generated with the output of the model available for review. Note that if you specify a layer ID for an operation that has multiple outputs, such as Split, VariadicSplit, TopK, or NonMaxSuppression, all output values will be used as outputs.

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7. Output sample

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8. Model Structure

https://digital-standard.com/threedpose/models/Resnet34_3inputs_448x448_20200609.onnx

ONNX (NCHW)	OpenVINO (NCHW)	TFLite (NHWC)

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9. My article

• [English] Converting PyTorch, ONNX, Caffe, and OpenVINO (NCHW) models to Tensorflow / TensorflowLite (NHWC) in a snap

• PyTorch, ONNX, Caffe, OpenVINO (NCHW) のモデルをTensorflow / TensorflowLite (NHWC) へお手軽に変換する

• tf.image.resizeを含むFull Integer Quantization (.tflite)モデルのEdgeTPUモデルへの変換後の推論時に発生する "main.ERROR - Only float32 and uint8 are supported currently, got -xxx.Node number n (op name) failed to invoke" エラーの回避方法

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10. Conversion Confirmed Models

1. u-2-net
2. mobilenet-v2-pytorch
3. midasnet
4. footprints
5. efficientnet-b0-pytorch
6. efficientdet-d0
7. dense_depth
8. deeplabv3
9. colorization-v2-norebal
10. age-gender-recognition-retail-0013
11. resnet
12. arcface
13. emotion-ferplus
14. mosaic
15. retinanet
16. shufflenet-v2
17. squeezenet
18. version-RFB-320
19. yolov4
20. yolov4x-mish
21. ThreeDPoseUnityBarracuda - Resnet34_3inputs_448x448
22. efficientnet-lite4
23. nanodet
24. yolov4-tiny
25. yolov5s
26. yolact
27. MiDaS v2
28. MODNet
29. Person Reidentification
30. DeepSort
31. DINO (Transformer)

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