Metadata-Version: 2.1
Name: openvino2tensorflow
Version: 1.21.5
Summary: This script converts the OpenVINO IR model to Tensorflow's saved_model, tflite, h5 and pb. in (NCHW) format
Home-page: https://github.com/PINTO0309/openvino2tensorflow
Author: Katsuya Hyodo
Author-email: rmsdh122@yahoo.co.jp
License: MIT License
Description: # openvino2tensorflow
        
        <p align="center">
          <img src="https://user-images.githubusercontent.com/33194443/104584047-4e688f80-56a5-11eb-8dc2-5816487239d0.png" />
        </p>
        
        This script converts the 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](https://github.com/PINTO0309/Tensorflow-bin) and [special custom TensorFLow Lite binaries](https://github.com/PINTO0309/TensorflowLite-bin) 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.**
        
        [![Downloads](https://static.pepy.tech/personalized-badge/openvino2tensorflow?period=total&units=none&left_color=grey&right_color=brightgreen&left_text=Downloads)](https://pepy.tech/project/openvino2tensorflow) ![GitHub](https://img.shields.io/github/license/PINTO0309/openvino2tensorflow?color=2BAF2B) [![PyPI](https://img.shields.io/pypi/v/openvino2tensorflow?color=2BAF2B)](https://pypi.org/project/openvino2tensorflow/)
        
        ![render1629515758354](https://user-images.githubusercontent.com/33194443/130308925-f859a799-8013-4c57-b378-f405bdd2d39f.gif)
        
        ## 1. Environment
        - TensorFlow v2.6.0+
        - OpenVINO 2021.4.582+
        - Python 3.6+
        - tensorflowjs **`pip3 install --upgrade tensorflowjs`**
        - **[tensorrt](https://docs.nvidia.com/deeplearning/tensorrt/install-guide/index.html)**
        - coremltools **`pip3 install --upgrade coremltools`**
        - onnx **`pip3 install --upgrade onnx`**
        - tf2onnx **`pip3 install --upgrade tf2onnx`**
        - tensorflow-datasets **`pip3 install --upgrade tensorflow-datasets`**
        - **[edgetpu_compiler](https://coral.ai/docs/edgetpu/compiler/#system-requirements)**
        - Intel-Media-SDK
        - Intel iHD GPU (iGPU) support
        - Docker
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 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
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 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](#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|Result|Identity|Output|
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 4. Setup
        ### 4-1. **[Environment construction pattern 1]** Execution by Docker (`strongly recommended`)
        You do not need to install any packages other than Docker.
        ```bash
        $ docker pull pinto0309/openvino2tensorflow
        or
        $ docker build -t 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 \
          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 \
          pinto0309/openvino2tensorflow:latest
        $ cd workdir
        
        # 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 \
          pinto0309/openvino2tensorflow:latest
        $ cd workdir
        
        # 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 \
          pinto0309/openvino2tensorflow:latest
        $ cd workdir
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 4-2. **[Environment construction pattern 2]** Execution by Host machine
        To install using the **[Python Package Index (PyPI)](https://pypi.org/project/openvino2tensorflow/)**, use the following command.
        
        ```bash
        $ pip3 install --user --upgrade openvino2tensorflow
        ```
        
        To install with the latest source code of the main branch, use the following command.
        
        ```bash
        $ pip3 install --user --upgrade git+https://github.com/PINTO0309/openvino2tensorflow
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 5. Usage
        ### 5-1. openvino to tensorflow convert
        ```bash
        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]
          [--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
                                tftrt 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.
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 5-2. saved_model to tflite convert
        ```bash
        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]
          [--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
                                tftrt 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.
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 5-3. pb to saved_model convert
        ```bash
        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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 5-4. pb to tflite convert
        ```bash
        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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 5-5. saved_model to pb convert
        ```bash
        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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 5-6. Extraction of IR weight
        ```bash
        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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 6-6. Checking the structure of saved_model
        ```
        $ saved_model_cli show \
          --dir saved_model \
          --tag_set serve \
          --signature_def serving_default
        ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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.
        ```
        $ 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
        ```json
        {
            "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"
                }
            ]
        }
        ```
        
        |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.<br>![Screenshot 2021-02-08 01:06:30](https://user-images.githubusercontent.com/33194443/107152221-068a0f00-69aa-11eb-9d9e-f48bb1c3f781.png)|
        |2-2|type|Fixed value replacement or type of operation to be added. "Const" or "Transpose" or "Reshape" or "Cast"|
        |2-3|replace_mode|"direct" or "npy" or "insert_before" or "insert_after".<br>"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".<br>![Screenshot 2021-08-10 23:16:05](https://user-images.githubusercontent.com/33194443/128883404-256e4872-0f1e-4dea-948d-4f5818e6da96.png)<br>"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.<br>![Screenshot 2021-08-10 23:17:22](https://user-images.githubusercontent.com/33194443/128883417-8a159a64-bb39-4c6d-92c2-d4da1c67ed2a.png)<br>"insert_before": Add **`Transpose`** or **`Reshape`** or **`Cast`** just before the operation specified by layer_id.<br>![Screenshot 2021-09-16 14:17:22](https://user-images.githubusercontent.com/33194443/133553884-76194fbf-640c-48f9-8db1-c29b396c15a0.png)<br>"insert_after": Add **`Transpose`** or **`Reshape`** or **`Cast`** just after the operation specified by layer_id.<br>![Screenshot 2021-08-10 23:12:52](https://user-images.githubusercontent.com/33194443/128882909-780567c7-970a-483f-960b-571f33437cb5.png)|
        |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".<br>![Screenshot 2021-08-22 01:48:43](https://user-images.githubusercontent.com/33194443/130329181-25282919-2701-4885-8c72-b640743c2800.png)|
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        #### 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](https://netron.app/)**. 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.
        ![Screenshot 2021-08-04 23:45:15](https://user-images.githubusercontent.com/33194443/128202697-1e9a5110-3482-424c-8c03-202023478571.png)
          2. Check the model structure as recorded in .xml. First, open **`yolox_nano_320x320.xml`** in your favorite IDE.
        ![Screenshot 2021-08-05 00:00:38](https://user-images.githubusercontent.com/33194443/128204779-3a446618-c3f4-4968-ab3d-50648652da53.png)
        ![Screenshot 2021-08-05 00:08:50](https://user-images.githubusercontent.com/33194443/128205851-c4effc4a-8033-49a4-887f-4af0829824b9.png)
          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`**.
        ![Screenshot 2021-08-05 00:17:31](https://user-images.githubusercontent.com/33194443/128207323-400f5145-46fd-4734-b186-408d4a8cc7d0.png)
        
        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`**.
        
        ![Screenshot 2021-08-05 00:26:29](https://user-images.githubusercontent.com/33194443/128208774-1dd27e57-e453-4942-8708-c118d5cec10c.png)
        
          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"`**.
        ![Screenshot 2021-08-05 01:02:00](https://user-images.githubusercontent.com/33194443/128214658-ec28bbc5-685b-4f92-b0ca-ca6e5389194c.png)
          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`**.
        ![Screenshot 2021-08-05 01:05:33](https://user-images.githubusercontent.com/33194443/128215161-9631100f-0bff-4c49-ad57-9f3a3e8ad7be.png)
          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":`**.
          ```json
          {
            "format_version": 2,
            "layers": [
                {
                    "layer_id": "659",
                    "type": "Const",
                    "replace_mode": "direct",
                    "values": [
                        0,
                        1,
                        2
                    ]
                }
            ]
          }
          ```
          ```json
          {
            "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
          ```
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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.
        
        ![ezgif com-gif-maker](https://user-images.githubusercontent.com/33194443/109318923-aba15480-7891-11eb-84aa-034f77125f34.gif)
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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](#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.
        ![error_sample2](https://user-images.githubusercontent.com/33194443/124498169-e181b700-ddf6-11eb-9200-83ba44c62410.png)
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ### 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`**.
        ![Screenshot 2021-09-06 21:33:19](https://user-images.githubusercontent.com/33194443/132218610-0577f219-158e-4554-a3a0-47f216b82816.png)
        ![Screenshot 2021-09-06 21:33:28](https://user-images.githubusercontent.com/33194443/132218771-b0e86036-411c-4f3c-8c39-0aed17ed0681.png)
        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.
        ![Screenshot 2021-09-06 21:43:17](https://user-images.githubusercontent.com/33194443/132219420-367bcae9-ae45-4c2b-a893-8311be487142.png)
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 7. Output sample
        ![Screenshot 2020-10-16 00:08:40](https://user-images.githubusercontent.com/33194443/96149093-e38fa700-0f43-11eb-8101-65fc20b2cc8f.png)
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 8. Model Structure
        **[https://digital-standard.com/threedpose/models/Resnet34_3inputs_448x448_20200609.onnx](https://github.com/digital-standard/ThreeDPoseUnityBarracuda#download-and-put-files)**
        
        |ONNX (NCHW)|OpenVINO (NCHW)|TFLite (NHWC)|
        |:--:|:--:|:--:|
        |![Resnet34_3inputs_448x448_20200609 onnx_](https://user-images.githubusercontent.com/33194443/96398683-62683680-1207-11eb-928d-e4cb6c8cc188.png)|![Resnet34_3inputs_448x448_20200609 xml](https://user-images.githubusercontent.com/33194443/96153010-23f12400-0f48-11eb-8186-4bbad73b517a.png)|![model_float32 tflite](https://user-images.githubusercontent.com/33194443/96153019-26ec1480-0f48-11eb-96be-0c405ee2cbf7.png)|
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 9. My article
        - **[[English] Converting PyTorch, ONNX, Caffe, and OpenVINO (NCHW) models to Tensorflow / TensorflowLite (NHWC) in a snap](https://qiita.com/PINTO/items/ed06e03eb5c007c2e102)**
        
        - **[PyTorch, ONNX, Caffe, OpenVINO (NCHW) のモデルをTensorflow / TensorflowLite (NHWC) へお手軽に変換する](https://qiita.com/PINTO/items/7a0bcaacc77bb5d6abb1)**
        
        - **[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](https://qiita.com/PINTO/items/6ff62da1d02089442c8c)**
        
        **[↥ Back to top](#openvino2tensorflow)**
        
        ## 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)
        
        **[↥ Back to top](#openvino2tensorflow)**
        
Platform: linux
Platform: unix
Requires-Python: >3.6
Description-Content-Type: text/markdown
