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tflite2tensorflow

Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite, ONNX, OpenVINO, Myriad Inference Engine blob and .pb from .tflite. 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. Supports inverse quantization of INT8 quantization model.

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

Downloads GitHub PyPI CodeQL

1. Supported Layers

Supported Layers

No. TFLite Layer TF Layer Remarks
1 CONV_2D tf.nn.conv2d
2 DEPTHWISE_CONV_2D tf.nn.depthwise_conv2d
3 MAX_POOL_2D tf.nn.max_pool
4 PAD tf.pad
5 MIRROR_PAD tf.raw_ops.MirrorPad
6 RELU tf.nn.relu
7 PRELU tf.keras.layers.PReLU
8 RELU6 tf.nn.relu6
9 RESHAPE tf.reshape
10 ADD tf.add
11 SUB tf.math.subtract
12 CONCATENATION tf.concat
13 LOGISTIC tf.math.sigmoid
14 TRANSPOSE_CONV tf.nn.conv2d_transpose
15 MUL tf.multiply
16 HARD_SWISH x*tf.nn.relu6(x+3)*0.16666667 Or x*tf.nn.relu6(x+3)*0.16666666
17 AVERAGE_POOL_2D tf.keras.layers.AveragePooling2D
18 FULLY_CONNECTED tf.keras.layers.Dense
19 RESIZE_BILINEAR tf.image.resize Or tf.image.resize_bilinear The behavior differs depending on the optimization options of openvino and edgetpu.
20 RESIZE_NEAREST_NEIGHBOR tf.image.resize Or tf.image.resize_nearest_neighbor The behavior differs depending on the optimization options of openvino and edgetpu.
21 MEAN tf.math.reduce_mean
22 SQUARED_DIFFERENCE tf.math.squared_difference
23 RSQRT tf.math.rsqrt
24 DEQUANTIZE (const)
25 FLOOR tf.math.floor
26 TANH tf.math.tanh
27 DIV tf.math.divide
28 FLOOR_DIV tf.math.floordiv
29 SUM tf.math.reduce_sum
30 POW tf.math.pow
31 SPLIT tf.split
32 SOFTMAX tf.nn.softmax
33 STRIDED_SLICE tf.strided_slice
34 TRANSPOSE ttf.transpose
35 SPACE_TO_DEPTH tf.nn.space_to_depth
36 DEPTH_TO_SPACE tf.nn.depth_to_space
37 REDUCE_MAX tf.math.reduce_max
38 Convolution2DTransposeBias tf.nn.conv2d_transpose, tf.math.add CUSTOM, MediaPipe
39 LEAKY_RELU tf.keras.layers.LeakyReLU
40 MAXIMUM tf.math.maximum
41 MINIMUM tf.math.minimum
42 MaxPoolingWithArgmax2D tf.raw_ops.MaxPoolWithArgmax CUSTOM, MediaPipe
43 MaxUnpooling2D tf.cast, tf.shape, tf.math.floordiv, tf.math.floormod, tf.ones_like, tf.shape, tf.concat, tf.reshape, tf.transpose, tf.scatter_nd CUSTOM, MediaPipe
44 GATHER tf.gather
45 CAST tf.cast
46 SLICE tf.slice
47 PACK tf.stack
48 UNPACK tf.unstack
49 ARG_MAX tf.math.argmax Or tf.math.reduce_max, tf.subtract, tf.math.minimum, tf.multiply The behavior differs depending on the optimization options of edgetpu.
50 EXP tf.exp
51 TOPK_V2 tf.math.top_k
52 LOG_SOFTMAX tf.nn.log_softmax
53 L2_NORMALIZATION tf.math.l2_normalize
54 LESS tf.math.less
55 LESS_EQUAL tf.math.less_equal
56 GREATER tf.math.greater
57 GREATER_EQUAL tf.math.greater_equal
58 NEG tf.math.negative
59 WHERE tf.where
60 SELECT tf.where
61 SELECT_V2 tf.where
62 PADV2 tf.raw_ops.PadV2
63 SIN tf.math.sin
64 TILE tf.tile
65 EQUAL tf.math.equal
66 NOT_EQUAL tf.math.not_equal
67 LOG tf.math.log
68 SQRT tf.math.sqrt
69 ARG_MIN tf.math.argmin or tf.math.negative,tf.math.argmax
70 REDUCE_PROD tf.math.reduce_prod
71 LOGICAL_OR tf.math.logical_or
72 LOGICAL_AND tf.math.logical_and
73 LOGICAL_NOT tf.math.logical_not
74 REDUCE_MIN tf.math.reduce_min or tf.math.negative,tf.math.reduce_max
75 REDUCE_ANY tf.math.reduce_any
76 SQUARE tf.math.square
77 ZEROS_LIKE tf.zeros_like
78 FILL tf.fill
79 FLOOR_MOD tf.math.floormod
80 RANGE tf.range
81 ABS tf.math.abs
82 UNIQUE tf.unique
83 CEIL tf.math.ceil
84 REVERSE_V2 tf.reverse
85 ADD_N tf.math.add_n
86 GATHER_ND tf.gather_nd
87 COS tf.math.cos
88 RANK tf.math.rank
89 ELU tf.nn.elu
90 WHILE tf.while_loop
91 REVERSE_SEQUENCE tf.reverse_sequence
92 MATRIX_DIAG tf.linalg.diag
93 ROUND tf.math.round
94 NON_MAX_SUPPRESSION_V4 tf.raw_ops.NonMaxSuppressionV4
95 NON_MAX_SUPPRESSION_V5 tf.raw_ops.NonMaxSuppressionV5, tf.raw_ops.NonMaxSuppressionV4, tf.raw_ops.NonMaxSuppressionV3
96 SCATTER_ND tf.scatter_nd
97 SEGMENT_SUM tf.math.segment_sum
98 CUMSUM tf.math.cumsum
99 BROADCAST_TO tf.broadcast_to
100 RFFT2D tf.signal.rfft2d
101 L2_POOL_2D tf.square, tf.keras.layers.AveragePooling2D, tf.sqrt
102 LOCAL_RESPONSE_NORMALIZATION tf.nn.local_response_normalization
103 RELU_N1_TO_1 tf.minimum, tf.maximum
104 SPLIT_V tf.raw_ops.SplitV
105 MATRIX_SET_DIAG tf.linalg.set_diag
106 SHAPE tf.shape
107 EXPAND_DIMS tf.expand_dims
108 SQUEEZE tf.squeeze
109 FlexRFFT tf.signal.rfft Flex OP
110 FlexImag tf.math.imag Flex OP
111 FlexReal tf.math.real Flex OP
112 FlexRFFT2D tf.signal.rfft2d Flex OP
113 FlexComplexAbs tf.raw_ops.ComplexAbs Flex OP
114 IMAG tf.math.imag
115 REAL tf.math.real
116 COMPLEX_ABS tf.raw_ops.ComplexAbs
117 TFLite_Detection_PostProcess tf.divide, tf.strided_slice, tf.math.argmax, tf.math.reduce_max, tf.math.multiply, tf.math.add, tf.math.exp, tf.math.subtract, tf.expand_dims, tf.gather, tf.reshape, tf.identity, tf.raw_ops.NonMaxSuppressionV5 CUSTOM
118 ONE_HOT tf.one_hot
119 FlexMultinomial tf.random.categorical Flex OP
120 FlexAll tf.math.reduce_all Flex OP
121 FlexErf tf.math.erf Flex OP
122 FlexRoll tf.roll Flex OP
123 CONV_3D tf.keras.layers.Conv3D
124 CONV_3D_TRANSPOSE tf.nn.conv3d_transpose
125 Densify (const)
126 SPACE_TO_BATCH_ND tf.space_to_batch_nd
127 BATCH_TO_SPACE_ND tf.compat.v1.batch_to_space_nd
128 TransformLandmarks tf.reshape, tf.linalg.matmul, tf.math.add CUSTOM, MediaPipe
129 TransformTensorBilinear tf.reshape, tf.linalg.matmul, tf.math.add, tf.tile, tf.math.floor, tf.math.subtract, tf.math.multiply, tf.math.reduce_prod, tf.cast, tf.math.maximum, tf.math.maximum, tf.concat, tf.gather_nd CUSTOM, MediaPipe
130 Landmarks2TransformMatrix tf.constant, tf.math.subtract, tf.math.norm, tf.math.divide, tf.linalg.matmul, tf.concat, tf.transpose, tf.gather, tf.math.reduce_min, tf.math.reduce_max, tf.math.multiply, tf.zeros, tf.math.add, tf.tile CUSTOM, MediaPipe

2. Environment

  • Python3.8+
  • TensorFlow v2.9.0+
  • TensorFlow Lite v2.9.0 with MediaPipe Custom OP, FlexDelegate and XNNPACK enabled
  • flatc v2.0.8
  • PyTorch v1.12.0 (with grid_sample)
  • TorchVision
  • TorchAudio
  • OpenVINO 2021.4.582+
  • TensorRT 8.4+
  • trtexec
  • pycuda 2021.1
  • tensorflowjs
  • coremltools
  • paddle2onnx
  • onnx
  • onnxruntime-gpu (CUDA, TensorRT, OpenVINO)
  • onnxruntime-extensions
  • onnx_graphsurgeon
  • onnx-simplifier
  • onnxconverter-common
  • onnxmltools
  • onnx-tensorrt
  • tf2onnx
  • torch2trt
  • onnx-tf
  • tensorflow-datasets
  • tf_slim
  • edgetpu_compiler
  • tflite2tensorflow
  • openvino2tensorflow
  • simple-onnx-processing-tools
  • gdown
  • pandas
  • matplotlib
  • paddlepaddle
  • paddle2onnx
  • pycocotools
  • scipy
  • Intel-Media-SDK
  • Intel iHD GPU (iGPU) support
  • OpenCL
  • gluoncv
  • LLVM
  • NNPACK
  • WSL2 OpenCL

3. Setup

3-1. [Environment construction pattern 1] Execution by Docker (strongly recommended)

You do not need to install any packages other than Docker. It consumes about 26.7GB of host storage.

$ docker pull ghcr.io/pinto0309/tflite2tensorflow:latest
or
$ docker build -t ghcr.io/pinto0309/tflite2tensorflow: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/tflite2tensorflow: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/tflite2tensorflow: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/tflite2tensorflow: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/tflite2tensorflow:latest

3-2. [Environment construction pattern 2] Execution by Host machine

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

$ pip3 install --user --upgrade tflite2tensorflow

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

$ pip3 install --user --upgrade git+https://github.com/PINTO0309/tflite2tensorflow

Installs a customized TensorFlow Lite runtime with support for MediaPipe Custom OP, FlexDelegate, and XNNPACK. If tflite_runtime does not install properly, please follow the instructions in the next article to build a custom build in the environment you are using. Add a custom OP to the TFLite runtime to build the whl installer (for Python), MaxPoolingWithArgmax2D, MaxUnpooling2D, Convolution2DTransposeBias, TransformLandmarks, TransformTensorBilinear, Landmarks2TransformMatrix

$ sudo pip3 uninstall -y \
    tensorboard-plugin-wit \
    tb-nightly \
    tensorboard \
    tf-estimator-nightly \
    tensorflow-gpu \
    tensorflow \
    tf-nightly \
    tensorflow_estimator \
    tflite_runtime

$ APPVER=v1.20.7
$ TENSORFLOWVER=2.8.0

### Customized version of TensorFlow Lite installation
$ wget https://github.com/PINTO0309/tflite2tensorflow/releases/download/${APPVER}/tflite_runtime-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl \
  && sudo chmod +x tflite_runtime-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl \
  && pip3 install --user --force-reinstall tflite_runtime-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl \
  && rm tflite_runtime-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl

### Install the Customized Full TensorFlow package
### (MediaPipe Custom OP, FlexDelegate, XNNPACK enabled)
$ wget https://github.com/PINTO0309/tflite2tensorflow/releases/download/${APPVER}/tflite_runtime-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl \
  && sudo chmod +x tensorflow-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl \
  && pip3 install --user --force-reinstall tensorflow-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl \
  && rm tensorflow-${TENSORFLOWVER}-cp38-none-linux_x86_64.whl

 or

### Install the Non-customized TensorFlow package
$ pip3 install --user tf-nightly

### Download schema.fbs
$ wget https://github.com/PINTO0309/tflite2tensorflow/raw/main/schema/schema.fbs

Build flatc

$ git clone -b v2.0.8 https://github.com/google/flatbuffers.git
$ cd flatbuffers && mkdir build && cd build
$ cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release ..
$ make -j$(nproc)

vvtvsu0y1791ow2ybdk61s9fv7e4 saxqukktcjncsk2hp7m8p2cns4q4

The Windows version of flatc v2.0.8 can be downloaded from here. https://github.com/google/flatbuffers/releases/download/v2.0.8/Windows.flatc.binary.zip

4. Usage / Execution sample

4-1. Command line options

usage: tflite2tensorflow
  [-h]
  --model_path MODEL_PATH
  --flatc_path FLATC_PATH
  --schema_path SCHEMA_PATH
  [--model_output_path MODEL_OUTPUT_PATH]
  [--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]
  [--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]
  [--output_coreml]
  [--optimizing_coreml]
  [--output_edgetpu]
  [--edgetpu_compiler_timeout EDGETPU_COMPILER_TIMEOUT]
  [--edgetpu_num_segments EDGETPU_NUM_SEGMENTS]
  [--output_onnx]
  [--onnx_opset ONNX_OPSET]
  [--onnx_extra_opset ONNX_EXTRA_OPSET]
  [--disable_onnx_nchw_conversion]
  [--disable_onnx_optimization]
  [--output_openvino_and_myriad]
  [--vpu_number_of_shaves VPU_NUMBER_OF_SHAVES]
  [--vpu_number_of_cmx_slices VPU_NUMBER_OF_CMX_SLICES]
  [--optimizing_for_openvino_and_myriad]
  [--rigorous_optimization_for_myriad]
  [--replace_swish_and_hardswish]
  [--optimizing_for_edgetpu]
  [--replace_prelu_and_minmax]
  [--disable_experimental_new_quantizer]
  [--disable_per_channel]
  [--optimizing_barracuda]
  [--locationids_of_the_terminating_output]

optional arguments:
  -h, --help
          show this help message and exit
  --model_path MODEL_PATH
          input tflite model path (*.tflite)
  --flatc_path FLATC_PATH
          flatc file path (flatc)
  --schema_path SCHEMA_PATH
          schema.fbs path (schema.fbs)
  --model_output_path MODEL_OUTPUT_PATH
          The output folder path of the converted model file
  --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
          Python's 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
          [20, 513, 513, 3] -> [Number of images, h, w, c]
  --output_tfjs
          tfjs model output switch
  --output_tftrt32
          tftrt float32 model output switch
  --output_tftrt16
          tftrt float16 model output switch
  --output_coreml
          coreml model output switch
  --optimizing_for_coreml
          Optimizing graph for coreml
  --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
  --onnx_extra_opset ONNX_EXTRA_OPSET
          The name of the onnx 'extra_opset' to enable.
          Default: ''
          'com.microsoft:1' or 'ai.onnx.contrib:1' or 'ai.onnx.ml:1'
  --disable_onnx_nchw_conversion
          Disable onnx NCHW conversion
  --disable_onnx_optimization
          Disable onnx optimization
  --output_openvino_and_myriad
          openvino model and 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
  --optimizing_for_openvino_and_myriad
          Optimizing graph for openvino/myriad
  --rigorous_optimization_for_myriad
          Replace operations that are not supported by myriad with operations
          that are as feasible as possible.
          e.g. 'Abs' -> 'Square' + 'Sqrt'
  --replace_swish_and_hardswish
          Replace swish and hard-swish with each other
  --optimizing_for_edgetpu
          Optimizing for edgetpu
  --replace_prelu_and_minmax
          Replace prelu and minimum/maximum with each other
  --disable_experimental_new_quantizer
          Disable MLIRs new quantization feature during INT8 quantization
          in TensorFlowLite.
  --disable_per_channel
          Disable per-channel quantization for tflite.
  --optimizing_barracuda
          Generates ONNX by replacing Barracuda unsupported layers
          with standard layers. For example, GatherND.
  --locationids_of_the_terminating_output
          A comma-separated list of LocationIDs to be used as output layers.
          e.g. --locationids_of_the_terminating_output 100,201,560
          Default: ''

4-2. Step 1 : Generating saved_model and FreezeGraph (.pb)

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_pb

or

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_pb \
  --optimizing_for_openvino_and_myriad

or

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_pb \
  --optimizing_for_openvino_and_myriad \
  --rigorous_optimization_for_myriad

or

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_pb \
  --optimizing_for_edgetpu

or

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_pb \
  --optimizing_for_coreml

or

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_pb \
  --optimizing_barracuda

4-3. Step 2 : Generation of quantized tflite, TFJS, TF-TRT, EdgeTPU, CoreML and ONNX

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --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 \
  --output_tftrt_float32 \
  --output_tftrt_float16 \
  --output_onnx \
  --onnx_opset 11 \
  --output_openvino_and_myriad

or

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ../flatc \
  --schema_path ../schema.fbs \
  --output_no_quant_float32_tflite \
  --output_dynamic_range_quant_tflite \
  --output_weight_quant_tflite \
  --output_float16_quant_tflite \
  --output_integer_quant_tflite \
  --output_edgetpu \
  --output_integer_quant_typ 'uint8' \
  --string_formulas_for_normalization 'data / 255.0' \
  --output_tfjs \
  --output_coreml \
  --output_tftrt_float32 \
  --output_tftrt_float16 \
  --output_onnx \
  --onnx_opset 11

4-4. Check the contents of the .npy file, which is a binary version of the image file

$ view_npy --npy_file_path 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.

image

5. Sample image

This is the result of converting MediaPipe's Meet Segmentation model (segm_full_v679.tflite / Float16 / Google Meet) to saved_model and then reconverting it to Float32 tflite. Replace the GPU-optimized Convolution2DTransposeBias layer with the standard TransposeConv and BiasAdd layers in a fully automatic manner. The weights and biases of the Float16 Dequantize layer are automatically back-quantized to Float32 precision. The generated saved_model in Float32 precision can be easily converted to Float16, INT8, EdgeTPU, TFJS, TF-TRT, CoreML, ONNX, OpenVINO, Myriad Inference Engine blob.

Before After
segm_full_v679 tflite model_float32 tflite