Awesome

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.

GitHub

1. Supported Layers

<details><summary>Supported Layers</summary><div>

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	xtf.nn.relu6(x+3)0.16666667 Or xtf.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

</div></details>

2. Environment

Python3.8+
TensorFlow v2.9.0+
TensorFlow Lite v2.9.0 with MediaPipe Custom OP, FlexDelegate and XNNPACK enabled
- Add a custom OP to the TFLite runtime to build the whl installer (for Python), MaxPoolingWithArgmax2D, MaxUnpooling2D, Convolution2DTransposeBias, TransformLandmarks, TransformTensorBilinear, Landmarks2TransformMatrix
- https://github.com/PINTO0309/TensorflowLite-bin
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

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

$ 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

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

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

$ 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

$ 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.

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