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

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

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

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 v1.12.0

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 flatc $ flatbuffers/1.12.0/download.sh $ sudo chmod +x flatc ### Download schema.fbs $ wget https://github.com/PINTO0309/tflite2tensorflow/raw/main/schema/schema.fbs

If the downloaded flatc does not work properly, please build it in your environment.

$ git clone -b v1.12.0 https://github.com/google/flatbuffers.git $ cd flatbuffers && mkdir build && cd build $ cmake -G "Unix Makefiles" -DCMAKE_BUILD_TYPE=Release .. $ make -j$(nproc)

The Windows version of flatc v1.12.0 can be downloaded from here. https://github.com/google/flatbuffers/releases/download/v1.12.0/flatc_windows.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.

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

tflite2tensorflow
tflite2tensorflow copied to clipboard

Metadata

tflite2tensorflow

1. Supported Layers

2. Environment

3. Setup

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

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

4. Usage / Execution sample

4-1. Command line options

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

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

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

5. Sample image

← Metadata

Owner

Metadata

tflite2tensorflow tflite2tensorflow copied to clipboard

Metadata

tflite2tensorflow

1. Supported Layers

2. Environment

3. Setup

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

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

4. Usage / Execution sample

4-1. Command line options

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

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

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

5. Sample image

← Metadata

Owner

Metadata

tflite2tensorflow
tflite2tensorflow copied to clipboard

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