Generate saved_model, tfjs, tf-trt, EdgeTPU, CoreML, quantized tflite and .pb from .tflite.

PyPI - Downloads GitHub PyPI

1. 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
20 RESIZE_NEAREST_NEIGHBOR tf.image.resize Or tf.image.resize_nearest_neighbor
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
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
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
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
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

2. Environment

3. Setup

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

$ pip3 install tflite2tensorflow --upgrade

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

$ pip3 install git+ --upgrade

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

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

### Customized version of TensorFlow Lite installation
$ sudo gdown --id 1RWZmfFgtxm3muunv6BSf4yU29SKKFXIh
$ sudo chmod +x tflite_runtime-2.4.1-py3-none-any.whl
$ sudo pip3 install tflite_runtime-2.4.1-py3-none-any.whl

### Install the full TensorFlow package
$ sudo pip3 install tensorflow==2.4.1
$ sudo pip3 install tf-nightly

### Download flatc
$ flatbuffers/1.12.0/

### Download schema.fbs
$ wget

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

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

vvtvsu0y1791ow2ybdk61s9fv7e4 saxqukktcjncsk2hp7m8p2cns4q4

The Windows version of flatc v1.12.0 can be downloaded from here.

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_PB]
                         [--output_no_quant_float32_tflite OUTPUT_NO_QUANT_FLOAT32_TFLITE]
                         [--output_weight_quant_tflite OUTPUT_WEIGHT_QUANT_TFLITE]
                         [--output_float16_quant_tflite OUTPUT_FLOAT16_QUANT_TFLITE]
                         [--output_integer_quant_tflite OUTPUT_INTEGER_QUANT_TFLITE]
                         [--output_full_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 TFDS_DOWNLOAD_FLG]
                         [--output_tfjs OUTPUT_TFJS]
                         [--output_tftrt OUTPUT_TFTRT]
                         [--output_coreml OUTPUT_COREML]
                         [--output_edgetpu OUTPUT_EDGETPU]
                         [--replace_swish_and_hardswish REPLACE_SWISH_AND_HARDSWISH]
                         [--optimizing_hardswish_for_edgetpu OPTIMIZING_HARDSWISH_FOR_EDGETPU]
                         [--replace_prelu_and_minmax REPLACE_PRELU_AND_MINMAX]

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 OUTPUT_PB
                        .pb output switch
  --output_no_quant_float32_tflite OUTPUT_NO_QUANT_FLOAT32_TFLITE
                        float32 tflite output switch
  --output_weight_quant_tflite OUTPUT_WEIGHT_QUANT_TFLITE
                        weight quant tflite output switch
  --output_float16_quant_tflite OUTPUT_FLOAT16_QUANT_TFLITE
                        float16 quant tflite output switch
  --output_integer_quant_tflite OUTPUT_INTEGER_QUANT_TFLITE
                        integer quant tflite output switch
  --output_full_integer_quant_tflite 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(Future Implementation)
  --ds_name_for_tfds_for_calibration DS_NAME_FOR_TFDS_FOR_CALIBRATION
                        Dataset name for TensorFlow Datasets for calibration.
  --split_name_for_tfds_for_calibration SPLIT_NAME_FOR_TFDS_FOR_CALIBRATION
                        Split name for TensorFlow Datasets for calibration.
  --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 TFDS_DOWNLOAD_FLG
                        True to automatically download datasets from
                        TensorFlow Datasets. True or False
  --output_tfjs OUTPUT_TFJS
                        tfjs model output switch
  --output_tftrt OUTPUT_TFTRT
                        tftrt model output switch
  --output_coreml OUTPUT_COREML
                        coreml model output switch
  --output_edgetpu OUTPUT_EDGETPU
                        edgetpu model output switch
  --replace_swish_and_hardswish REPLACE_SWISH_AND_HARDSWISH
                        [Future support] Replace swish and hard-swish with
                        each other
  --optimizing_hardswish_for_edgetpu OPTIMIZING_HARDSWISH_FOR_EDGETPU
                        Optimizing hardswish for edgetpu
  --replace_prelu_and_minmax REPLACE_PRELU_AND_MINMAX
                        Replace prelu and minimum/maximum with each other

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 True


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

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

$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ./flatc \
  --schema_path schema.fbs \
  --output_no_quant_float32_tflite True \
  --output_weight_quant_tflite True \
  --output_float16_quant_tflite True \
  --output_integer_quant_tflite True \
  --string_formulas_for_normalization 'data / 255.0' \
  --output_tfjs True \
  --output_coreml True \
  --output_tftrt True


$ tflite2tensorflow \
  --model_path segm_full_v679.tflite \
  --flatc_path ./flatc \
  --schema_path schema.fbs \
  --output_no_quant_float32_tflite True \
  --output_weight_quant_tflite True \
  --output_float16_quant_tflite True \
  --output_integer_quant_tflite True \
  --output_edgetpu True \
  --string_formulas_for_normalization 'data / 255.0' \
  --output_tfjs True \
  --output_coreml True \
  --output_tftrt True

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, and OpenVINO.

Before After
segm_full_v679 tflite model_float32 tflite