tflite2tensorflow

<p align="center"> <img src="https://user-images.githubusercontent.com/33194443/105187518-38ac0c00-5b76-11eb-869b-b518df146924.png" /> </p>

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.

<p align="center"> <img src="https://user-images.githubusercontent.com/33194443/166147152-79cd691e-7b47-4338-acee-34791ac2d1f3.gif" /> </p>

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|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|

</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=$X