# Converter for TFlite to Onnx

## Description

Convert an TFlite model to Onnx.
We parse TFlite model use:
1. flatc(https://google.github.io/flatbuffers/flatbuffers_guide_using_schema_compiler.html) 
2. tensorflow lite schema file(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs)

### Prerequisites

We test our script using:
* **Python 3**:    Python==3.7.7
* **ONNX**:    onnx==1.6
* **Tensorflow**:    tensorflow==1.15.0
* **FlatBuffer**:    flatbuffers==1.12
* **Python-igraph**:    python-igraph==0.8.3

## Basic Usage

1. run `python path/to/tflite2onnx.py -h` to check the parameter.
* 1.1. -tflite: the path to the tflite file
* 1.2. -save_path: final onnx file path to save
2. run `python tflite2onnx.py -tflite YOUR_TFLITE_PATH -save_path ONNX_SAVE_PATH` to convert the model,

## Tested Model Table

### tensorflow object detection model zoo:
#### https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/tf1_detection_zoo.md
#### ( use export_tflite_ssd_graph.py with --add_postprocessing_op=false )
* Mobile_models/ssd_mobiledet_cpu_coco
* Mobile_models/ssd_mobilenet_v3_large_coco
* Mobile_models/ssd_mobilenet_v3_small_coco
* Mobile_models/ssd_mobilenet_v2_mnasfpn_coco

### tflite model hub (Floating point models):
#### https://www.tensorflow.org/lite/guide/hosted_models
* Mobilenet_V1 series
* Mobilenet_V2 series
* Inception_V3
* Inception_V4
* Inception_ResNet_V2
* SqueezeNet
* DenseNet
* ResNet_V2_101
* EfficientNet-Lite


## Now Supported Operators List
* ADD
* AVERAGE_POOL_2D
* CONCATENATION
* CONV_2D
* DEPTH_TO_SPACE
* DEPTHWISE_CONV_2D
* ELU
* FULLY_CONNECTED
* L2_NORMALIZATION
* LEAKY_RELU
* LOGISTIC
* MUL
* MEAN
* MAXIMUM
* MAX_POOL_2D
* PAD
* PRELU
* RELU
* RELU6
* RESHAPE
* RESIZE_BILINEAR
* RESIZE_NEAREST_NEIGHBOR
* SOFTMAX
* SPACE_TO_DEPTH
* SQUEEZE
* TRANSPOSE_CONV


## Example 1: Convert model to the onnx optimized for Kneron Toolchain
#### Step0 (Optional). Download example tflite model
###### (Our provided example model is LFS-tracked file, sometimes 'git lfs pull' is needed)
    git lfs pull
#### Step1. Convert tflite to onnx  
###### (Originally, we add transpose nodes for the channel order difference between onnx and tflite. Use '-release_mode True' could ignore those transpose nodes)
    python ./onnx_tflite/tflite2onnx.py -tflite ./example/example.tflite -save_path ./example/example.onnx -release_mode True
#### Step2. Convert onnx to the onnx which is optimized for Kneron Toolchain
    python ../optimizer_scripts/onnx2onnx.py ./example/example.onnx


## Example 2: Convert part of tflite model to onnx
    python ./onnx_tflite/tflite2onnx.py -tflite ./example/example.tflite -save_path ./example/example.onnx -release_mode True -bottom_nodes sequential_1/model1/block_14_add/add


## *Note 1:
* "./example/example.tflite" and "./onnx_tflite/flatc/flatc" are large file and not necessary. So we let them being tracked with GIT-LFS. You can use 'git lfs pull' to download them. Since the limit of lfs transfer size. They will not be available in the master branch. You can checkout the branch 'lfs' to get them.
    git checkout lfs

## *Note 2 (convert tensorflow to tflite):
* Here is the official guide to convert tensorflow model and keras model to tflite:
    https://www.tensorflow.org/lite/guide/get_started#tensorflow_lite_converter

    Note that current tflite2onnx only support "float32" tflite,and current official tflite not support all tensorflow op  
    ( ref: https://www.tensorflow.org/lite/guide/get_started#ops_compatibility )  
    you might need to cut some unsupported nodes.