Convert Pytorch model to onnx or tflite, and the converted model can be visualized by Netron

Related tags

Deep Learning model-converter
Overview

Model converter

Convert Pytorch model to onnx or tflite, and the converted model can be visualized by Netron

You can use this project to:

  1. Pytorch -> onnx (float32)
  2. Pytorch -> onnx -> tflite (float32)
  3. Pytorch -> onnx -> tflite (int8)

Requirements

torch2onnx

pytorch
onnx
opencv-python

torch2tflite

tensorflow ~= 2.5
torch == 1.8.1
tensorflow-addons ~= 0.15
opencv-python ~= 4.5.4
onnx ~= 1.10
onnx-tf ~= 1.9
numpy >= 1.19

(opencv-python is optional)

Usage

torch2onnx (float32)

from converter import Torch2onnxConverter

converter = Torch2onnxConverter(model_path, target_shape=(3,224,224))
converter.convert()

torch2tflite (float32)

from converter import Torch2TFLiteConverter

converter = Torch2TFLiteConverter(tmp_path, tflite_model_save_path='model_float32.lite', target_shape=(224,224,3))
converter.convert()

torch2tflite (int8)

from converter import Torch2TFLiteConverter

converter = Torch2TFLiteConverter(tmp_path, tflite_model_save_path='model_int8.lite', target_shape=(224,224,3),
                                    representative_dataset=representative_dataset)
converter.convert()

More details can be found in Torch2onnxConverter and Torch2TfliteConverter __init__ method.

Note that target_shape is different for Pytorch and Tensorflow.

Example

  1. torch2onnx example

  2. torch2tflite example

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