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

Last update: Nov 19, 2022

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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:

Pytorch -> onnx (float32)
Pytorch -> onnx -> tflite (float32)
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.

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

Related tags

Overview

Model converter

Requirements

Usage

Example

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