keras2onnx 1.7.0
Converts Machine Learning models to ONNX for use in Windows ML
License: MIT License

# Introduction
The keras2onnx model converter enables users to convert Keras models into the [ONNX](https://onnx.ai) model format.
Initially, the Keras converter was developed in the project [onnxmltools](https://github.com/onnx/onnxmltools). keras2onnx converter development was moved into an [independent repository](https://github.com/onnx/keras-onnx) to support more kinds of Keras models and reduce the complexity of mixing multiple converters.

Most of the common Keras layers have been supported for conversion. Please refer to the [Keras documentation](https://keras.io/layers/about-keras-layers/) or [tf.keras docs](https://www.tensorflow.org/api_docs/python/tf/keras/layers) for details on Keras layers.

Windows Machine Learning (WinML) users can use [WinMLTools](https://docs.microsoft.com/en-us/windows/ai/windows-ml/convert-model-winmltools) which wrap its call on keras2onnx to convert the Keras models. If you want to use the keras2onnx converter, please refer to the [WinML Release Notes](https://docs.microsoft.com/en-us/windows/ai/windows-ml/release-notes) to identify the corresponding ONNX opset number for your WinML version.

keras2onnx has been tested on **Python 3.5, 3.6, and 3.7**, with **tensorflow 1.x/2.0/2.1**  (CI build). It does not support **Python 2.x**.

# Install
You can install latest release of Keras2ONNX from PyPi: **Due to some reason, the package release paused, please install it from the source, and the support of keras or tf.keras over tensorflow 2.x is only available in the source.**

```
pip install keras2onnx
```
or install from source:

```
pip install -U git+https://github.com/microsoft/onnxconverter-common
pip install -U git+https://github.com/onnx/keras-onnx
```
Before running the converter, please notice that tensorflow has to be installed in your python environment,
you can choose **tensorflow**/**tensorflow-cpu** package(CPU version) or **tensorflow-gpu**(GPU version)

# Notes
Keras2ONNX supports the new Keras subclassing model which was introduced in tensorflow 2.0 since the version **1.6.5**. Some typical subclassing models like [huggingface/transformers](https://github.com/huggingface/transformers) have been converted into ONNX and validated by ONNXRuntime.<br>

Since its version 2.3, the [multi-backend Keras (keras.io)](https://keras.io/#multi-backend-keras-and-tfkeras) stops the support of the tensorflow version above 2.0. The auther suggests to switch to tf.keras for the new features.
## Multi-backend Keras and tf.keras:
Both Keras model types are now supported in the keras2onnx converter. If in the user python env, Keras package was installed from [Keras.io](https://keras.io/) and tensorflow package version is 1.x, the converter converts the model as it was created by the keras.io package. Otherwise, it will convert it through [tf.keras](https://www.tensorflow.org/guide/keras).<br>

If you want to override this behaviour, please specify the environment variable TF_KERAS=1 before invoking the converter python API.
# Development
Keras2ONNX depends on [onnxconverter-common](https://github.com/microsoft/onnxconverter-common). In practice, the latest code of this converter requires the latest version of onnxconverter-common, so if you install this converter from its source code, please install the onnxconverter-common in source code mode before keras2onnx installation.

# Validated pre-trained Keras models
Most Keras models could be converted successfully by calling ```keras2onnx.convert_keras```, including CV, GAN, NLP, Speech and etc. See the tutorial [here](https://github.com/onnx/keras-onnx/tree/master/tutorial). However some models with a lot of custom operations need custom conversion, the following are some examples,
like [YOLOv3](https://github.com/qqwweee/keras-yolo3), and [Mask RCNN](https://github.com/matterport/Mask_RCNN).


## Scripts
It will be useful to convert the models from Keras to ONNX from a python script.
You can use the following API:
```
import keras2onnx
keras2onnx.convert_keras(model, name=None, doc_string='', target_opset=None, channel_first_inputs=None):
    # type: (keras.Model, str, str, int, []) -> onnx.ModelProto
    """
    :param model: keras model
    :param name: the converted onnx model internal name
    :param doc_string:
    :param target_opset:
    :param channel_first_inputs: A list of channel first input.
    :return:
    """
```

Use the following script to convert keras application models to onnx, and then perform inference:
```
import numpy as np
from keras.preprocessing import image
from keras.applications.resnet50 import preprocess_input
import keras2onnx
import onnxruntime

# image preprocessing
img_path = 'street.jpg'   # make sure the image is in img_path
img_size = 224
img = image.load_img(img_path, target_size=(img_size, img_size))
x = image.img_to_array(img)
x = np.expand_dims(x, axis=0)
x = preprocess_input(x)

# load keras model
from keras.applications.resnet50 import ResNet50
model = ResNet50(include_top=True, weights='imagenet')

# convert to onnx model
onnx_model = keras2onnx.convert_keras(model, model.name)

# runtime prediction
content = onnx_model.SerializeToString()
sess = onnxruntime.InferenceSession(content)
x = x if isinstance(x, list) else [x]
feed = dict([(input.name, x[n]) for n, input in enumerate(sess.get_inputs())])
pred_onnx = sess.run(None, feed)
```

The inference result is a list which aligns with keras model prediction result `model.predict()`.
An alternative way to load onnx model to runtime session is to save the model first:
```
temp_model_file = 'model.onnx'
keras2onnx.save_model(onnx_model, temp_model_file)
sess = onnxruntime.InferenceSession(temp_model_file)
```

## Contribute
We welcome contributions in the form of feedback, ideas, or code.

## License
[MIT License](LICENSE)