We stopped active development of keras2onnx and keras2onnx is now frozen to tf-2.3 and onnx-1.10. keras2onnx
To convert your Keras models you can head over to tf2onnx which can convert Tensorflow, Keras, Tflite and Tensorflow.js models. All keras2onnx unit tests have been added to the tf2onnx ci pipeline to make sure there are no avoidable regressions. The tf2onnx api tf2onnx.convert.from_keras() is similar to the keras2onnx api and we hope transition is painless.
You can find a simple tutorial how to convert keras models using tf2onnx here.
If you run into issue or need help with the transition, please open issue against tf2onnx here.
The keras2onnx model converter enables users to convert Keras models into the ONNX model format. Initially, the Keras converter was developed in the project onnxmltools. keras2onnx converter development was moved into an independent repository to support more kinds of Keras models and reduce the complexity of mixing multiple converters.
Windows Machine Learning (WinML) users can use 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 to identify the corresponding ONNX opset number for your WinML version.
keras2onnx has been tested on Python 3.5 - 3.8, with tensorflow 1.x/2.0 - 2.2 (CI build). It does not support Python 2.x.
You can install latest release of Keras2ONNX from PyPi:
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)
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 have been converted into ONNX and validated by ONNXRuntime.
Since its version 2.3, the multi-backend Keras (keras.io) 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 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.
If you want to override this behaviour, please specify the environment variable TF_KERAS=1 before invoking the converter python API.
Keras2ONNX depends on 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. However some models with a lot of custom operations need custom conversion, the following are some examples,
like YOLOv3, and Mask RCNN.
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
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)
We welcome contributions in the form of feedback, ideas, or code.