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94 lines
2.6 KiB
Python
94 lines
2.6 KiB
Python
# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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"""
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.. _l-example-backend-api-tensorflow:
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ONNX Runtime for Keras
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======================
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The following demonstrates how to compute the predictions
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of a pretrained deep learning model obtained from
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`keras <https://keras.io/>`_
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with *onnxruntime*. The conversion requires
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`keras <https://keras.io/>`_,
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`tensorflow <https://www.tensorflow.org/>`_,
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`keras-onnx <https://github.com/onnx/keras-onnx/>`_,
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`onnxmltools <https://pypi.org/project/onnxmltools/>`_
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but then only *onnxruntime* is required
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to compute the predictions.
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"""
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import os
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if not os.path.exists('dense121.onnx'):
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from keras.applications.densenet import DenseNet121
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model = DenseNet121(include_top=True, weights='imagenet')
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from keras2onnx import convert_keras
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onx = convert_keras(model, 'dense121.onnx')
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with open("dense121.onnx", "wb") as f:
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f.write(onx.SerializeToString())
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##################################
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# Let's load an image (source: wikipedia).
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from keras.preprocessing.image import array_to_img, img_to_array, load_img
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img = load_img('Sannosawa1.jpg')
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ximg = img_to_array(img)
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import matplotlib.pyplot as plt
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plt.imshow(ximg / 255)
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plt.axis('off')
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#############################################
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# Let's load the model with onnxruntime.
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import onnxruntime as rt
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from onnxruntime.capi.onnxruntime_pybind11_state import InvalidGraph
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try:
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sess = rt.InferenceSession('dense121.onnx')
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ok = True
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except (InvalidGraph, TypeError, RuntimeError) as e:
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# Probably a mismatch between onnxruntime and onnx version.
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print(e)
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ok = False
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if ok:
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print("The model expects input shape:", sess.get_inputs()[0].shape)
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print("image shape:", ximg.shape)
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#######################################
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# Let's resize the image.
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if ok:
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from skimage.transform import resize
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import numpy
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ximg224 = resize(ximg / 255, (224, 224, 3), anti_aliasing=True)
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ximg = ximg224[numpy.newaxis, :, :, :]
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ximg = ximg.astype(numpy.float32)
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print("new shape:", ximg.shape)
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##################################
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# Let's compute the output.
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if ok:
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input_name = sess.get_inputs()[0].name
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res = sess.run(None, {input_name: ximg})
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prob = res[0]
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print(prob.ravel()[:10]) # Too big to be displayed.
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##################################
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# Let's get more comprehensive results.
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if ok:
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from keras.applications.densenet import decode_predictions
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decoded = decode_predictions(prob)
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import pandas
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df = pandas.DataFrame(decoded[0], columns=["class_id", "name", "P"])
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print(df)
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