mirror of
https://github.com/saymrwulf/onnxruntime.git
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249 lines
4.8 KiB
ReStructuredText
249 lines
4.8 KiB
ReStructuredText
.. only:: html
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.. note::
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:class: sphx-glr-download-link-note
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Click :ref:`here <sphx_glr_download_auto_examples_plot_dl_keras.py>` to download the full example code
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.. rst-class:: sphx-glr-example-title
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.. _sphx_glr_auto_examples_plot_dl_keras.py:
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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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.. code-block:: default
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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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onx.ir_version = 6
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with open("dense121.onnx", "wb") as f:
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f.write(onx.SerializeToString())
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Let's load an image (source: wikipedia).
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.. code-block:: default
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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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.. image:: /auto_examples/images/sphx_glr_plot_dl_keras_001.png
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:alt: plot dl keras
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:class: sphx-glr-single-img
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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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(-0.5, 1279.5, 959.5, -0.5)
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Let's load the model with onnxruntime.
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.. code-block:: default
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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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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The model expects input shape: ['N', 224, 224, 3]
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image shape: (960, 1280, 3)
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Let's resize the image.
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.. code-block:: default
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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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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new shape: (1, 224, 224, 3)
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Let's compute the output.
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.. code-block:: default
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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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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[2.0847994e-05 9.0344076e-07 1.6248664e-06 4.8085840e-06 6.5068948e-06
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9.4026956e-07 1.9977851e-06 4.6639366e-07 9.4333046e-07 3.2267365e-06]
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Let's get more comprehensive results.
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.. code-block:: default
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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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.. rst-class:: sphx-glr-script-out
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Out:
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.. code-block:: none
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Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json
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8192/35363 [=====>........................] - ETA: 0s
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40960/35363 [==================================] - 0s 0us/step
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class_id name P
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0 n09468604 valley 0.673279
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1 n09193705 alp 0.267428
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2 n09399592 promontory 0.013859
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3 n09246464 cliff 0.013251
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4 n03792972 mountain_tent 0.007756
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.. rst-class:: sphx-glr-timing
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**Total running time of the script:** ( 0 minutes 6.749 seconds)
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.. _sphx_glr_download_auto_examples_plot_dl_keras.py:
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.. only :: html
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.. container:: sphx-glr-footer
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:class: sphx-glr-footer-example
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.. container:: sphx-glr-download sphx-glr-download-python
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:download:`Download Python source code: plot_dl_keras.py <plot_dl_keras.py>`
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.. container:: sphx-glr-download sphx-glr-download-jupyter
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:download:`Download Jupyter notebook: plot_dl_keras.ipynb <plot_dl_keras.ipynb>`
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.. only:: html
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.. rst-class:: sphx-glr-signature
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`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_
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