.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "build/examples_auto_module/demo_auto_detection.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        Click :ref:`here <sphx_glr_download_build_examples_auto_module_demo_auto_detection.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_build_examples_auto_module_demo_auto_detection.py:

03. Train classifier or detector with HPO using GluonCV Auto task
====================================================================

The previous image classification example shows the basic usages to train/evaluate/predict
using estimators provided by `gluoncv.auto.estimators`. Similarly, you can train object detectors
using  `SSDEstimator`, `YOLOv3Estimator`, `CenterNetEstimator`, `FasterRCNNEstimator`.

In this tutorial, we will move forward a little bit, into the hyper-parameter tunning space!
We will show you how to offload an experiment to the backend HPO searcher, to offer better result if
computational cost is abundant.

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HPO with GluonCV auto tasks
---------------------------

.. GENERATED FROM PYTHON SOURCE LINES 16-20

.. code-block:: default

    from gluoncv.auto.tasks.image_classification import ImageClassification
    from gluoncv.auto.tasks.object_detection import ObjectDetection
    import autogluon.core as ag








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In this tutorial, we use a small sample dataset for object detection
For image classification, please refer to :ref:`sphx_glr_build_examples_auto_module_train_image_classifier_basic.py`.

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.. code-block:: default

    train = ObjectDetection.Dataset.from_voc(
        'https://autogluon.s3.amazonaws.com/datasets/tiny_motorbike.zip')
    train, val, test = train.random_split(val_size=0.1, test_size=0.1)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    tiny_motorbike/
    ├── Annotations/
    ├── ImageSets/
    └── JPEGImages/




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Define search space
-------------------
We show a minimal example to run HPO for object detection.
For image classification, the code change is negalected, just swap
the dataset and `ObjectDetection` to `ImageClassification`.

We use very conservative search space to reduce CI runtime,
and we will cap the number of trials to be 1 to reduce building time,
feel free to adjust the `num_trials`, `time_limits`, `epochs` and tune
other search spaces with `ag.Categorical`, `ag.Int`, `ag.Real` for example
in order to achieve better results

.. GENERATED FROM PYTHON SOURCE LINES 39-49

.. code-block:: default

    time_limits = 60 * 60  # 1hr
    search_args = {'lr': ag.Categorical(1e-3, 1e-2),
                   'num_trials': 1,
                   'epochs': 2,
                   'num_workers': 16,
                   'batch_size': ag.Categorical(4, 8),
                   'ngpus_per_trial': 1,
                   'search_strategy': 'random',
                   'time_limits': time_limits}








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Construct a object detection task based on the config.

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.. code-block:: default

    task = ObjectDetection(search_args)








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Automatically fit a model.

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.. code-block:: default

    detector = task.fit(train, val)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1512: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
            data: None
      input_sym_arg_type = in_param.infer_type()[0]
    Downloading /root/.mxnet/models/ssd_512_resnet50_v1_coco-c4835162.zip from https://apache-mxnet.s3-accelerate.dualstack.amazonaws.com/gluon/models/ssd_512_resnet50_v1_coco-c4835162.zip...

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    /usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1512: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
            data: None
      input_sym_arg_type = in_param.infer_type()[0]
    /usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1512: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
            data: None
      input_sym_arg_type = in_param.infer_type()[0]




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Evaluate the final model on test set.

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.. code-block:: default

    test_map = detector.evaluate(test)
    print("mAP on test dataset:")
    for category, score in zip(*test_map):
        print(category, score)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    mAP on test dataset:
    motorbike 0.557581493355552
    bus nan
    boat nan
    cow 0.0
    person 0.4424933160156914
    bicycle nan
    car 1.0000000000000002
    dog nan
    chair nan
    pottedplant nan
    mAP 0.5000187023428109




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Save our final model.

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.. code-block:: default

    detector.save('detector.pkl')








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load the model and run some prediction

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.. code-block:: default

    detector2 = ObjectDetection.load('detector.pkl')
    pred = detector2.predict(test.iloc[0]['image'])
    print(pred)





.. rst-class:: sphx-glr-script-out

 Out:

 .. code-block:: none

    /usr/local/lib/python3.6/dist-packages/mxnet/gluon/block.py:1512: UserWarning: Cannot decide type for the following arguments. Consider providing them as input:
            data: None
      input_sym_arg_type = in_param.infer_type()[0]
       predict_class  ...                                       predict_rois
    0            car  ...  {'xmin': 0.2950457036495209, 'ymin': 0.1961218...
    1            car  ...  {'xmin': 0.1063406690955162, 'ymin': 0.1650179...
    2         person  ...  {'xmin': 0.9072394967079163, 'ymin': 0.0386775...
    3            car  ...  {'xmin': 0.497192919254303, 'ymin': 0.32654511...
    4         person  ...  {'xmin': 0.1039300486445427, 'ymin': 0.0214233...
    ..           ...  ...                                                ...
    76        person  ...  {'xmin': 0.9762840270996094, 'ymin': 0.0143931...
    77     motorbike  ...  {'xmin': 0.7977412939071655, 'ymin': 0.1901351...
    78        person  ...  {'xmin': 0.14319948852062225, 'ymin': 0.497429...
    79        person  ...  {'xmin': 0.7110783457756042, 'ymin': 0.1970322...
    80        person  ...  {'xmin': 0.1752810776233673, 'ymin': 0.1064255...

    [81 rows x 3 columns]




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Pin the object detector algorithms
----------------------------------
By default, the scheduler choose algorithms from
`SSDEstimator`, `YOLOv3Estimator`, `CenterNetEstimator`, `FasterRCNNEstimator`.
If you want to train a specific algorithm during HPO, you may pin the estimator.

For example, this tells the searcher to use YOLOv3 models only

.. GENERATED FROM PYTHON SOURCE LINES 82-92

.. code-block:: default

    search_args = {'lr': ag.Categorical(1e-3, 1e-2),
                   'num_trials': 1,
                   'estimator': 'yolo3',
                   'epochs': 2,
                   'num_workers': 16,
                   'batch_size': ag.Categorical(4, 8),
                   'ngpus_per_trial': 1,
                   'search_strategy': 'random',
                   'time_limits': time_limits}








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Specify models for transfer learning
------------------------------------
Allowing transfer learning from previous trained models is the key to
fast convergence and high performance model with very limited training data.
By default, we enable transfer learning from object detection networks trained
on COCO dataset, downloaded automatically from gluoncv model zoo.

The selected pre-trained models by default is 'ssd_512_resnet50_v1_coco',
'yolo3_darknet53_coco', 'faster_rcnn_resnet50_v1b_coco' and 'center_net_resnet50_v1b_coco'.

You may continue to use them, or replace with your favarite models, e.g.,

.. GENERATED FROM PYTHON SOURCE LINES 104-115

.. code-block:: default

    search_args = {'lr': ag.Categorical(1e-3, 1e-2),
                   'num_trials': 1,
                   'transfer': ag.Categorical('ssd_512_mobilenet1.0_coco',
                                              'yolo3_mobilenet1.0_coco'),
                   'epochs': 10,
                   'num_workers': 16,
                   'batch_size': ag.Categorical(4, 8),
                   'ngpus_per_trial': 1,
                   'search_strategy': 'random',
                   'time_limits': time_limits}








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Now you are ready to use HPO feature in gluoncv.auto!
Stay tuned as we are rolling out more features and tutorials for
faster and more convenient training/test/inference experiences.


.. rst-class:: sphx-glr-timing

   **Total running time of the script:** ( 1 minutes  0.965 seconds)


.. _sphx_glr_download_build_examples_auto_module_demo_auto_detection.py:


.. only :: html

 .. container:: sphx-glr-footer
    :class: sphx-glr-footer-example



  .. container:: sphx-glr-download sphx-glr-download-python

     :download:`Download Python source code: demo_auto_detection.py <demo_auto_detection.py>`



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