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

    Click :ref:`here <sphx_glr_download_auto_examples_resume_train_segm.py>` to download the full example code
.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_resume_train_segm.py:


Example 09: Resume training a network for segmentation
======================================================

This script resumes an earlier training of a MS-D network for 
segmentation (i.e. labeling)
Run generatedata.py first to generate required training data, and
train_segm.py to generate a partially trained network.


.. code-block:: default


    # Import code
    import msdnet
    import glob

    # Define training data
    # First, create lists of input files (noisy) and target files (labels)
    flsin = sorted(glob.glob('train/noisy/*.tiff'))
    flstg = sorted(glob.glob('train/label/*.tiff'))
    # Create list of datapoints (i.e. input/target pairs)
    dats = []
    for i in range(len(flsin)):
        # Create datapoint with file names
        d = msdnet.data.ImageFileDataPoint(flsin[i],flstg[i])
        # Convert datapoint to one-hot, using labels 0, 1, 2, 3, and 4,
        # which are the labels given in each label TIFF file.
        d_oh = msdnet.data.OneHotDataPoint(d, [0,1,2,3,4])
        # Augment data by rotating and flipping
        d_augm = msdnet.data.RotateAndFlipDataPoint(d_oh)
        # Add augmented datapoint to list
        dats.append(d_augm)
    # Note: The above can also be achieved using a utility function for such 'simple' cases:
    # dats = msdnet.utils.load_simple_data('train/noisy/*.tiff', 'train/label/*.tiff', augment=True, labels=[0,1,2,3,4])

    # Use image batches of a single image
    bprov = msdnet.data.BatchProvider(dats,1)

    # Define validation data (not using augmentation)
    flsin = sorted(glob.glob('val/noisy/*.tiff'))
    flstg = sorted(glob.glob('val/label/*.tiff'))
    datsv = []
    for i in range(len(flsin)):
        d = msdnet.data.ImageFileDataPoint(flsin[i],flstg[i])
        d_oh = msdnet.data.OneHotDataPoint(d, [0,1,2,3,4])
        datsv.append(d_oh)
    # Note: The above can also be achieved using a utility function for such 'simple' cases:
    # datsv = msdnet.utils.load_simple_data('train/noisy/*.tiff', 'train/label/*.tiff', augment=False, labels=[0,1,2,3,4])

    # Load network, training algorithm, and validation object from checkpoint of previous training
    n, t, val = msdnet.train.restore_training('segm_params.checkpoint', msdnet.network.SegmentationMSDNet, msdnet.train.AdamAlgorithm, msdnet.validate.MSEValidation, datsv, gpu=True)

    # Log error metrics to console
    consolelog = msdnet.loggers.ConsoleLogger()
    # Log error metrics to file
    filelog = msdnet.loggers.FileLogger('log_segm.txt')
    # Log typical, worst, and best images to image files
    imagelog = msdnet.loggers.ImageLabelLogger('log_segm', onlyifbetter=True)
    # Log typical, worst, and best images to image files
    # Output probability map for a single channel (in this case, channel 3)
    singlechannellog = msdnet.loggers.ImageLogger('log_segm_singlechannel', chan_out=3, onlyifbetter=True)

    # Train network until program is stopped manually
    # Network parameters are saved in segm_params.h5
    # Validation is run after every len(datsv) (=25)
    # training steps.
    msdnet.train.train(n, t, val, bprov, 'segm_params_resumed.h5',loggers=[consolelog,filelog,imagelog,singlechannellog], val_every=len(datsv))


.. _sphx_glr_download_auto_examples_resume_train_segm.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

     :download:`Download Jupyter notebook: resume_train_segm.ipynb <resume_train_segm.ipynb>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_