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

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

.. _sphx_glr_auto_examples_train_segm_tomography.py:


Example 07: Train a network for segmentation (tomography)
=========================================================

This script trains a MS-D network for segmentation (i.e. labeling)
Run generatedata.py first to generate required training data.


.. code-block:: default


    # Import code
    import msdnet
    import glob

    # Define dilations in [1,10] as in paper.
    dilations = msdnet.dilations.IncrementDilations(10)

    # Create main network object for segmentation, with 100 layers,
    # [1,10] dilations, 5 input channels (5 slices), 4 output channels (one for each label), 
    # using the GPU (set gpu=False to use CPU)
    n = msdnet.network.SegmentationMSDNet(100, dilations, 5, 4, gpu=True)

    # Initialize network parameters
    n.initialize()

    # Define training data
    # First, create lists of input files (low quality) and target files (labels)
    flsin = sorted(glob.glob('tomo_train/lowqual/*.tiff'))
    flstg = sorted(glob.glob('tomo_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, and 3,
        # which are the labels given in each label TIFF file.
        d_oh = msdnet.data.OneHotDataPoint(d, [0,1,2,3])
        # Add datapoint to list
        dats.append(d_oh)
    # Note: The above can also be achieved using a utility function for such 'simple' cases:
    # dats = msdnet.utils.load_simple_data('tomo_train/lowqual/*.tiff', 'tomo_train/label/*.tiff', augment=False, labels=[0,1,2,3])

    # Convert input slices to input slabs (i.e. multiple slices as input)
    dats = msdnet.data.convert_to_slabs(dats, 2, flip=True)
    # Augment data by rotating and flipping
    dats_augm = [msdnet.data.RotateAndFlipDataPoint(d) for d in dats]
    
    # Normalize input and output of network to zero mean and unit variance using
    # training data images
    n.normalizeinout(dats)

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

    # Define validation data (not using augmentation)
    flsin = sorted(glob.glob('tomo_val/lowqual/*.tiff'))
    flstg = sorted(glob.glob('tomo_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])
        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('tomo_val/lowqual/*.tiff', 'tomo_val/label/*.tiff', augment=False, labels=[0,1,2,3])

    # Convert input slices to input slabs (i.e. multiple slices as input)
    datsv = msdnet.data.convert_to_slabs(datsv, 2, flip=False)

    # Validate with Mean-Squared Error
    val = msdnet.validate.MSEValidation(datsv)

    # Use ADAM training algorithms
    t = msdnet.train.AdamAlgorithm(n)

    # Log error metrics to console
    consolelog = msdnet.loggers.ConsoleLogger()
    # Log error metrics to file
    filelog = msdnet.loggers.FileLogger('log_tomo_segm.txt')
    # Log typical, worst, and best images to image files
    imagelog = msdnet.loggers.ImageLabelLogger('log_tomo_segm', chan_in=2, 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_tomo_segm_singlechannel', chan_in=2, 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) (=256)
    # training steps.
    msdnet.train.train(n, t, val, bprov, 'tomo_segm_params.h5',loggers=[consolelog,filelog,imagelog,singlechannellog], val_every=len(datsv))


.. _sphx_glr_download_auto_examples_train_segm_tomography.py:


.. only :: html

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



  .. container:: sphx-glr-download

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



  .. container:: sphx-glr-download

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


.. only:: html

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

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