<reponame>mikhailkin/dataset
"""  <NAME>. et al "`V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation
<https://arxiv.org/abs/1606.04797>`_"
"""
import tensorflow as tf
import numpy as np

from ... import is_best_practice
from .layers import conv_block
from . import TFModel
from .resnet import ResNet


class VNet(TFModel):
    """ VNet

    **Configuration**

    inputs : dict
        dict with 'images' and 'masks' (see :meth:`._make_inputs`)

    body : dict
        num_blocks : int
            number of downsampling blocks (default=5)

        filters : list of int
            number of filters in each block (default=[16, 32, 64, 128, 256])

    head : dict
        num_classes : int
            number of semantic classes
    """
    @classmethod
    def default_config(cls):
        config = TFModel.default_config()

        filters = 16   # number of filters in the first block
        config['body/layout'] = ['cna', 'cna'*2] + ['cna'*3] * 3
        num_blocks = len(config['body']['layout'])
        config['body/filters'] = 2 ** np.arange(num_blocks) * filters
        config['body/kernel_size'] = 5
        config['body/upsample'] = dict(layout='tna', factor=2)
        config['head'] = dict(layout='c', kernel_size=1)

        config['loss'] = 'ce'
        if is_best_practice('optimizer'):
            config['optimizer'] = 'Adam'
        else:
            config['decay'] = ('invtime', dict(learning_rate=1e-4, decay_steps=25000, decay_rate=10))
            config['optimizer'] = ('Momentum', dict(momentum=.99))
        return config

    def build_config(self, names=None):
        config = super().build_config(names)
        if config.get('head/num_classes') is None:
            config['head/num_classes'] = self.num_classes('targets')
        return config

    @classmethod
    def body(cls, inputs, name='body', **kwargs):
        """ Base layers

        Parameters
        ----------
        inputs : tf.Tensor
            input tensor
        name : str
            scope name

        Returns
        -------
        tf.Tensor
        """
        kwargs = cls.fill_params('body', **kwargs)
        layout, filters = cls.pop(['layout', 'filters'], kwargs)

        with tf.variable_scope(name):
            x, inputs = inputs, None
            encoder_outputs = []
            for i, ifilters in enumerate(filters):
                x = cls.encoder_block(x, layout=layout[i], filters=ifilters, downsample=i > 0,
                                      name='encoder-'+str(i), **kwargs)
                encoder_outputs.append(x)

            for i, ifilters in enumerate(filters[-2::-1]):
                x = cls.decoder_block((x, encoder_outputs[-i-2]), layout=layout[-i-1], filters=ifilters*2,
                                      name='decoder-'+str(i), **kwargs)

        return x

    @classmethod
    def encoder_block(cls, inputs, name, downsample=True, **kwargs):
        """ 5x5x5 convolutions and 2x2x2 max pooling with stride 2

        Parameters
        ----------
        inputs : tf.Tensor
            input tensor
        name : str
            scope name

        Returns
        -------
        tf.Tensor
        """
        kwargs = cls.fill_params('body', **kwargs)
        layout, kernel_size = cls.pop(['layout', 'kernel_size'], kwargs)
        x, inputs = inputs, None
        with tf.variable_scope(name):
            if downsample:
                x = conv_block(x, layout='cna', kernel_size=2, strides=2, name='downsample', **kwargs)
            x = ResNet.block(x, layout=layout, kernel_size=kernel_size, downsample=0, name='conv', **kwargs)
        return x

    @classmethod
    def decoder_block(cls, inputs, name, **kwargs):
        """ 2x2x2 transposed convolution + 5x5x5 convolutions

        Parameters
        ----------
        inputs : tf.Tensor
            input tensor
        name : str
            scope name

        Returns
        -------
        tf.Tensor
        """
        kwargs = cls.fill_params('body', **kwargs)
        layout, filters, kernel_size = cls.pop(['layout', 'filters', 'kernel_size'], kwargs)
        upsample_args = cls.pop('upsample', kwargs)

        with tf.variable_scope(name):
            x, skip = inputs
            inputs = None
            x = cls.upsample(x, filters=filters, name='upsample', **upsample_args, **kwargs)
            x = cls.crop(x, skip, data_format=kwargs.get('data_format'))
            axis = cls.channels_axis(kwargs.get('data_format'))
            x = tf.concat((skip, x), axis=axis)
            x = ResNet.block(x, layout=layout, filters=filters, kernel_size=kernel_size, downsample=0,
                             name='conv', **kwargs)
        return x

    @classmethod
    def head(cls, inputs, num_classes, name='head', **kwargs):
        """ 1x1 convolution

        Parameters
        ----------
        inputs : tf.Tensor
            input tensor
        num_classes : int
            number of classes (and number of filters in the last 1x1 convolution)
        name : str
            scope name

        Returns
        -------
        tf.Tensor
        """
        kwargs = cls.fill_params('head', **kwargs)
        x = conv_block(inputs, filters=num_classes, name=name, **kwargs)
        return x
