layers.py

from keras.engine.topology import Layer
from keras.utils.generic_utils import get_custom_objects
from keras import backend as K
import itertools

''' Theano Backend function '''


def depth_to_space_th(input, scale, data_format=None):
    ''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
    import theano.tensor as T
    if data_format is None:
        data_format = K.image_dim_ordering()
    data_format = data_format.lower()
    input = K._preprocess_conv2d_input(input, data_format)

    b, k, row, col = input.shape
    output_shape = (b, k // (scale ** 2), row * scale, col * scale)

    out = T.zeros(output_shape)
    r = scale

    for y, x in itertools.product(range(scale), repeat=2):
        out = T.inc_subtensor(out[:, :, y::r, x::r], input[:, r * y + x:: r * r, :, :])

    out = K._postprocess_conv2d_output(out, input, None, None, None, data_format)
    return out


''' Tensorflow Backend Function (NOT TESTED '''


# TODO: Test on Tensorflow backend
def depth_to_space_tf(input, scale, data_format=None):
    ''' Uses phase shift algorithm to convert channels/depth for spatial resolution '''
    import tensorflow as tf
    if data_format is None:
        data_format = K.image_dim_ordering()
    data_format = data_format.lower()
    input = K._preprocess_conv2d_input(input, data_format)
    out = tf.depth_to_space(input, scale)
    out = K._postprocess_conv2d_output(out, data_format)
    return out


class SubPixelUpscaling(Layer):
    """ Sub-pixel convolutional upscaling layer based on the paper "Real-Time Single Image
    and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network"
    (https://arxiv.org/abs/1609.05158).
    This layer requires a Convolution2D prior to it, having output filters computed according to
    the formula :
        filters = k * (scale_factor * scale_factor)
        where k = a user defined number of filters (generally larger than 32)
              scale_factor = the upscaling factor (generally 2)
    This layer performs the depth to space operation on the convolution filters, and returns a
    tensor with the size as defined below.
    # Example :
    ```python
        # A standard subpixel upscaling block
        x = Convolution2D(256, 3, 3, padding='same', activation='relu')(...)
        u = SubPixelUpscaling(scale_factor=2)(x)
        [Optional]
        x = Convolution2D(256, 3, 3, padding='same', activation='relu')(u)
    ```
        In practice, it is useful to have a second convolution layer after the
        SubPixelUpscaling layer to speed up the learning process.
        However, if you are stacking multiple SubPixelUpscaling blocks, it may increase
        the number of parameters greatly, so the Convolution layer after SubPixelUpscaling
        layer can be removed.
    # Arguments
        scale_factor: Upscaling factor.
        data_format: Can be None, 'channels_first' or 'channels_last'.
    # Input shape
        4D tensor with shape:
        `(samples, k * (scale_factor * scale_factor) channels, rows, cols)` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, rows, cols, k * (scale_factor * scale_factor) channels)` if data_format='channels_last'.
    # Output shape
        4D tensor with shape:
        `(samples, k channels, rows * scale_factor, cols * scale_factor))` if data_format='channels_first'
        or 4D tensor with shape:
        `(samples, rows * scale_factor, cols * scale_factor, k channels)` if data_format='channels_last'.
    """

    def __init__(self, scale_factor=2, data_format=None, **kwargs):
        super(SubPixelUpscaling, self).__init__(**kwargs)

        self.scale_factor = scale_factor
        self.data_format = K.image_dim_ordering() if data_format == None else data_format

    def build(self, input_shape):
        pass

    def call(self, x, mask=None):
        if K.backend() == 'th':
            y = depth_to_space_th(x, self.scale_factor, self.data_format)
        else:
            y = depth_to_space_tf(x, self.scale_factor, self.data_format)
        return y

    def compute_output_shape(self, input_shape):
        if self.data_format == 'channels_first':
            b, k, r, c = input_shape
            return (b, k // (self.scale_factor ** 2), r * self.scale_factor, c * self.scale_factor)
        else:
            b, r, c, k = input_shape
            return (b, r * self.scale_factor, c * self.scale_factor, k // (self.scale_factor ** 2))

    def get_config(self):
        config = {'scale_factor': self.scale_factor,
                  'data_format': self.data_format}
        base_config = super(SubPixelUpscaling, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))


get_custom_objects().update({'SubPixelUpscaling': SubPixelUpscaling})