from __future__ import absolute_import
from keras import backend as K
from keras.engine import Layer
from keras.utils.generic_utils import get_custom_objects
from keras.utils.conv_utils import normalize_data_format
if K.backend() == 'theano':
import theano_backend as K_BACKEND
else:
import tensorflow_backend as K_BACKEND
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 = normalize_data_format(data_format)
def build(self, input_shape):
pass
def call(self, x, mask=None):
y = K_BACKEND.depth_to_space(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})
