#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
from tensorlayer.layers.core import Layer
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.decorators import private_method
__all__ = [
'SubpixelConv1d',
'SubpixelConv2d',
]
[docs]class SubpixelConv2d(Layer):
"""It is a 2D sub-pixel up-sampling layer, usually be used
for Super-Resolution applications, see `SRGAN <https://github.com/tensorlayer/srgan/>`__ for example.
Parameters
------------
prev_layer : :class:`Layer`
Previous layer,
scale : int
The up-scaling ratio, a wrong setting will lead to dimension size error.
n_out_channel : int or None
The number of output channels.
- If None, automatically set n_out_channel == the number of input channels / (scale x scale).
- The number of input channels == (scale x scale) x The number of output channels.
act : activation function
The activation function of this layer.
name : str
A unique layer name.
Examples
---------
>>> # examples here just want to tell you how to set the n_out_channel.
>>> import numpy as np
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> x = np.random.rand(2, 16, 16, 4)
>>> X = tf.placeholder("float32", shape=(2, 16, 16, 4), name="X")
>>> net = tl.layers.InputLayer(X, name='input')
>>> net = tl.layers.SubpixelConv2d(net, scale=2, n_out_channel=1, name='subpixel_conv2d')
>>> sess = tf.Session()
>>> y = sess.run(net.outputs, feed_dict={X: x})
>>> print(x.shape, y.shape)
(2, 16, 16, 4) (2, 32, 32, 1)
>>> x = np.random.rand(2, 16, 16, 4*10)
>>> X = tf.placeholder("float32", shape=(2, 16, 16, 4*10), name="X")
>>> net = tl.layers.InputLayer(X, name='input2')
>>> net = tl.layers.SubpixelConv2d(net, scale=2, n_out_channel=10, name='subpixel_conv2d2')
>>> y = sess.run(net.outputs, feed_dict={X: x})
>>> print(x.shape, y.shape)
(2, 16, 16, 40) (2, 32, 32, 10)
>>> x = np.random.rand(2, 16, 16, 25*10)
>>> X = tf.placeholder("float32", shape=(2, 16, 16, 25*10), name="X")
>>> net = tl.layers.InputLayer(X, name='input3')
>>> net = tl.layers.SubpixelConv2d(net, scale=5, n_out_channel=None, name='subpixel_conv2d3')
>>> y = sess.run(net.outputs, feed_dict={X: x})
>>> print(x.shape, y.shape)
(2, 16, 16, 250) (2, 80, 80, 10)
References
------------
- `Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network <https://arxiv.org/pdf/1609.05158.pdf>`__
"""
# github/Tetrachrome/subpixel https://github.com/Tetrachrome/subpixel/blob/master/subpixel.py
@deprecated_alias(net='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, scale=2, n_out_channel=None, act=None, name='subpixel_conv2d'):
super(SubpixelConv2d, self).__init__(prev_layer=prev_layer, act=act, name=name)
if n_out_channel is None:
if int(self.inputs.get_shape()[-1]) / (scale**2) % 1 != 0:
raise Exception(
"SubpixelConv2d: The number of input channels == (scale x scale) x The number of output channels"
)
n_out_channel = int(int(self.inputs.get_shape()[-1]) / (scale**2))
logging.info(
"SubpixelConv2d %s: scale: %d n_out_channel: %s act: %s" %
(self.name, scale, n_out_channel, self.act.__name__ if self.act is not None else 'No Activation')
)
with tf.variable_scope(name):
self.outputs = self._apply_activation(self._PS(self.inputs, r=scale, n_out_channels=n_out_channel))
self._add_layers(self.outputs)
@private_method
def _PS(self, X, r, n_out_channels):
_err_log = "SubpixelConv2d: The number of input channels == (scale x scale) x The number of output channels"
if n_out_channels >= 1:
if int(X.get_shape()[-1]) != (r**2) * n_out_channels:
raise Exception(_err_log)
# bsize, a, b, c = X.get_shape().as_list()
# bsize = tf.shape(X)[0] # Handling Dimension(None) type for undefined batch dim
# Xs=tf.split(X,r,3) #b*h*w*r*r
# Xr=tf.concat(Xs,2) #b*h*(r*w)*r
# X=tf.reshape(Xr,(bsize,r*a,r*b,n_out_channel)) # b*(r*h)*(r*w)*c
X = tf.depth_to_space(X, r)
else:
raise RuntimeError(_err_log)
return X
[docs]class SubpixelConv1d(Layer):
"""It is a 1D sub-pixel up-sampling layer.
Calls a TensorFlow function that directly implements this functionality.
We assume input has dim (batch, width, r)
Parameters
------------
net : :class:`Layer`
Previous layer with output shape of (batch, width, r).
scale : int
The up-scaling ratio, a wrong setting will lead to Dimension size error.
act : activation function
The activation function of this layer.
name : str
A unique layer name.
Examples
----------
>>> import tensorflow as tf
>>> import tensorlayer as tl
>>> t_signal = tf.placeholder('float32', [10, 100, 4], name='x')
>>> n = tl.layers.InputLayer(t_signal, name='in')
>>> n = tl.layers.SubpixelConv1d(n, scale=2, name='s')
>>> print(n.outputs.shape)
(10, 200, 2)
References
-----------
`Audio Super Resolution Implementation <https://github.com/kuleshov/audio-super-res/blob/master/src/models/layers/subpixel.py>`__.
"""
@deprecated_alias(net='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(self, prev_layer, scale=2, act=None, name='subpixel_conv1d'):
super(SubpixelConv1d, self).__init__(prev_layer=prev_layer, act=act, name=name)
logging.info(
"SubpixelConv1d %s: scale: %d act: %s" %
(self.name, scale, self.act.__name__ if self.act is not None else 'No Activation')
)
with tf.name_scope(name):
self.outputs = self._apply_activation(self._PS(self.inputs, r=scale))
self._add_layers(self.outputs)
@private_method
def _PS(self, I, r):
X = tf.transpose(I, [2, 1, 0]) # (r, w, b)
X = tf.batch_to_space_nd(X, [r], [[0, 0]]) # (1, r*w, b)
X = tf.transpose(X, [2, 1, 0])
return X