#! /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
__all__ = [
'UpSampling2dLayer',
'DownSampling2dLayer',
]
[docs]class UpSampling2dLayer(Layer):
"""The :class:`UpSampling2dLayer` class is a up-sampling 2D layer.
See `tf.image.resize_images <https://www.tensorflow.org/api_docs/python/tf/image/resize_images>`__.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer with 4-D Tensor of the shape (batch, height, width, channels) or 3-D Tensor of the shape (height, width, channels).
size : tuple of int/float
(height, width) scale factor or new size of height and width.
is_scale : boolean
If True (default), the `size` is a scale factor; otherwise, the `size` is the numbers of pixels of height and width.
method : int
The resize method selected through the index. Defaults index is 0 which is ResizeMethod.BILINEAR.
- Index 0 is ResizeMethod.BILINEAR, Bilinear interpolation.
- Index 1 is ResizeMethod.NEAREST_NEIGHBOR, Nearest neighbor interpolation.
- Index 2 is ResizeMethod.BICUBIC, Bicubic interpolation.
- Index 3 ResizeMethod.AREA, Area interpolation.
align_corners : boolean
If True, align the corners of the input and output. Default is False.
name : str
A unique layer name.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
size,
is_scale=True,
method=0,
align_corners=False,
name='upsample2d_layer',
):
super(UpSampling2dLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"UpSampling2dLayer %s: is_scale: %s size: %s method: %d align_corners: %s" %
(self.name, is_scale, size, method, align_corners)
)
if not isinstance(size, (list, tuple)) and len(size) == 2:
raise AssertionError()
if len(self.inputs.get_shape()) == 3:
if is_scale:
size_h = size[0] * tf.shape(self.inputs)[0]
size_w = size[1] * tf.shape(self.inputs)[1]
size = [size_h, size_w]
elif len(self.inputs.get_shape()) == 4:
if is_scale:
size_h = size[0] * tf.shape(self.inputs)[1]
size_w = size[1] * tf.shape(self.inputs)[2]
size = [size_h, size_w]
else:
raise Exception("Donot support shape %s" % tf.shape(self.inputs))
with tf.variable_scope(name):
try:
self.outputs = tf.image.resize_images(
self.inputs, size=size, method=method, align_corners=align_corners
)
except Exception: # for TF 0.10
self.outputs = tf.image.resize_images(
self.inputs, new_height=size[0], new_width=size[1], method=method, align_corners=align_corners
)
self._add_layers(self.outputs)
[docs]class DownSampling2dLayer(Layer):
"""The :class:`DownSampling2dLayer` class is down-sampling 2D layer.
See `tf.image.resize_images <https://www.tensorflow.org/versions/master/api_docs/python/image/resizing#resize_images>`__.
Parameters
----------
prev_layer : :class:`Layer`
Previous layer with 4-D Tensor in the shape of (batch, height, width, channels) or 3-D Tensor in the shape of (height, width, channels).
size : tuple of int/float
(height, width) scale factor or new size of height and width.
is_scale : boolean
If True (default), the `size` is the scale factor; otherwise, the `size` are numbers of pixels of height and width.
method : int
The resize method selected through the index. Defaults index is 0 which is ResizeMethod.BILINEAR.
- Index 0 is ResizeMethod.BILINEAR, Bilinear interpolation.
- Index 1 is ResizeMethod.NEAREST_NEIGHBOR, Nearest neighbor interpolation.
- Index 2 is ResizeMethod.BICUBIC, Bicubic interpolation.
- Index 3 ResizeMethod.AREA, Area interpolation.
align_corners : boolean
If True, exactly align all 4 corners of the input and output. Default is False.
name : str
A unique layer name.
"""
@deprecated_alias(layer='prev_layer', end_support_version=1.9) # TODO remove this line for the 1.9 release
def __init__(
self,
prev_layer,
size,
is_scale=True,
method=0,
align_corners=False,
name='downsample2d_layer',
):
super(DownSampling2dLayer, self).__init__(prev_layer=prev_layer, name=name)
logging.info(
"DownSampling2dLayer %s: is_scale: %s size: %s method: %d, align_corners: %s" %
(self.name, is_scale, size, method, align_corners)
)
if not isinstance(size, (list, tuple)) and len(size) == 2:
raise AssertionError()
if len(self.inputs.get_shape()) == 3:
if is_scale:
size_h = size[0] * tf.shape(self.inputs)[0]
size_w = size[1] * tf.shape(self.inputs)[1]
size = [size_h, size_w]
elif len(self.inputs.get_shape()) == 4:
if is_scale:
size_h = size[0] * tf.shape(self.inputs)[1]
size_w = size[1] * tf.shape(self.inputs)[2]
size = [size_h, size_w]
else:
raise Exception("Do not support shape %s" % tf.shape(self.inputs))
with tf.variable_scope(name):
try:
self.outputs = tf.image.resize_images(
self.inputs, size=size, method=method, align_corners=align_corners
)
except Exception: # for TF 0.10
self.outputs = tf.image.resize_images(
self.inputs, new_height=size[0], new_width=size[1], method=method, align_corners=align_corners
)
self._add_layers(self.outputs)