#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module
__all__ = [
'UpSampling2d',
'DownSampling2d',
]
[docs]class UpSampling2d(Module):
"""The :class:`UpSampling2d` class is a up-sampling 2D layer.
See `tf.image.resize_images <https://www.tensorflow.org/api_docs/python/tf/image/resize_images>`__.
Parameters
----------
scale : int or tuple of int
(scale_height, scale_width) scale factor. scale_height = new_height/height, scale_width = new_width/width.
method : str
The resize method selected through the given string. Default 'bilinear'.
- 'bilinear', Bilinear interpolation.
- 'nearest', Nearest neighbor interpolation.
- 'bicubic', Bicubic interpolation.
- 'area', Area interpolation.
antialias : boolean
Whether to use an anti-aliasing filter when downsampling an image.
data_format : str
channels_last 'channel_last' (default) or channels_first.
name : None or str
A unique layer name.
Examples
---------
With TensorLayer
>>> ni = tlx.nn.Input([10, 50, 50, 32], name='input')
>>> ni = tlx.nn.UpSampling2d(scale=(2, 2))(ni)
>>> output shape : [10, 100, 100, 32]
"""
def __init__(self, scale, method='bilinear', antialias=False, data_format='channels_last', name=None, ksize=None):
super(UpSampling2d, self).__init__(name)
self.method = method
self.antialias = antialias
self.data_format = data_format
self.ksize = ksize
logging.info(
"UpSampling2d %s: scale: %s method: %s antialias: %s" % (self.name, scale, self.method, self.antialias)
)
if isinstance(scale, (list, tuple)) and len(scale) != 2:
raise ValueError("out_size must be int or tuple/list of length 2")
self.scale = (scale, scale) if isinstance(scale, int) else scale
self.build(None)
self._built = True
def __repr__(self):
s = '{classname}(scale={scale}, method={method}'
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, scale=self.scale, method=self.method, name=self.name)
def build(self, inputs_shape):
self.resize = tlx.ops.Resize(
scale=self.scale, method=self.method, antialias=self.antialias, data_format=self.data_format
)
def forward(self, inputs):
"""
Parameters
------------
inputs : :class:`Tensor`
Inputs tensors with 4-D Tensor of the shape (batch, height, width, channels)
"""
outputs = self.resize(inputs)
if not self._nodes_fixed and self._build_graph:
self._add_node(inputs, outputs)
self._nodes_fixed = True
return outputs
[docs]class DownSampling2d(Module):
"""The :class:`DownSampling2d` 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
----------
scale : int or tuple of int
(new_height, new_width) scale factor.scale_height = new_height/height, scale_width = new_width/width.
method : str
The resize method selected through the given string. Default 'bilinear'.
- 'bilinear', Bilinear interpolation.
- 'nearest', Nearest neighbor interpolation.
- 'bicubic', Bicubic interpolation.
- 'area', Area interpolation.
antialias : boolean
Whether to use an anti-aliasing filter when downsampling an image.
data_format : str
channels_last 'channel_last' (default) or channels_first.
name : None or str
A unique layer name.
Examples
---------
With TensorLayer
>>> ni = tlx.nn.Input([10, 50, 50, 32], name='input')
>>> ni = tlx.nn.DownSampling2d(scale=(2, 2))(ni)
>>> output shape : [10, 25, 25, 32]
"""
def __init__(self, scale, method='bilinear', antialias=False, data_format='channels_last', name=None, ksize=None):
super(DownSampling2d, self).__init__(name)
self.method = method
self.antialias = antialias
self.data_format = data_format
self.ksize = ksize
logging.info(
"DownSampling2d %s: scale: %s method: %s antialias: %s" % (self.name, scale, self.method, self.antialias)
)
if isinstance(scale, (list, tuple)) and len(scale) != 2:
raise ValueError("scale must be int or tuple/list of length 2")
self.scale = (scale, scale) if isinstance(scale, int) else scale
self.build(None)
self._built = True
def __repr__(self):
s = '{classname}(scale={scale}, method={method}'
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, scale=self.scale, method=self.method, name=self.name)
def build(self, inputs_shape):
scale = [1.0 / self.scale[0], 1.0 / self.scale[1]]
self.resize = tlx.ops.Resize(
scale=scale, method=self.method, antialias=self.antialias, data_format=self.data_format
)
def forward(self, inputs):
"""
Parameters
------------
inputs : :class:`Tensor`
Inputs tensors with 4-D Tensor of the shape (batch, height, width, channels)
"""
outputs = self.resize(inputs)
if not self._nodes_fixed and self._build_graph:
self._add_node(inputs, outputs)
self._nodes_fixed = True
return outputs