#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorlayerx as tlx
from tensorlayerx import logging
from tensorlayerx.nn.core import Module
__all__ = [
'PadLayer',
'ZeroPad1d',
'ZeroPad2d',
'ZeroPad3d',
]
[docs]class PadLayer(Module):
"""The :class:`PadLayer` class is a padding layer for any mode and dimension.
Please see `tf.pad <https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/pad>`__ for usage.
Parameters
----------
padding : list of lists of 2 ints, or a Tensor of type int32.
The int32 values to pad.
mode : str
"CONSTANT", "REFLECT", or "SYMMETRIC" (case-insensitive).
name : None or str
A unique layer name.
Examples
--------
With TensorLayer
>>> net = tlx.nn.Input([10, 224, 224, 3], name='input')
>>> padlayer = tlx.nn.PadLayer([[0, 0], [3, 3], [3, 3], [0, 0]], "REFLECT", name='inpad')(net)
>>> print(padlayer)
>>> output shape : (10, 230, 230, 3)
"""
def __init__(
self,
padding=None,
mode='CONSTANT',
constant_values=0,
name=None, # 'pad_layer',
):
super().__init__(name)
self.padding = padding
self.mode = mode
self.constant_values = constant_values
logging.info("PadLayer %s: padding: %s mode: %s" % (self.name, self.padding, self.mode))
if self.padding is None:
raise Exception(
"padding should be a Tensor of type int32. see https://www.tensorflow.org/versions/r2.0/api_docs/python/tf/pad"
)
self.build()
self._built = True
def __repr__(self):
s = '{classname}(padding={padding}, mode={mode}'
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
self.pad = tlx.ops.Pad(paddings=self.padding, mode=self.mode, constant_values=self.constant_values)
def forward(self, inputs):
outputs = self.pad(inputs)
if not self._nodes_fixed and self._build_graph:
self._add_node(inputs, outputs)
self._nodes_fixed = True
return outputs
[docs]class ZeroPad1d(Module):
"""
The :class:`ZeroPad1d` class is a 1D padding layer for signal [batch, length, channel].
Parameters
----------
padding : int, or tuple of 2 ints
- If int, zeros to add at the beginning and end of the padding dimension (axis 1).
- If tuple of 2 ints, zeros to add at the beginning and at the end of the padding dimension.
name : None or str
A unique layer name.
Examples
--------
With TensorLayer
>>> net = tlx.nn.Input([10, 100, 1], name='input')
>>> pad1d = tlx.nn.ZeroPad1d(padding=(3, 3))(net)
>>> print(pad1d)
>>> output shape : (10, 106, 1)
"""
def __init__(
self,
padding,
name=None, # 'zeropad1d',
):
super().__init__(name)
self.padding = padding
logging.info("ZeroPad1d %s: padding: %s" % (self.name, str(padding)))
if not isinstance(self.padding, (int, tuple, dict)):
raise AssertionError()
self.build()
self._built = True
def __repr__(self):
s = '{classname}(padding={padding}'
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
self.layer = tlx.ops.ZeroPadding1D(padding=self.padding)
def forward(self, inputs):
outputs = self.layer(inputs)
if not self._nodes_fixed and self._build_graph:
self._add_node(inputs, outputs)
self._nodes_fixed = True
return outputs
[docs]class ZeroPad2d(Module):
"""
The :class:`ZeroPad2d` class is a 2D padding layer for image [batch, height, width, channel].
Parameters
----------
padding : tuple of 2 ints or int, or tuple of 2 tuples of 2 ints.
- If int, the same symmetric padding is applied to width and height.
- If tuple of 2 ints, interpreted as two different symmetric padding values for height and width as ``(symmetric_height_pad, symmetric_width_pad)``.
- If tuple of 2 tuples of 2 ints, interpreted as ``((top_pad, bottom_pad), (left_pad, right_pad))``.
name : None or str
A unique layer name.
Examples
--------
With TensorLayer
>>> net = tlx.nn.Input([10, 100, 100, 3], name='input')
>>> pad2d = tlx.nn.ZeroPad2d(padding=((3, 3), (4, 4)))(net)
>>> print(pad2d)
>>> output shape : (10, 106, 108, 3)
"""
def __init__(
self,
padding,
name=None, # 'zeropad2d',
):
super().__init__(name)
self.padding = padding
logging.info("ZeroPad2d %s: padding: %s" % (self.name, str(self.padding)))
if not isinstance(self.padding, (int, tuple)):
raise AssertionError("Padding should be of type `int` or `tuple`")
self.build()
self._built = True
def __repr__(self):
s = '{classname}(padding={padding}'
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
self.layer = tlx.ops.ZeroPadding2D(padding=self.padding)
def forward(self, inputs):
outputs = self.layer(inputs)
if not self._nodes_fixed and self._build_graph:
self._add_node(inputs, outputs)
self._nodes_fixed = True
return outputs
[docs]class ZeroPad3d(Module):
"""
The :class:`ZeroPad3d` class is a 3D padding layer for volume [batch, depth, height, width, channel].
Parameters
----------
padding : int, or tuple of 2 ints, or tuple of 2 tuples of 2 ints.
- If int, the same symmetric padding is applied to width and height.
- If tuple of 2 ints, interpreted as two different symmetric padding values for height and width as ``(symmetric_dim1_pad, symmetric_dim2_pad, symmetric_dim3_pad)``.
- If tuple of 2 tuples of 2 ints, interpreted as ``((left_dim1_pad, right_dim1_pad), (left_dim2_pad, right_dim2_pad), (left_dim3_pad, right_dim3_pad))``.
name : None or str
A unique layer name.
Examples
--------
With TensorLayer
>>> net = tlx.nn.Input([10, 100, 100, 100, 3], name='input')
>>> pad3d = tlx.nn.ZeroPad3d(padding=((3, 3), (4, 4), (5, 5)))(net)
>>> print(pad3d)
>>> output shape : (10, 106, 108, 110, 3)
"""
def __init__(
self,
padding,
name=None, # 'zeropad3d',
):
super().__init__(name)
self.padding = padding
logging.info("ZeroPad3d %s: padding: %s" % (self.name, str(self.padding)))
if not isinstance(self.padding, (int, tuple)):
raise AssertionError()
self.build()
self._built = True
def __repr__(self):
s = '{classname}(padding={padding}'
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape=None):
self.layer = tlx.ops.ZeroPadding3D(padding=self.padding)
def forward(self, inputs):
outputs = self.layer(inputs)
if not self._nodes_fixed and self._build_graph:
self._add_node(inputs, outputs)
self._nodes_fixed = True
return outputs