#! /usr/bin/python
# -*- coding: utf-8 -*-
import numpy as np
import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import get_collection_trainable
__all__ = [
# 'DeConv1d' # TODO: Shall be implemented
'DeConv2d',
'DeConv3d',
]
[docs]class DeConv2d(Layer):
"""Simplified version of :class:`DeConv2dLayer`, see `tf.nn.conv3d_transpose <https://tensorflow.google.cn/versions/r2.0/api_docs/python/tf/nn/conv2d_transpose>`__.
Parameters
----------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (height, width).
strides : tuple of int
The stride step (height, width).
padding : str
The padding algorithm type: "SAME" or "VALID".
act : activation function
The activation function of this layer.
data_format : str
"channels_last" (NHWC, default) or "channels_first" (NCHW).
dilation_rate : int of tuple of int
The dilation rate to use for dilated convolution
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip biases.
in_channels : int
The number of in channels.
name : None or str
A unique layer name.
Examples
--------
With TensorLayer
>>> net = tl.layers.Input([5, 100, 100, 32], name='input')
>>> deconv2d = tl.layers.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), in_channels=32, name='DeConv2d_1')
>>> print(deconv2d)
>>> tensor = tl.layers.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), name='DeConv2d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3),
strides=(2, 2),
act=None,
padding='SAME',
dilation_rate=(1, 1),
data_format='channels_last',
W_init=tl.initializers.truncated_normal(stddev=0.02),
b_init=tl.initializers.constant(value=0.0),
in_channels=None,
name=None # 'decnn2d'
):
super().__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self.strides = strides
self.padding = padding
self.data_format = data_format
self.dilation_rate = dilation_rate
self.W_init = W_init
self.b_init = b_init
self.in_channels = in_channels
# Attention: To build, we need not only the in_channels!
# if self.in_channels:
# self.build(None)
# self._built = True
logging.info(
"DeConv2d {}: n_filters: {} strides: {} padding: {} act: {} dilation: {}".format(
self.name,
str(n_filter),
str(strides),
padding,
self.act.__name__ if self.act is not None else 'No Activation',
dilation_rate,
)
)
if len(strides) != 2:
raise ValueError("len(strides) should be 2, DeConv2d and DeConv2dLayer are different.")
def __repr__(self):
actstr = self.act.__name__ if self.act is not None else 'No Activation'
s = (
'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
', strides={strides}, padding={padding}'
)
if self.dilation_rate != (1, ) * len(self.dilation_rate):
s += ', dilation={dilation_rate}'
if self.b_init is None:
s += ', bias=False'
s += (', ' + actstr)
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape):
self.layer = tf.keras.layers.Conv2DTranspose(
filters=self.n_filter,
kernel_size=self.filter_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
dilation_rate=self.dilation_rate,
activation=self.act,
use_bias=(True if self.b_init is not None else False),
kernel_initializer=self.W_init,
bias_initializer=self.b_init,
# dtype=tf.float32,
name=self.name,
)
if self.data_format == "channels_first":
self.in_channels = inputs_shape[1]
else:
self.in_channels = inputs_shape[-1]
_out = self.layer(
tf.convert_to_tensor(np.random.uniform(size=inputs_shape), dtype=np.float32)
) #np.random.uniform([1] + list(inputs_shape))) # initialize weights
outputs_shape = _out.shape
self._trainable_weights = self.layer.weights
def forward(self, inputs):
outputs = self.layer(inputs)
return outputs
[docs]class DeConv3d(Layer):
"""Simplified version of :class:`DeConv3dLayer`, see `tf.nn.conv3d_transpose <https://tensorflow.google.cn/versions/r2.0/api_docs/python/tf/nn/conv3d_transpose>`__.
Parameters
----------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (depth, height, width).
strides : tuple of int
The stride step (depth, height, width).
padding : str
The padding algorithm type: "SAME" or "VALID".
act : activation function
The activation function of this layer.
data_format : str
"channels_last" (NDHWC, default) or "channels_first" (NCDHW).
W_init : initializer
The initializer for the weight matrix.
b_init : initializer or None
The initializer for the bias vector. If None, skip bias.
in_channels : int
The number of in channels.
name : None or str
A unique layer name.
Examples
--------
With TensorLayer
>>> net = tl.layers.Input([5, 100, 100, 100, 32], name='input')
>>> deconv3d = tl.layers.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), in_channels=32, name='DeConv3d_1')
>>> print(deconv3d)
>>> tensor = tl.layers.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), name='DeConv3d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3, 3),
strides=(2, 2, 2),
padding='SAME',
act=None,
data_format='channels_last',
W_init=tl.initializers.truncated_normal(stddev=0.02),
b_init=tl.initializers.constant(value=0.0),
in_channels=None,
name=None # 'decnn3d'
):
super().__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self.strides = strides
self.padding = padding
self.data_format = data_format
self.W_init = W_init
self.b_init = b_init
self.in_channels = in_channels,
# Attention: To build, we need not only the in_channels!
# if self.in_channels:
# self.build(None)
# self._built = True
logging.info(
"DeConv3d %s: n_filters: %s strides: %s pad: %s act: %s" % (
self.name, str(n_filter), str(strides), padding,
self.act.__name__ if self.act is not None else 'No Activation'
)
)
if len(strides) != 3:
raise ValueError("len(strides) should be 3, DeConv3d and DeConv3dLayer are different.")
def __repr__(self):
actstr = self.act.__name__ if self.act is not None else 'No Activation'
s = (
'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
', strides={strides}, padding={padding}'
)
# if self.dilation_rate != (1,) * len(self.dilation_rate):
# s += ', dilation={dilation_rate}'
if self.b_init is None:
s += ', bias=False'
s += (', ' + actstr)
if self.name is not None:
s += ', name=\'{name}\''
s += ')'
return s.format(classname=self.__class__.__name__, **self.__dict__)
def build(self, inputs_shape):
self.layer = tf.keras.layers.Conv3DTranspose(
filters=self.n_filter,
kernel_size=self.filter_size,
strides=self.strides,
padding=self.padding,
data_format=self.data_format,
activation=self.act,
use_bias=(True if self.b_init is not None else False),
kernel_initializer=self.W_init,
bias_initializer=self.b_init,
name=self.name,
)
if self.data_format == "channels_first":
self.in_channels = inputs_shape[1]
else:
self.in_channels = inputs_shape[-1]
_out = self.layer(
tf.convert_to_tensor(np.random.uniform(size=inputs_shape), dtype=np.float32)
) #self.layer(np.random.uniform([1] + list(inputs_shape))) # initialize weights
outputs_shape = _out.shape
# self._add_weights(self.layer.weights)
self._trainable_weights = self.layer.weights
def forward(self, inputs):
outputs = self.layer(inputs)
return outputs