#! /usr/bin/python
# -*- coding: utf-8 -*-
from tensorlayerx.nn.core import Module
import tensorlayerx as tlx
from tensorlayerx import logging
__all__ = [
'Conv1d',
'Conv2d',
'Conv3d',
'DeConv1d',
'DeConv2d',
'DeConv3d',
]
[docs]class Conv1d(Module):
"""Simplified version of :class:`Conv1dLayer`.
Parameters
----------
n_filter : int
The number of filters
filter_size : int
The filter size
stride : int
The stride step
dilation_rate : int
Specifying the dilation rate to use for dilated convolution.
act : activation function
The function that is applied to the layer activations
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channel_last" (NWC, default) or "channels_first" (NCW).
W_init : initializer or str
The initializer for the weight matrix.
b_init : initializer or None or str
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 = tlx.nn.Input([8, 100, 1], name='input')
>>> conv1d = tlx.nn.Conv1d(n_filter=32, filter_size=5, stride=2, b_init=None, in_channels=1, name='conv1d_1')
>>> print(conv1d)
>>> tensor = tlx.nn.Conv1d(n_filter=32, filter_size=5, stride=2, act=tlx.ReLU, name='conv1d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=5,
stride=1,
act=None,
padding='SAME',
data_format="channels_last",
dilation_rate=1,
W_init='truncated_normal',
b_init='constant',
in_channels=None,
name=None # 'conv1d'
):
super().__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self.stride = stride
self.padding = padding
self.data_format = data_format
self.dilation_rate = dilation_rate
self.W_init = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"Conv1d %s: n_filter: %d filter_size: %s stride: %d pad: %s act: %s" % (
self.name, n_filter, filter_size, stride, padding,
self.act.__class__.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
s = (
'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
', stride={stride}, padding={padding}'
)
if self.dilation_rate != 1:
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):
if self.data_format == 'channels_last':
self.data_format = 'NWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
elif self.data_format == 'channels_first':
self.data_format = 'NCW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
else:
raise Exception("data_format should be either channels_last or channels_first")
self.filter_shape = (self.filter_size, self.in_channels, self.n_filter)
# TODO : check
self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
self.b_init_flag = False
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.bias_add = tlx.ops.BiasAdd(self.data_format)
self.b_init_flag = True
self.conv1d = tlx.ops.Conv1D(
stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation_rate,
out_channel=self.n_filter, k_size=self.filter_size
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.conv1d(inputs, self.W)
if self.b_init_flag:
outputs = self.bias_add(outputs, self.b)
if self.act_init_flag:
outputs = self.act(outputs)
return outputs
[docs]class Conv2d(Module):
"""Simplified version of :class:`Conv2dLayer`.
Parameters
----------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (height, width).
strides : tuple of int
The sliding window strides of corresponding input dimensions.
It must be in the same order as the ``shape`` parameter.
dilation_rate : tuple of int
Specifying the dilation rate to use for dilated convolution.
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channels_last" (NHWC, default) or "channels_first" (NCHW).
W_init : initializer or str
The initializer for the the weight matrix.
b_init : initializer or None or str
The initializer for the 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 = tlx.nn.Input([8, 400, 400, 3], name='input')
>>> conv2d = tlx.nn.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), b_init=None, in_channels=3, name='conv2d_1')
>>> print(conv2d)
>>> tensor = tlx.nn.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tlx.ReLU, name='conv2d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3),
strides=(1, 1),
act=None,
padding='SAME',
data_format='channels_last',
dilation_rate=(1, 1),
W_init='truncated_normal',
b_init='constant',
in_channels=None,
name=None, # 'conv2d',
):
super(Conv2d, self).__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self._strides = self.strides = strides
self.padding = padding
self.data_format = data_format
self._dilation_rate = self.dilation_rate = dilation_rate
self.W_init = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"Conv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
self.name, n_filter, str(filter_size), str(strides), padding,
self.act.__class__.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__class__.__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):
if self.data_format == 'channels_last':
self.data_format = 'NHWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
self._strides = [1, self._strides[0], self._strides[1], 1]
self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1]
elif self.data_format == 'channels_first':
self.data_format = 'NCHW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
self._strides = [1, 1, self._strides[0], self._strides[1]]
self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]]
else:
raise Exception("data_format should be either channels_last or channels_first")
#TODO channels first filter shape [out_channel, in_channel, filter_h, filter_w]
self.filter_shape = (self.filter_size[0], self.filter_size[1], self.in_channels, self.n_filter)
self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
self.b_init_flag = False
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.bias_add = tlx.ops.BiasAdd(self.data_format)
self.b_init_flag = True
self.conv2d = tlx.ops.Conv2D(
strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate,
out_channel=self.n_filter, k_size=(self.filter_size[0], self.filter_size[1])
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.conv2d(inputs, self.W)
if self.b_init_flag:
outputs = self.bias_add(outputs, self.b)
if self.act_init_flag:
outputs = self.act(outputs)
return outputs
[docs]class Conv3d(Module):
"""Simplified version of :class:`Conv3dLayer`.
Parameters
----------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (height, width).
strides : tuple of int
The sliding window strides of corresponding input dimensions.
It must be in the same order as the ``shape`` parameter.
dilation_rate : tuple of int
Specifying the dilation rate to use for dilated convolution.
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channels_last" (NDHWC, default) or "channels_first" (NCDHW).
W_init : initializer or str
The initializer for the the weight matrix.
b_init : initializer or None or str
The initializer for the 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 = tlx.nn.Input([8, 20, 20, 20, 3], name='input')
>>> conv3d = tlx.nn.Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), b_init=None, in_channels=3, name='conv3d_1')
>>> print(conv3d)
>>> tensor = tlx.nn.Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), act=tlx.ReLU, name='conv3d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3, 3),
strides=(1, 1, 1),
act=None,
padding='SAME',
data_format='channels_last',
dilation_rate=(1, 1, 1),
W_init='truncated_normal',
b_init='constant',
in_channels=None,
name=None # 'conv3d',
):
super().__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self._strides = self.strides = strides
self.padding = padding
self.data_format = data_format
self._dilation_rate = self.dilation_rate = dilation_rate
self.W_init = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"Conv3d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
self.name, n_filter, str(filter_size), str(strides), padding,
self.act.__class__.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__class__.__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):
if self.data_format == 'channels_last':
self.data_format = 'NDHWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
self._strides = [1, self._strides[0], self._strides[1], self._strides[2], 1]
self._dilation_rate = [1, self.dilation_rate[0], self.dilation_rate[1], self.dilation_rate[2], 1]
elif self.data_format == 'channels_first':
self.data_format = 'NCDHW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
self._strides = [1, 1, self._strides[0], self._strides[1], self._strides[2]]
self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1], self._dilation_rate[2]]
else:
raise Exception("data_format should be either channels_last or channels_first")
self.filter_shape = (
self.filter_size[0], self.filter_size[1], self.filter_size[2], self.in_channels, self.n_filter
)
self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
self.b_init_flag = False
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.bias_add = tlx.ops.BiasAdd(self.data_format)
self.b_init_flag = True
self.conv3d = tlx.ops.Conv3D(
strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate,
out_channel=self.n_filter, k_size=(self.filter_size[0], self.filter_size[1], self.filter_size[2])
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.conv3d(inputs, self.W)
if self.b_init_flag:
outputs = self.bias_add(outputs, self.b)
if self.act_init_flag:
outputs = self.act(outputs)
return outputs
class DeConv1d(Module):
"""Simplified version of :class:`Deconv1dlayer`.
Parameters
----------
n_filter : int
The number of filters
filter_size : int
The filter size
strides : int or list
An int or list of `ints` that has length `1` or `3`. The number of entries by which the filter is moved right at each step.
output_shape : a 1-D Tensor
containing three elements, representing the output shape of the deconvolution op.
dilation_rate : int or list
Specifying the dilation rate to use for dilated convolution.
act : activation function
The function that is applied to the layer activations
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channel_last" (NWC, default) or "channels_first" (NCW).
W_init : initializer or str
The initializer for the weight matrix.
b_init : initializer or None or str
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 = tlx.nn.Input([8, 100, 1], name='input')
>>> conv1d = tlx.nn.DeConv1d(n_filter=32, filter_size=5, stride=2, b_init=None, in_channels=1, name='Deonv1d_1')
>>> print(conv1d)
>>> tensor = tlx.nn.DeConv1d(n_filter=32, filter_size=5, stride=2, act=tlx.ReLU, name='Deconv1d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=15,
stride=1,
act=None,
padding='SAME',
data_format="channels_last",
dilation_rate=1,
W_init='truncated_normal',
b_init='constant',
in_channels=None,
name=None # 'conv1d_transpose'
):
super(DeConv1d, self).__init__(name, act=act)
self.n_filter = n_filter
self.filter_size = filter_size
self.stride = stride
self.padding = padding
self.data_format = data_format
self.dilation_rate = dilation_rate
self.W_init = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"DeConv1d %s: n_filter: %d filter_size: %s stride: %d pad: %s act: %s" % (
self.name, n_filter, filter_size, stride, padding,
self.act.__class__.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
s = (
'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
', stride={stride}, padding={padding}'
)
if self.dilation_rate != 1:
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):
if self.data_format == 'channels_last':
self.data_format = 'NWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
elif self.data_format == 'channels_first':
self.data_format = 'NCW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
else:
raise Exception("data_format should be either channels_last or channels_first")
self.filter_shape = (self.filter_size, self.n_filter, self.in_channels)
# TODO : check
self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
self.b_init_flag = False
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.bias_add = tlx.ops.BiasAdd(self.data_format)
self.b_init_flag = True
self.conv1d_transpose = tlx.ops.Conv1d_transpose(
stride=self.stride,
padding=self.padding,
data_format=self.data_format,
dilations=self.dilation_rate,
out_channel=self.n_filter,
k_size=self.filter_size,
in_channels=self.in_channels,
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.conv1d_transpose(inputs, self.W)
if self.b_init_flag:
outputs = self.bias_add(outputs, self.b)
if self.act_init_flag:
outputs = self.act(outputs)
return outputs
[docs]class DeConv2d(Module):
"""Simplified version of :class:`Deconv2dLayer`.
Parameters
----------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size.
strides : tuple of int
The sliding window strides of corresponding input dimensions.
It must be in the same order as the ``shape`` parameter.
output_shape : A 1-D Tensor
representing the output shape of the deconvolution op.
dilation_rate : tuple of int
Specifying the dilation rate to use for dilated convolution.
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channels_last" (NHWC, default) or "channels_first" (NCHW).
W_init : initializer or str
The initializer for the the weight matrix.
b_init : initializer or None or str
The initializer for the 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 = tlx.nn.Input([8, 400, 400, 3], name='input')
>>> conv2d_transpose = tlx.nn.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), b_init=None, in_channels=3, name='conv2d_transpose_1')
>>> print(conv2d_transpose)
>>> tensor = tlx.nn.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tlx.ReLU, name='conv2d_transpose_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3),
strides=(1, 1),
act=None,
padding='SAME',
data_format='channels_last',
dilation_rate=(1, 1),
W_init='truncated_normal',
b_init='constant',
in_channels=None,
name=None, # 'conv2d_transpose',
):
super(DeConv2d, self).__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 = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"DeConv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
self.name, n_filter, str(filter_size), str(strides), padding,
self.act.__class__.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__class__.__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):
if self.data_format == 'channels_last':
self.data_format = 'NHWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
elif self.data_format == 'channels_first':
self.data_format = 'NCHW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
else:
raise Exception("data_format should be either channels_last or channels_first")
#TODO channels first filter shape [out_channel, in_channel, filter_h, filter_w]
self.filter_shape = (self.filter_size[0], self.filter_size[1], self.n_filter, self.in_channels)
self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init, transposed=True)
self.b_init_flag = False
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.bias_add = tlx.ops.BiasAdd(self.data_format)
self.b_init_flag = True
self.conv2d_transpose = tlx.ops.Conv2d_transpose(
strides=self.strides, padding=self.padding, data_format=self.data_format, dilations=self.dilation_rate,
out_channel=self.n_filter, k_size=(self.filter_size[0], self.filter_size[1]), in_channels=self.in_channels
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.conv2d_transpose(inputs, self.W)
if self.b_init_flag:
outputs = self.bias_add(outputs, self.b)
if self.act_init_flag:
outputs = self.act(outputs)
return outputs
[docs]class DeConv3d(Module):
"""Simplified version of :class:`Deconv3dLayer`.
Parameters
----------
n_filter : int
The number of filters.
filter_size : tuple of int
The filter size (depth, height, width).
output_shape:
A 1-D Tensor representing the output shape of the deconvolution op.
strides : tuple of int
The sliding window strides of corresponding input dimensions.
It must be in the same order as the ``shape`` parameter.
dilation_rate : tuple of int
Specifying the dilation rate to use for dilated convolution.
act : activation function
The activation function of this layer.
padding : str
The padding algorithm type: "SAME" or "VALID".
data_format : str
"channels_last" (NDHWC, default) or "channels_first" (NCDHW).
W_init : initializer or str
The initializer for the the weight matrix.
b_init : initializer or None or str
The initializer for the 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 = tlx.nn.Input([8, 20, 20, 20, 3], name='input')
>>> deconv3d = tlx.nn.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), b_init=None, in_channels=3, name='deconv3d_1')
>>> print(deconv3d)
>>> tensor = tlx.nn.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), act=tlx.ReLU, name='deconv3d_2')(net)
>>> print(tensor)
"""
def __init__(
self,
n_filter=32,
filter_size=(3, 3, 3),
strides=(1, 1, 1),
act=None,
padding='SAME',
data_format='channels_last',
dilation_rate=(1, 1, 1),
W_init='truncated_normal',
b_init='constant',
in_channels=None,
name=None # 'deconv3d',
):
super(DeConv3d, self).__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 = self.str_to_init(W_init)
self.b_init = self.str_to_init(b_init)
self.in_channels = in_channels
if self.in_channels:
self.build(None)
self._built = True
logging.info(
"DeConv3d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
self.name, n_filter, str(filter_size), str(strides), padding,
self.act.__class__.__name__ if self.act is not None else 'No Activation'
)
)
def __repr__(self):
actstr = self.act.__class__.__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):
if self.data_format == 'channels_last':
self.data_format = 'NDHWC'
if self.in_channels is None:
self.in_channels = inputs_shape[-1]
elif self.data_format == 'channels_first':
self.data_format = 'NCDHW'
if self.in_channels is None:
self.in_channels = inputs_shape[1]
else:
raise Exception("data_format should be either channels_last or channels_first")
self.filter_shape = (
self.filter_size[0], self.filter_size[1], self.filter_size[2], self.n_filter, self.in_channels
)
self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init, transposed=True)
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.b_init_flag = False
if self.b_init:
self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
self.bias_add = tlx.ops.BiasAdd(self.data_format)
self.b_init_flag = True
self.conv3d_transpose = tlx.ops.Conv3d_transpose(
strides=self.strides, padding=self.padding, data_format=self.data_format, dilations=self.dilation_rate,
out_channel=self.n_filter, k_size=(self.filter_size[0], self.filter_size[1], self.filter_size[2]),
in_channels=self.in_channels
)
self.act_init_flag = False
if self.act:
self.act_init_flag = True
def forward(self, inputs):
if self._forward_state == False:
if self._built == False:
self.build(tlx.get_tensor_shape(inputs))
self._built = True
self._forward_state = True
outputs = self.conv3d_transpose(inputs, self.W)
if self.b_init_flag:
outputs = self.bias_add(outputs, self.b)
if self.act_init_flag:
outputs = self.act(outputs)
return outputs