|
- #! /usr/bin/python
- # -*- coding: utf-8 -*-
-
- from tensorlayer.layers.core import Module
- import tensorlayer as tl
- from tensorlayer import logging
-
- __all__ = [
- 'Conv1d',
- 'Conv2d',
- 'Conv3d',
- 'DeConv1d',
- 'DeConv2d',
- 'DeConv3d',
- ]
-
-
- 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 = tl.layers.Input([8, 100, 1], name='input')
- >>> conv1d = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, b_init=None, in_channels=1, name='conv1d_1')
- >>> print(conv1d)
- >>> tensor = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, act=tl.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 = tl.ops.BiasAdd(self.data_format)
- self.b_init_flag = True
-
- self.conv1d = tl.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(tl.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
-
-
- 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 = tl.layers.Input([8, 400, 400, 3], name='input')
- >>> conv2d = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), b_init=None, in_channels=3, name='conv2d_1')
- >>> print(conv2d)
- >>> tensor = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tl.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 = tl.ops.BiasAdd(self.data_format)
- self.b_init_flag = True
-
- self.conv2d = tl.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(tl.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
-
-
- 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 = tl.layers.Input([8, 20, 20, 20, 3], name='input')
- >>> conv3d = tl.layers.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 = tl.layers.Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), act=tl.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 = tl.ops.BiasAdd(self.data_format)
- self.b_init_flag = True
-
- self.conv3d = tl.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(tl.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 = tl.layers.Input([8, 100, 1], name='input')
- >>> conv1d = tl.layers.DeConv1d(n_filter=32, filter_size=5, stride=2, b_init=None, in_channels=1, name='Deonv1d_1')
- >>> print(conv1d)
- >>> tensor = tl.layers.DeConv1d(n_filter=32, filter_size=5, stride=2, act=tl.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 = tl.ops.BiasAdd(self.data_format)
- self.b_init_flag = True
-
- self.conv1d_transpose = tl.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(tl.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
-
-
- 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 = tl.layers.Input([8, 400, 400, 3], name='input')
- >>> conv2d_transpose = tl.layers.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 = tl.layers.DeConv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tl.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 = tl.ops.BiasAdd(self.data_format)
- self.b_init_flag = True
-
- self.conv2d_transpose = tl.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(tl.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
-
-
- 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 = tl.layers.Input([8, 20, 20, 20, 3], name='input')
- >>> deconv3d = tl.layers.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 = tl.layers.DeConv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), act=tl.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 = tl.ops.BiasAdd(self.data_format)
- self.b_init_flag = True
-
- self.conv3d_transpose = tl.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(tl.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
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