forked from TensorLayer/tensorlayer3
883 lines
32 KiB
Python
883 lines
32 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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from tensorlayer.layers.core import Module
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import tensorlayer as tl
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from tensorlayer import logging
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__all__ = [
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'Conv1d',
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'Conv2d',
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'Conv3d',
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'DeConv1d',
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'DeConv2d',
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'DeConv3d',
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]
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class Conv1d(Module):
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"""Simplified version of :class:`Conv1dLayer`.
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Parameters
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----------
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n_filter : int
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The number of filters
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filter_size : int
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The filter size
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stride : int
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The stride step
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dilation_rate : int
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Specifying the dilation rate to use for dilated convolution.
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act : activation function
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The function that is applied to the layer activations
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padding : str
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The padding algorithm type: "SAME" or "VALID".
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data_format : str
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"channel_last" (NWC, default) or "channels_first" (NCW).
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W_init : initializer
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The initializer for the weight matrix.
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b_init : initializer or None
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The initializer for the bias vector. If None, skip biases.
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in_channels : int
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The number of in channels.
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name : None or str
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A unique layer name
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Examples
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--------
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With TensorLayer
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>>> net = tl.layers.Input([8, 100, 1], name='input')
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>>> conv1d = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, b_init=None, in_channels=1, name='conv1d_1')
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>>> print(conv1d)
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>>> tensor = tl.layers.Conv1d(n_filter=32, filter_size=5, stride=2, act=tl.ReLU, name='conv1d_2')(net)
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>>> print(tensor)
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"""
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def __init__(
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self,
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n_filter=32,
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filter_size=5,
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stride=1,
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act=None,
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padding='SAME',
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data_format="channels_last",
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dilation_rate=1,
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W_init=tl.initializers.truncated_normal(stddev=0.02),
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b_init=tl.initializers.constant(value=0.0),
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in_channels=None,
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name=None # 'conv1d'
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):
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super().__init__(name, act=act)
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self.n_filter = n_filter
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self.filter_size = filter_size
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self.stride = stride
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self.padding = padding
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self.data_format = data_format
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self.dilation_rate = dilation_rate
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self.W_init = W_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels:
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self.build(None)
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self._built = True
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logging.info(
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"Conv1d %s: n_filter: %d filter_size: %s stride: %d pad: %s act: %s" % (
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self.name, n_filter, filter_size, stride, padding,
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self.act.__class__.__name__ if self.act is not None else 'No Activation'
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)
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)
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def __repr__(self):
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actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
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s = (
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'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
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', stride={stride}, padding={padding}'
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)
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if self.dilation_rate != 1:
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s += ', dilation={dilation_rate}'
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if self.b_init is None:
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s += ', bias=False'
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s += (', ' + actstr)
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if self.data_format == 'channels_last':
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self.data_format = 'NWC'
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if self.in_channels is None:
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self.in_channels = inputs_shape[-1]
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elif self.data_format == 'channels_first':
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self.data_format = 'NCW'
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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else:
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raise Exception("data_format should be either channels_last or channels_first")
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self.filter_shape = (self.filter_size, self.in_channels, self.n_filter)
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# TODO : check
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self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
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self.b_init_flag = False
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if self.b_init:
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self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
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self.bias_add = tl.ops.BiasAdd(self.data_format)
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self.b_init_flag = True
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self.conv1d = tl.ops.Conv1D(
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stride=self.stride, padding=self.padding, data_format=self.data_format, dilations=self.dilation_rate,
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out_channel=self.n_filter, k_size=self.filter_size
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)
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self.act_init_flag = False
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if self.act:
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self.act_init_flag = True
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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outputs = self.conv1d(inputs, self.W)
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if self.b_init_flag:
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outputs = self.bias_add(outputs, self.b)
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if self.act_init_flag:
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outputs = self.act(outputs)
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return outputs
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class Conv2d(Module):
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"""Simplified version of :class:`Conv2dLayer`.
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Parameters
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----------
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n_filter : int
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The number of filters.
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filter_size : tuple of int
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The filter size (height, width).
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strides : tuple of int
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The sliding window strides of corresponding input dimensions.
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It must be in the same order as the ``shape`` parameter.
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dilation_rate : tuple of int
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Specifying the dilation rate to use for dilated convolution.
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act : activation function
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The activation function of this layer.
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padding : str
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The padding algorithm type: "SAME" or "VALID".
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data_format : str
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"channels_last" (NHWC, default) or "channels_first" (NCHW).
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W_init : initializer
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The initializer for the the weight matrix.
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b_init : initializer or None
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The initializer for the the bias vector. If None, skip biases.
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in_channels : int
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The number of in channels.
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name : None or str
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A unique layer name.
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Examples
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--------
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With TensorLayer
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>>> net = tl.layers.Input([8, 400, 400, 3], name='input')
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>>> conv2d = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), b_init=None, in_channels=3, name='conv2d_1')
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>>> print(conv2d)
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>>> tensor = tl.layers.Conv2d(n_filter=32, filter_size=(3, 3), strides=(2, 2), act=tl.ReLU, name='conv2d_2')(net)
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>>> print(tensor)
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"""
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def __init__(
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self,
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n_filter=32,
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filter_size=(3, 3),
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strides=(1, 1),
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act=None,
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padding='SAME',
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data_format='channels_last',
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dilation_rate=(1, 1),
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W_init=tl.initializers.truncated_normal(stddev=0.02),
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b_init=tl.initializers.constant(value=0.0),
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in_channels=None,
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name=None, # 'conv2d',
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):
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super(Conv2d, self).__init__(name, act=act)
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self.n_filter = n_filter
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self.filter_size = filter_size
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self._strides = self.strides = strides
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self.padding = padding
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self.data_format = data_format
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self._dilation_rate = self.dilation_rate = dilation_rate
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self.W_init = W_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels:
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self.build(None)
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self._built = True
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logging.info(
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"Conv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
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self.name, n_filter, str(filter_size), str(strides), padding,
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self.act.__class__.__name__ if self.act is not None else 'No Activation'
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)
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)
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def __repr__(self):
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actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
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s = (
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'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
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', strides={strides}, padding={padding}'
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)
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if self.dilation_rate != (1, ) * len(self.dilation_rate):
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s += ', dilation={dilation_rate}'
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if self.b_init is None:
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s += ', bias=False'
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s += (', ' + actstr)
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if self.data_format == 'channels_last':
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self.data_format = 'NHWC'
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if self.in_channels is None:
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self.in_channels = inputs_shape[-1]
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self._strides = [1, self._strides[0], self._strides[1], 1]
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self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1]
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elif self.data_format == 'channels_first':
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self.data_format = 'NCHW'
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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self._strides = [1, 1, self._strides[0], self._strides[1]]
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self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]]
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else:
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raise Exception("data_format should be either channels_last or channels_first")
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#TODO channels first filter shape [out_channel, in_channel, filter_h, filter_w]
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self.filter_shape = (self.filter_size[0], self.filter_size[1], self.in_channels, self.n_filter)
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self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
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self.b_init_flag = False
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if self.b_init:
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self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
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self.bias_add = tl.ops.BiasAdd(self.data_format)
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self.b_init_flag = True
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self.conv2d = tl.ops.Conv2D(
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strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate,
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out_channel=self.n_filter, k_size=(self.filter_size[0], self.filter_size[1])
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)
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self.act_init_flag = False
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if self.act:
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self.act_init_flag = True
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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outputs = self.conv2d(inputs, self.W)
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if self.b_init_flag:
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outputs = self.bias_add(outputs, self.b)
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if self.act_init_flag:
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outputs = self.act(outputs)
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return outputs
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class Conv3d(Module):
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"""Simplified version of :class:`Conv3dLayer`.
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Parameters
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---AppData\Local\Continuum\anaconda3\envs\ms_tf\lib\site-packages\mindspore\common\api.py", line 412, in compile
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result = self._executor.compile(obj, args_list, phase, use_vm)
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RuntimeError: Unable to cast from non-held to held instance (T& to Holder<T>) of type 'std:-------
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n_filter : int
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The number of filters.
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filter_size : tuple of int
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The filter size (height, width).
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strides : tuple of int
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The sliding window strides of corresponding input dimensions.
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It must be in the same order as the ``shape`` parameter.
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dilation_rate : tuple of int
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Specifying the dilation rate to use for dilated convolution.
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act : activation function
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The activation function of this layer.
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padding : str
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The padding algorithm type: "SAME" or "VALID".
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data_format : str
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"channels_last" (NDHWC, default) or "channels_first" (NCDHW).
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W_init : initializer
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The initializer for the the weight matrix.
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b_init : initializer or None
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The initializer for the the bias vector. If None, skip biases.
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in_channels : int
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The number of in channels.
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name : None or str
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A unique layer name.
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Examples
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--------
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With TensorLayer
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>>> net = tl.layers.Input([8, 20, 20, 20, 3], name='input')
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>>> 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')
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>>> print(conv3d)
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>>> tensor = tl.layers.Conv3d(n_filter=32, filter_size=(3, 3, 3), strides=(2, 2, 2), act=tl.ReLU, name='conv3d_2')(net)
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>>> print(tensor)
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"""
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def __init__(
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self,
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n_filter=32,
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filter_size=(3, 3, 3),
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strides=(1, 1, 1),
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act=None,
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padding='SAME',
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data_format='channels_last',
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dilation_rate=(1, 1, 1),
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W_init=tl.initializers.truncated_normal(stddev=0.02),
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b_init=tl.initializers.constant(value=0.0),
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in_channels=None,
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name=None # 'conv3d',
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):
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super().__init__(name, act=act)
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self.n_filter = n_filter
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self.filter_size = filter_size
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self._strides = self.strides = strides
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self.padding = padding
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self.data_format = data_format
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self._dilation_rate = self.dilation_rate = dilation_rate
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self.W_init = W_init
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self.b_init = b_init
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self.in_channels = in_channels
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if self.in_channels:
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self.build(None)
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self._built = True
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logging.info(
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"Conv3d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
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self.name, n_filter, str(filter_size), str(strides), padding,
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self.act.__class__.__name__ if self.act is not None else 'No Activation'
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)
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)
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def __repr__(self):
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actstr = self.act.__class__.__name__ if self.act is not None else 'No Activation'
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s = (
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'{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
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', strides={strides}, padding={padding}'
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)
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if self.dilation_rate != (1, ) * len(self.dilation_rate):
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s += ', dilation={dilation_rate}'
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if self.b_init is None:
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s += ', bias=False'
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s += (', ' + actstr)
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if self.name is not None:
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s += ', name=\'{name}\''
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s += ')'
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return s.format(classname=self.__class__.__name__, **self.__dict__)
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def build(self, inputs_shape):
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if self.data_format == 'channels_last':
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self.data_format = 'NDHWC'
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if self.in_channels is None:
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self.in_channels = inputs_shape[-1]
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self._strides = [1, self._strides[0], self._strides[1], self._strides[2], 1]
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self._dilation_rate = [1, self.dilation_rate[0], self.dilation_rate[1], self.dilation_rate[2], 1]
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elif self.data_format == 'channels_first':
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self.data_format = 'NCDHW'
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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self._strides = [1, 1, self._strides[0], self._strides[1], self._strides[2]]
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self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1], self._dilation_rate[2]]
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else:
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raise Exception("data_format should be either channels_last or channels_first")
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self.filter_shape = (
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self.filter_size[0], self.filter_size[1], self.filter_size[2], self.in_channels, self.n_filter
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)
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self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
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self.b_init_flag = False
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if self.b_init:
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self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
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self.bias_add = tl.ops.BiasAdd(self.data_format)
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self.b_init_flag = True
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self.conv3d = tl.ops.Conv3D(
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strides=self._strides, padding=self.padding, data_format=self.data_format, dilations=self._dilation_rate,
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out_channel=self.n_filter, k_size=(self.filter_size[0], self.filter_size[1], self.filter_size[2])
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)
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self.act_init_flag = False
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if self.act:
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self.act_init_flag = True
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def forward(self, inputs):
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if self._forward_state == False:
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if self._built == False:
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self.build(tl.get_tensor_shape(inputs))
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self._built = True
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self._forward_state = True
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outputs = self.conv3d(inputs, self.W)
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if self.b_init_flag:
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outputs = self.bias_add(outputs, self.b)
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if self.act_init_flag:
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outputs = self.act(outputs)
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return outputs
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class DeConv1d(Module):
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"""Simplified version of :class:`Deconv1dlayer`.
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Parameters
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----------
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n_filter : int
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The number of filters
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filter_size : int
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The filter size
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strides : int or list
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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.
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output_shape : a 1-D Tensor
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containing three elements, representing the output shape of the deconvolution op.
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dilation_rate : int or list
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Specifying the dilation rate to use for dilated convolution.
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act : activation function
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The function that is applied to the layer activations
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padding : str
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The padding algorithm type: "SAME" or "VALID".
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data_format : str
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"channel_last" (NWC, default) or "channels_first" (NCW).
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W_init : initializer
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|
The initializer for the weight matrix.
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b_init : initializer or None
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|
The initializer for the bias vector. If None, skip biases.
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in_channels : int
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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=tl.initializers.truncated_normal(stddev=0.02),
|
|
b_init=tl.initializers.constant(value=0.0),
|
|
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 = W_init
|
|
self.b_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
|
|
The initializer for the the weight matrix.
|
|
b_init : initializer or None
|
|
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=tl.initializers.truncated_normal(stddev=0.02),
|
|
b_init=tl.initializers.constant(value=0.0),
|
|
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 = W_init
|
|
self.b_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
|
|
---AppData\Local\Continuum\anaconda3\envs\ms_tf\lib\site-packages\mindspore\common\api.py", line 412, in compile
|
|
result = self._executor.compile(obj, args_list, phase, use_vm)
|
|
RuntimeError: Unable to cast from non-held to held instance (T& to Holder<T>) of type 'std:-------
|
|
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
|
|
The initializer for the the weight matrix.
|
|
b_init : initializer or None
|
|
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=tl.initializers.truncated_normal(stddev=0.02),
|
|
b_init=tl.initializers.constant(value=0.0),
|
|
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 = W_init
|
|
self.b_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
|