forked from TensorLayer/tensorlayer3
168 lines
6.3 KiB
Python
168 lines
6.3 KiB
Python
#! /usr/bin/python
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# -*- coding: utf-8 -*-
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import tensorlayer as tl
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from tensorlayer import logging
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from tensorlayer.layers.core import Module
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from tensorlayer.backend import BACKEND
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__all__ = [
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'GroupConv2d',
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]
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class GroupConv2d(Module):
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"""The :class:`GroupConv2d` class is 2D grouped convolution, see `here <https://blog.yani.io/filter-group-tutorial/>`__.
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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.
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stride : tuple of int
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The stride step.
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n_group : int
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The number of groups.
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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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dilation_rate : tuple of int
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Specifying the dilation rate to use for dilated convolution.
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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, 24, 24, 32], name='input')
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>>> groupconv2d = tl.layers.QuanConv2d(
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... n_filter=64, filter_size=(3, 3), strides=(2, 2), n_group=2, name='group'
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... )(net)
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>>> print(groupconv2d)
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>>> output shape : (8, 12, 12, 64)
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"""
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def __init__(
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self, n_filter=32, filter_size=(1, 1), strides=(1, 1), n_group=1, act=None, padding='SAME',
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data_format="channels_last", dilation_rate=(1, 1), W_init=tl.initializers.truncated_normal(stddev=0.02),
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b_init=tl.initializers.constant(value=0.0), in_channels=None, name=None
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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.n_group = n_group
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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 n_group: %d pad: %s act: %s" % (
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self.name, n_filter, str(filter_size), str(strides), n_group, 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}, n_group = {n_group}, 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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if self.n_group < 1:
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raise ValueError(
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"The n_group must be a integer greater than or equal to 1, but we got :{}".format(self.n_group)
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)
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if self.in_channels % self.n_group != 0:
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raise ValueError(
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"The channels of input must be divisible by n_group, but we got: the channels of input"
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"is {}, the n_group is {}.".format(self.in_channels, self.n_group)
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)
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if self.n_filter % self.n_group != 0:
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raise ValueError(
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"The number of filters must be divisible by n_group, but we got: the number of filters "
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"is {}, the n_group is {}. ".format(self.n_filter, self.n_group)
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)
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# TODO channels first filter shape [out_channel, in_channel/n_group, filter_h, filter_w]
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self.filter_shape = (
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self.filter_size[0], self.filter_size[1], int(self.in_channels / self.n_group), 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.group_conv2d = tl.ops.GroupConv2D(
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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]), groups=self.n_group
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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.group_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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