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- #! /usr/bin/python
- # -*- coding: utf-8 -*-
-
- import tensorlayer as tl
- from tensorlayer import logging
- from tensorlayer.layers.core import Module
-
- __all__ = [
- 'GroupConv2d',
- ]
-
-
- class GroupConv2d(Module):
- """The :class:`GroupConv2d` class is 2D grouped convolution, see `here <https://blog.yani.io/filter-group-tutorial/>`__.
-
- Parameters
- --------------
- n_filter : int
- The number of filters.
- filter_size : tuple of int
- The filter size.
- stride : tuple of int
- The stride step.
- n_group : int
- The number of groups.
- 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).
- dilation_rate : tuple of int
- Specifying the dilation rate to use for dilated convolution.
- 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, 24, 24, 32], name='input')
- >>> groupconv2d = tl.layers.GroupConv2d(
- ... n_filter=64, filter_size=(3, 3), strides=(2, 2), n_group=2, name='group'
- ... )(net)
- >>> print(groupconv2d)
- >>> output shape : (8, 12, 12, 64)
-
- """
-
- def __init__(
- self, n_filter=32, filter_size=(1, 1), strides=(1, 1), n_group=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
- ):
- super().__init__(name, act=act)
- self.n_filter = n_filter
- self.filter_size = filter_size
- self._strides = self.strides = strides
- self.n_group = n_group
- 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 n_group: %d pad: %s act: %s" % (
- self.name, n_filter, str(filter_size), str(strides), n_group, 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}, n_group = {n_group}, 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")
-
- if self.n_group < 1:
- raise ValueError(
- "The n_group must be a integer greater than or equal to 1, but we got :{}".format(self.n_group)
- )
-
- if self.in_channels % self.n_group != 0:
- raise ValueError(
- "The channels of input must be divisible by n_group, but we got: the channels of input"
- "is {}, the n_group is {}.".format(self.in_channels, self.n_group)
- )
-
- if self.n_filter % self.n_group != 0:
- raise ValueError(
- "The number of filters must be divisible by n_group, but we got: the number of filters "
- "is {}, the n_group is {}. ".format(self.n_filter, self.n_group)
- )
-
- # TODO channels first filter shape [out_channel, in_channel/n_group, filter_h, filter_w]
- self.filter_shape = (
- self.filter_size[0], self.filter_size[1], int(self.in_channels / self.n_group), 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.group_conv2d = tl.ops.GroupConv2D(
- 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]), groups=self.n_group
- )
-
- 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.group_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
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