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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__ = ['TernaryConv2d']
-
-
- class TernaryConv2d(Module):
- """
- The :class:`TernaryConv2d` class is a 2D ternary CNN layer, which weights are either -1 or 1 or 0 while inference.
-
- Note that, the bias vector would not be tenarized.
-
- 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.
- act : activation function
- The activation function of this layer.
- padding : str
- The padding algorithm type: "SAME" or "VALID".
- use_gemm : boolean
- If True, use gemm instead of ``tf.matmul`` for inference.
- TODO: support gemm
- 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 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, 12, 12, 32], name='input')
- >>> ternaryconv2d = tl.layers.TernaryConv2d(
- ... n_filter=64, filter_size=(5, 5), strides=(1, 1), act=tl.ReLU, padding='SAME', name='ternaryconv2d'
- ... )(net)
- >>> print(ternaryconv2d)
- >>> output shape : (8, 12, 12, 64)
-
- """
-
- def __init__(
- self,
- n_filter=32,
- filter_size=(3, 3),
- strides=(1, 1),
- act=None,
- padding='SAME',
- use_gemm=False,
- data_format="channels_last",
- dilation_rate=(1, 1),
- W_init='truncated_normal',
- b_init='constant',
- in_channels=None,
- name=None # 'ternary_cnn2d',
- ):
- super().__init__(name, act=act)
- self.n_filter = n_filter
- self.filter_size = filter_size
- self.strides = self._strides = strides
- self.padding = padding
- self.use_gemm = use_gemm
- 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(
- "TernaryConv2d %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'
- )
- )
-
- if use_gemm:
- raise Exception("TODO. The current version use tf.matmul for inferencing.")
-
- if len(self.strides) != 2:
- raise ValueError("len(strides) should be 2.")
-
- def __repr__(self):
- actstr = self.act.__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")
-
- 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)
- if self.b_init:
- self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
- self.bias_add = tl.ops.BiasAdd(data_format=self.data_format)
-
- self.ternary_conv = tl.ops.TernaryConv(
- weights=self.W, strides=self._strides, padding=self.padding, data_format=self.data_format,
- dilations=self._dilation_rate
- )
-
- 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.ternary_conv(inputs)
-
- if self.b_init:
- outputs = self.bias_add(outputs, self.b)
- if self.act:
- outputs = self.act(outputs)
-
- return outputs
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