187 lines
6.6 KiB
Python
187 lines
6.6 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 tensorflow.python.training import moving_averages
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from tensorlayer.layers.utils import (
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quantize_active_overflow, quantize_weight_overflow, mean_var_with_update, w_fold, bias_fold
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)
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__all__ = [
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'QuanDenseWithBN',
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]
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class QuanDenseWithBN(Module):
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"""The :class:`QuanDenseWithBN` class is a quantized fully connected layer with BN, which weights are 'bitW' bits and the output of the previous layer
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are 'bitA' bits while inferencing.
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# TODO The QuanDenseWithBN only supports TensorFlow backend.
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Parameters
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----------
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n_units : int
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The number of units of this layer.
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act : activation function
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The activation function of this layer.
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decay : float
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A decay factor for `ExponentialMovingAverage`.
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Suggest to use a large value for large dataset.
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epsilon : float
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Eplison.
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is_train : boolean
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Is being used for training or inference.
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beta_init : initializer or None
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The initializer for initializing beta, if None, skip beta.
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Usually you should not skip beta unless you know what happened.
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gamma_init : initializer or None
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The initializer for initializing gamma, if None, skip gamma.
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bitW : int
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The bits of this layer's parameter
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bitA : int
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The bits of the output of previous layer
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use_gemm : boolean
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If True, use gemm instead of ``tf.matmul`` for inferencing. (TODO).
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W_init : initializer
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The initializer for the the weight matrix.
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W_init_args : dictionary
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The arguments for the weight matrix initializer.
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in_channels: int
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The number of channels of the previous layer.
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If None, it will be automatically detected when the layer is forwarded for the first time.
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name : a str
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A unique layer name.
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Examples
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---------
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>>> import tensorlayer as tl
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>>> net = tl.layers.Input([50, 256])
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>>> layer = tl.layers.QuanDenseWithBN(128, act='relu', name='qdbn1')(net)
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>>> net = tl.layers.QuanDenseWithBN(256, act='relu', name='qdbn2')(net)
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"""
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def __init__(
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self,
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n_units=100,
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act=None,
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decay=0.9,
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epsilon=1e-5,
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is_train=False,
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bitW=8,
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bitA=8,
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gamma_init=tl.initializers.truncated_normal(stddev=0.05),
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beta_init=tl.initializers.truncated_normal(stddev=0.05),
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use_gemm=False,
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W_init=tl.initializers.truncated_normal(stddev=0.05),
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W_init_args=None,
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in_channels=None,
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name=None, # 'quan_dense_with_bn',
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):
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super(QuanDenseWithBN, self).__init__(act=act, W_init_args=W_init_args, name=name)
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self.n_units = n_units
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self.decay = decay
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self.epsilon = epsilon
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self.is_train = is_train
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self.bitW = bitW
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self.bitA = bitA
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self.gamma_init = gamma_init
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self.beta_init = beta_init
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self.use_gemm = use_gemm
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self.W_init = W_init
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self.in_channels = in_channels
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if self.in_channels is not None:
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self.build((None, self.in_channels))
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self._built = True
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logging.info(
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"QuanDenseLayerWithBN %s: %d %s" %
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(self.name, n_units, self.act.__class__.__name__ if self.act is not None else 'No Activation')
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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 = ('{classname}(n_units={n_units}, ' + actstr)
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s += ', bitW={bitW}, bitA={bitA}'
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if self.in_channels is not None:
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s += ', in_channels=\'{in_channels}\''
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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.in_channels is None and len(inputs_shape) != 2:
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raise Exception("The input dimension must be rank 2, please reshape or flatten it")
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if self.in_channels is None:
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self.in_channels = inputs_shape[1]
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if self.use_gemm:
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raise Exception("TODO. The current version use tf.matmul for inferencing.")
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n_in = inputs_shape[-1]
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self.W = self._get_weights("weights", shape=(n_in, self.n_units), init=self.W_init)
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para_bn_shape = (self.n_units, )
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if self.gamma_init:
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self.scale_para = self._get_weights("gamm_weights", shape=para_bn_shape, init=self.gamma_init)
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else:
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self.scale_para = None
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if self.beta_init:
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self.offset_para = self._get_weights("beta_weights", shape=para_bn_shape, init=self.beta_init)
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else:
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self.offset_para = None
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self.moving_mean = self._get_weights(
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"moving_mean", shape=para_bn_shape, init=tl.initializers.constant(1.0), trainable=False
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)
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self.moving_variance = self._get_weights(
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"moving_variacne", shape=para_bn_shape, init=tl.initializers.constant(1.0), trainable=False
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)
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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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x = inputs
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inputs = quantize_active_overflow(inputs, self.bitA)
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mid_out = tl.ops.matmul(x, self.W)
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mean, variance = tl.ops.moments(x=mid_out, axes=list(range(len(mid_out.get_shape()) - 1)))
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update_moving_mean = moving_averages.assign_moving_average(
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self.moving_mean, mean, self.decay, zero_debias=False
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) # if zero_debias=True, has bias
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update_moving_variance = moving_averages.assign_moving_average(
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self.moving_variance, variance, self.decay, zero_debias=False
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) # if zero_debias=True, has bias
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if self.is_train:
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mean, var = mean_var_with_update(update_moving_mean, update_moving_variance, mean, variance)
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else:
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mean, var = self.moving_mean, self.moving_variance
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_w_fold = w_fold(self.W, self.scale_para, var, self.epsilon)
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W = quantize_weight_overflow(_w_fold, self.bitW)
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outputs = tl.ops.matmul(inputs, W)
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if self.beta_init:
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_bias_fold = bias_fold(self.offset_para, self.scale_para, mean, var, self.epsilon)
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outputs = tl.ops.bias_add(outputs, _bias_fold)
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else:
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outputs = outputs
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if self.act:
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outputs = self.act(outputs)
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else:
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outputs = outputs
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return outputs
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