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- import paddle
- import numpy as np
- import os
- import paddle.nn as nn
- import paddleslim
- class PACT(paddle.nn.Layer):
- def __init__(self):
- super(PACT, self).__init__()
- alpha_attr = paddle.ParamAttr(
- name=self.full_name() + ".pact",
- initializer=paddle.nn.initializer.Constant(value=20),
- learning_rate=1.0,
- regularizer=paddle.regularizer.L2Decay(2e-5))
- self.alpha = self.create_parameter(
- shape=[1], attr=alpha_attr, dtype='float32')
- def forward(self, x):
- out_left = paddle.nn.functional.relu(x - self.alpha)
- out_right = paddle.nn.functional.relu(-self.alpha - x)
- x = x - out_left + out_right
- return x
- quant_config = {
- # weight preprocess type, default is None and no preprocessing is performed.
- 'weight_preprocess_type': None,
- # activation preprocess type, default is None and no preprocessing is performed.
- 'activation_preprocess_type': None,
- # weight quantize type, default is 'channel_wise_abs_max'
- 'weight_quantize_type': 'channel_wise_abs_max',
- # activation quantize type, default is 'moving_average_abs_max'
- 'activation_quantize_type': 'moving_average_abs_max',
- # weight quantize bit num, default is 8
- 'weight_bits': 8,
- # activation quantize bit num, default is 8
- 'activation_bits': 8,
- # data type after quantization, such as 'uint8', 'int8', etc. default is 'int8'
- 'dtype': 'int8',
- # window size for 'range_abs_max' quantization. default is 10000
- 'window_size': 10000,
- # The decay coefficient of moving average, default is 0.9
- 'moving_rate': 0.9,
- # for dygraph quantization, layers of type in quantizable_layer_type will be quantized
- 'quantizable_layer_type': ['Conv2D', 'Linear'],
- }
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