WangZhen
6 years ago
parent
451896fce4
commit
e2ff300b02
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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from . import quantization_performer
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from .quantization_performer import *
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__all__ = quantization_performer.__all__
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# copyright (c) 2018 paddlepaddle authors. all rights reserved.
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#
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# licensed under the apache license, version 2.0 (the "license");
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# you may not use this file except in compliance with the license.
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# you may obtain a copy of the license at
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#
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# http://www.apache.org/licenses/license-2.0
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#
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# unless required by applicable law or agreed to in writing, software
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# distributed under the license is distributed on an "as is" basis,
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# without warranties or conditions of any kind, either express or implied.
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# see the license for the specific language governing permissions and
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# limitations under the license.
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import unittest
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import random
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import numpy as np
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import paddle
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import paddle.fluid as fluid
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import six
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from paddle.fluid.framework import Program
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from paddle.fluid.contrib.slim.quantization import QuantizationPerformer
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from paddle.fluid.contrib.slim.graph import PyGraph
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from paddle.fluid import core
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def linear_fc(num):
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data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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hidden = data
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for _ in six.moves.xrange(num):
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hidden = fluid.layers.fc(hidden, size=128, act='relu')
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loss = fluid.layers.cross_entropy(input=hidden, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def residual_block(num):
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def conv_bn_layer(input,
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ch_out,
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filter_size,
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stride,
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padding,
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act='relu',
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bias_attr=False):
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tmp = fluid.layers.conv2d(
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input=input,
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filter_size=filter_size,
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num_filters=ch_out,
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stride=stride,
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padding=padding,
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act=None,
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bias_attr=bias_attr)
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return fluid.layers.batch_norm(input=tmp, act=act)
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data = fluid.layers.data(name='image', shape=[1, 32, 32], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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hidden = data
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for _ in six.moves.xrange(num):
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conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
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short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
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hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
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fc = fluid.layers.fc(input=hidden, size=10)
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loss = fluid.layers.cross_entropy(input=fc, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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class TestQuantizationPerformer(unittest.TestCase):
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def setUp(self):
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# since quant_op and dequant_op is not ready, use cos and sin for test
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self.weight_quant_op_type = 'fake_quantize_abs_max'
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self.dequant_op_type = 'fake_dequantize_max_abs'
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self.quantizable_op_and_inputs = {
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'conv2d': ['Input', 'Filter'],
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'depthwise_conv2d': ['Input', 'Filter'],
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'mul': ['X', 'Y']
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}
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self.quantizable_op_grad_and_inputs = {
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'conv2d_grad': ['Input', 'Filter'],
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'depthwise_conv2d_grad': ['Input', 'Filter'],
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'mul_grad': ['X', 'Y']
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}
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def linear_fc_quant(self, quant_type):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = linear_fc(3)
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opt = fluid.optimizer.Adam(learning_rate=0.001)
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opt.minimize(loss)
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graph = PyGraph(core.Graph(main.desc))
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performer = QuantizationPerformer(activation_quantize_type=quant_type)
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performer.quantize_transform(graph, False)
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marked_nodes = set()
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for op in graph.all_ops():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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graph.draw_graph('.', 'quantize_fc_' + quant_type, marked_nodes)
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def test_linear_fc_quant_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_abs_max'
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self.linear_fc_quant('abs_max')
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def test_linear_fc_quant_range_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_range_abs_max'
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self.linear_fc_quant('range_abs_max')
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def residual_block_quant(self, quant_type):
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = residual_block(2)
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opt = fluid.optimizer.Adam(learning_rate=0.001)
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opt.minimize(loss)
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graph = PyGraph(core.Graph(main.desc))
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performer = QuantizationPerformer(activation_quantize_type=quant_type)
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performer.quantize_transform(graph, False)
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marked_nodes = set()
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for op in graph.all_ops():
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if op.name().find('quantize') > -1:
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marked_nodes.add(op)
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graph.draw_graph('.', 'quantize_residual_' + quant_type, marked_nodes)
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def test_residual_block_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_abs_max'
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self.residual_block_quant('abs_max')
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def test_residual_block_range_abs_max(self):
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self.act_quant_op_type = 'fake_quantize_range_abs_max'
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self.residual_block_quant('range_abs_max')
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if __name__ == '__main__':
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unittest.main()
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