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test_quant_aware.py
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# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import sys
sys.path.append("../")
import unittest
import paddle
import paddle.fluid as fluid
from paddleslim.quant import quant_aware, convert
sys.path.append("../demo")
from models import MobileNet
from layers import conv_bn_layer
import paddle.dataset.mnist as reader
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
import numpy as np
class TestQuantAwareCase1(unittest.TestCase):
def get_model(self):
image = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
model = MobileNet()
out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
startup_prog = fluid.default_startup_program()
train_prog = fluid.default_main_program()
return startup_prog, train_prog
def get_op_number(self, prog):
graph = IrGraph(core.Graph(prog.desc), for_test=False)
quant_op_nums = 0
op_nums = 0
for op in graph.all_op_nodes():
if op.name() in ['conv2d', 'depthwise_conv2d', 'mul']:
op_nums += 1
elif 'fake_' in op.name():
quant_op_nums += 1
return op_nums, quant_op_nums
def test_quant_op(self):
startup_prog, train_prog = self.get_model()
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
config_1 = {
'weight_quantize_type': 'channel_wise_abs_max',
'activation_quantize_type': 'moving_average_abs_max',
'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
}
quant_prog_1 = quant_aware(
train_prog, place, config=config_1, for_test=True)
op_nums_1, quant_op_nums_1 = self.get_op_number(quant_prog_1)
convert_prog_1 = convert(quant_prog_1, place, config=config_1)
convert_op_nums_1, convert_quant_op_nums_1 = self.get_op_number(
convert_prog_1)
config_1['not_quant_pattern'] = ['last_fc']
quant_prog_2 = quant_aware(
train_prog, place, config=config_1, for_test=True)
op_nums_2, quant_op_nums_2 = self.get_op_number(quant_prog_2)
convert_prog_2 = convert(quant_prog_2, place, config=config_1)
convert_op_nums_2, convert_quant_op_nums_2 = self.get_op_number(
convert_prog_2)
self.assertTrue(op_nums_1 == op_nums_2)
# test quant_aware op numbers
self.assertTrue(op_nums_1 * 4 == quant_op_nums_1)
# test convert op numbers
self.assertTrue(convert_op_nums_1 * 2 == convert_quant_op_nums_1)
# test skip_quant
self.assertTrue(quant_op_nums_1 - 4 == quant_op_nums_2)
self.assertTrue(convert_quant_op_nums_1 - 2 == convert_quant_op_nums_2)
class TestQuantAwareCase2(unittest.TestCase):
def test_accuracy(self):
image = fluid.layers.data(
name='image', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
model = MobileNet()
out = model.net(input=image, class_dim=10)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5))
optimizer.minimize(avg_cost)
main_prog = fluid.default_main_program()
val_prog = main_prog.clone(for_test=True)
place = fluid.CUDAPlace(0) if fluid.is_compiled_with_cuda(
) else fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
feeder = fluid.DataFeeder([image, label], place, program=main_prog)
train_reader = paddle.fluid.io.batch(
paddle.dataset.mnist.train(), batch_size=64)
eval_reader = paddle.fluid.io.batch(
paddle.dataset.mnist.test(), batch_size=64)
def train(program):
iter = 0
for data in train_reader():
cost, top1, top5 = exe.run(
program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1
if iter % 100 == 0:
print(
'train iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
format(iter, cost, top1, top5))
def test(program):
iter = 0
result = [[], [], []]
for data in eval_reader():
cost, top1, top5 = exe.run(
program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc_top1, acc_top5])
iter += 1
if iter % 100 == 0:
print(
'eval iter={}, avg loss {}, acc_top1 {}, acc_top5 {}'.
format(iter, cost, top1, top5))
result[0].append(cost)
result[1].append(top1)
result[2].append(top5)
print(' avg loss {}, acc_top1 {}, acc_top5 {}'.format(
np.mean(result[0]), np.mean(result[1]), np.mean(result[2])))
return np.mean(result[1]), np.mean(result[2])
train(main_prog)
top1_1, top5_1 = test(main_prog)
config = {
'weight_quantize_type': 'channel_wise_abs_max',
'activation_quantize_type': 'moving_average_abs_max',
'quantize_op_types': ['depthwise_conv2d', 'mul', 'conv2d'],
}
quant_train_prog = quant_aware(
main_prog, place, config, for_test=False)
quant_eval_prog = quant_aware(val_prog, place, config, for_test=True)
train(quant_train_prog)
quant_eval_prog, int8_prog = convert(
quant_eval_prog, place, config, save_int8=True)
top1_2, top5_2 = test(quant_eval_prog)
# values before quantization and after quantization should be close
print("before quantization: top1: {}, top5: {}".format(top1_1, top5_1))
print("after quantization: top1: {}, top5: {}".format(top1_2, top5_2))
if __name__ == '__main__':
unittest.main()