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test_quant_aware_user_defined.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
from paddleslim.quant import quant_aware, convert
from static_case import StaticCase
sys.path.append("../demo")
from models import MobileNet
from layers import conv_bn_layer
import paddle.dataset.mnist as reader
import numpy as np
from paddle.fluid.layer_helper import LayerHelper
def pact(x):
helper = LayerHelper("pact", **locals())
dtype = 'float32'
init_thres = 20
u_param_attr = paddle.ParamAttr(
name=x.name + '_pact',
initializer=paddle.nn.initializer.Constant(value=init_thres),
regularizer=paddle.regularizer.L2Decay(0.0001),
learning_rate=1)
u_param = helper.create_parameter(attr=u_param_attr, shape=[1], dtype=dtype)
part_a = paddle.nn.functional.relu(x - u_param)
part_b = paddle.nn.functional.relu(-u_param - x)
x = x - part_a + part_b
return x
def get_optimizer():
return paddle.optimizer.Momentum(0.0001, 0.9)
class TestQuantAwareCase1(StaticCase):
def get_model(self):
image = paddle.static.data(
name='image', shape=[None, 1, 28, 28], dtype='float32')
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet()
out = model.net(input=image, class_dim=10)
cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = paddle.mean(x=cost)
startup_prog = paddle.static.default_startup_program()
train_prog = paddle.static.default_main_program()
return startup_prog, train_prog
def test_accuracy(self):
image = paddle.static.data(
name='image', shape=[None, 1, 28, 28], dtype='float32')
image.stop_gradient = False
label = paddle.static.data(name='label', shape=[None, 1], dtype='int64')
model = MobileNet()
out = model.net(input=image, class_dim=10)
cost = paddle.nn.functional.loss.cross_entropy(input=out, label=label)
avg_cost = paddle.mean(x=cost)
acc_top1 = paddle.metric.accuracy(input=out, label=label, k=1)
acc_top5 = paddle.metric.accuracy(input=out, label=label, k=5)
optimizer = paddle.optimizer.Momentum(
momentum=0.9,
learning_rate=0.01,
weight_decay=paddle.regularizer.L2Decay(4e-5))
optimizer.minimize(avg_cost)
main_prog = paddle.static.default_main_program()
val_prog = main_prog.clone(for_test=True)
place = paddle.CUDAPlace(0) if paddle.is_compiled_with_cuda(
) else paddle.CPUPlace()
exe = paddle.static.Executor(place)
exe.run(paddle.static.default_startup_program())
def transform(x):
return np.reshape(x, [1, 28, 28])
train_dataset = paddle.vision.datasets.MNIST(
mode='train', backend='cv2', transform=transform)
test_dataset = paddle.vision.datasets.MNIST(
mode='test', backend='cv2', transform=transform)
train_loader = paddle.io.DataLoader(
train_dataset,
places=place,
feed_list=[image, label],
drop_last=True,
return_list=False,
batch_size=64)
valid_loader = paddle.io.DataLoader(
test_dataset,
places=place,
feed_list=[image, label],
batch_size=64,
return_list=False)
def train(program):
iter = 0
for data in train_loader():
cost, top1, top5 = exe.run(
program,
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 valid_loader():
cost, top1, top5 = exe.run(
program,
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_pact = quant_aware(
main_prog,
place,
config,
for_test=False,
act_preprocess_func=pact,
optimizer_func=get_optimizer,
executor=exe)
quant_eval_prog = quant_aware(val_prog, place, config, for_test=True)
train(quant_train_prog_pact)
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()