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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
from typing import List
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 numpy as np
@unittest.skip("Deprecated quantization for static graph in PaddleSlim>=2.6.0")
class TestQuantAwareCase(StaticCase):
def test_accuracy(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)
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 = paddle.static.default_main_program().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)
ops_with_weights = [
'depthwise_conv2d',
'mul',
'conv2d',
]
ops_without_weights = [
'relu',
]
config = {
'weight_quantize_type': 'channel_wise_abs_max',
'activation_quantize_type': 'moving_average_abs_max',
'quantize_op_types': ops_with_weights + ops_without_weights,
}
quant_train_prog = quant_aware(main_prog, place, config, for_test=False)
quant_eval_prog = quant_aware(val_prog, place, config, for_test=True)
# Step1: check the quantizers count in qat graph
quantizers_count_in_qat = self.count_op(quant_eval_prog,
['quantize_linear'])
ops_with_weights_count = self.count_op(quant_eval_prog,
ops_with_weights)
ops_without_weights_count = self.count_op(quant_eval_prog,
ops_without_weights)
self.assertEqual(ops_with_weights_count * 2 + ops_without_weights_count,
quantizers_count_in_qat)
with paddle.static.program_guard(quant_eval_prog):
paddle.static.save_inference_model("./models/mobilenet_qat", [
image, label
], [avg_cost, acc_top1, acc_top5], exe)
train(quant_train_prog)
convert_eval_prog = convert(quant_eval_prog, place, config)
with paddle.static.program_guard(convert_eval_prog):
paddle.static.save_inference_model("./models/mobilenet_onnx", [
image, label
], [avg_cost, acc_top1, acc_top5], exe)
top1_2, top5_2 = test(convert_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))
# Step2: check the quantizers count in onnx graph
quantizers_count = self.count_op(convert_eval_prog, ['quantize_linear'])
observers_count = self.count_op(quant_eval_prog,
['moving_average_abs_max_scale'])
self.assertEqual(quantizers_count, ops_with_weights_count +
ops_without_weights_count + observers_count)
# Step3: check the quantization skipping
config['not_quant_pattern'] = ['last_fc']
skip_quant_prog = quant_aware(
main_prog, place, config=config, for_test=True)
skip_quantizers_count_in_qat = self.count_op(skip_quant_prog,
['quantize_linear'])
skip_ops_with_weights_count = self.count_op(skip_quant_prog,
ops_with_weights)
skip_ops_without_weights_count = self.count_op(skip_quant_prog,
ops_without_weights)
self.assertEqual(skip_ops_without_weights_count,
ops_without_weights_count)
self.assertEqual(skip_ops_with_weights_count, ops_with_weights_count)
self.assertEqual(skip_quantizers_count_in_qat + 2,
quantizers_count_in_qat)
skip_quant_prog_onnx = convert(skip_quant_prog, place, config=config)
skip_quantizers_count_in_onnx = self.count_op(skip_quant_prog_onnx,
['quantize_linear'])
self.assertEqual(quantizers_count, skip_quantizers_count_in_onnx)
def count_op(self, prog, ops: List[str]):
graph = paddle.framework.IrGraph(
paddle.framework.core.Graph(prog.desc), for_test=False)
op_nums = 0
for op in graph.all_op_nodes():
if op.name() in ops:
op_nums += 1
return op_nums
if __name__ == '__main__':
unittest.main()