提交 a72f988a 编写于 作者: L Liufang Sang 提交者: whs

[PaddleSlim]Yolov3 quantization demo (#3440)

上级 b860c3ba
>运行该示例前请安装Paddle1.6或更高版本
# 检测模型量化压缩示例
## 概述
该示例使用PaddleSlim提供的[量化压缩策略](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/tutorial.md#1-quantization-aware-training%E9%87%8F%E5%8C%96%E4%BB%8B%E7%BB%8D)对分类模型进行压缩。
在阅读该示例前,建议您先了解以下内容:
- [检测模型的常规训练方法](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection)
- [PaddleSlim使用文档](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md)
## 配置文件说明
关于配置文件如何编写您可以参考:
- [PaddleSlim配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#122-%E9%85%8D%E7%BD%AE%E6%96%87%E4%BB%B6%E7%9A%84%E4%BD%BF%E7%94%A8)
- [量化策略配置文件编写说明](https://github.com/PaddlePaddle/models/blob/develop/PaddleSlim/docs/usage.md#21-%E9%87%8F%E5%8C%96%E8%AE%AD%E7%BB%83)
其中save_out_nodes需要得到检测结果的Variable的名称,下面介绍如何确定save_out_nodes的参数
以MobileNet V1为例,可在compress.py中构建好网络之后,直接打印Variable得到Variable的名称信息。
代码示例:
```
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# print(eval_values)
```
根据运行结果可看到Variable的名字为:`multiclass_nms_0.tmp_0`
## 训练
根据 [PaddleCV/PaddleDetection/tools/train.py](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/tools/train.py) 编写压缩脚本compress.py。
在该脚本中定义了Compressor对象,用于执行压缩任务。
通过`python compress.py --help`查看可配置参数,简述如下:
- config: 检测库的配置,其中配置了训练超参数、数据集信息等。
- slim_file: PaddleSlim的配置文件,参见[配置文件说明](#配置文件说明)
您可以通过运行脚本`run.sh`运行该示例,请确保已正确下载[pretrained model](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/image_classification#%E5%B7%B2%E5%8F%91%E5%B8%83%E6%A8%A1%E5%9E%8B%E5%8F%8A%E5%85%B6%E6%80%A7%E8%83%BD)
### 训练时的模型结构
这部分介绍来源于[量化low-level API介绍](https://github.com/PaddlePaddle/models/tree/develop/PaddleSlim/quant_low_level_api#1-%E9%87%8F%E5%8C%96%E8%AE%AD%E7%BB%83low-level-apis%E4%BB%8B%E7%BB%8D)
PaddlePaddle框架中有四个和量化相关的IrPass, 分别是QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass以及TransformForMobilePass。在训练时,对网络应用了QuantizationTransformPass,作用是在网络中的conv2d、depthwise_conv2d、mul等算子的各个输入前插入连续的量化op和反量化op,并改变相应反向算子的某些输入。示例图如下:
<p align="center">
<img src="./images/TransformPass.png" height=400 width=520 hspace='10'/> <br />
<strong>图1:应用QuantizationTransformPass后的结果</strong>
</p>
### 保存断点(checkpoint)
如果在配置文件中设置了`checkpoint_path`, 则在压缩任务执行过程中会自动保存断点,当任务异常中断时,
重启任务会自动从`checkpoint_path`路径下按数字顺序加载最新的checkpoint文件。如果不想让重启的任务从断点恢复,
需要修改配置文件中的`checkpoint_path`,或者将`checkpoint_path`路径下文件清空。
>注意:配置文件中的信息不会保存在断点中,重启前对配置文件的修改将会生效。
## 评估
如果在配置文件中设置了`checkpoint_path`,则每个epoch会保存一个量化后的用于评估的模型,
该模型会保存在`${checkpoint_path}/${epoch_id}/eval_model/`路径下,包含`__model__``__params__`两个文件。
其中,`__model__`用于保存模型结构信息,`__params__`用于保存参数(parameters)信息。模型结构和训练时一样。
如果不需要保存评估模型,可以在定义Compressor对象时,将`save_eval_model`选项设置为False(默认为True)。
脚本<a href="eval.py">slim/quantization/eval.py</a>中为使用该模型在评估数据集上做评估的示例。
## 预测
如果在配置文件的量化策略中设置了`float_model_save_path`, `int8_model_save_path`, `mobile_model_save_path`, 在训练结束后,会保存模型量化压缩之后用于预测的模型。接下来介绍这三种预测模型的区别。
### float预测模型
在介绍量化训练时的模型结构时介绍了PaddlePaddle框架中有四个和量化相关的IrPass, 分别是QuantizationTransformPass、QuantizationFreezePass、ConvertToInt8Pass以及TransformForMobilePass。float预测模型是在应用QuantizationFreezePass并删除eval_program中多余的operators之后,保存的模型。
QuantizationFreezePass主要用于改变IrGraph中量化op和反量化op的顺序,即将类似图1中的量化op和反量化op顺序改变为图2中的布局。除此之外,QuantizationFreezePass还会将`conv2d``depthwise_conv2d``mul`等算子的权重离线量化为int8_t范围内的值(但数据类型仍为float32),以减少预测过程中对权重的量化操作,示例如图2:
<p align="center">
<img src="./images/FreezePass.png" height=400 width=420 hspace='10'/> <br />
<strong>图2:应用QuantizationFreezePass后的结果</strong>
</p>
### int8预测模型
在对训练网络进行QuantizationFreezePass之后,执行ConvertToInt8Pass,
其主要目的是将执行完QuantizationFreezePass后输出的权重类型由`FP32`更改为`INT8`。换言之,用户可以选择将量化后的权重保存为float32类型(不执行ConvertToInt8Pass)或者int8_t类型(执行ConvertToInt8Pass),示例如图3:
<p align="center">
<img src="./images/ConvertToInt8Pass.png" height=400 width=400 hspace='10'/> <br />
<strong>图3:应用ConvertToInt8Pass后的结果</strong>
</p>
### mobile预测模型
经TransformForMobilePass转换后,用户可得到兼容[paddle-lite](https://github.com/PaddlePaddle/Paddle-Lite)移动端预测库的量化模型。paddle-mobile中的量化op和反量化op的名称分别为`quantize``dequantize``quantize`算子和PaddlePaddle框架中的`fake_quantize_abs_max`算子簇的功能类似,`dequantize` 算子和PaddlePaddle框架中的`fake_dequantize_max_abs`算子簇的功能相同。若选择paddle-mobile执行量化训练输出的模型,则需要将`fake_quantize_abs_max`等算子改为`quantize`算子以及将`fake_dequantize_max_abs`等算子改为`dequantize`算子,示例如图4:
<p align="center">
<img src="./images/TransformForMobilePass.png" height=400 width=400 hspace='10'/> <br />
<strong>图4:应用TransformForMobilePass后的结果</strong>
</p>
### python预测
### PaddleLite预测
float预测模型可使用PaddleLite进行加载预测,可参见教程[Paddle-Lite如何加载运行量化模型](https://github.com/PaddlePaddle/Paddle-Lite/wiki/model_quantization)
## 从评估模型保存预测模型
[配置文件说明](#配置文件说明)中可以看到,在 `end_epoch` 时将保存可用于预测的 `float`, `int8`, `mobile`模型,但是在训练之前不能准确地保存结果最好的epoch的结果,因此,提供了从`${checkpoint_path}/${epoch_id}/eval_model/`下保存的评估模型转化为预测模型的接口 `freeze.py `, 需要配置的参数为:
- model_path, 加载的模型路径,`为${checkpoint_path}/${epoch_id}/eval_model/`
- weight_quant_type 模型参数的量化方式,和配置文件中的类型保持一致
- save_path `float`, `int8`, `mobile`模型的保存路径,分别为 `${save_path}/float/`, `${save_path}/int8/`, `${save_path}/mobile/`
## 示例结果
### MobileNetV1
| weight量化方式 | activation量化方式| Box ap |Paddle Fluid inference time(ms)| Paddle Lite inference time(ms)|
|---|---|---|---|---|
|baseline|- |76.2%|- |-|
|abs_max|abs_max|- |- |-|
|abs_max|moving_average_abs_max|- |- |-|
|channel_wise_abs_max|abs_max|- |- |-|
>训练超参:
## FAQ
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import multiprocessing
import numpy as np
import datetime
from collections import deque
import sys
sys.path.append("../../")
from paddle.fluid.contrib.slim import Compressor
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
def set_paddle_flags(**kwargs):
for key, value in kwargs.items():
if os.environ.get(key, None) is None:
os.environ[key] = str(value)
# NOTE(paddle-dev): All of these flags should be set before
# `import paddle`. Otherwise, it would not take any effect.
set_paddle_flags(
FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
)
from paddle import fluid
from ppdet.core.workspace import load_config, merge_config, create
from ppdet.data.data_feed import create_reader
from ppdet.utils.eval_utils import parse_fetches, eval_results
from ppdet.utils.stats import TrainingStats
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu
import ppdet.utils.checkpoint as checkpoint
from ppdet.modeling.model_input import create_feed
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
"""
Run evaluation program, return program outputs.
"""
iter_id = 0
results = []
if len(cls) != 0:
values = []
for i in range(len(cls)):
_, accum_map = cls[i].get_map_var()
cls[i].reset(exe)
values.append(accum_map)
images_num = 0
start_time = time.time()
has_bbox = 'bbox' in keys
for data in reader():
data = test_feed.feed(data)
feed_data = {'image': data['image'],
'im_size': data['im_size']}
outs = exe.run(compile_program,
feed=feed_data,
fetch_list=values[0],
return_numpy=False)
outs.append(data['gt_box'])
outs.append(data['gt_label'])
outs.append(data['is_difficult'])
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
results.append(res)
if iter_id % 100 == 0:
logger.info('Test iter {}'.format(iter_id))
iter_id += 1
images_num += len(res['bbox'][1][0]) if has_bbox else 1
logger.info('Test finish iter {}'.format(iter_id))
end_time = time.time()
fps = images_num / (end_time - start_time)
if has_bbox:
logger.info('Total number of images: {}, inference time: {} fps.'.
format(images_num, fps))
else:
logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
images_num, fps))
return results
def main():
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
if 'log_iter' not in cfg:
cfg.log_iter = 20
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
if 'train_feed' not in cfg:
train_feed = create(main_arch + 'TrainFeed')
else:
train_feed = create(cfg.train_feed)
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
lr_builder = create('LearningRate')
optim_builder = create('OptimizerBuilder')
# build program
startup_prog = fluid.Program()
train_prog = fluid.Program()
with fluid.program_guard(train_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
train_pyreader, feed_vars = create_feed(train_feed)
train_fetches = model.train(feed_vars)
loss = train_fetches['loss']
lr = lr_builder()
optimizer = optim_builder(lr)
optimizer.minimize(loss)
train_reader = create_reader(train_feed, cfg.max_iters * devices_num,
FLAGS.dataset_dir)
train_pyreader.decorate_sample_list_generator(train_reader, place)
# parse train fetches
train_keys, train_values, _ = parse_fetches(train_fetches)
train_values.append(lr)
train_fetch_list=[]
for k, v in zip(train_keys, train_values):
train_fetch_list.append((k, v))
print("train_fetch_list: {}".format(train_fetch_list))
eval_prog = fluid.Program()
with fluid.program_guard(eval_prog, startup_prog):
with fluid.unique_name.guard():
model = create(main_arch)
eval_pyreader, test_feed_vars = create_feed(eval_feed, use_pyreader=False)
fetches = model.eval(test_feed_vars)
eval_prog = eval_prog.clone(True)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
#eval_pyreader.decorate_sample_list_generator(eval_reader, place)
test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
# parse eval fetches
extra_keys = []
if cfg.metric == 'COCO':
extra_keys = ['im_info', 'im_id', 'im_shape']
if cfg.metric == 'VOC':
extra_keys = ['gt_box', 'gt_label', 'is_difficult']
eval_keys, eval_values, eval_cls = parse_fetches(fetches, eval_prog,
extra_keys)
# print(eval_values)
eval_fetch_list=[]
for k, v in zip(eval_keys, eval_values):
eval_fetch_list.append((k, v))
exe.run(startup_prog)
start_iter = 0
checkpoint.load_pretrain(exe, train_prog, cfg.pretrain_weights)
def eval_func(program, scope):
#place = fluid.CPUPlace()
#exe = fluid.Executor(place)
results = eval_run(exe, program, eval_reader,
eval_keys, eval_values, eval_cls, test_data_feed)
best_box_ap_list = []
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes,
resolution, False, FLAGS.output_eval)
if len(best_box_ap_list) == 0:
best_box_ap_list.append(box_ap_stats[0])
elif box_ap_stats[0] > best_box_ap_list[0]:
best_box_ap_list[0] = box_ap_stats[0]
checkpoint.save(exe, train_prog, os.path.join(save_dir,"best_model"))
logger.info("Best test box ap: {}".format(
best_box_ap_list[0]))
return best_box_ap_list[0]
test_feed = [('image', test_feed_vars['image'].name),
('im_size', test_feed_vars['im_size'].name)]
com = Compressor(
place,
fluid.global_scope(),
train_prog,
train_reader=train_pyreader,
train_feed_list=None,
train_fetch_list=train_fetch_list,
eval_program=eval_prog,
eval_reader=eval_reader,
eval_feed_list=test_feed,
eval_func={'map': eval_func},
eval_fetch_list=[eval_fetch_list[0]],
train_optimizer=None)
com.config(FLAGS.slim_file)
com.run()
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument(
"-s",
"--slim_file",
default=None,
type=str,
help="Config file of PaddleSlim.")
parser.add_argument(
"--output_eval",
default=None,
type=str,
help="Evaluation directory, default is current directory.")
parser.add_argument(
"-d",
"--dataset_dir",
default=None,
type=str,
help="Dataset path, same as DataFeed.dataset.dataset_dir")
FLAGS = parser.parse_args()
main()
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import multiprocessing
import numpy as np
import datetime
from collections import deque
import sys
sys.path.append("../../")
from paddle.fluid.contrib.slim import Compressor
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
def set_paddle_flags(**kwargs):
for key, value in kwargs.items():
if os.environ.get(key, None) is None:
os.environ[key] = str(value)
# NOTE(paddle-dev): All of these flags should be set before
# `import paddle`. Otherwise, it would not take any effect.
set_paddle_flags(
FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
)
from paddle import fluid
from ppdet.core.workspace import load_config, merge_config, create
from ppdet.data.data_feed import create_reader
from ppdet.utils.eval_utils import parse_fetches, eval_results
from ppdet.utils.stats import TrainingStats
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu
import ppdet.utils.checkpoint as checkpoint
from ppdet.modeling.model_input import create_feed
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
"""
Run evaluation program, return program outputs.
"""
iter_id = 0
results = []
images_num = 0
start_time = time.time()
has_bbox = 'bbox' in keys
for data in reader():
data = test_feed.feed(data)
feed_data = {'image': data['image'],
'im_size': data['im_size']}
outs = exe.run(compile_program,
feed=feed_data,
fetch_list=values[0],
return_numpy=False)
outs.append(data['gt_box'])
outs.append(data['gt_label'])
outs.append(data['is_difficult'])
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
results.append(res)
if iter_id % 100 == 0:
logger.info('Test iter {}'.format(iter_id))
iter_id += 1
images_num += len(res['bbox'][1][0]) if has_bbox else 1
logger.info('Test finish iter {}'.format(iter_id))
end_time = time.time()
fps = images_num / (end_time - start_time)
if has_bbox:
logger.info('Total number of images: {}, inference time: {} fps.'.
format(images_num, fps))
else:
logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
images_num, fps))
return results
def main():
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
if 'log_iter' not in cfg:
cfg.log_iter = 20
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
eval_pyreader, test_feed_vars = create_feed(eval_feed, use_pyreader=False)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
#eval_pyreader.decorate_sample_list_generator(eval_reader, place)
test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
assert os.path.exists(FLAGS.model_path)
infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model(
dirname=FLAGS.model_path, executor=exe,
model_filename='model',
params_filename='params')
eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult']
eval_values = ['multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult']
eval_cls = []
eval_values[0] = fetch_targets[0]
results = eval_run(exe, infer_prog, eval_reader,
eval_keys, eval_values, eval_cls, test_data_feed)
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
eval_results(results, eval_feed, cfg.metric, cfg.num_classes,
resolution, False, FLAGS.output_eval)
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument(
"-m",
"--model_path",
default=None,
type=str,
help="path of checkpoint")
parser.add_argument(
"--output_eval",
default=None,
type=str,
help="Evaluation directory, default is current directory.")
parser.add_argument(
"-d",
"--dataset_dir",
default=None,
type=str,
help="Dataset path, same as DataFeed.dataset.dataset_dir")
FLAGS = parser.parse_args()
main()
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
import multiprocessing
import numpy as np
import datetime
from collections import deque
import sys
sys.path.append("../../")
from paddle.fluid.contrib.slim import Compressor
from paddle.fluid.framework import IrGraph
from paddle.fluid import core
from paddle.fluid.contrib.slim.quantization import QuantizationTransformPass
from paddle.fluid.contrib.slim.quantization import QuantizationFreezePass
from paddle.fluid.contrib.slim.quantization import ConvertToInt8Pass
from paddle.fluid.contrib.slim.quantization import TransformForMobilePass
def set_paddle_flags(**kwargs):
for key, value in kwargs.items():
if os.environ.get(key, None) is None:
os.environ[key] = str(value)
# NOTE(paddle-dev): All of these flags should be set before
# `import paddle`. Otherwise, it would not take any effect.
set_paddle_flags(
FLAGS_eager_delete_tensor_gb=0, # enable GC to save memory
)
from paddle import fluid
from ppdet.core.workspace import load_config, merge_config, create
from ppdet.data.data_feed import create_reader
from ppdet.utils.eval_utils import parse_fetches, eval_results
from ppdet.utils.stats import TrainingStats
from ppdet.utils.cli import ArgsParser
from ppdet.utils.check import check_gpu
import ppdet.utils.checkpoint as checkpoint
from ppdet.modeling.model_input import create_feed
import logging
FORMAT = '%(asctime)s-%(levelname)s: %(message)s'
logging.basicConfig(level=logging.INFO, format=FORMAT)
logger = logging.getLogger(__name__)
def eval_run(exe, compile_program, reader, keys, values, cls, test_feed):
"""
Run evaluation program, return program outputs.
"""
iter_id = 0
results = []
images_num = 0
start_time = time.time()
has_bbox = 'bbox' in keys
for data in reader():
data = test_feed.feed(data)
feed_data = {'image': data['image'],
'im_size': data['im_size']}
outs = exe.run(compile_program,
feed=feed_data,
fetch_list=values[0],
return_numpy=False)
outs.append(data['gt_box'])
outs.append(data['gt_label'])
outs.append(data['is_difficult'])
res = {
k: (np.array(v), v.recursive_sequence_lengths())
for k, v in zip(keys, outs)
}
results.append(res)
if iter_id % 100 == 0:
logger.info('Test iter {}'.format(iter_id))
iter_id += 1
images_num += len(res['bbox'][1][0]) if has_bbox else 1
logger.info('Test finish iter {}'.format(iter_id))
end_time = time.time()
fps = images_num / (end_time - start_time)
if has_bbox:
logger.info('Total number of images: {}, inference time: {} fps.'.
format(images_num, fps))
else:
logger.info('Total iteration: {}, inference time: {} batch/s.'.format(
images_num, fps))
return results
def main():
cfg = load_config(FLAGS.config)
if 'architecture' in cfg:
main_arch = cfg.architecture
else:
raise ValueError("'architecture' not specified in config file.")
merge_config(FLAGS.opt)
if 'log_iter' not in cfg:
cfg.log_iter = 20
# check if set use_gpu=True in paddlepaddle cpu version
check_gpu(cfg.use_gpu)
if cfg.use_gpu:
devices_num = fluid.core.get_cuda_device_count()
else:
devices_num = int(
os.environ.get('CPU_NUM', multiprocessing.cpu_count()))
if 'eval_feed' not in cfg:
eval_feed = create(main_arch + 'EvalFeed')
else:
eval_feed = create(cfg.eval_feed)
place = fluid.CUDAPlace(0) if cfg.use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
eval_pyreader, test_feed_vars = create_feed(eval_feed, use_pyreader=False)
eval_reader = create_reader(eval_feed, args_path=FLAGS.dataset_dir)
#eval_pyreader.decorate_sample_list_generator(eval_reader, place)
test_data_feed = fluid.DataFeeder(test_feed_vars.values(), place)
assert os.path.exists(FLAGS.model_path)
infer_prog, feed_names, fetch_targets = fluid.io.load_inference_model(
dirname=FLAGS.model_path, executor=exe,
model_filename='__model__',
params_filename='__params__')
eval_keys = ['bbox', 'gt_box', 'gt_label', 'is_difficult']
eval_values = ['multiclass_nms_0.tmp_0', 'gt_box', 'gt_label', 'is_difficult']
eval_cls = []
eval_values[0] = fetch_targets[0]
results = eval_run(exe, infer_prog, eval_reader,
eval_keys, eval_values, eval_cls, test_data_feed)
resolution = None
if 'mask' in results[0]:
resolution = model.mask_head.resolution
box_ap_stats = eval_results(results, eval_feed, cfg.metric, cfg.num_classes,
resolution, False, FLAGS.output_eval)
logger.info("freeze the graph for inference")
test_graph = IrGraph(core.Graph(infer_prog.desc), for_test=True)
freeze_pass = QuantizationFreezePass(
scope=fluid.global_scope(),
place=place,
weight_quantize_type=FLAGS.weight_quant_type)
freeze_pass.apply(test_graph)
server_program = test_graph.to_program()
fluid.io.save_inference_model(
dirname=os.path.join(FLAGS.save_path, 'float'),
feeded_var_names=feed_names,
target_vars=fetch_targets,
executor=exe,
main_program=server_program,
model_filename='model',
params_filename='params')
logger.info("convert the weights into int8 type")
convert_int8_pass = ConvertToInt8Pass(
scope=fluid.global_scope(),
place=place)
convert_int8_pass.apply(test_graph)
server_int8_program = test_graph.to_program()
fluid.io.save_inference_model(
dirname=os.path.join(FLAGS.save_path, 'int8'),
feeded_var_names=feed_names,
target_vars=fetch_targets,
executor=exe,
main_program=server_int8_program,
model_filename='model',
params_filename='params')
logger.info("convert the freezed pass to paddle-lite execution")
mobile_pass = TransformForMobilePass()
mobile_pass.apply(test_graph)
mobile_program = test_graph.to_program()
fluid.io.save_inference_model(
dirname=os.path.join(FLAGS.save_path, 'mobile'),
feeded_var_names=feed_names,
target_vars=fetch_targets,
executor=exe,
main_program=mobile_program,
model_filename='model',
params_filename='params')
if __name__ == '__main__':
parser = ArgsParser()
parser.add_argument(
"-m",
"--model_path",
default=None,
type=str,
help="path of checkpoint")
parser.add_argument(
"--output_eval",
default=None,
type=str,
help="Evaluation directory, default is current directory.")
parser.add_argument(
"-d",
"--dataset_dir",
default=None,
type=str,
help="Dataset path, same as DataFeed.dataset.dataset_dir")
parser.add_argument(
"--weight_quant_type",
default='abs_max',
type=str,
help="quantization type for weight")
parser.add_argument(
"--save_path",
default='./output',
type=str,
help="path to save quantization inference model")
FLAGS = parser.parse_args()
main()
version: 1.0
strategies:
quantization_strategy:
class: 'QuantizationStrategy'
start_epoch: 0
end_epoch: 0
float_model_save_path: './output/yolov3/float'
mobile_model_save_path: './output/yolov3/mobile'
int8_model_save_path: './output/yolov3/int8'
weight_bits: 8
activation_bits: 8
weight_quantize_type: 'abs_max'
activation_quantize_type: 'moving_average_abs_max'
save_in_nodes: ['image', 'im_size']
save_out_nodes: ['multiclass_nms_0.tmp_0']
compressor:
epoch: 1
checkpoint_path: './checkpoints/yolov3/'
strategies:
- quantization_strategy
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