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更换文档检测模型

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liuyebo
2024-08-27 14:42:45 +08:00
parent aea6f19951
commit 1514e09c40
2072 changed files with 254336 additions and 4967 deletions
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paddle_detection/deploy/pptracking/python/benchmark_utils.py Normal file
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# Copyright (c) 2021 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 os
import logging
import paddle
import paddle.inference as paddle_infer
from pathlib import Path
CUR_DIR = os.path.dirname(os.path.abspath(__file__))
LOG_PATH_ROOT = f"{CUR_DIR}/../../output"
class PaddleInferBenchmark(object):
def __init__(self,
config,
model_info: dict={},
data_info: dict={},
perf_info: dict={},
resource_info: dict={},
**kwargs):
"""
Construct PaddleInferBenchmark Class to format logs.
args:
config(paddle.inference.Config): paddle inference config
model_info(dict): basic model info
{'model_name': 'resnet50'
'precision': 'fp32'}
data_info(dict): input data info
{'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info(dict): performance result
{'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info(dict):
cpu and gpu resources
{'cpu_rss': 100
'gpu_rss': 100
'gpu_util': 60}
"""
# PaddleInferBenchmark Log Version
self.log_version = "1.0.3"
# Paddle Version
self.paddle_version = paddle.__version__
self.paddle_commit = paddle.__git_commit__
paddle_infer_info = paddle_infer.get_version()
self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
# model info
self.model_info = model_info
# data info
self.data_info = data_info
# perf info
self.perf_info = perf_info
try:
# required value
self.model_name = model_info['model_name']
self.precision = model_info['precision']
self.batch_size = data_info['batch_size']
self.shape = data_info['shape']
self.data_num = data_info['data_num']
self.inference_time_s = round(perf_info['inference_time_s'], 4)
except:
self.print_help()
raise ValueError(
"Set argument wrong, please check input argument and its type")
self.preprocess_time_s = perf_info.get('preprocess_time_s', 0)
self.postprocess_time_s = perf_info.get('postprocess_time_s', 0)
self.with_tracker = True if 'tracking_time_s' in perf_info else False
self.tracking_time_s = perf_info.get('tracking_time_s', 0)
self.total_time_s = perf_info.get('total_time_s', 0)
self.inference_time_s_90 = perf_info.get("inference_time_s_90", "")
self.inference_time_s_99 = perf_info.get("inference_time_s_99", "")
self.succ_rate = perf_info.get("succ_rate", "")
self.qps = perf_info.get("qps", "")
# conf info
self.config_status = self.parse_config(config)
# mem info
if isinstance(resource_info, dict):
self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
self.cpu_vms_mb = int(resource_info.get('cpu_vms_mb', 0))
self.cpu_shared_mb = int(resource_info.get('cpu_shared_mb', 0))
self.cpu_dirty_mb = int(resource_info.get('cpu_dirty_mb', 0))
self.cpu_util = round(resource_info.get('cpu_util', 0), 2)
self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
self.gpu_mem_util = round(resource_info.get('gpu_mem_util', 0), 2)
else:
self.cpu_rss_mb = 0
self.cpu_vms_mb = 0
self.cpu_shared_mb = 0
self.cpu_dirty_mb = 0
self.cpu_util = 0
self.gpu_rss_mb = 0
self.gpu_util = 0
self.gpu_mem_util = 0
# init benchmark logger
self.benchmark_logger()
def benchmark_logger(self):
"""
benchmark logger
"""
# remove other logging handler
for handler in logging.root.handlers[:]:
logging.root.removeHandler(handler)
# Init logger
FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
log_output = f"{LOG_PATH_ROOT}/{self.model_name}.log"
Path(f"{LOG_PATH_ROOT}").mkdir(parents=True, exist_ok=True)
logging.basicConfig(
level=logging.INFO,
format=FORMAT,
handlers=[
logging.FileHandler(
filename=log_output, mode='w'),
logging.StreamHandler(),
])
self.logger = logging.getLogger(__name__)
self.logger.info(
f"Paddle Inference benchmark log will be saved to {log_output}")
def parse_config(self, config) -> dict:
"""
parse paddle predictor config
args:
config(paddle.inference.Config): paddle inference config
return:
config_status(dict): dict style config info
"""
if isinstance(config, paddle_infer.Config):
config_status = {}
config_status['runtime_device'] = "gpu" if config.use_gpu(
) else "cpu"
config_status['ir_optim'] = config.ir_optim()
config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
config_status['precision'] = self.precision
config_status['enable_mkldnn'] = config.mkldnn_enabled()
config_status[
'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
)
elif isinstance(config, dict):
config_status['runtime_device'] = config.get('runtime_device', "")
config_status['ir_optim'] = config.get('ir_optim', "")
config_status['enable_tensorrt'] = config.get('enable_tensorrt', "")
config_status['precision'] = config.get('precision', "")
config_status['enable_mkldnn'] = config.get('enable_mkldnn', "")
config_status['cpu_math_library_num_threads'] = config.get(
'cpu_math_library_num_threads', "")
else:
self.print_help()
raise ValueError(
"Set argument config wrong, please check input argument and its type"
)
return config_status
def report(self, identifier=None):
"""
print log report
args:
identifier(string): identify log
"""
if identifier:
identifier = f"[{identifier}]"
else:
identifier = ""
self.logger.info("\n")
self.logger.info(
"---------------------- Paddle info ----------------------")
self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
self.logger.info(f"{identifier} log_api_version: {self.log_version}")
self.logger.info(
"----------------------- Conf info -----------------------")
self.logger.info(
f"{identifier} runtime_device: {self.config_status['runtime_device']}"
)
self.logger.info(
f"{identifier} ir_optim: {self.config_status['ir_optim']}")
self.logger.info(f"{identifier} enable_memory_optim: {True}")
self.logger.info(
f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
)
self.logger.info(
f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
self.logger.info(
f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
)
self.logger.info(
"----------------------- Model info ----------------------")
self.logger.info(f"{identifier} model_name: {self.model_name}")
self.logger.info(f"{identifier} precision: {self.precision}")
self.logger.info(
"----------------------- Data info -----------------------")
self.logger.info(f"{identifier} batch_size: {self.batch_size}")
self.logger.info(f"{identifier} input_shape: {self.shape}")
self.logger.info(f"{identifier} data_num: {self.data_num}")
self.logger.info(
"----------------------- Perf info -----------------------")
self.logger.info(
f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, cpu_vms: {self.cpu_vms_mb}, cpu_shared_mb: {self.cpu_shared_mb}, cpu_dirty_mb: {self.cpu_dirty_mb}, cpu_util: {self.cpu_util}%"
)
self.logger.info(
f"{identifier} gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%, gpu_mem_util: {self.gpu_mem_util}%"
)
self.logger.info(
f"{identifier} total time spent(s): {self.total_time_s}")
if self.with_tracker:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}, "
f"tracking_time(ms): {round(self.tracking_time_s*1000, 1)}")
else:
self.logger.info(
f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, "
f"inference_time(ms): {round(self.inference_time_s*1000, 1)}, "
f"postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
)
if self.inference_time_s_90:
self.looger.info(
f"{identifier} 90%_cost: {self.inference_time_s_90}, 99%_cost: {self.inference_time_s_99}, succ_rate: {self.succ_rate}"
)
if self.qps:
self.logger.info(f"{identifier} QPS: {self.qps}")
def print_help(self):
"""
print function help
"""
print("""Usage:
==== Print inference benchmark logs. ====
config = paddle.inference.Config()
model_info = {'model_name': 'resnet50'
'precision': 'fp32'}
data_info = {'batch_size': 1
'shape': '3,224,224'
'data_num': 1000}
perf_info = {'preprocess_time_s': 1.0
'inference_time_s': 2.0
'postprocess_time_s': 1.0
'total_time_s': 4.0}
resource_info = {'cpu_rss_mb': 100
'gpu_rss_mb': 100
'gpu_util': 60}
log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
log('Test')
""")
def __call__(self, identifier=None):
"""
__call__
args:
identifier(string): identify log
"""
self.report(identifier)