delete benchmark
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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import argparse
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import os
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import time
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import logging
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import paddle
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import paddle.inference as paddle_infer
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from pathlib import Path
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CUR_DIR = os.path.dirname(os.path.abspath(__file__))
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class PaddleInferBenchmark(object):
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def __init__(self,
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config,
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model_info: dict={},
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data_info: dict={},
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perf_info: dict={},
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resource_info: dict={},
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save_log_path: str="",
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**kwargs):
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"""
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Construct PaddleInferBenchmark Class to format logs.
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args:
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config(paddle.inference.Config): paddle inference config
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model_info(dict): basic model info
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{'model_name': 'resnet50'
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'precision': 'fp32'}
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data_info(dict): input data info
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{'batch_size': 1
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'shape': '3,224,224'
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'data_num': 1000}
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perf_info(dict): performance result
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{'preprocess_time_s': 1.0
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'inference_time_s': 2.0
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'postprocess_time_s': 1.0
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'total_time_s': 4.0}
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resource_info(dict):
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cpu and gpu resources
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{'cpu_rss': 100
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'gpu_rss': 100
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'gpu_util': 60}
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"""
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# PaddleInferBenchmark Log Version
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self.log_version = 1.0
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# Paddle Version
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self.paddle_version = paddle.__version__
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self.paddle_commit = paddle.__git_commit__
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paddle_infer_info = paddle_infer.get_version()
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self.paddle_branch = paddle_infer_info.strip().split(': ')[-1]
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# model info
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self.model_info = model_info
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# data info
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self.data_info = data_info
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# perf info
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self.perf_info = perf_info
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try:
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self.model_name = model_info['model_name']
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self.precision = model_info['precision']
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self.batch_size = data_info['batch_size']
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self.shape = data_info['shape']
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self.data_num = data_info['data_num']
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self.preprocess_time_s = round(perf_info['preprocess_time_s'], 4)
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self.inference_time_s = round(perf_info['inference_time_s'], 4)
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self.postprocess_time_s = round(perf_info['postprocess_time_s'], 4)
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self.total_time_s = round(perf_info['total_time_s'], 4)
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except:
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self.print_help()
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raise ValueError(
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"Set argument wrong, please check input argument and its type")
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# conf info
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self.config_status = self.parse_config(config)
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self.save_log_path = save_log_path
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# mem info
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if isinstance(resource_info, dict):
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self.cpu_rss_mb = int(resource_info.get('cpu_rss_mb', 0))
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self.gpu_rss_mb = int(resource_info.get('gpu_rss_mb', 0))
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self.gpu_util = round(resource_info.get('gpu_util', 0), 2)
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else:
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self.cpu_rss_mb = 0
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self.gpu_rss_mb = 0
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self.gpu_util = 0
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# init benchmark logger
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self.benchmark_logger()
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def benchmark_logger(self):
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"""
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benchmark logger
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"""
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# Init logger
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FORMAT = '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
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log_output = f"{self.save_log_path}/{self.model_name}.log"
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Path(f"{self.save_log_path}").mkdir(parents=True, exist_ok=True)
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logging.basicConfig(
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level=logging.INFO,
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format=FORMAT,
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handlers=[
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logging.FileHandler(
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filename=log_output, mode='w'),
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logging.StreamHandler(),
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])
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self.logger = logging.getLogger(__name__)
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self.logger.info(
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f"Paddle Inference benchmark log will be saved to {log_output}")
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def parse_config(self, config) -> dict:
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"""
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parse paddle predictor config
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args:
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config(paddle.inference.Config): paddle inference config
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return:
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config_status(dict): dict style config info
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"""
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config_status = {}
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config_status['runtime_device'] = "gpu" if config.use_gpu() else "cpu"
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config_status['ir_optim'] = config.ir_optim()
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config_status['enable_tensorrt'] = config.tensorrt_engine_enabled()
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config_status['precision'] = self.precision
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config_status['enable_mkldnn'] = config.mkldnn_enabled()
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config_status[
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'cpu_math_library_num_threads'] = config.cpu_math_library_num_threads(
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)
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return config_status
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def report(self, identifier=None):
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"""
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print log report
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args:
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identifier(string): identify log
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"""
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if identifier:
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identifier = f"[{identifier}]"
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else:
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identifier = ""
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self.logger.info("\n")
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self.logger.info(
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"---------------------- Paddle info ----------------------")
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self.logger.info(f"{identifier} paddle_version: {self.paddle_version}")
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self.logger.info(f"{identifier} paddle_commit: {self.paddle_commit}")
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self.logger.info(f"{identifier} paddle_branch: {self.paddle_branch}")
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self.logger.info(f"{identifier} log_api_version: {self.log_version}")
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self.logger.info(
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"----------------------- Conf info -----------------------")
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self.logger.info(
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f"{identifier} runtime_device: {self.config_status['runtime_device']}"
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)
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self.logger.info(
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f"{identifier} ir_optim: {self.config_status['ir_optim']}")
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self.logger.info(f"{identifier} enable_memory_optim: {True}")
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self.logger.info(
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f"{identifier} enable_tensorrt: {self.config_status['enable_tensorrt']}"
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)
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self.logger.info(
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f"{identifier} enable_mkldnn: {self.config_status['enable_mkldnn']}")
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self.logger.info(
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f"{identifier} cpu_math_library_num_threads: {self.config_status['cpu_math_library_num_threads']}"
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)
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self.logger.info(
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"----------------------- Model info ----------------------")
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self.logger.info(f"{identifier} model_name: {self.model_name}")
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self.logger.info(f"{identifier} precision: {self.precision}")
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self.logger.info(
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"----------------------- Data info -----------------------")
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self.logger.info(f"{identifier} batch_size: {self.batch_size}")
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self.logger.info(f"{identifier} input_shape: {self.shape}")
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self.logger.info(f"{identifier} data_num: {self.data_num}")
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self.logger.info(
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"----------------------- Perf info -----------------------")
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self.logger.info(
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f"{identifier} cpu_rss(MB): {self.cpu_rss_mb}, gpu_rss(MB): {self.gpu_rss_mb}, gpu_util: {self.gpu_util}%"
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)
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self.logger.info(
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f"{identifier} total time spent(s): {self.total_time_s}")
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self.logger.info(
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f"{identifier} preprocess_time(ms): {round(self.preprocess_time_s*1000, 1)}, inference_time(ms): {round(self.inference_time_s*1000, 1)}, postprocess_time(ms): {round(self.postprocess_time_s*1000, 1)}"
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)
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def print_help(self):
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"""
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print function help
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"""
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print("""Usage:
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==== Print inference benchmark logs. ====
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config = paddle.inference.Config()
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model_info = {'model_name': 'resnet50'
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'precision': 'fp32'}
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data_info = {'batch_size': 1
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'shape': '3,224,224'
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'data_num': 1000}
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perf_info = {'preprocess_time_s': 1.0
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'inference_time_s': 2.0
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'postprocess_time_s': 1.0
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'total_time_s': 4.0}
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resource_info = {'cpu_rss_mb': 100
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'gpu_rss_mb': 100
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'gpu_util': 60}
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log = PaddleInferBenchmark(config, model_info, data_info, perf_info, resource_info)
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log('Test')
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""")
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def __call__(self, identifier=None):
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"""
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__call__
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args:
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identifier(string): identify log
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"""
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self.report(identifier)
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@ -219,15 +219,12 @@ class TextRecognizer(object):
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i])
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input_tensor.copy_from_cpu(inputs[i])
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self.predictor.run()
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self.rec_times.inference_time.end()
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outputs = []
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for output_tensor in self.output_tensors:
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output = output_tensor.copy_to_cpu()
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outputs.append(output)
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preds = {"predict": outputs[2]}
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else:
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self.rec_times.preprocess_time.end()
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self.rec_times.inference_time.start()
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self.input_tensor.copy_from_cpu(norm_img_batch)
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self.predictor.run()
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@ -239,8 +236,6 @@ class TextRecognizer(object):
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rec_result = self.postprocess_op(preds)
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for rno in range(len(rec_result)):
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rec_res[indices[beg_img_no + rno]] = rec_result[rno]
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self.rec_times.postprocess_time.end()
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self.rec_times.img_num += int(norm_img_batch.shape[0])
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return rec_res, time.time() - st
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@ -33,8 +33,7 @@ import tools.infer.predict_det as predict_det
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import tools.infer.predict_cls as predict_cls
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from ppocr.utils.utility import get_image_file_list, check_and_read_gif
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from ppocr.utils.logging import get_logger
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from tools.infer.utility import draw_ocr_box_txt, get_current_memory_mb
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import tools.infer.benchmark_utils as benchmark_utils
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from tools.infer.utility import draw_ocr_box_txt
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logger = get_logger()
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