opt benchmark

benchmark/run_det.sh

 # 提供可稳定复现性能的脚本，默认在标准docker环境内py37执行： paddlepaddle/paddle:latest-gpu-cuda10.1-cudnn7  paddle=2.1.2  py=37
 # 执行目录：需说明
-cd PaddleOCR
+#cd PaddleOCR
 # 1 安装该模型需要的依赖 (如需开启优化策略请注明)
 python3.7 -m pip install -r requirements.txt
 # 2 拷贝该模型需要数据、预训练模型
-wget -p ./tain_data/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data  && tar xf icdar2015.tar && cd ../
-wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
+#wget -p ./tain_data/  https://paddleocr.bj.bcebos.com/dygraph_v2.0/test/icdar2015.tar && cd train_data  && tar xf icdar2015.tar && cd ../
+#wget -p ./pretrain_models/ https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/ResNet50_vd_pretrained.pdparams
 # 3 批量运行（如不方便批量，1，2需放到单个模型中）
 
 model_mode_list=(det_mv3_db det_r50_vd_east)
 fp_item_list=(fp32)
-bs_list=(256 128)
+bs_list=(4 8)
 for model_mode in ${model_mode_list[@]}; do
       for fp_item in ${fp_item_list[@]}; do
           for bs_item in ${bs_list[@]}; do
             echo "index is speed, 1gpus, begin, ${model_name}"
             run_mode=sp
-            CUDA_VISIBLE_DEVICES=0 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}     #  (5min)
+            CUDA_VISIBLE_DEVICES=3 bash benchmark/run_benchmark_det.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode}     #  (5min)
             sleep 60
             echo "index is speed, 8gpus, run_mode is multi_process, begin, ${model_name}"
-            run_mode=mp
-            CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} 
-            sleep 60
+            #run_mode=mp
+            #CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 bash benchmark/run_benchmark.sh ${run_mode} ${bs_item} ${fp_item} 10 ${model_mode} 
+            #sleep 60
             done
       done
 done

ppocr/utils/profiler.py

+# copyright (c) 2021 PaddlePaddle Authors. All Rights Reserve.
+#
+# 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
+import paddle
+
+# A global variable to record the number of calling times for profiler
+# functions. It is used to specify the tracing range of training steps.
+_profiler_step_id = 0
+
+# A global variable to avoid parsing from string every time.
+_profiler_options = None
+
+
+class ProfilerOptions(object):
+    '''
+    Use a string to initialize a ProfilerOptions.
+    The string should be in the format: "key1=value1;key2=value;key3=value3".
+    For example:
+      "profile_path=model.profile"
+      "batch_range=[50, 60]; profile_path=model.profile"
+      "batch_range=[50, 60]; tracer_option=OpDetail; profile_path=model.profile"
+    ProfilerOptions supports following key-value pair:
+      batch_range      - a integer list, e.g. [100, 110].
+      state            - a string, the optional values are 'CPU', 'GPU' or 'All'. 
+      sorted_key       - a string, the optional values are 'calls', 'total',
+                         'max', 'min' or 'ave.
+      tracer_option    - a string, the optional values are 'Default', 'OpDetail',
+                         'AllOpDetail'.
+      profile_path     - a string, the path to save the serialized profile data,
+                         which can be used to generate a timeline.
+      exit_on_finished - a boolean.
+    '''
+
+    def __init__(self, options_str):
+        assert isinstance(options_str, str)
+
+        self._options = {
+            'batch_range': [10, 20],
+            'state': 'All',
+            'sorted_key': 'total',
+            'tracer_option': 'Default',
+            'profile_path': '/tmp/profile',
+            'exit_on_finished': True
+        }
+        self._parse_from_string(options_str)
+
+    def _parse_from_string(self, options_str):
+        for kv in options_str.replace(' ', '').split(';'):
+            key, value = kv.split('=')
+            if key == 'batch_range':
+                value_list = value.replace('[', '').replace(']', '').split(',')
+                value_list = list(map(int, value_list))
+                if len(value_list) >= 2 and value_list[0] >= 0 and value_list[
+                        1] > value_list[0]:
+                    self._options[key] = value_list
+            elif key == 'exit_on_finished':
+                self._options[key] = value.lower() in ("yes", "true", "t", "1")
+            elif key in [
+                    'state', 'sorted_key', 'tracer_option', 'profile_path'
+            ]:
+                self._options[key] = value
+
+    def __getitem__(self, name):
+        if self._options.get(name, None) is None:
+            raise ValueError(
+                "ProfilerOptions does not have an option named %s." % name)
+        return self._options[name]
+
+
+def add_profiler_step(options_str=None):
+    '''
+    Enable the operator-level timing using PaddlePaddle's profiler.
+    The profiler uses a independent variable to count the profiler steps.
+    One call of this function is treated as a profiler step.
+    
+    Args:
+      profiler_options - a string to initialize the ProfilerOptions.
+                         Default is None, and the profiler is disabled.
+    '''
+    if options_str is None:
+        return
+
+    global _profiler_step_id
+    global _profiler_options
+
+    if _profiler_options is None:
+        _profiler_options = ProfilerOptions(options_str)
+
+    if _profiler_step_id == _profiler_options['batch_range'][0]:
+        paddle.utils.profiler.start_profiler(
+            _profiler_options['state'], _profiler_options['tracer_option'])
+    elif _profiler_step_id == _profiler_options['batch_range'][1]:
+        paddle.utils.profiler.stop_profiler(_profiler_options['sorted_key'],
+                                            _profiler_options['profile_path'])
+        if _profiler_options['exit_on_finished']:
+            sys.exit(0)
+
+    _profiler_step_id += 1

tools/program.py

@@ -31,6 +31,7 @@ from ppocr.utils.stats import TrainingStats
 from ppocr.utils.save_load import save_model
 from ppocr.utils.utility import print_dict
 from ppocr.utils.logging import get_logger
+from ppocr.utils import profiler
 from ppocr.data import build_dataloader
 import numpy as np