add_profiler_nextvlad (#4058)

PaddleCV/PaddleVideo/train.py

@@ -104,6 +104,17 @@ def parse_args():
         type=ast.literal_eval,
         default=False,
         help='If set True, enable continuous evaluation job.')
+    # NOTE: args for profiler, used for benchmark
+    parser.add_argument(
+        '--profiler_path',
+        type=str,
+        default='./',
+        help='the path to store profiler output file. used for benchmark.')
+    parser.add_argument(
+        '--is_profiler',
+        type=int,
+        default=0,
+        help='the switch profiler. used for benchmark.')
     args = parser.parse_args()
     return args
 
@@ -236,7 +247,9 @@ def train(args):
         compiled_test_prog=compiled_valid_prog,  #test_exe=valid_exe,
         test_dataloader=valid_dataloader,
         test_fetch_list=valid_fetch_list,
-        test_metrics=valid_metrics)
+        test_metrics=valid_metrics,
+        is_profiler=args.is_profiler,
+        profiler_path=args.profiler_path)
 
 
 if __name__ == "__main__":

PaddleCV/PaddleVideo/utils/train_utils.py

@@ -18,6 +18,7 @@ import time
 import numpy as np
 import paddle
 import paddle.fluid as fluid
+from paddle.fluid import profiler
 import logging
 import shutil
 
@@ -76,7 +77,8 @@ def train_with_dataloader(exe, train_prog, compiled_train_prog, train_dataloader
                         log_interval = 0, valid_interval = 0, save_dir = './', \
                         save_model_name = 'model', fix_random_seed = False, \
                         compiled_test_prog = None, test_dataloader = None, \
-                        test_fetch_list = None, test_metrics = None):
+                        test_fetch_list = None, test_metrics = None, \
+                        is_profiler = None, profiler_path = None):
     if not train_dataloader:
         logger.error("[TRAIN] get dataloader failed.")
     epoch_periods = []
@@ -98,6 +100,13 @@ def train_with_dataloader(exe, train_prog, compiled_train_prog, train_dataloader
                 train_metrics.calculate_and_log_out(train_outs, \
                         info = '[TRAIN] Epoch {}, iter {} '.format(epoch, train_iter))
             train_iter += 1
+ 
+            # NOTE: profiler tools, used for benchmark
+            if is_profiler and epoch == 0 and train_iter == log_interval:
+                profiler.start_profiler("All")
+            elif is_profiler and epoch == 0 and train_iter == log_interval + 5:
+                profiler.stop_profiler("total", profiler_path)
+                return
 
         if len(epoch_periods) < 1:
             logger.info(