add search_times

ppcls/configs/StrategySearch/person.yaml

@@ -3,6 +3,7 @@ distill_config_file: ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_dis
 
 gpus: 0,1,2,3
 output_dir: output/search_person
+search_times: 3
 search_dict:
   - search_key: lrs
     replace_config:
@@ -27,6 +28,7 @@ search_dict:
     search_values:
       - [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
       - [0.0, 0.4, 0.4, 0.8, 0.8, 1.0]
+      - [1.0. 1.0. 1.0. 1.0. 1.0. 1.0]
 teacher:
   rm_keys:
     - Arch.lr_mult_list

tools/search_strategy.py

@@ -7,6 +7,8 @@ __dir__ = os.path.dirname(os.path.abspath(__file__))
 sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
 
 import subprocess
+import numpy as np
+
 from ppcls.utils import config
 
 
@@ -18,7 +20,8 @@ def get_result(log_dir):
     return res
 
 
-def search_train(search_list, base_program, base_output_dir, search_key, config_replace_value, model_name):
+def search_train(search_list, base_program, base_output_dir, search_key,
+                 config_replace_value, model_name, search_times=1):
     best_res = 0.
     best = search_list[0]
     all_result = {}
@@ -28,15 +31,19 @@ def search_train(search_list, base_program, base_output_dir, search_key, config_
             program += ["-o", "{}={}".format(v, search_i)]
             if v == "Arch.name":
                 model_name = search_i
-        output_dir = "{}/{}_{}".format(base_output_dir, search_key, search_i).replace(".", "_")
-        program += ["-o", "Global.output_dir={}".format(output_dir)]
-        process = subprocess.Popen(program)
-        process.communicate()
-        res = get_result("{}/{}".format(output_dir, model_name))
-        all_result[str(search_i)] = res
-        if res > best_res:
+        res_list = []
+        for j in range(search_times):
+            output_dir = "{}/{}_{}_{}".format(base_output_dir, search_key, search_i, j).replace(".", "_")
+            program += ["-o", "Global.output_dir={}".format(output_dir)]
+            process = subprocess.Popen(program)
+            process.communicate()
+            res = get_result("{}/{}".format(output_dir, model_name))
+            res_list.append(res)
+        all_result[str(search_i)] = res_list
+
+        if np.mean(res_list) > best_res:
             best = search_i
-            best_res = res
+            best_res = np.mean(res_list)
     all_result["best"] = best
     return all_result
 
@@ -52,13 +59,15 @@ def search_strategy():
     base_program = ["python3.7", "-m", "paddle.distributed.launch", "--gpus={}".format(gpus),
                     "tools/train.py", "-c", base_config_file]
     base_output_dir = configs["output_dir"]
+    search_times = configs["search_times"]
     search_dict = configs.get("search_dict")
     all_results = {}
     for search_i in search_dict:
         search_key = search_i["search_key"]
         search_values = search_i["search_values"]
         replace_config = search_i["replace_config"]
-        res = search_train(search_values, base_program, base_output_dir, search_key, replace_config, model_name)
+        res = search_train(search_values, base_program, base_output_dir,
+                           search_key, replace_config, model_name, search_times)
         all_results[search_key] = res
         best = res.get("best")
         for v in replace_config: