dbg

ppcls/configs/StrategySearch/person.yaml

@@ -23,13 +23,16 @@ search_dict:
     search_values: [0.0, 0.1, 0.5]
   lr_mult_list:
     replace_config:
-      - Arch.models.1.Student.lr_mult_list
+      - Arch.lr_mult_list
     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]
 teacher:
   rm_keys:
-    - Arch.models.1.Student.lr_mult_list
+    - Arch.lr_mult_list
   search_values:
     - ResNet101_vd
     - ResNet50_vd
+final_replace:
+  Arch.lr_mult_list: Arch.models.1.Student.lr_mult_list
+

ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml

@@ -33,7 +33,6 @@ Arch:
     - Teacher:
         name: ResNet101_vd
         class_num: *class_num
-        pretrained: "./output/TEACHER_ResNet101_vd/ResNet101_vd/best_model"
     - Student:
         name: PPLCNet_x1_0
         class_num: *class_num

tools/search_strategy.py

+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import os
+import sys
+__dir__ = os.path.dirname(os.path.abspath(__file__))
+sys.path.append(os.path.abspath(os.path.join(__dir__, '../')))
+
 import subprocess
 from ppcls.utils import config
 
@@ -6,11 +14,11 @@ def get_result(log_dir):
     log_file = "{}/train.log".format(log_dir)
     with open(log_file, "r") as f:
         raw = f.read()
-    res = float(raw.split("best metric ")[-1].split("]")[0])
+    res = float(raw.split("best metric: ")[-1].split("]")[0])
     return res
 
 
-def search_train(search_list, base_program, base_output_dir, search_key, config_replace_value):
+def search_train(search_list, base_program, base_output_dir, search_key, config_replace_value, model_name):
     best_res = 0.
     best = search_list[0]
     all_result = {}
@@ -18,11 +26,14 @@ def search_train(search_list, base_program, base_output_dir, search_key, config_
         program = base_program.copy()
         for v in config_replace_value:
             program += ["-o", "{}={}".format(v, search_i)]
-        output_dir = "{}/{}_{}".format(base_output_dir, search_key, search_i.replace(".", "_"))
+            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)]
-        subprocess.Popen(program)
-        res = get_result(output_dir)
-        all_result[search_i] = res
+        process = subprocess.Popen(program)
+        process.communicate()
+        res = get_result("{}/{}".format(output_dir, model_name))
+        all_result[str(search_i)] = res
         if res > best_res:
             best = search_i
             best_res = res
@@ -34,16 +45,19 @@ def search_strategy():
     args = config.parse_args()
     configs = config.get_config(args.config, overrides=args.override, show=False)
     base_config_file = configs["base_config_file"]
+    distill_config_file = configs["distill_config_file"]
+    model_name = config.get_config(base_config_file)["Arch"]["name"]
     gpus = configs["gpus"]
+    gpus = ",".join([str(i) for i in gpus])
     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_dict = configs.get("search_dict")
     all_results = {}
     for search_key in search_dict:
-        search_values = configs[search_key]["search_values"]
+        search_values = search_dict[search_key]["search_values"]
         replace_config = search_dict[search_key]["replace_config"]
-        res = search_train(search_values, base_program, base_output_dir, search_key, replace_config)
+        res = search_train(search_values, base_program, base_output_dir, search_key, replace_config, model_name)
         all_results[search_key] = res
         best = res.get("best")
         for v in replace_config:
@@ -56,22 +70,33 @@ def search_strategy():
         teacher_rm_keys = teacher_configs["rm_keys"]
         rm_indices = []
         for rm_k in teacher_rm_keys:
-            rm_indices.append(base_program.index(rm_k))
-        rm_indices = sorted(rm_indices)
-        for rm_index in rm_indices[:, :, -1]:
-            teacher_program.pop(rm_index + 1)
+            for ind, ki in enumerate(base_program):
+              if rm_k in ki:
+                rm_indices.append(ind)
+        print(rm_indices)
+        for rm_index in rm_indices[::-1]:
             teacher_program.pop(rm_index)
-        replace_config = "-o Arch.name"
+            teacher_program.pop(rm_index-1)
+        replace_config = ["Arch.name"]
         teacher_list = teacher_configs["search_values"]
-        res = search_train(teacher_list, teacher_program, base_output_dir, "teacher", replace_config)
+        res = search_train(teacher_list, teacher_program, base_output_dir, "teacher", replace_config, model_name)
         all_results["teacher"] = res
         best = res.get("best")
-        t_pretrained = "{}/{}_{}".format(base_output_dir, "teacher", best.replace(".", "_"))
+        t_pretrained = "{}/{}_{}/{}/best_model".format(base_output_dir, "teacher", best, best)
         base_program += ["-o", "Arch.models.0.Teacher.name={}".format(best),
                          "-o", "Arch.models.0.Teacher.pretrained={}".format(t_pretrained)]
     output_dir = "{}/search_res".format(base_output_dir)
     base_program += ["-o", "Global.output_dir={}".format(output_dir)]
+    final_replace = configs.get('final_replace')
+    for i in range(len(base_program)):
+      base_program[i] = base_program[i].replace(base_config_file, distill_config_file)
+      for k in final_replace:
+        v = final_replace[k]
+        base_program[i] = base_program[i].replace(k, v)
+
     subprocess.Popen(base_program)
+    subprocess.communicate()
+    print(all_results, base_program)
 
 
 if __name__ == '__main__':