add python search tools

ppcls/configs/StrategySearch/person.yaml

+base_config_file: ppcls/configs/cls_demo/person/PPLCNet/PPLCNet_x1_0.yaml
+distill_config_file: ppcls/configs/cls_demo/person/Distillation/PPLCNet_x1_0_distillation.yaml
+
+gpus: 0,1,2,3,4,5,6,7
+output_dir: output/search_person
+search_dict:
+  lrs:
+    replace_config:
+      - Optimizer.lr.learning_rate
+    search_values: [0.0075, 0.01, 0.0125]
+  resolutions:
+    replace_config:
+      - DataLoader.Train.dataset.transform_ops.1.RandCropImage.size
+      - DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.img_size
+    search_values: [176, 192, 224]
+  ra_probs:
+    replace_config:
+      - DataLoader.Train.dataset.transform_ops.3.TimmAutoAugment.prob
+    search_values: [0.0, 0.1, 0.5]
+  re_probs:
+    replace_config:
+      - DataLoader.Train.dataset.transform_ops.5.RandomErasing.EPSILON
+    search_values: [0.0, 0.1, 0.5]
+  lr_mult_list:
+    replace_config:
+      - Arch.models.1.Student.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
+  search_values:
+    - ResNet101_vd
+    - ResNet50_vd

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

@@ -72,7 +72,7 @@ DataLoader:
     dataset:
       name: ImageNetDataset
       image_root: ./dataset/person/
-      cls_label_path: ./dataset/person/train_list.txt
+      cls_label_path: ./dataset/person/train_list_for_distill.txt
       transform_ops:
         - DecodeImage:
             to_rgb: True

tools/search_strategy.py

+import subprocess
+from ppcls.utils import config
+
+
+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])
+    return res
+
+
+def search_train(search_list, base_program, base_output_dir, search_key, config_replace_value):
+    best_res = 0.
+    best = search_list[0]
+    all_result = {}
+    for search_i in search_list:
+        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(".", "_"))
+        program += ["-o", "Global.output_dir={}".format(output_dir)]
+        subprocess.Popen(program)
+        res = get_result(output_dir)
+        all_result[search_i] = res
+        if res > best_res:
+            best = search_i
+            best_res = res
+    all_result["best"] = best
+    return all_result
+
+
+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"]
+    gpus = configs["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"]
+        replace_config = search_dict[search_key]["replace_config"]
+        res = search_train(search_values, base_program, base_output_dir, search_key, replace_config)
+        all_results[search_key] = res
+        best = res.get("best")
+        for v in replace_config:
+            base_program += ["-o", "{}={}".format(v, best)]
+
+    teacher_configs = configs.get("teacher", None)
+    if teacher_configs is not None:
+        teacher_program = base_program.copy()
+        # remove incompatible keys
+        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)
+            teacher_program.pop(rm_index)
+        replace_config = "-o Arch.name"
+        teacher_list = teacher_configs["search_values"]
+        res = search_train(teacher_list, teacher_program, base_output_dir, "teacher", replace_config)
+        all_results["teacher"] = res
+        best = res.get("best")
+        t_pretrained = "{}/{}_{}".format(base_output_dir, "teacher", best.replace(".", "_"))
+        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)]
+    subprocess.Popen(base_program)
+
+
+if __name__ == '__main__':
+    search_strategy()