Move str2bool to config_utils

.github/workflows/basic_test.yml

@@ -36,6 +36,7 @@ jobs:
           python -m black ./lib/spaces -l 88 --check --diff --verbose
           python -m black ./lib/trade_models -l 88 --check --diff --verbose
           python -m black ./lib/procedures -l 88 --check --diff --verbose
+          python -m black ./lib/config_utils -l 88 --check --diff --verbose
 
       - name: Test Search Space
         run: |

exps/trading/baselines.py

@@ -30,6 +30,7 @@ lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 
+from config_utils import arg_str2bool
 from procedures.q_exps import update_gpu
 from procedures.q_exps import update_market
 from procedures.q_exps import run_exp
@@ -182,6 +183,12 @@ if __name__ == "__main__":
         help="The market indicator.",
     )
     parser.add_argument("--times", type=int, default=5, help="The repeated run times.")
+    parser.add_argument(
+        "--shared_dataset",
+        type=arg_str2bool,
+        default=False,
+        help="Whether to share the dataset for all algorithms?",
+    )
     parser.add_argument(
         "--gpu", type=int, default=0, help="The GPU ID used for train / test."
     )
@@ -189,9 +196,13 @@ if __name__ == "__main__":
         "--alg",
         type=str,
         choices=list(alg2configs.keys()),
+        nargs="+",
         required=True,
-        help="The algorithm name.",
+        help="The algorithm name(s).",
     )
     args = parser.parse_args()
 
-    main(args, alg2configs[args.alg])
+    if len(args.alg) == 1:
+        main(args, alg2configs[args.alg[0]])
+    else:
+        print("-")

exps/trading/organize_results.py

@@ -15,6 +15,7 @@ lib_dir = (Path(__file__).parent / ".." / ".." / "lib").resolve()
 if str(lib_dir) not in sys.path:
     sys.path.insert(0, str(lib_dir))
 
+from config_utils import arg_str2bool
 import qlib
 from qlib.config import REG_CN
 from qlib.workflow import R
@@ -184,16 +185,6 @@ if __name__ == "__main__":
 
     parser = argparse.ArgumentParser("Show Results")
 
-    def str2bool(v):
-        if isinstance(v, bool):
-            return v
-        elif v.lower() in ("yes", "true", "t", "y", "1"):
-            return True
-        elif v.lower() in ("no", "false", "f", "n", "0"):
-            return False
-        else:
-            raise argparse.ArgumentTypeError("Boolean value expected.")
-
     parser.add_argument(
         "--save_dir",
         type=str,
@@ -203,7 +194,7 @@ if __name__ == "__main__":
     )
     parser.add_argument(
         "--verbose",
-        type=str2bool,
+        type=arg_str2bool,
         default=False,
         help="Print detailed log information or not.",
     )
@@ -228,7 +219,7 @@ if __name__ == "__main__":
         info_dict["heads"],
         info_dict["values"],
         info_dict["names"],
-        space=14,
+        space=18,
         verbose=True,
         sort_key=True,
     )

lib/config_utils/__init__.py

@@ -1,13 +1,19 @@
 ##################################################
 # Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
 ##################################################
-from .configure_utils    import load_config, dict2config, configure2str
+# general config related functions
-from .basic_args         import obtain_basic_args
+from .config_utils import load_config, dict2config, configure2str
-from .attention_args     import obtain_attention_args
+# the args setting for different experiments
-from .random_baseline    import obtain_RandomSearch_args
+from .basic_args import obtain_basic_args
-from .cls_kd_args        import obtain_cls_kd_args
+from .attention_args import obtain_attention_args
-from .cls_init_args      import obtain_cls_init_args
+from .random_baseline import obtain_RandomSearch_args
+from .cls_kd_args import obtain_cls_kd_args
+from .cls_init_args import obtain_cls_init_args
 from .search_single_args import obtain_search_single_args
-from .search_args        import obtain_search_args
+from .search_args import obtain_search_args
+
 # for network pruning
-from .pruning_args       import obtain_pruning_args
+from .pruning_args import obtain_pruning_args
+
+# utils for args
+from .args_utils import arg_str2bool

lib/config_utils/args_utils.py

@@ -0,0 +1,12 @@
+import argparse
+
+
+def arg_str2bool(v):
+    if isinstance(v, bool):
+        return v
+    elif v.lower() in ("yes", "true", "t", "y", "1"):
+        return True
+    elif v.lower() in ("no", "false", "f", "n", "0"):
+        return False
+    else:
+        raise argparse.ArgumentTypeError("Boolean value expected.")

lib/config_utils/attention_args.py

@@ -1,22 +1,32 @@
 import random, argparse
 from .share_args import add_shared_args
 
-def obtain_attention_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--resume'      ,     type=str,                   help='Resume path.')
-  parser.add_argument('--init_model'  ,     type=str,                   help='The initialization model path.')
-  parser.add_argument('--model_config',     type=str,                   help='The path to the model configuration')
-  parser.add_argument('--optim_config',     type=str,                   help='The path to the optimizer configuration')
-  parser.add_argument('--procedure'   ,     type=str,                   help='The procedure basic prefix.')
-  parser.add_argument('--att_channel' ,     type=int,                   help='.')
-  parser.add_argument('--att_spatial' ,     type=str,                   help='.')
-  parser.add_argument('--att_active'  ,     type=str,                   help='.')
-  add_shared_args( parser )
-  # Optimization options
-  parser.add_argument('--batch_size',       type=int,   default=2,      help='Batch size for training.')
-  args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+def obtain_attention_args():
-    args.rand_seed = random.randint(1, 100000)
+    parser = argparse.ArgumentParser(
-  assert args.save_dir is not None, 'save-path argument can not be None'
+        description="Train a classification model on typical image classification datasets.",
-  return args
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--resume", type=str, help="Resume path.")
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
+    parser.add_argument(
+        "--model_config", type=str, help="The path to the model configuration"
+    )
+    parser.add_argument(
+        "--optim_config", type=str, help="The path to the optimizer configuration"
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument("--att_channel", type=int, help=".")
+    parser.add_argument("--att_spatial", type=str, help=".")
+    parser.add_argument("--att_active", type=str, help=".")
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
+
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "save-path argument can not be None"
+    return args

lib/config_utils/basic_args.py

@@ -4,21 +4,41 @@
 import random, argparse
 from .share_args import add_shared_args
 
-def obtain_basic_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--resume'      ,     type=str,                   help='Resume path.')
-  parser.add_argument('--init_model'  ,     type=str,                   help='The initialization model path.')
-  parser.add_argument('--model_config',     type=str,                   help='The path to the model configuration')
-  parser.add_argument('--optim_config',     type=str,                   help='The path to the optimizer configuration')
-  parser.add_argument('--procedure'   ,     type=str,                   help='The procedure basic prefix.')
-  parser.add_argument('--model_source',     type=str,  default='normal',help='The source of model defination.')
-  parser.add_argument('--extra_model_path', type=str,  default=None,    help='The extra model ckp file (help to indicate the searched architecture).')
-  add_shared_args( parser )
-  # Optimization options
-  parser.add_argument('--batch_size',       type=int,  default=2,       help='Batch size for training.')
-  args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+def obtain_basic_args():
-    args.rand_seed = random.randint(1, 100000)
+    parser = argparse.ArgumentParser(
-  assert args.save_dir is not None, 'save-path argument can not be None'
+        description="Train a classification model on typical image classification datasets.",
-  return args
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--resume", type=str, help="Resume path.")
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
+    parser.add_argument(
+        "--model_config", type=str, help="The path to the model configuration"
+    )
+    parser.add_argument(
+        "--optim_config", type=str, help="The path to the optimizer configuration"
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument(
+        "--model_source",
+        type=str,
+        default="normal",
+        help="The source of model defination.",
+    )
+    parser.add_argument(
+        "--extra_model_path",
+        type=str,
+        default=None,
+        help="The extra model ckp file (help to indicate the searched architecture).",
+    )
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
+
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "save-path argument can not be None"
+    return args

lib/config_utils/cls_init_args.py

@@ -1,20 +1,32 @@
 import random, argparse
 from .share_args import add_shared_args
 
-def obtain_cls_init_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--resume'      ,     type=str,                   help='Resume path.')
-  parser.add_argument('--init_model'  ,     type=str,                   help='The initialization model path.')
-  parser.add_argument('--model_config',     type=str,                   help='The path to the model configuration')
-  parser.add_argument('--optim_config',     type=str,                   help='The path to the optimizer configuration')
-  parser.add_argument('--procedure'   ,     type=str,                   help='The procedure basic prefix.')
-  parser.add_argument('--init_checkpoint',  type=str,                   help='The checkpoint path to the initial model.')
-  add_shared_args( parser )
-  # Optimization options
-  parser.add_argument('--batch_size',       type=int,   default=2,      help='Batch size for training.')
-  args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+def obtain_cls_init_args():
-    args.rand_seed = random.randint(1, 100000)
+    parser = argparse.ArgumentParser(
-  assert args.save_dir is not None, 'save-path argument can not be None'
+        description="Train a classification model on typical image classification datasets.",
-  return args
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--resume", type=str, help="Resume path.")
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
+    parser.add_argument(
+        "--model_config", type=str, help="The path to the model configuration"
+    )
+    parser.add_argument(
+        "--optim_config", type=str, help="The path to the optimizer configuration"
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument(
+        "--init_checkpoint", type=str, help="The checkpoint path to the initial model."
+    )
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
+
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "save-path argument can not be None"
+    return args

lib/config_utils/cls_kd_args.py

@@ -1,23 +1,43 @@
 import random, argparse
 from .share_args import add_shared_args
 
-def obtain_cls_kd_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--resume'      ,     type=str,                   help='Resume path.')
-  parser.add_argument('--init_model'  ,     type=str,                   help='The initialization model path.')
-  parser.add_argument('--model_config',     type=str,                   help='The path to the model configuration')
-  parser.add_argument('--optim_config',     type=str,                   help='The path to the optimizer configuration')
-  parser.add_argument('--procedure'   ,     type=str,                   help='The procedure basic prefix.')
-  parser.add_argument('--KD_checkpoint',    type=str,                   help='The teacher checkpoint in knowledge distillation.')
-  parser.add_argument('--KD_alpha'    ,     type=float,                 help='The alpha parameter in knowledge distillation.')
-  parser.add_argument('--KD_temperature',   type=float,                 help='The temperature parameter in knowledge distillation.')
-  #parser.add_argument('--KD_feature',       type=float,                 help='Knowledge distillation at the feature level.')
-  add_shared_args( parser )
-  # Optimization options
-  parser.add_argument('--batch_size',       type=int,   default=2,      help='Batch size for training.')
-  args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+def obtain_cls_kd_args():
-    args.rand_seed = random.randint(1, 100000)
+    parser = argparse.ArgumentParser(
-  assert args.save_dir is not None, 'save-path argument can not be None'
+        description="Train a classification model on typical image classification datasets.",
-  return args
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
+    )
+    parser.add_argument("--resume", type=str, help="Resume path.")
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
+    parser.add_argument(
+        "--model_config", type=str, help="The path to the model configuration"
+    )
+    parser.add_argument(
+        "--optim_config", type=str, help="The path to the optimizer configuration"
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument(
+        "--KD_checkpoint",
+        type=str,
+        help="The teacher checkpoint in knowledge distillation.",
+    )
+    parser.add_argument(
+        "--KD_alpha", type=float, help="The alpha parameter in knowledge distillation."
+    )
+    parser.add_argument(
+        "--KD_temperature",
+        type=float,
+        help="The temperature parameter in knowledge distillation.",
+    )
+    # parser.add_argument('--KD_feature',       type=float,                 help='Knowledge distillation at the feature level.')
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
+
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "save-path argument can not be None"
+    return args

lib/config_utils/config_utils.py

@@ -0,0 +1,135 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+#
+import os, json
+from os import path as osp
+from pathlib import Path
+from collections import namedtuple
+
+support_types = ("str", "int", "bool", "float", "none")
+
+
+def convert_param(original_lists):
+    assert isinstance(original_lists, list), "The type is not right : {:}".format(
+        original_lists
+    )
+    ctype, value = original_lists[0], original_lists[1]
+    assert ctype in support_types, "Ctype={:}, support={:}".format(ctype, support_types)
+    is_list = isinstance(value, list)
+    if not is_list:
+        value = [value]
+    outs = []
+    for x in value:
+        if ctype == "int":
+            x = int(x)
+        elif ctype == "str":
+            x = str(x)
+        elif ctype == "bool":
+            x = bool(int(x))
+        elif ctype == "float":
+            x = float(x)
+        elif ctype == "none":
+            if x.lower() != "none":
+                raise ValueError(
+                    "For the none type, the value must be none instead of {:}".format(x)
+                )
+            x = None
+        else:
+            raise TypeError("Does not know this type : {:}".format(ctype))
+        outs.append(x)
+    if not is_list:
+        outs = outs[0]
+    return outs
+
+
+def load_config(path, extra, logger):
+    path = str(path)
+    if hasattr(logger, "log"):
+        logger.log(path)
+    assert os.path.exists(path), "Can not find {:}".format(path)
+    # Reading data back
+    with open(path, "r") as f:
+        data = json.load(f)
+    content = {k: convert_param(v) for k, v in data.items()}
+    assert extra is None or isinstance(
+        extra, dict
+    ), "invalid type of extra : {:}".format(extra)
+    if isinstance(extra, dict):
+        content = {**content, **extra}
+    Arguments = namedtuple("Configure", " ".join(content.keys()))
+    content = Arguments(**content)
+    if hasattr(logger, "log"):
+        logger.log("{:}".format(content))
+    return content
+
+
+def configure2str(config, xpath=None):
+    if not isinstance(config, dict):
+        config = config._asdict()
+
+    def cstring(x):
+        return '"{:}"'.format(x)
+
+    def gtype(x):
+        if isinstance(x, list):
+            x = x[0]
+        if isinstance(x, str):
+            return "str"
+        elif isinstance(x, bool):
+            return "bool"
+        elif isinstance(x, int):
+            return "int"
+        elif isinstance(x, float):
+            return "float"
+        elif x is None:
+            return "none"
+        else:
+            raise ValueError("invalid : {:}".format(x))
+
+    def cvalue(x, xtype):
+        if isinstance(x, list):
+            is_list = True
+        else:
+            is_list, x = False, [x]
+        temps = []
+        for temp in x:
+            if xtype == "bool":
+                temp = cstring(int(temp))
+            elif xtype == "none":
+                temp = cstring("None")
+            else:
+                temp = cstring(temp)
+            temps.append(temp)
+        if is_list:
+            return "[{:}]".format(", ".join(temps))
+        else:
+            return temps[0]
+
+    xstrings = []
+    for key, value in config.items():
+        xtype = gtype(value)
+        string = "  {:20s} : [{:8s}, {:}]".format(
+            cstring(key), cstring(xtype), cvalue(value, xtype)
+        )
+        xstrings.append(string)
+    Fstring = "{\n" + ",\n".join(xstrings) + "\n}"
+    if xpath is not None:
+        parent = Path(xpath).resolve().parent
+        parent.mkdir(parents=True, exist_ok=True)
+        if osp.isfile(xpath):
+            os.remove(xpath)
+        with open(xpath, "w") as text_file:
+            text_file.write("{:}".format(Fstring))
+    return Fstring
+
+
+def dict2config(xdict, logger):
+    assert isinstance(xdict, dict), "invalid type : {:}".format(type(xdict))
+    Arguments = namedtuple("Configure", " ".join(xdict.keys()))
+    content = Arguments(**xdict)
+    if hasattr(logger, "log"):
+        logger.log("{:}".format(content))
+    return content

lib/config_utils/configure_utils.py

@@ -1,106 +0,0 @@
-# Copyright (c) Facebook, Inc. and its affiliates.
-# All rights reserved.
-#
-# This source code is licensed under the license found in the
-# LICENSE file in the root directory of this source tree.
-#
-import os, json
-from os import path as osp
-from pathlib import Path
-from collections import namedtuple
-
-support_types = ('str', 'int', 'bool', 'float', 'none')
-
-
-def convert_param(original_lists):
-  assert isinstance(original_lists, list), 'The type is not right : {:}'.format(original_lists)
-  ctype, value = original_lists[0], original_lists[1]
-  assert ctype in support_types, 'Ctype={:}, support={:}'.format(ctype, support_types)
-  is_list = isinstance(value, list)
-  if not is_list: value = [value]
-  outs = []
-  for x in value:
-    if ctype == 'int':
-      x = int(x)
-    elif ctype == 'str':
-      x = str(x)
-    elif ctype == 'bool':
-      x = bool(int(x))
-    elif ctype == 'float':
-      x = float(x)
-    elif ctype == 'none':
-      if x.lower() != 'none':
-        raise ValueError('For the none type, the value must be none instead of {:}'.format(x))
-      x = None
-    else:
-      raise TypeError('Does not know this type : {:}'.format(ctype))
-    outs.append(x)
-  if not is_list: outs = outs[0]
-  return outs
-
-
-def load_config(path, extra, logger):
-  path = str(path)
-  if hasattr(logger, 'log'): logger.log(path)
-  assert os.path.exists(path), 'Can not find {:}'.format(path)
-  # Reading data back
-  with open(path, 'r') as f:
-    data = json.load(f)
-  content = { k: convert_param(v) for k,v in data.items()}
-  assert extra is None or isinstance(extra, dict), 'invalid type of extra : {:}'.format(extra)
-  if isinstance(extra, dict): content = {**content, **extra}
-  Arguments = namedtuple('Configure', ' '.join(content.keys()))
-  content   = Arguments(**content)
-  if hasattr(logger, 'log'): logger.log('{:}'.format(content))
-  return content
-
-
-def configure2str(config, xpath=None):
-  if not isinstance(config, dict):
-    config = config._asdict()
-  def cstring(x):
-    return "\"{:}\"".format(x)
-  def gtype(x):
-    if isinstance(x, list): x = x[0]
-    if isinstance(x, str)  : return 'str'
-    elif isinstance(x, bool) : return 'bool'
-    elif isinstance(x, int): return 'int'
-    elif isinstance(x, float): return 'float'
-    elif x is None           : return 'none'
-    else: raise ValueError('invalid : {:}'.format(x))
-  def cvalue(x, xtype):
-    if isinstance(x, list): is_list = True
-    else:
-      is_list, x = False, [x]
-    temps = []
-    for temp in x:
-      if xtype == 'bool'  : temp = cstring(int(temp))
-      elif xtype == 'none': temp = cstring('None')
-      else                : temp = cstring(temp)
-      temps.append( temp )
-    if is_list:
-      return "[{:}]".format( ', '.join( temps ) )
-    else:
-      return temps[0]
-
-  xstrings = []
-  for key, value in config.items():
-    xtype  = gtype(value)
-    string = '  {:20s} : [{:8s}, {:}]'.format(cstring(key), cstring(xtype), cvalue(value, xtype))
-    xstrings.append(string)
-  Fstring = '{\n' + ',\n'.join(xstrings) + '\n}'
-  if xpath is not None:
-    parent = Path(xpath).resolve().parent
-    parent.mkdir(parents=True, exist_ok=True)
-    if osp.isfile(xpath): os.remove(xpath)
-    with open(xpath, "w") as text_file:
-      text_file.write('{:}'.format(Fstring))
-  return Fstring
-
-
-def dict2config(xdict, logger):
-  assert isinstance(xdict, dict), 'invalid type : {:}'.format( type(xdict) )
-  Arguments = namedtuple('Configure', ' '.join(xdict.keys()))
-  content   = Arguments(**xdict)
-  if hasattr(logger, 'log'): logger.log('{:}'.format(content))
-  return content

lib/config_utils/pruning_args.py

@@ -1,26 +1,48 @@
 import os, sys, time, random, argparse
 from .share_args import add_shared_args
 
-def obtain_pruning_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
-  parser.add_argument('--resume'      ,     type=str,                   help='Resume path.')
-  parser.add_argument('--init_model'  ,     type=str,                   help='The initialization model path.')
-  parser.add_argument('--model_config',     type=str,                   help='The path to the model configuration')
-  parser.add_argument('--optim_config',     type=str,                   help='The path to the optimizer configuration')
-  parser.add_argument('--procedure'   ,     type=str,                   help='The procedure basic prefix.')
-  parser.add_argument('--keep_ratio'  ,     type=float,                 help='The left channel ratio compared to the original network.')
-  parser.add_argument('--model_version',    type=str,                   help='The network version.')
-  parser.add_argument('--KD_alpha'    ,     type=float,                 help='The alpha parameter in knowledge distillation.')
-  parser.add_argument('--KD_temperature',   type=float,                 help='The temperature parameter in knowledge distillation.')
-  parser.add_argument('--Regular_W_feat',   type=float,                 help='The .')
-  parser.add_argument('--Regular_W_conv',   type=float,                 help='The .')
-  add_shared_args( parser )
-  # Optimization options
-  parser.add_argument('--batch_size',       type=int,  default=2,       help='Batch size for training.')
-  args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+def obtain_pruning_args():
-    args.rand_seed = random.randint(1, 100000)
+    parser = argparse.ArgumentParser(
-  assert args.save_dir is not None, 'save-path argument can not be None'
+        description="Train a classification model on typical image classification datasets.",
-  assert args.keep_ratio > 0 and args.keep_ratio <= 1, 'invalid keep ratio : {:}'.format(args.keep_ratio)
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-  return args
+    )
+    parser.add_argument("--resume", type=str, help="Resume path.")
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
+    parser.add_argument(
+        "--model_config", type=str, help="The path to the model configuration"
+    )
+    parser.add_argument(
+        "--optim_config", type=str, help="The path to the optimizer configuration"
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument(
+        "--keep_ratio",
+        type=float,
+        help="The left channel ratio compared to the original network.",
+    )
+    parser.add_argument("--model_version", type=str, help="The network version.")
+    parser.add_argument(
+        "--KD_alpha", type=float, help="The alpha parameter in knowledge distillation."
+    )
+    parser.add_argument(
+        "--KD_temperature",
+        type=float,
+        help="The temperature parameter in knowledge distillation.",
+    )
+    parser.add_argument("--Regular_W_feat", type=float, help="The .")
+    parser.add_argument("--Regular_W_conv", type=float, help="The .")
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
+
+    if args.rand_seed is None or args.rand_seed < 0:
+        args.rand_seed = random.randint(1, 100000)
+    assert args.save_dir is not None, "save-path argument can not be None"
+    assert (
+        args.keep_ratio > 0 and args.keep_ratio <= 1
+    ), "invalid keep ratio : {:}".format(args.keep_ratio)
+    return args

lib/config_utils/random_baseline.py

@@ -3,22 +3,42 @@ from .share_args import add_shared_args
 
 
 def obtain_RandomSearch_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser = argparse.ArgumentParser(
-  parser.add_argument('--resume'      ,     type=str,                   help='Resume path.')
+        description="Train a classification model on typical image classification datasets.",
-  parser.add_argument('--init_model'  ,     type=str,                   help='The initialization model path.')
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-  parser.add_argument('--expect_flop',      type=float,                 help='The expected flop keep ratio.')
+    )
-  parser.add_argument('--arch_nums'   ,     type=int,                   help='The maximum number of running random arch generating..')
+    parser.add_argument("--resume", type=str, help="Resume path.")
-  parser.add_argument('--model_config',     type=str,                   help='The path to the model configuration')
+    parser.add_argument("--init_model", type=str, help="The initialization model path.")
-  parser.add_argument('--optim_config',     type=str,                   help='The path to the optimizer configuration')
+    parser.add_argument(
-  parser.add_argument('--random_mode', type=str, choices=['random', 'fix'], help='The path to the optimizer configuration')
+        "--expect_flop", type=float, help="The expected flop keep ratio."
-  parser.add_argument('--procedure'   ,     type=str,                   help='The procedure basic prefix.')
+    )
-  add_shared_args( parser )
+    parser.add_argument(
-  # Optimization options
+        "--arch_nums",
-  parser.add_argument('--batch_size',       type=int,   default=2,      help='Batch size for training.')
+        type=int,
-  args = parser.parse_args()
+        help="The maximum number of running random arch generating..",
+    )
+    parser.add_argument(
+        "--model_config", type=str, help="The path to the model configuration"
+    )
+    parser.add_argument(
+        "--optim_config", type=str, help="The path to the optimizer configuration"
+    )
+    parser.add_argument(
+        "--random_mode",
+        type=str,
+        choices=["random", "fix"],
+        help="The path to the optimizer configuration",
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+    if args.rand_seed is None or args.rand_seed < 0:
-    args.rand_seed = random.randint(1, 100000)
+        args.rand_seed = random.randint(1, 100000)
-  assert args.save_dir is not None, 'save-path argument can not be None'
+    assert args.save_dir is not None, "save-path argument can not be None"
-  #assert args.flop_ratio_min < args.flop_ratio_max, 'flop-ratio {:} vs {:}'.format(args.flop_ratio_min, args.flop_ratio_max)
+    # assert args.flop_ratio_min < args.flop_ratio_max, 'flop-ratio {:} vs {:}'.format(args.flop_ratio_min, args.flop_ratio_max)
-  return args
+    return args

lib/config_utils/search_args.py

@@ -3,30 +3,51 @@ from .share_args import add_shared_args
 
 
 def obtain_search_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser = argparse.ArgumentParser(
-  parser.add_argument('--resume'        ,   type=str,                   help='Resume path.')
+        description="Train a classification model on typical image classification datasets.",
-  parser.add_argument('--model_config'  ,   type=str,                   help='The path to the model configuration')
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-  parser.add_argument('--optim_config'  ,   type=str,                   help='The path to the optimizer configuration')
+    )
-  parser.add_argument('--split_path'    ,   type=str,                   help='The split file path.')
+    parser.add_argument("--resume", type=str, help="Resume path.")
-  #parser.add_argument('--arch_para_pure',   type=int,                   help='The architecture-parameter pure or not.')
+    parser.add_argument(
-  parser.add_argument('--gumbel_tau_max',   type=float,                 help='The maximum tau for Gumbel.')
+        "--model_config", type=str, help="The path to the model configuration"
-  parser.add_argument('--gumbel_tau_min',   type=float,                 help='The minimum tau for Gumbel.')
+    )
-  parser.add_argument('--procedure'     ,   type=str,                   help='The procedure basic prefix.')
+    parser.add_argument(
-  parser.add_argument('--FLOP_ratio'    ,   type=float,                 help='The expected FLOP ratio.')
+        "--optim_config", type=str, help="The path to the optimizer configuration"
-  parser.add_argument('--FLOP_weight'   ,   type=float,                 help='The loss weight for FLOP.')
+    )
-  parser.add_argument('--FLOP_tolerant' ,   type=float,                 help='The tolerant range for FLOP.')
+    parser.add_argument("--split_path", type=str, help="The split file path.")
-  # ablation studies
+    # parser.add_argument('--arch_para_pure',   type=int,                   help='The architecture-parameter pure or not.')
-  parser.add_argument('--ablation_num_select', type=int,                help='The number of randomly selected channels.')
+    parser.add_argument(
-  add_shared_args( parser )
+        "--gumbel_tau_max", type=float, help="The maximum tau for Gumbel."
-  # Optimization options
+    )
-  parser.add_argument('--batch_size'    ,   type=int,   default=2,      help='Batch size for training.')
+    parser.add_argument(
-  args = parser.parse_args()
+        "--gumbel_tau_min", type=float, help="The minimum tau for Gumbel."
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument("--FLOP_ratio", type=float, help="The expected FLOP ratio.")
+    parser.add_argument("--FLOP_weight", type=float, help="The loss weight for FLOP.")
+    parser.add_argument(
+        "--FLOP_tolerant", type=float, help="The tolerant range for FLOP."
+    )
+    # ablation studies
+    parser.add_argument(
+        "--ablation_num_select",
+        type=int,
+        help="The number of randomly selected channels.",
+    )
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+    if args.rand_seed is None or args.rand_seed < 0:
-    args.rand_seed = random.randint(1, 100000)
+        args.rand_seed = random.randint(1, 100000)
-  assert args.save_dir is not None, 'save-path argument can not be None'
+    assert args.save_dir is not None, "save-path argument can not be None"
-  assert args.gumbel_tau_max is not None and args.gumbel_tau_min is not None
+    assert args.gumbel_tau_max is not None and args.gumbel_tau_min is not None
-  assert args.FLOP_tolerant is not None and args.FLOP_tolerant > 0, 'invalid FLOP_tolerant : {:}'.format(FLOP_tolerant)
+    assert (
-  #assert args.arch_para_pure is not None, 'arch_para_pure is not None: {:}'.format(args.arch_para_pure)
+        args.FLOP_tolerant is not None and args.FLOP_tolerant > 0
-  #args.arch_para_pure = bool(args.arch_para_pure)
+    ), "invalid FLOP_tolerant : {:}".format(FLOP_tolerant)
-  return args
+    # assert args.arch_para_pure is not None, 'arch_para_pure is not None: {:}'.format(args.arch_para_pure)
+    # args.arch_para_pure = bool(args.arch_para_pure)
+    return args

lib/config_utils/search_single_args.py

@@ -3,29 +3,46 @@ from .share_args import add_shared_args
 
 
 def obtain_search_single_args():
-  parser = argparse.ArgumentParser(description='Train a classification model on typical image classification datasets.', formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    parser = argparse.ArgumentParser(
-  parser.add_argument('--resume'        ,   type=str,                   help='Resume path.')
+        description="Train a classification model on typical image classification datasets.",
-  parser.add_argument('--model_config'  ,   type=str,                   help='The path to the model configuration')
+        formatter_class=argparse.ArgumentDefaultsHelpFormatter,
-  parser.add_argument('--optim_config'  ,   type=str,                   help='The path to the optimizer configuration')
+    )
-  parser.add_argument('--split_path'    ,   type=str,                   help='The split file path.')
+    parser.add_argument("--resume", type=str, help="Resume path.")
-  parser.add_argument('--search_shape'  ,   type=str,                   help='The shape to be searched.')
+    parser.add_argument(
-  #parser.add_argument('--arch_para_pure',   type=int,                   help='The architecture-parameter pure or not.')
+        "--model_config", type=str, help="The path to the model configuration"
-  parser.add_argument('--gumbel_tau_max',   type=float,                 help='The maximum tau for Gumbel.')
+    )
-  parser.add_argument('--gumbel_tau_min',   type=float,                 help='The minimum tau for Gumbel.')
+    parser.add_argument(
-  parser.add_argument('--procedure'     ,   type=str,                   help='The procedure basic prefix.')
+        "--optim_config", type=str, help="The path to the optimizer configuration"
-  parser.add_argument('--FLOP_ratio'    ,   type=float,                 help='The expected FLOP ratio.')
+    )
-  parser.add_argument('--FLOP_weight'   ,   type=float,                 help='The loss weight for FLOP.')
+    parser.add_argument("--split_path", type=str, help="The split file path.")
-  parser.add_argument('--FLOP_tolerant' ,   type=float,                 help='The tolerant range for FLOP.')
+    parser.add_argument("--search_shape", type=str, help="The shape to be searched.")
-  add_shared_args( parser )
+    # parser.add_argument('--arch_para_pure',   type=int,                   help='The architecture-parameter pure or not.')
-  # Optimization options
+    parser.add_argument(
-  parser.add_argument('--batch_size'    ,   type=int,   default=2,      help='Batch size for training.')
+        "--gumbel_tau_max", type=float, help="The maximum tau for Gumbel."
-  args = parser.parse_args()
+    )
+    parser.add_argument(
+        "--gumbel_tau_min", type=float, help="The minimum tau for Gumbel."
+    )
+    parser.add_argument("--procedure", type=str, help="The procedure basic prefix.")
+    parser.add_argument("--FLOP_ratio", type=float, help="The expected FLOP ratio.")
+    parser.add_argument("--FLOP_weight", type=float, help="The loss weight for FLOP.")
+    parser.add_argument(
+        "--FLOP_tolerant", type=float, help="The tolerant range for FLOP."
+    )
+    add_shared_args(parser)
+    # Optimization options
+    parser.add_argument(
+        "--batch_size", type=int, default=2, help="Batch size for training."
+    )
+    args = parser.parse_args()
 
-  if args.rand_seed is None or args.rand_seed < 0:
+    if args.rand_seed is None or args.rand_seed < 0:
-    args.rand_seed = random.randint(1, 100000)
+        args.rand_seed = random.randint(1, 100000)
-  assert args.save_dir is not None, 'save-path argument can not be None'
+    assert args.save_dir is not None, "save-path argument can not be None"
-  assert args.gumbel_tau_max is not None and args.gumbel_tau_min is not None
+    assert args.gumbel_tau_max is not None and args.gumbel_tau_min is not None
-  assert args.FLOP_tolerant is not None and args.FLOP_tolerant > 0, 'invalid FLOP_tolerant : {:}'.format(FLOP_tolerant)
+    assert (
-  #assert args.arch_para_pure is not None, 'arch_para_pure is not None: {:}'.format(args.arch_para_pure)
+        args.FLOP_tolerant is not None and args.FLOP_tolerant > 0
-  #args.arch_para_pure = bool(args.arch_para_pure)
+    ), "invalid FLOP_tolerant : {:}".format(FLOP_tolerant)
-  return args
+    # assert args.arch_para_pure is not None, 'arch_para_pure is not None: {:}'.format(args.arch_para_pure)
+    # args.arch_para_pure = bool(args.arch_para_pure)
+    return args

lib/config_utils/share_args.py

@@ -1,17 +1,39 @@
 import os, sys, time, random, argparse
 
-def add_shared_args( parser ):
+
-  # Data Generation
+def add_shared_args(parser):
-  parser.add_argument('--dataset',          type=str,                   help='The dataset name.')
+    # Data Generation
-  parser.add_argument('--data_path',        type=str,                   help='The dataset name.')
+    parser.add_argument("--dataset", type=str, help="The dataset name.")
-  parser.add_argument('--cutout_length',    type=int,                   help='The cutout length, negative means not use.')
+    parser.add_argument("--data_path", type=str, help="The dataset name.")
-  # Printing
+    parser.add_argument(
-  parser.add_argument('--print_freq',       type=int,   default=100,    help='print frequency (default: 200)')
+        "--cutout_length", type=int, help="The cutout length, negative means not use."
-  parser.add_argument('--print_freq_eval',  type=int,   default=100,    help='print frequency (default: 200)')
+    )
-  # Checkpoints
+    # Printing
-  parser.add_argument('--eval_frequency',   type=int,   default=1,      help='evaluation frequency (default: 200)')
+    parser.add_argument(
-  parser.add_argument('--save_dir',         type=str,                   help='Folder to save checkpoints and log.')
+        "--print_freq", type=int, default=100, help="print frequency (default: 200)"
-  # Acceleration
+    )
-  parser.add_argument('--workers',          type=int,   default=8,      help='number of data loading workers (default: 8)')
+    parser.add_argument(
-  # Random Seed
+        "--print_freq_eval",
-  parser.add_argument('--rand_seed',        type=int,   default=-1,     help='manual seed')
+        type=int,
+        default=100,
+        help="print frequency (default: 200)",
+    )
+    # Checkpoints
+    parser.add_argument(
+        "--eval_frequency",
+        type=int,
+        default=1,
+        help="evaluation frequency (default: 200)",
+    )
+    parser.add_argument(
+        "--save_dir", type=str, help="Folder to save checkpoints and log."
+    )
+    # Acceleration
+    parser.add_argument(
+        "--workers",
+        type=int,
+        default=8,
+        help="number of data loading workers (default: 8)",
+    )
+    # Random Seed
+    parser.add_argument("--rand_seed", type=int, default=-1, help="manual seed")