# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. from paddlerec.core.utils import envs class ValueFormat: def __init__(self, type, value, value_handler): self.type = type self.value = value self.value_handler = value_handler self.help = help def is_valid(self, name, value): ret = self.is_type_valid(name, value) if not ret: return ret ret = self.is_value_valid(name, value) return ret def is_type_valid(self, name, value): if self.type == "int": if not isinstance(value, int): print("\nattr {} should be int, but {} now\n".format( name, self.type)) return False return True elif self.type == "str": if not isinstance(value, str): print("\nattr {} should be str, but {} now\n".format( name, self.type)) return False return True elif self.type == "strs": if not isinstance(value, list): print("\nattr {} should be list(str), but {} now\n".format( name, self.type)) return False for v in value: if not isinstance(v, str): print("\nattr {} should be list(str), but list({}) now\n". format(name, type(v))) return False return True elif self.type == "ints": if not isinstance(value, list): print("\nattr {} should be list(int), but {} now\n".format( name, self.type)) return False for v in value: if not isinstance(v, int): print("\nattr {} should be list(int), but list({}) now\n". format(name, type(v))) return False return True else: print("\nattr {}'s type is {}, can not be supported now\n".format( name, type(value))) return False def is_value_valid(self, name, value): ret = self.value_handler(value) return ret def in_value_handler(name, value, values): if value not in values: print("\nattr {}'s value is {}, but {} is expected\n".format( name, value, values)) return False return True def eq_value_handler(name, value, values): if value != values: print("\nattr {}'s value is {}, but == {} is expected\n".format( name, value, values)) return False return True def ge_value_handler(name, value, values): if value < values: print("\nattr {}'s value is {}, but >= {} is expected\n".format( name, value, values)) return False return True def le_value_handler(name, value, values): if value > values: print("\nattr {}'s value is {}, but <= {} is expected\n".format( name, value, values)) return False return True def register(): validations = {} validations["train.workspace"] = ValueFormat("str", None, eq_value_handler) validations["train.device"] = ValueFormat("str", ["cpu", "gpu"], in_value_handler) validations["train.epochs"] = ValueFormat("int", 1, ge_value_handler) validations["train.engine"] = ValueFormat( "str", ["single", "local_cluster", "cluster"], in_value_handler) requires = ["workspace", "dataset", "mode", "runner", "phase"] return validations, requires def yaml_validation(config): all_checkers, require_checkers = register() _config = envs.load_yaml(config) flattens = envs.flatten_environs(_config) for required in require_checkers: if required not in flattens.keys(): print("\ncan not find {} in yaml, which is required\n".format( required)) return False for name, flatten in flattens.items(): checker = all_checkers.get(name, None) if not checker: continue ret = checker.is_valid(name, flattens) if not ret: return False return True