def _decode_cfg_value(v):
"""Decodes a raw config value (e.g., from a yaml config files or command
line argument) into a Python object.
"""
# Configs parsed from raw yaml will contain dictionary keys that need to be
# converted to AttrDict objects
if isinstance(v, dict):
return AttrDict(v)
# All remaining processing is only applied to strings
if not isinstance(v, string_types):
return v
# Try to interpret `v` as a:
# string, number, tuple, list, dict, boolean, or None
try:
v = literal_eval(v)
# The following two excepts allow v to pass through when it represents a
# string.
#
# Longer explanation:
# The type of v is always a string (before calling literal_eval), but
# sometimes it *represents* a string and other times a data structure, like
# a list. In the case that v represents a string, what we got back from the
# yaml parser is 'foo' *without quotes* (so, not '"foo"'). literal_eval is
# ok with '"foo"', but will raise a ValueError if given 'foo'. In other
# cases, like paths (v = 'foo/bar' and not v = '"foo/bar"'), literal_eval
# will raise a SyntaxError.
except ValueError:
pass
except SyntaxError:
pass
return v
from __future__ import print_function from __future__ import unicode_literals import yaml import io import os import copy from ast import literal_eval import numpy as np # from past.builtins import basestring from six import string_types from configs.collections import AttrDict __C = AttrDict() cfg = __C # Training options __C.TRAIN = AttrDict() __C.TRAIN.WEIGHTS = '' __C.TRAIN.BATCH_SIZE = 32 __C.TRAIN.START_EPOCH = 0 __C.TRAIN.MAX_EPOCH = 200
def merge_cfg_from_file(cfg_filename):
"""Load a yaml config file and merge it into the global config."""
with open(cfg_filename, 'r') as f:
yaml_cfg = AttrDict(load_cfg(f))
_merge_a_into_b(yaml_cfg, __C)
from __future__ import print_function from __future__ import unicode_literals import yaml import io import os import copy from ast import literal_eval import numpy as np # from past.builtins import basestring from six import string_types from configs.collections import AttrDict __C = AttrDict() cfg = __C # Training options __C.TRAIN = AttrDict() __C.TRAIN.WEIGHTS = '' __C.TRAIN.BATCH_SIZE = 32 __C.TRAIN.START_EPOCH = 0 __C.TRAIN.MAX_EPOCH = 200