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- import numpy as np
- try:
- from yaml import CLoader as Loader, CDumper as Dumper
- except ImportError:
- from yaml import Loader, Dumper
- import yaml
- from os.path import isfile
- def feature_file_name(subset, part_info, model_info):
- return "{subset}{suffix}.{model}.npz".format(
- subset=subset,
- suffix=part_info.feature_suffix,
- model=model_info.folder)
- def read_info_file(fpath):
- with open(fpath) as f:
- return attr_dict(yaml.load(f, Loader=Loader))
- def random_idxs(idxs, rnd=None, n_parts=None):
- if rnd is None or isinstance(rnd, int):
- rnd = np.random.RandomState(rnd)
- else:
- assert isinstance(rnd, np.random.RandomState), \
- "'rnd' should be either a random seed or a RandomState instance!"
- n_parts = n_parts or rnd.randint(1, len(idxs))
- res = rnd.choice(idxs, n_parts, replace=False)
- res.sort()
- return res
- class attr_dict(dict):
- def __getattr__(self, name):
- if name in self:
- return self[name]
- else:
- return super(attr_dict, self).get(name)
- def __getitem__(self, key):
- res = super(attr_dict, self).__getitem__(key)
- if isinstance(res, dict):
- return attr_dict(res)
- return res
- def __getstate__(self): return self.__dict__
- def __setstate__(self, d): self.__dict__.update(d)
- class _MetaInfo(object):
- def __init__(self, **kwargs):
- for name, value in kwargs.items():
- setattr(self, name, value)
- self.structure = []
- from .image import asarray, dimensions
- from .dataset import new_iterator
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