# -*- coding: utf-8 -*-
# Copyright (c) 2016-     Facebook, Inc            (Adam Paszke)
# Copyright (c) 2014-     Facebook, Inc            (Soumith Chintala)
# Copyright (c) 2011-2014 Idiap Research Institute (Ronan Collobert)
# Copyright (c) 2012-2014 Deepmind Technologies    (Koray Kavukcuoglu)
# Copyright (c) 2011-2012 NEC Laboratories America (Koray Kavukcuoglu)
# Copyright (c) 2011-2013 NYU                      (Clement Farabet)
# Copyright (c) 2006-2010 NEC Laboratories America (Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston)
# Copyright (c) 2006      Idiap Research Institute (Samy Bengio)
# Copyright (c) 2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio, Johnny Mariethoz)
# ---------------------------------------------------------------------
# This file has been modified by Megvii ("Megvii Modifications").
# All Megvii Modifications are Copyright (C) 2014-2021 Megvii Inc. All rights reserved.
# ----------------------------------------------------------------------
import re

import numpy as np

np_str_obj_array_pattern = re.compile(r"[aO]")
default_collate_err_msg_format = (
    "default_collator: inputs must contain numpy arrays, numbers, "
    "Unicode strings, bytes, dicts or lists; found {}"

[文档]class Collator: r"""Used for merging a list of samples to form a mini-batch of Tensor(s). Used when using batched loading from a dataset. Modified from """ def apply(self, inputs): elem = inputs[0] elem_type = type(elem) if ( elem_type.__module__ == "numpy" and elem_type.__name__ != "str_" and elem_type.__name__ != "string_" ): elem = inputs[0] if elem_type.__name__ == "ndarray": # array of string classes and object if is not None: raise TypeError(default_collate_err_msg_format.format(elem.dtype)) return np.ascontiguousarray(np.stack(inputs)) elif elem.shape == (): # scalars return np.array(inputs) elif isinstance(elem, float): return np.array(inputs, dtype=np.float64) elif isinstance(elem, int): return np.array(inputs) elif isinstance(elem, (str, bytes)): return inputs elif isinstance(elem, return {key: self.apply([d[key] for d in inputs]) for key in elem} elif isinstance(elem, tuple) and hasattr(elem, "_fields"): # namedtuple return elem_type(*(self.apply(samples) for samples in zip(*inputs))) elif isinstance(elem, transposed = zip(*inputs) return [self.apply(samples) for samples in transposed] raise TypeError(default_collate_err_msg_format.format(elem_type))