paddlenlp.data.collate 源代码

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import numpy as np

__all__ = ['Stack', 'Pad', 'Tuple', 'Dict']


[文档]class Stack(object): """ Stacks the input data samples to construct the batch. The N input samples must have the same shape/length and will be stacked to construct a batch. Args: axis (int, optional): The axis in the result data along which the input data are stacked. Default: 0. dtype (str|numpy.dtype, optional): The value type of the output. If it is set to None, the type of input data is used. Default: None. """ def __init__(self, axis=0, dtype=None): self._axis = axis self._dtype = dtype
[文档] def __call__(self, data): """ Batchifies the input data by stacking. Args: data (list[numpy.ndarray]): The input data samples. It is a list. Each element is a numpy.ndarray or list. Returns: numpy.ndarray: Stacked batch data. Example: .. code-block:: python from paddlenlp.data import Stack a = [1, 2, 3, 4] b = [3, 4, 5, 6] c = [5, 6, 7, 8] result = Stack()([a, b, c]) ''' [[1, 2, 3, 4], [3, 4, 5, 6], [5, 6, 7, 8]] ''' """ data = np.stack( data, axis=self._axis).astype(self._dtype) if self._dtype else np.stack( data, axis=self._axis) return data
[文档]class Pad(object): """ Pads the input data samples to the largest length at `axis`. Args: pad_val (float|int, optional): The padding value. Default: 0. axis (int, optional): The axis to pad the arrays. The arrays will be padded to the largest length at `axis`. For example, assume the input arrays have shape (10, 8, 5), (6, 8, 5), (3, 8, 5) and the axis is 0. Each input will be padded into (10, 8, 5) and then stacked to form the final output, which has shape (3, 10, 8, 5). Default: 0. ret_length (bool|numpy.dtype, optional): If it is bool, indicate whether to return the valid length in the output, and the data type of returned length is int32 if True. If it is numpy.dtype, indicate the data type of returned length. Default: None. dtype (numpy.dtype, optional): The value type of the output. If it is set to None, the input data type is used. Default: None. pad_right (bool, optional): Whether the padding direction is right-side. If True, it indicates we pad to the right side, while False indicates we pad to the left side. Default: True. """ def __init__(self, pad_val=0, axis=0, ret_length=None, dtype=None, pad_right=True): self._pad_val = pad_val self._axis = axis self._ret_length = ret_length self._dtype = dtype self._pad_right = pad_right
[文档] def __call__(self, data): """ Batchifies the input data by padding. The input will be padded to the largest dimension at `axis` and then stacked to form the final output. In addition, the function will output the original dimensions at the `axis` if `ret_length` is not None or False. Args: data (list[numpy.ndarray|list]): The input data samples. It is a list. Each element is a numpy.ndarray or list. Returns: numpy.ndarray|tuple[numpy.ndarray]: If `ret_length` is False, it is a numpy.ndarray representing the padded batch data and the shape is (N, …). Otherwise, it is a tuple, besides the padded batch data, the tuple also includes a numpy.ndarray representing original length at `axis` of all input samples, which shaped `(N,)`. Example: .. code-block:: python from paddlenlp.data import Pad a = [1, 2, 3, 4] b = [5, 6, 7] c = [8, 9] result = Pad(pad_val=0)([a, b, c]) ''' [[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]] ''' """ arrs = [np.asarray(ele) for ele in data] original_length = [ele.shape[self._axis] for ele in arrs] max_size = max(original_length) ret_shape = list(arrs[0].shape) ret_shape[self._axis] = max_size ret_shape = (len(arrs), ) + tuple(ret_shape) ret = np.full( shape=ret_shape, fill_value=self._pad_val, dtype=arrs[0].dtype if self._dtype is None else self._dtype) for i, arr in enumerate(arrs): if arr.shape[self._axis] == max_size: ret[i] = arr else: slices = [slice(None) for _ in range(arr.ndim)] if self._pad_right: slices[self._axis] = slice(0, arr.shape[self._axis]) else: slices[self._axis] = slice(max_size - arr.shape[self._axis], max_size) if slices[self._axis].start != slices[self._axis].stop: slices = [slice(i, i + 1)] + slices ret[tuple(slices)] = arr if self._ret_length: return ret, np.asarray( original_length, dtype="int32") if self._ret_length == True else np.asarray( original_length, self._ret_length) else: return ret
[文档]class Tuple(object): """ Wraps multiple batchify functions together. The input functions will be applied to the corresponding input fields. Each sample should be a list or tuple containing multiple fields. The i'th batchify function stored in Tuple will be applied on the i'th field. For example, when data sample is (nd_data, label), you can wrap two batchify functions using `Tuple(DataBatchify, LabelBatchify)` to batchify nd_data and label correspondingly. Args: fn (callable|list[callable]|tuple[callable]): The batchify functions to wrap. It is a callable function or a list/tuple of callable functions. args (tuple[callable]): The additional batchify functions to wrap. """ def __init__(self, fn, *args): if isinstance(fn, (list, tuple)): assert len(args) == 0, 'Input pattern not understood. The input of Tuple can be ' \ 'Tuple(A, B, C) or Tuple([A, B, C]) or Tuple((A, B, C)). ' \ 'Received fn=%s, args=%s' % (str(fn), str(args)) self._fn = fn else: self._fn = (fn, ) + args for i, ele_fn in enumerate(self._fn): assert callable( ele_fn ), 'Batchify functions must be callable! type(fn[%d]) = %s' % ( i, str(type(ele_fn)))
[文档] def __call__(self, data): """ Batchifies data samples by applying each function on the corresponding data field, and each data field is produced by stacking the field data of samples. Args: data (list|tuple): The samples to batchfy. Each sample in list/tuple should contain `N` fields. Returns: tuple: A tuple composed of results from all including batchifying functions. Example: .. code-block:: python from paddlenlp.data import Stack, Pad, Tuple data = [ [[1, 2, 3, 4], [1]], [[5, 6, 7], [0]], [[8, 9], [1]], ] batchify_fn = Tuple(Pad(pad_val=0), Stack()) ids, label = batchify_fn(data) ''' ids: [[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]] label: [[1], [0], [1]] ''' """ assert len(data[0]) == len(self._fn),\ 'The number of attributes in each data sample should contain' \ ' {} elements'.format(len(self._fn)) ret = [] for i, ele_fn in enumerate(self._fn): result = ele_fn([ele[i] for ele in data]) if isinstance(result, (tuple, list)): ret.extend(result) else: ret.append(result) return tuple(ret)
[文档]class Dict(object): """ Wraps multiple batchify functions together. The input functions will be applied to the corresponding input fields. Each sample should be a dict containing multiple fields. Each batchify function with key stored in `Dict` will be applied on the field which has the same key. For example, when data sample is {'tokens': tokens, 'labels': labels}, you can wrap two batchify functions using `Dict({'tokens': DataBatchify, 'labels': LabelBatchify})` to batchify tokens and labels correspondingly. Args: fn (dict): The batchify functions to wrap. It is a dict, which values is callable functions. """ def __init__(self, fn): assert isinstance(fn, (dict)), 'Input pattern not understood. The input of Dict must be a dict with key of input column name and value of collate_fn ' \ 'Received fn=%s' % (str(fn)) self._fn = fn for col_name, ele_fn in self._fn.items(): assert callable( ele_fn ), 'Batchify functions must be callable! type(fn[%d]) = %s' % ( col_name, str(type(ele_fn)))
[文档] def __call__(self, data): """ Batchifies data samples by applying each function on the corresponding data field, and each data field is produced by stacking the field data with the same key as batchify functions of all samples. Args: data (list[dict]|tuple[dict]): The samples to batchfy. Each sample in list/tuple is a dict with `N` key-values. Returns: tuple: A tuple composed of results from all including batchifying functions. Example: .. code-block:: python from paddlenlp.data import Stack, Pad, Dict data = [ {'labels':[1], 'token_ids':[1, 2, 3, 4]}, {'labels':[0], 'token_ids':[5, 6, 7]}, {'labels':[1], 'token_ids':[8, 9]}, ] batchify_fn = Dict({'token_ids':Pad(pad_val=0), 'labels':Stack()}) ids, label = batchify_fn(data) ''' ids: [[1, 2, 3, 4], [5, 6, 7, 0], [8, 9, 0, 0]] label: [[1], [0], [1]] ''' """ ret = [] for col_name, ele_fn in self._fn.items(): result = ele_fn([ele[col_name] for ele in data]) if isinstance(result, (tuple, list)): ret.extend(result) else: ret.append(result) return tuple(ret)