paddlenlp.data.sampler 源代码

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# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
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#     http://www.apache.org/licenses/LICENSE-2.0
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import collections
import functools
import math
import six

import numpy as np
import paddle.distributed as dist


[文档]class SamplerHelper(object): """ The class is to help construct iterable sampler used for :class:`paddle.io.DataLoader`. It wraps a dataset and uses its :meth:`__getitem__` method. Every subclass of :class:`SamplerHelper` has to provide an :meth:`__iter__` method, providing a way to iterate over indices of dataset elements, and a :meth:`__len__` method that returns the length of the returned iterators. The class also can be used as batch iterator instead of indices iterator when `iterator` yield samples rather than indices by initializing `iterator` with a iterable dataset. .. note:: The :meth:`__len__` method isn't strictly required by :class:`paddle.io.DataLoader`, but is expected in any calculation involving the length of a :class:`paddle.io.DataLoader`. Args: dataset (Dataset): Input dataset for :class:`SamplerHelper`. iterable (Iterable, optional): Iterator of dataset. Default: None. """ # chain sampler def __init__(self, dataset, iterable=None): self.data_source = dataset self.iterable = iterable if isinstance(dataset, collections.Iterable) and iterable is None: # iterable-style datasets self.iterable = dataset def __iter__(self): if self.iterable is None: return iter(range(len(self.data_source))) elif isinstance(self.iterable, collections.Iterable): return iter(self.iterable) elif callable(self.iterable): return self.iterable() else: raise ValueError( "`iterable` should be None, instance of Iterable or callable " "producing generator.") def __len__(self): # Allow some samplers have different length with `len(data_source)`, # such as batch sampler. if hasattr(self, "_length"): return self._length else: return len(self.data_source) @property def length(self): """ Returns the length. """ # since `len()` only produce integer, use length property to get None # for uncertain length. samplers can set length if necessary. try: length = len(self) except Exception: length = None return length @length.setter def length(self, length): self._length = length def apply(self, fn): # Transformation functions would be performed. It includes # :meth:`shuffle`, :meth:`sort`, :meth:`fit` and :meth:`shard`. # Args: # fn (callable): Transformation functions to be performed. # Returns: # SamplerHelper: A new transformed :class:`SamplerHelper` object. rs = fn(self) if isinstance(rs, (list, tuple)): iterable, data_source = rs else: iterable, data_source = rs, self.data_source sampler = type(self)(data_source, iterable) return sampler
[文档] def shuffle(self, buffer_size=-1, seed=None): """ Shuffles the dataset according to the given buffer size and random seed. Args: buffer_size (int, optional): Buffer size for shuffle. If `buffer_size < 0` or more than the length of the dataset, `buffer_size` is the length of the dataset. Default: -1. seed (int, optional): Seed for the random. Default: None. Returns: SamplerHelper: A new shuffled :class:`SamplerHelper` object. Example: .. code-block:: python from paddlenlp.data import SamplerHelper from paddle.io import Dataset class MyDataset(Dataset): def __init__(self): super(MyDataset, self).__init__() self.data = [ [[1, 2, 3, 4], [1]], [[5, 6, 7], [0]], [[8, 9], [1]], ] def __getitem__(self, index): data = self.data[index][0] label = self.data[index][1] return data, label def __len__(self): return len(self.data) dataset = MyDataset() sampler = SamplerHelper(dataset) print(list(sampler)) # indices of dataset elements # [0, 1, 2] sampler = sampler.shuffle(seed=2) print(list(sampler)) # indices of dataset elements # [2, 1, 0] """ if seed is not None: random_generator = np.random.RandomState(seed) else: # use the global random generator random_generator = np.random def _impl(): buf = [] for idx in iter(self): buf.append(idx) if buffer_size > 0 and len(buf) >= buffer_size: random_generator.shuffle(buf) for b in buf: yield b buf = [] if len(buf) > 0: random_generator.shuffle(buf) for b in buf: yield b return type(self)(self.data_source, _impl)
[文档] def sort(self, cmp=None, key=None, reverse=False, buffer_size=-1): """ Sorts the dataset according to given callable :meth:`cmp` or :meth:`key`. Args: cmp (callable, optional): The function of comparison. Default: None. key (callable, optional): The function of key. Default: None. reverse (bool, optional): Whether to reverse when sorting the data samples. If True, it means in descending order, and False means in ascending order. Default: False. buffer_size (int, optional): Buffer size for sort. If `buffer_size < 0` or `buffer_size` is more than the length of the data, `buffer_size` will be set to the length of the data. Default: -1. Returns: SamplerHelper: A new sorted :class:`SamplerHelper` object. Example: .. code-block:: python from paddlenlp.data import SamplerHelper from paddle.io import Dataset class MyDataset(Dataset): def __init__(self): super(MyDataset, self).__init__() self.data = [ [[1, 2, 3, 4], [1]], [[5, 6, 7], [0]], [[8, 9], [1]], ] def __getitem__(self, index): data = self.data[index][0] label = self.data[index][1] return data, label def __len__(self): return len(self.data) dataset = MyDataset() sampler = SamplerHelper(dataset) print(list(sampler)) # indices of dataset elements # [0, 1, 2] # Sorted in ascending order by the length of the first field # of the sample key = (lambda x, data_source: len(data_source[x][0])) sampler = sampler.sort(key=key) print(list(sampler)) # indices of dataset elements # [2, 1, 0] """ if key: key_wrapper = (lambda x: key(x, self.data_source)) elif cmp: key_wrapper = functools.cmp_to_key( lambda x, y: cmp(x, y, self.data_source)) else: key_wrapper = (lambda x: len(self.data_source[x])) def _impl(): data_source = self.data_source buf = [] for idx in iter(self): buf.append(idx) if buffer_size > 0 and len(buf) >= buffer_size: buf = sorted(buf, key=key_wrapper, reverse=reverse) for b in buf: yield b buf = [] if len(buf) > 0: buf = sorted(buf, key=key_wrapper, reverse=reverse) for b in buf: yield b return type(self)(self.data_source, _impl)
[文档] def batch(self, batch_size, drop_last=False, batch_size_fn=None, key=None): """ Batches the dataset according to given `batch_size`. Args: batch_size (int): The batch size. drop_last (bool, optional): Whether to drop the last mini batch. Default: False. batch_size_fn (callable, optional): It accepts four arguments: index of data source, the length of minibatch, the size of minibatch so far and data source, and it returns the size of mini batch so far. Actually, the returned value can be anything and would used as argument `size_so_far` in `key`. If None, it would return the length of mini match. Default: None. key (callable, optional): The function of key. It accepts the size of minibatch so far and the length of minibatch, and returns what to be compared with `batch_size`. If None, only the size of mini batch so far would be compared with `batch_size`. Default: None. Returns: SamplerHelper: A new batched :class:`SamplerHelper` object. Example: .. code-block:: python from paddlenlp.data import SamplerHelper from paddle.io import Dataset class MyDataset(Dataset): def __init__(self): super(MyDataset, self).__init__() self.data = [ [[1, 2, 3, 4], [1]], [[5, 6, 7], [0]], [[8, 9], [1]], ] def __getitem__(self, index): data = self.data[index][0] label = self.data[index][1] return data, label def __len__(self): return len(self.data) dataset = MyDataset() sampler = SamplerHelper(dataset) print(list(sampler)) # indices of dataset elements # [0, 1, 2] sampler = sampler.batch(batch_size=2) print(list(sampler)) # indices of dataset elements # [[0, 1], [2]] """ _key = lambda size_so_far, minibatch_len: size_so_far ori_batch_size_fn = batch_size_fn if batch_size_fn is None: batch_size_fn = lambda new, count, sofar, data_source: count key = _key if key is None else key def _impl(): data_source = self.data_source minibatch, size_so_far = [], 0 for idx in iter(self): minibatch.append(idx) size_so_far = batch_size_fn(idx, len(minibatch), size_so_far, data_source) if key(size_so_far, len(minibatch)) == batch_size: yield minibatch minibatch, size_so_far = [], 0 elif key(size_so_far, len(minibatch)) > batch_size: if len(minibatch) == 1: raise ValueError( "Please increase the value of `batch_size`, or limit the max length of batch." ) yield minibatch[:-1] minibatch, size_so_far = minibatch[-1:], batch_size_fn( idx, 1, 0, data_source) if minibatch and not drop_last: yield minibatch sampler = type(self)(self.data_source, _impl) if ori_batch_size_fn is None and self.length is not None: sampler.length = (self.length + int(not drop_last) * (batch_size - 1)) // batch_size else: sampler.length = None return sampler
[文档] def shard(self, num_replicas=None, rank=None): """ Slices the dataset for multi GPU training. Args: num_replicas (int, optional): The number of training process, and is also the number of GPU cards used in training. If None, it will be set by :meth:`paddle.distributed.get_world_size` method. Default: None. rank (int, optional): The id of current training process. Equal to the value of the environment variable PADDLE_TRAINER_ID. If None, it will be intialized by :meth:`paddle.distributed.get_rank` method. Default: None. Returns: SamplerHelper: A new sliced :class:`SamplerHelper` object. Example: .. code-block:: python from paddlenlp.data import SamplerHelper from paddle.io import Dataset class MyDataset(Dataset): def __init__(self): super(MyDataset, self).__init__() self.data = [ [[1, 2, 3, 4], [1]], [[5, 6, 7], [0]], [[8, 9], [1]], ] def __getitem__(self, index): data = self.data[index][0] label = self.data[index][1] return data, label def __len__(self): return len(self.data) dataset = MyDataset() sampler = SamplerHelper(dataset) print(list(sampler)) # indices of dataset elements # [0, 1, 2] sampler = sampler.shard(num_replicas=2) print(list(sampler)) # indices of dataset elements # [0, 2] """ if num_replicas is None: num_replicas = dist.get_world_size() if rank is None: rank = dist.get_rank() def _impl(): for i, idx in enumerate(self): if i % num_replicas == rank: yield idx if i % num_replicas != num_replicas - 1 and rank > i % num_replicas: # use last samples to make it evenly divisible yield idx sampler = type(self)(self.data_source, _impl) if self.length is not None: sampler.length = int(math.ceil(self.length * 1.0 / num_replicas)) else: sampler.length = None return sampler
def list(self): # Produce a sampler with a `listiterator` when calling `iter`. Since # `list` would fetch all contents at time, thus it can get accurate # length. def _impl(): indices = list(iter(self)) self.length = len(indices) return iter(indices) return type(self)(self.data_source, _impl)