Source code for mindspore.dataset.engine.samplers

# Copyright 2019 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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# ==============================================================================
"""
The sampler module provides several samplers to generate data from datasets.
The provided samplers include: DistributedSampler, PKSampler, RandomSampler,
SequentialSampler, SubsetRandomSampler, and WeightedRandomSampler.
Users can also define a custom sampler by extending from the Sampler class.
"""

import numpy as np
import mindspore._c_dataengine as cde
import mindspore.dataset as ds

class Sampler:
    """
    Base class for user defined sampler.
    A user defined sampler can be used with any existing dataset with sampler support.

    A required  _iter_() method should by overridden by the user for sample index generation.
    An optional reset() method can be overridden for per repeat reset,

    dataset_size and num_samples will be set by dataset once a dataset iterator is created.

    Examples:
        >>> import mindspore.dataset as ds
        >>>
        >>> class ReverseSampler(ds,Sampler):
        >>>     def __iter__(self):
        >>>         for i in range(self.dataset_size - 1, -1, -1):
        >>>             yield i
        >>>
        >>> ds = ds.ImageFolderDataset(path, sampler=ReverseSampler())
    """

    def __init__(self, num_samples=None):
        self.dataset_size = 0
        self.child_sampler = None
        self.num_samples = num_samples

    def __iter__(self):
        """
        User defined iterator, must be overridden.
        _handshake is guaranteed to be called prior to iterator construction.
        """
        raise NotImplementedError

    def reset(self):
        """
        Per repeat reset callback, override this method if necessary
        """

    # Initialization handshake callback
    # Do not override this method!
    def _handshake(self, ds_size, num_samples):
        self.dataset_size = ds_size
        self.num_samples = num_samples

    # Indices fetcher
    # Do not override this method!
    def _get_indices(self):
        sampler_iter = iter(self)
        ret = []
        for _ in range(self.num_samples):
            try:
                idx = next(sampler_iter)
                ret.append(idx)
            except StopIteration:
                break
        return np.array(ret)

    # Instance fetcher
    # Do not override this method!
    def create(self):
        num_samples = self.num_samples if self.num_samples is not None else 0
        c_sampler = cde.PythonSampler(num_samples, self)
        c_child_sampler = self.create_child()
        c_sampler.add_child(c_child_sampler)
        return c_sampler

    def add_child(self, sampler):
        self.child_sampler = sampler

    def get_child(self):
        return self.child_sampler

    def create_child(self):
        c_child_sampler = None
        if self.child_sampler is not None:
            c_child_sampler = self.child_sampler.create()

        return c_child_sampler

    def is_shuffled(self):
        if self.child_sampler is None:
            return False

        return self.child_sampler.is_shuffled()

    def is_sharded(self):
        if self.child_sampler is None:
            return False

        return self.child_sampler.is_sharded()

    def get_num_samples(self):
        if self.num_samples is None:
            return None
        return self._get_indices().size


class BuiltinSampler:
    """
    Base class for BuiltinSampler.

    User should not extend this class.
    """
    def __init__(self, num_samples=None):
        self.child_sampler = None
        self.num_samples = num_samples

    def create(self):
        pass

    def add_child(self, sampler):
        self.child_sampler = sampler

    def get_child(self):
        return self.child_sampler

    def create_child(self):
        c_child_sampler = None
        if self.child_sampler is not None:
            c_child_sampler = self.child_sampler.create()
        return c_child_sampler

    def create_child_for_minddataset(self):
        c_child_sampler = None
        if self.child_sampler is not None:
            c_child_sampler = self.child_sampler.create_for_minddataset()
        return c_child_sampler

    def is_shuffled(self):
        raise NotImplementedError("Sampler must implement is_shuffled.")

    def is_sharded(self):
        raise NotImplementedError("Sampler must implement is_sharded.")

    def get_num_samples(self):
        """
        All samplers can contain a numeric num_samples value (or it can be set to None).
        A child sampler can exist or be None.
        If a child sampler exists, then the child sampler count can be a numeric value or None.
        These conditions impact the resultant sampler count that is used.
        The following table shows the possible results from calling this function.

        .. list-table::
           :widths: 25 25 25 25
           :header-rows: 1

           * - child sampler
             - num_samples
             - child_samples
             - result
           * - T
             - x
             - y
             - min(x, y)
           * - T
             - x
             - None
             - x
           * - T
             - None
             - y
             - y
           * - T
             - None
             - None
             - None
           * - None
             - x
             - n/a
             - x
           * - None
             - None
             - n/a
             - None

        Returns:
            int, The number of samples, or None
        """
        if self.child_sampler is not None:
            child_samples = self.child_sampler.get_num_samples()
            if self.num_samples is not None:
                if child_samples is not None:
                    return min(self.num_samples, child_samples)

                return self.num_samples

            return child_samples

        return self.num_samples


[docs]class DistributedSampler(BuiltinSampler): """ A sampler that accesses a shard of the dataset. Args: num_shards (int): Number of shards to divide the dataset into. shard_id (int): Shard ID of the current shard within num_shards. shuffle (bool, optional): If True, the indices are shuffled (default=True). num_samples (int, optional): The number of samples to draw (default=None, all elements). offset(int, optional): Offset from shard when the element of dataset is allocated (default=-1). Examples: >>> import mindspore.dataset as ds >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> >>> # creates a distributed sampler with 10 shards in total. This shard is shard 5. >>> sampler = ds.DistributedSampler(10, 5) >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler) Raises: ValueError: If num_shards is not positive. ValueError: If shard_id is smaller than 0 or equal to num_shards or larger than num_shards. ValueError: If shuffle is not a boolean value. """ def __init__(self, num_shards, shard_id, shuffle=True, num_samples=None, offset=-1): if num_shards <= 0: raise ValueError("num_shards should be a positive integer value, but got num_shards={}".format(num_shards)) if shard_id < 0 or shard_id >= num_shards: raise ValueError("shard_id is invalid, shard_id={}".format(shard_id)) if not isinstance(shuffle, bool): raise ValueError("shuffle should be a boolean value, but got shuffle={}".format(shuffle)) if num_samples is not None: if num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(num_samples)) self.num_shards = num_shards self.shard_id = shard_id self.shuffle = shuffle self.seed = 0 self.offset = offset super().__init__(num_samples) def create(self): num_samples = self.num_samples if self.num_samples is not None else 0 # each time user calls create_dict_iterator() (to do repeat) sampler would get a different seed to shuffle self.seed += 1 c_sampler = cde.DistributedSampler(num_samples, self.num_shards, self.shard_id, self.shuffle, self.seed, self.offset) c_child_sampler = self.create_child() c_sampler.add_child(c_child_sampler) return c_sampler def create_for_minddataset(self): num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.MindrecordDistributedSampler(self.num_shards, self.shard_id, self.shuffle, self.seed, num_samples, self.offset) c_child_sampler = self.create_child_for_minddataset() c_sampler.add_child(c_child_sampler) return c_sampler def is_shuffled(self): if self.child_sampler is None: return self.shuffle return self.child_sampler.is_shuffled() def is_sharded(self): if self.child_sampler is None: return self.num_shards > 1 return self.child_sampler.is_sharded() def set_offset(self, offset): self.offset = offset return self
[docs]class PKSampler(BuiltinSampler): """ Samples K elements for each P class in the dataset. Args: num_val (int): Number of elements to sample for each class. num_class (int, optional): Number of classes to sample (default=None, all classes). shuffle (bool, optional): If True, the class IDs are shuffled (default=False). class_column (str, optional): Name of column with class labels for MindDataset (default='label'). num_samples (int, optional): The number of samples to draw (default=None, all elements). Examples: >>> import mindspore.dataset as ds >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> >>> # creates a PKSampler that will get 3 samples from every class. >>> sampler = ds.PKSampler(3) >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler) Raises: ValueError: If num_val is not positive. NotImplementedError: If num_class is not None. ValueError: If shuffle is not boolean. """ def __init__(self, num_val, num_class=None, shuffle=False, class_column='label', num_samples=None): if num_val <= 0: raise ValueError("num_val should be a positive integer value, but got num_val={}".format(num_val)) if num_class is not None: raise NotImplementedError("Not support specify num_class") if not isinstance(shuffle, bool): raise ValueError("shuffle should be a boolean value, but got shuffle={}".format(shuffle)) if num_samples is not None: if num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(num_samples)) self.num_val = num_val self.shuffle = shuffle self.class_column = class_column # work for minddataset super().__init__(num_samples) def create(self): num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.PKSampler(num_samples, self.num_val, self.shuffle) c_child_sampler = self.create_child() c_sampler.add_child(c_child_sampler) return c_sampler def is_shuffled(self): if self.child_sampler is None: return self.shuffle return self.child_sampler.is_shuffled() def is_sharded(self): if self.child_sampler is None: return False return self.child_sampler.is_sharded() def create_for_minddataset(self): if not self.class_column or not isinstance(self.class_column, str): raise ValueError("class_column should be a not empty string value, \ but got class_column={}".format(class_column)) num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.MindrecordPkSampler(self.num_val, self.class_column, self.shuffle, num_samples) c_child_sampler = self.create_child_for_minddataset() c_sampler.add_child(c_child_sampler) return c_sampler
[docs]class RandomSampler(BuiltinSampler): """ Samples the elements randomly. Args: replacement (bool, optional): If True, put the sample ID back for the next draw (default=False). num_samples (int, optional): Number of elements to sample (default=None, all elements). Examples: >>> import mindspore.dataset as ds >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> >>> # creates a RandomSampler >>> sampler = ds.RandomSampler() >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler) Raises: ValueError: If replacement is not boolean. ValueError: If num_samples is not positive. """ def __init__(self, replacement=False, num_samples=None): if not isinstance(replacement, bool): raise ValueError("replacement should be a boolean value, but got replacement={}".format(replacement)) if num_samples is not None: if num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(num_samples)) self.deterministic = False self.replacement = replacement self.reshuffle_each_epoch = True super().__init__(num_samples) def create(self): num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.RandomSampler(num_samples, self.replacement, self.reshuffle_each_epoch) c_child_sampler = self.create_child() c_sampler.add_child(c_child_sampler) return c_sampler def create_for_minddataset(self): num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.MindrecordRandomSampler(num_samples, self.replacement, self.reshuffle_each_epoch) c_child_sampler = self.create_child_for_minddataset() c_sampler.add_child(c_child_sampler) return c_sampler def is_shuffled(self): return True def is_sharded(self): if self.child_sampler is None: return False return self.child_sampler.is_sharded()
[docs]class SequentialSampler(BuiltinSampler): """ Samples the dataset elements sequentially, same as not having a sampler. Args: start_index (int, optional): Index to start sampling at. (dafault=None, start at first ID) num_samples (int, optional): Number of elements to sample (default=None, all elements). Examples: >>> import mindspore.dataset as ds >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> >>> # creates a SequentialSampler >>> sampler = ds.SequentialSampler() >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler) """ def __init__(self, start_index=None, num_samples=None): if num_samples is not None: if num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(num_samples)) if start_index is not None: if start_index < 0: raise ValueError("start_index should be a positive integer " "value or 0, but got start_index={}".format(start_index)) self.start_index = start_index super().__init__(num_samples) def create(self): start_index = self.start_index if self.start_index is not None else 0 num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.SequentialSampler(num_samples, start_index) c_child_sampler = self.create_child() c_sampler.add_child(c_child_sampler) return c_sampler def create_for_minddataset(self): start_index = self.start_index if self.start_index is not None else 0 num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.MindrecordSequentialSampler(num_samples, start_index) c_child_sampler = self.create_child_for_minddataset() c_sampler.add_child(c_child_sampler) return c_sampler def is_shuffled(self): if self.child_sampler is None: return False return self.child_sampler.is_shuffled() def is_sharded(self): if self.child_sampler is None: return False return self.child_sampler.is_sharded()
[docs]class SubsetRandomSampler(BuiltinSampler): """ Samples the elements randomly from a sequence of indices. Args: indices (list[int]): A sequence of indices. num_samples (int, optional): Number of elements to sample (default=None, all elements). Examples: >>> import mindspore.dataset as ds >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> >>> indices = [0, 1, 2, 3, 7, 88, 119] >>> >>> # creates a SubsetRandomSampler, will sample from the provided indices >>> sampler = ds.SubsetRandomSampler() >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler) """ def __init__(self, indices, num_samples=None): if num_samples is not None: if num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(num_samples)) if not isinstance(indices, list): indices = [indices] self.indices = indices super().__init__(num_samples) def create(self): num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.SubsetRandomSampler(num_samples, self.indices) c_child_sampler = self.create_child() c_sampler.add_child(c_child_sampler) return c_sampler def is_shuffled(self): return True def is_sharded(self): if self.child_sampler is None: return False return self.child_sampler.is_sharded() def create_for_minddataset(self): c_sampler = cde.MindrecordSubsetRandomSampler(self.indices, ds.config.get_seed()) c_child_sampler = self.create_child_for_minddataset() c_sampler.add_child(c_child_sampler) return c_sampler def get_num_samples(self): num_samples = super().get_num_samples() if num_samples is None: return len(self.indices) return min(len(self.indices), num_samples)
[docs]class WeightedRandomSampler(BuiltinSampler): """ Samples the elements from [0, len(weights) - 1] randomly with the given weights (probabilities). Args: weights (list[float]): A sequence of weights, not necessarily summing up to 1. num_samples (int, optional): Number of elements to sample (default=None, all elements). replacement (bool): If True, put the sample ID back for the next draw (default=True). Examples: >>> import mindspore.dataset as ds >>> >>> dataset_dir = "path/to/imagefolder_directory" >>> >>> weights = [0.9, 0.01, 0.4, 0.8, 0.1, 0.1, 0.3] >>> >>> # creates a WeightedRandomSampler that will sample 4 elements without replacement >>> sampler = ds.WeightedRandomSampler(weights, 4) >>> data = ds.ImageFolderDataset(dataset_dir, num_parallel_workers=8, sampler=sampler) Raises: ValueError: If num_samples is not positive. ValueError: If replacement is not boolean. """ def __init__(self, weights, num_samples=None, replacement=True): if not isinstance(weights, list): weights = [weights] if num_samples is not None: if num_samples <= 0: raise ValueError("num_samples should be a positive integer " "value, but got num_samples={}".format(num_samples)) if not isinstance(replacement, bool): raise ValueError("replacement should be a boolean value, but got replacement={}".format(replacement)) self.weights = weights self.replacement = replacement super().__init__(num_samples) def create(self): num_samples = self.num_samples if self.num_samples is not None else 0 c_sampler = cde.WeightedRandomSampler(num_samples, self.weights, self.replacement) c_child_sampler = self.create_child() c_sampler.add_child(c_child_sampler) return c_sampler def is_shuffled(self): return True def is_sharded(self): if self.child_sampler is None: return False return self.child_sampler.is_sharded()