add Sampler/SequenceSampler/RandomSampler

python/paddle/fluid/dataloader/__init__.py

@@ -23,6 +23,10 @@
 from . import dataloader_iter
 from .dataloader_iter import *
 
+from . import sampler
+from .sampler import *
+
 __all__ = dataset.__all__ \
         + batch_sampler.__all__ \
-        + dataloader_iter.__all__
+        + dataloader_iter.__all__ \
+        + sampler.__all__

python/paddle/fluid/dataloader/batch_sampler.py

@@ -16,12 +16,13 @@
 from __future__ import division
 
 import numpy as np
+from .sampler import Sampler, SequenceSampler
 from .dataset import Dataset, IterableDataset
 
 __all__ = ["BatchSampler"]
 
 
-class BatchSampler(object):
+class BatchSampler(Sampler):
     """
     A base implement of batch sampler used by `paddle.io.DataLoader`
     which yield mini-batch indices(a list/tuple with length as
@@ -41,10 +42,11 @@ class BatchSampler(object):
                 implement or other python object which implemented
                 :code:`__len__` for BatchSampler to get indices as the
                 range of :attr:`dataset` length. Default None.
-        indices (list|tuple): a substitution parameter for
-                :attr:`dataset` either :attr:`dataset` or
-                :attr:`indices` should be set, give the whole
-                indices to sampler from directly. Default None.
+        sampler (Sampler): this could be a :code:`paddle.io.Dataset`
+                instance which implemented :code:`__iter__` to yield
+                sample indices. :attr:`sampler` and :attr:`dataset`
+                can not be set in the same time.  If :attr:`sampler`
+                is set, :attr:`shuffle` should not be set. Default None.
         shuffle(bool): whether to shuffle indices order before genrating
                 batch indices. Default False.
         batch_size(int): sample indice number in a mini-batch indices.
@@ -58,16 +60,7 @@ class BatchSampler(object):
         
         .. code-block:: python
             
-            from paddle.io import BatchSampler, Dataset
-
-            # init with indices
-            bs = BatchSampler(indices=list(range(100)),
-                              shuffle=True,
-                              batch_size=8,
-                              drop_last=True)
-
-            for batch_indices in bs:
-                print(batch_indices)
+            from paddle.io import RandomSampler, BatchSampler, Dataset
 
             # init with dataset
             class RandomDataset(Dataset):
@@ -90,34 +83,42 @@ def __len__(self):
             for batch_indices in bs:
                 print(batch_indices)
 
+            # init with sampler
+            sampler = RandomSampler(RandomDataset(100))
+            bs = BatchSampler(sampler=sampler,
+                              shuffle=True,
+                              batch_size=8,
+                              drop_last=True)
+
+            for batch_indices in bs:
+                print(batch_indices)
+
+
     see `paddle.io.DataLoader`
 
     """
 
     def __init__(self,
                  dataset=None,
-                 indices=None,
+                 sampler=None,
                  shuffle=False,
                  batch_size=1,
                  drop_last=False):
         if dataset is None:
-            assert indices is not None, \
-                "either dataset or indices should be set"
-            assert isinstance(indices, list) or isinstance(indices, tuple), \
-                "indices should be a list or tuple, but got {}".format(type(indices))
-            self.indices = indices
-            self.sampler_iter = None
+            assert sampler is not None, \
+                "either dataset or sampler should be set"
+            assert isinstance(sampler, Sampler), \
+                "sampler should be a paddle.io.Sampler, but got {}".format(type(sampler))
+            assert not shuffle, "shuffle should be False when sampler is set"
+            self.sampler = sampler
         else:
-            if isinstance(dataset, IterableDataset):
-                self.sampler_iter = iter(
-                    _InfiniteIterableSampler(dataset, batch_size))
-            else:
-                self.sampler_iter = None
-                assert isinstance(dataset, Dataset), \
-                    "dataset should be an instance of paddle.io.Dataset"
-                assert indices is None, \
-                    "should not set both dataset and indices"
-                self.indices = list(range(len(dataset)))
+            assert isinstance(dataset, Dataset), \
+                "dataset should be a paddle.io.Dataset"
+            assert not isinstance(dataset, IterableDataset), \
+                "dataset should not be a paddle.io.IterableDataset"
+            assert sampler is None, \
+                "should not set both dataset and sampler"
+            self.sampler = SequenceSampler(dataset)
 
         assert isinstance(batch_size, int) and batch_size > 0, \
             "batch_size should be a positive integer, but got {}".format(batch_size)
@@ -130,15 +131,8 @@ def __init__(self,
         self.drop_last = drop_last
 
     def __iter__(self):
-        if self.sampler_iter:
-            yield next(self.sampler_iter)
-
-        if self.shuffle:
-            np.random.shuffle(self.indices)
-        _iter = iter(self.indices)
-
         batch_indices = []
-        for idx in _iter:
+        for idx in self.sampler:
             batch_indices.append(idx)
             if len(batch_indices) == self.batch_size:
                 yield batch_indices
@@ -147,10 +141,7 @@ def __iter__(self):
             yield batch_indices
 
     def __len__(self):
-        if self.sampler_iter:
-            raise RuntimeError("'{}' should not be called for IterableDataset".
-                               format('__len__'))
-        num_samples = len(self.indices)
+        num_samples = len(self.sampler)
         num_samples += int(not self.drop_last) * (self.batch_size - 1)
         return num_samples // self.batch_size
 

python/paddle/fluid/dataloader/sampler.py

@@ -0,0 +1,232 @@
+#   Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+# limitations under the License.
+
+from __future__ import print_function
+from __future__ import division
+
+import numpy as np
+
+__all__ = ["Sampler", "SequenceSampler", "RandomSampler"]
+
+
+class Sampler(object):
+    """
+    An abstract class to encapsulate methods and behaviors of samplers.
+
+    All sampler used by :code:`paddle.io.BatchSampler` should be a subclass
+    of :code:`paddle.io.Sampler`, BatchSampler subclasses should
+    implement following methods:
+
+    :code:`__iter__`: return sample index iterably, which iterate over indices
+    of dataset elements
+
+    :code:`__len__`: the number of sample in :attr:`data_source`
+
+
+    Args:
+        data_source(Dataset, optional): this could be an instance of
+                :code:`paddle.io.Dataset` other Python object which
+                implemented :code:`__len__` for Sampler to get indices
+                as the range of :attr:`dataset` length. Default None.
+
+    Returns:
+        Sampler: an iterable object for sample indices iterating
+
+    Examples:
+        
+        .. code-block:: python
+            
+            from paddle.io import Dataset, Sampler
+
+            class RandomDataset(Dataset):
+                def __init__(self, num_samples):
+                    self.num_samples = num_samples
+            
+                def __getitem__(self, idx):
+                    image = np.random.random([784]).astype('float32')
+                    label = np.random.randint(0, 9, (1, )).astype('int64')
+                    return image, label
+                
+                def __len__(self):
+                    return self.num_samples
+
+            class MySampler(Sampler):
+                def __init__(self, data_source):
+                    self.data_source = data_source
+
+                def __iter__(self):
+                    return iter(range(len(self.data_source)))
+
+                def __len__(self):
+                    return len(self.data_source)
+            
+            sampler = MySampler(data_source=RandomDataset(100))
+
+            for index in sampler:
+                print(index)
+
+    see `paddle.io.BatchSampler`
+    see `paddle.io.DataLoader`
+
+    """
+
+    def __init__(self, data_source=None):
+        self.data_source = data_source
+
+    def __iter__(self):
+        raise NotImplementedError
+
+    # Not define __len__ method in this base class here for __len__
+    # is not needed in same sence, e.g. paddle.io.IterableDataset
+
+
+class SequenceSampler(Sampler):
+    """
+    Iterate samples sequentially, yield :code:`0, 1, 2, ..., len(data_source) -1`
+    generally,
+
+    Args:
+        data_source(Dataset): dataset to sample, this could be an
+                instance of :code:`paddle.io.Dataset` other Python
+                object which implemented :code:`__len__`.
+
+    Returns:
+        Sampler: a Sampler yield sample index sequentially
+
+    Examples:
+
+        .. code-block:: python
+            
+            from paddle.io import Dataset, SequenceSampler
+
+            class RandomDataset(Dataset):
+                def __init__(self, num_samples):
+                    self.num_samples = num_samples
+            
+                def __getitem__(self, idx):
+                    image = np.random.random([784]).astype('float32')
+                    label = np.random.randint(0, 9, (1, )).astype('int64')
+                    return image, label
+                
+                def __len__(self):
+                    return self.num_samples
+
+            sampler = SequenceSampler(data_source=RandomDataset(100))
+
+            for index in sampler:
+                print(index)
+
+    see `paddle.io.Sampler`
+    """
+
+    def __init__(self, data_source):
+        self.data_source = data_source
+
+    def __iter__(self):
+        return iter(range(len(self.data_source)))
+
+    def __len__(self):
+        return len(self.data_source)
+
+
+class RandomSampler(Sampler):
+    """
+    Iterate samples randomly, yield shuffled indices, if :attr:`replacement=False`,
+    yield shuffled indices of the whole data souce, if :attr:`replacement=True`,
+    :attr:`num_samples` can set to specify the sample number to draw.
+
+    Args:
+        data_source(Dataset): dataset to sample, this could be an
+                instance of :code:`paddle.io.Dataset` other Python
+                object which implemented :code:`__len__`.
+        replacement(bool): If False, sample the whole dataset, If False,
+                set :attr:`num_samples` for how many sample to draw. Default False.
+        num_samples(int): set sample number to draw if :attr:`replacement`
+                is True. Default None.
+        generator(Generator): specify a generator to sample the data source. Default None
+        
+    Returns:
+        Sampler: a Sampler yield sample index randomly
+
+    Examples:
+
+        .. code-block:: python
+            
+            from paddle.io import Dataset, RandomSampler
+
+            class RandomDataset(Dataset):
+                def __init__(self, num_samples):
+                    self.num_samples = num_samples
+            
+                def __getitem__(self, idx):
+                    image = np.random.random([784]).astype('float32')
+                    label = np.random.randint(0, 9, (1, )).astype('int64')
+                    return image, label
+                
+                def __len__(self):
+                    return self.num_samples
+
+            sampler = RandomSampler(data_souce=RandomDataset(100))
+
+            for index in sampler:
+                print(index)
+
+    see `paddle.io.Sampler`
+    """
+
+    def __init__(self,
+                 data_source,
+                 replacement=False,
+                 num_samples=None,
+                 generator=None):
+        self.data_source = data_source
+        self.replacement = replacement
+        self._num_samples = num_samples
+        self.generator = generator
+
+        if not isinstance(self.replacement, bool):
+            raise TypeError("expect boolean value for replacement, but got "
+                            "replacement={}".format(self.replacement))
+
+        if self._num_samples is not None and not replacement:
+            raise ValueError(
+                "num_samples should not be specified while replacement is False")
+
+        if not isinstance(self.num_samples, int) or self.num_samples <= 0:
+            raise ValueError("num_samples should be a positive integer, "
+                             "but got num_samples={}".format(self.num_samples))
+
+    @property
+    def num_samples(self):
+        if self._num_samples is None:
+            return len(self.data_source)
+        return self._num_samples
+
+    def __iter__(self):
+        n = len(self.data_source)
+        if self.generator:
+            for index in self.generator:
+                yield index
+        else:
+            if self.replacement:
+                for index in np.random.choice(
+                        np.arange(n), self.num_samples, replace=True).tolist():
+                    yield index
+            else:
+                for index in np.random.choice(
+                        np.arange(n), n, replace=False).tolist():
+                    yield index
+
+    def __len__(self):
+        return self.num_samples