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Author: samedii

README.rst

@@ -52,75 +52,36 @@ a more extensive list on API and usage.
         .multi_sample
         .sample_proportion
 
-Construct informative batches
------------------------------
+Merge / stratify / oversample datastreams
+-----------------------------------------
+The fruit datastreams given below repeatedly yields the string of its fruit
+type.
 
->>> datastream = Datastream.merge([
-...     (apple_datastream, 2),
-...     (pear_datastream, 1),
-...     (banana_datastream, 1),
-... ])
-... next(iter(datastream.data_loader(batch_size=6)))
-['apple', 'apple', 'pear', 'banana', 'apple', 'apple']
+.. code-block:: python
 
+    >>> datastream = Datastream.merge([
+    ...     (apple_datastream, 2),
+    ...     (pear_datastream, 1),
+    ...     (banana_datastream, 1),
+    ... ])
+    >>> next(iter(datastream.data_loader(batch_size=8)))
+    ['apple', 'apple', 'pear', 'banana', 'apple', 'apple', 'pear', 'banana']
 
 Zip independently sampled datastreams
 -------------------------------------
+The fruit datastreams given below repeatedly yields the string of its fruit
+type.
 
 .. code-block:: python
 
-    datastream = Datastream.zip([
-        apple_datastream,
-        Datastream.merge([pear_datastream, banana_datastream])
-    ])
-
-Pipeline functions
-------------------
-
-.. code-block:: python
-
-    from PIL import Image
-    from imgaug import augmenters as iaa
-    from datastream import Dataset
-
-    augmenter = iaa.Sequential([...])
-
-    def preprocess(image, class_names):
-        ...
-
-    dataset = (
-        Dataset.from_dataframe(df)
-        .map(lambda row: (
-            row['image_path'],
-            row['class_names'],
-        ))
-        .map(lambda image_path, class_names: (
-            Image.open(image_path),
-            class_names,
-        ))
-        .map(lambda image, class_names: (
-            augmenter.augment(image=image),
-            class_names,
-        ))
-        .map(preprocess)
-    )
-
-Datastream to pytorch data loader
----------------------------------
-
-.. code-block:: python
-
-    data_loader = (
-        Datastream(dataset)
-        .data_loader(
-            batch_size=32,
-            num_workers=8,
-            n_batches_per_epoch=100,
-        )
-    )
+    >>> datastream = Datastream.zip([
+    ...     apple_datastream,
+    ...     Datastream.merge([pear_datastream, banana_datastream]),
+    ... ])
+    >>> next(iter(datastream.data_loader(batch_size=4)))
+    [('apple', 'pear'), ('apple', 'banana'), ('apple', 'pear'), ('apple', 'banana')]
 
 More usage examples
 -------------------
-
 See the `documentation <https://pytorch-datastream.readthedocs.io/en/latest/>`_
-for examples with oversampling / stratification and weighted sampling.
+for more usage examples.

datastream/datastream.py

@@ -30,7 +30,7 @@
 
 class Datastream(BaseModel, Generic[T]):
     '''
-    ``Datastream`` combines a ``Dataset`` and a sampler into a stream of
+    ``Datastream[T]`` combines a ``Dataset[T]`` and a sampler into a stream of
     examples. By default the samples are drawn without replacement until the
     full dataset is exhausted. The proportion of the dataset that should be
     drawn before allowing replacement can be changed with ``.sample_proportion``.
@@ -119,9 +119,9 @@ def zip(datastreams: List[Datastream]) -> Datastream[Tuple]:
     def map(
         self: Datastream[T], function: Callable[Union[[T], [...]], R]
     ) -> Datastream[R]:
-        '''
-        Creates a new Datastream with a new mapped dataset. See ``Dataset.map``
-        for details.
+        ''' 
+        Creates a new Datastream with a new mapped dataset. See
+        :func:`Dataset.map` for details.
         '''
         return Datastream(
             self.dataset.map(function),