chore: import upstream snapshot with attribution

nemo/core/classes/dataset.py

@@ -0,0 +1,108 @@
+# Copyright (c) 2020, NVIDIA CORPORATION.  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 dataclasses import dataclass
+from typing import Optional
+
+from torch.utils import data
+
+from nemo.core.classes import Serialization, Typing, typecheck
+
+__all__ = ['Dataset', 'IterableDataset']
+
+
+class Dataset(data.Dataset, Typing, Serialization):
+    """Dataset with output ports
+
+    Please Note: Subclasses of IterableDataset should *not* implement input_types.
+    """
+
+    def _collate_fn(self, batch):
+        """
+        A default implementation of a collation function.
+        Users should override this method to define custom data loaders.
+        """
+        return data.dataloader.default_collate(batch)
+
+    @typecheck()
+    def collate_fn(self, batch):
+        """
+        This is the method that user pass as functor to DataLoader.
+        The method optionally performs neural type checking and add types to the outputs.
+
+        Please note, subclasses of Dataset should not implement `input_types`.
+
+        Usage:
+
+        .. code-block:: python
+
+            dataloader = torch.utils.data.DataLoader(
+                    ....,
+                    collate_fn=dataset.collate_fn,
+                    ....
+            )
+
+        Returns:
+            Collated batch, with or without types.
+        """
+        if self.input_types is not None:
+            raise TypeError("Datasets should not implement `input_types` as they are not checked")
+
+        # Simply forward the inner `_collate_fn`
+        return self._collate_fn(batch)
+
+
+class IterableDataset(data.IterableDataset, Typing, Serialization):
+    """Iterable Dataset with output ports
+
+    Please Note: Subclasses of IterableDataset should *not* implement input_types.
+    """
+
+    def _collate_fn(self, batch):
+        """
+        A default implementation of a collation function.
+        Users should override this method to define custom data loaders.
+        """
+        return data.dataloader.default_collate(batch)
+
+    @typecheck()
+    def collate_fn(self, batch):
+        """
+        This is the method that user pass as functor to DataLoader.
+        The method optionally performs neural type checking and add types to the outputs.
+
+        # Usage:
+        dataloader = torch.utils.data.DataLoader(
+                ....,
+                collate_fn=dataset.collate_fn,
+                ....
+        )
+
+        Returns:
+            Collated batch, with or without types.
+        """
+        if self.input_types is not None:
+            raise TypeError("Datasets should not implement `input_types` as they are not checked")
+
+        # Simply forward the inner `_collate_fn`
+        return self._collate_fn(batch)
+
+
+@dataclass
+class DatasetConfig:
+    # ...
+    batch_size: int = 32
+    drop_last: bool = False
+    shuffle: bool = False
+    num_workers: Optional[int] = 0
+    pin_memory: bool = True