chore: import upstream snapshot with attribution

python/paddle/io/dataloader/fetcher.py

@@ -0,0 +1,86 @@
+#   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.
+
+
+class _DatasetFetcher:
+    def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
+        self.dataset = dataset
+        self.auto_collate_batch = auto_collate_batch
+        self.collate_fn = collate_fn
+        self.drop_last = drop_last
+
+    # NOTE: fetch function here perform the whole pipeline of dataset
+    #       reading and data transforms of a batch in each calling, this
+    #       may take a long time inside, if DataLoader is exit outside,
+    #       fetch need to perceive exit situation, so we pass done_event
+    #       here for fetch to check exit status
+    # NOTE: if DataLoader exit by `break`, performing GPU tensor operations,
+    #       e.g. to_tensor may cause SIGSEGV in thread, so we pass the
+    #       done_event argument to check DataLoader exit status between
+    #       each sample processing in the batch
+    def fetch(self, batch_indices, done_event=None):
+        raise NotImplementedError(
+            f"'fetch' not implement for class {self.__class__.__name__}"
+        )
+
+
+class _IterableDatasetFetcher(_DatasetFetcher):
+    def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
+        super().__init__(dataset, auto_collate_batch, collate_fn, drop_last)
+        self.dataset_iter = iter(dataset)
+
+    def fetch(self, batch_indices, done_event=None):
+        if self.auto_collate_batch:
+            data = []
+            for _ in batch_indices:
+                if done_event is None or not done_event.is_set():
+                    try:
+                        data.append(next(self.dataset_iter))
+                    except StopIteration:
+                        break
+                else:
+                    return None
+
+            if len(data) == 0 or (
+                self.drop_last and len(data) < len(batch_indices)
+            ):
+                raise StopIteration
+
+        else:
+            data = next(self.dataset_iter)
+
+        if self.collate_fn:
+            data = self.collate_fn(data)
+        return data
+
+
+class _MapDatasetFetcher(_DatasetFetcher):
+    def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
+        super().__init__(dataset, auto_collate_batch, collate_fn, drop_last)
+
+    def fetch(self, batch_indices, done_event=None):
+        if self.auto_collate_batch:
+            data = []
+            for idx in batch_indices:
+                if done_event is None or not done_event.is_set():
+                    data.append(self.dataset[idx])
+                else:
+                    return None
+
+        else:
+            data = self.dataset[batch_indices]
+
+        if self.collate_fn:
+            data = self.collate_fn(data)
+        return data