wehub-resource-sync
87 lines
3.2 KiB
Python
87 lines
3.2 KiB
Python
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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class _DatasetFetcher:
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def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
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self.dataset = dataset
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self.auto_collate_batch = auto_collate_batch
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self.collate_fn = collate_fn
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self.drop_last = drop_last
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# NOTE: fetch function here perform the whole pipeline of dataset
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# reading and data transforms of a batch in each calling, this
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# may take a long time inside, if DataLoader is exit outside,
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# fetch need to perceive exit situation, so we pass done_event
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# here for fetch to check exit status
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# NOTE: if DataLoader exit by `break`, performing GPU tensor operations,
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# e.g. to_tensor may cause SIGSEGV in thread, so we pass the
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# done_event argument to check DataLoader exit status between
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# each sample processing in the batch
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def fetch(self, batch_indices, done_event=None):
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raise NotImplementedError(
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f"'fetch' not implement for class {self.__class__.__name__}"
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)
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class _IterableDatasetFetcher(_DatasetFetcher):
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def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
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super().__init__(dataset, auto_collate_batch, collate_fn, drop_last)
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self.dataset_iter = iter(dataset)
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def fetch(self, batch_indices, done_event=None):
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if self.auto_collate_batch:
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data = []
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for _ in batch_indices:
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if done_event is None or not done_event.is_set():
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try:
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data.append(next(self.dataset_iter))
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except StopIteration:
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break
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else:
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return None
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if len(data) == 0 or (
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self.drop_last and len(data) < len(batch_indices)
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):
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raise StopIteration
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else:
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data = next(self.dataset_iter)
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if self.collate_fn:
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data = self.collate_fn(data)
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return data
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class _MapDatasetFetcher(_DatasetFetcher):
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def __init__(self, dataset, auto_collate_batch, collate_fn, drop_last):
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super().__init__(dataset, auto_collate_batch, collate_fn, drop_last)
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def fetch(self, batch_indices, done_event=None):
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if self.auto_collate_batch:
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data = []
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for idx in batch_indices:
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if done_event is None or not done_event.is_set():
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data.append(self.dataset[idx])
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else:
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return None
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else:
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data = self.dataset[batch_indices]
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if self.collate_fn:
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data = self.collate_fn(data)
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return data
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