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69 lines
2.4 KiB
69 lines
2.4 KiB
3 years ago
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
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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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import time
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import paddle
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import numbers
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import numpy as np
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from paddle.distributed import ParallelEnv
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from paddle.io import DistributedBatchSampler
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from .repeat_dataset import RepeatDataset
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from ..utils.registry import Registry, build_from_config
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DATASETS = Registry("DATASETS")
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def build_dataset(cfg):
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name = cfg.pop('name')
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if name == 'RepeatDataset':
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dataset_ = build_from_config(cfg['dataset'], DATASETS)
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dataset = RepeatDataset(dataset_, cfg['times'])
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else:
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dataset = dataset = DATASETS.get(name)(**cfg)
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return dataset
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def build_dataloader(cfg, is_train=True, distributed=True):
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cfg_ = cfg.copy()
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batch_size = cfg_.pop('batch_size', 1)
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num_workers = cfg_.pop('num_workers', 0)
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use_shared_memory = cfg_.pop('use_shared_memory', True)
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dataset = build_dataset(cfg_)
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if distributed:
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sampler = DistributedBatchSampler(dataset,
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batch_size=batch_size,
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shuffle=True if is_train else False,
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drop_last=True if is_train else False)
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dataloader = paddle.io.DataLoader(dataset,
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batch_sampler=sampler,
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num_workers=num_workers,
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use_shared_memory=use_shared_memory)
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else:
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dataloader = paddle.io.DataLoader(dataset,
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batch_size=batch_size,
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shuffle=True if is_train else False,
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drop_last=True if is_train else False,
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use_shared_memory=use_shared_memory,
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num_workers=num_workers)
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return dataloader
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