80 lines
3.2 KiB
Python
80 lines
3.2 KiB
Python
# Copyright (c) 2021 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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from __future__ import absolute_import
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from __future__ import division
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from paddle.io import DistributedBatchSampler, Sampler
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from ppcls.utils import logger
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from ppcls.data.dataloader.mix_dataset import MixDataset
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from ppcls.data import dataloader
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class MixSampler(DistributedBatchSampler):
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def __init__(self, dataset, batch_size, sample_configs, iter_per_epoch):
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super().__init__(dataset, batch_size)
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assert isinstance(dataset,
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MixDataset), "MixSampler only support MixDataset"
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self.sampler_list = []
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self.batch_size = batch_size
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self.start_list = []
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self.length = iter_per_epoch
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dataset_list = dataset.get_dataset_list()
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batch_size_left = self.batch_size
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self.iter_list = []
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for i, config_i in enumerate(sample_configs):
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self.start_list.append(dataset_list[i][1])
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sample_method = config_i.pop("name")
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ratio_i = config_i.pop("ratio")
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if i < len(sample_configs) - 1:
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batch_size_i = int(self.batch_size * ratio_i)
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batch_size_left -= batch_size_i
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else:
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batch_size_i = batch_size_left
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assert batch_size_i <= len(dataset_list[i][2])
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config_i["batch_size"] = batch_size_i
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if sample_method == "DistributedBatchSampler":
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sampler_i = DistributedBatchSampler(dataset_list[i][2],
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**config_i)
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else:
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sampler_i = getattr(dataloader, sample_method)(
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dataset_list[i][2], **config_i)
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self.sampler_list.append(sampler_i)
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self.iter_list.append(iter(sampler_i))
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self.length += len(dataset_list[i][2]) * ratio_i
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self.iter_counter = 0
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def __iter__(self):
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while self.iter_counter < self.length:
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batch = []
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for i, iter_i in enumerate(self.iter_list):
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batch_i = next(iter_i, None)
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if batch_i is None:
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iter_i = iter(self.sampler_list[i])
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self.iter_list[i] = iter_i
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batch_i = next(iter_i, None)
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assert batch_i is not None, "dataset {} return None".format(
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i)
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batch += [idx + self.start_list[i] for idx in batch_i]
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if len(batch) == self.batch_size:
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self.iter_counter += 1
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yield batch
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else:
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logger.info("Some dataset reaches end")
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self.iter_counter = 0
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def __len__(self):
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return self.length
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