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79 lines
3.2 KiB
79 lines
3.2 KiB
# 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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