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| import math
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| import random
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| from torch.utils.data import ConcatDataset, Dataset
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| from torch.utils.data.sampler import (
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| BatchSampler,
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| RandomSampler,
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| Sampler,
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| SequentialSampler,
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| )
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| class ScheduledSampler(Sampler):
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| """A sampler that samples data from a given concat-dataset.
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| Args:
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| concat_dataset (ConcatDataset): a concatenated dataset consisting of all datasets
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| batch_size (int): batch size
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| holistic_shuffle (bool): whether to shuffle the whole dataset or not
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| logger (logging.Logger): logger to print warning message
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| Usage:
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| For cfg.train.batch_size = 3, cfg.train.holistic_shuffle = False, cfg.train.drop_last = True:
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| >>> list(ScheduledSampler(ConcatDataset([0, 1, 2], [3, 4, 5], [6, 7, 8]])))
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| [3, 4, 5, 0, 1, 2, 6, 7, 8]
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| """
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| def __init__(
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| self, concat_dataset, batch_size, holistic_shuffle, logger=None, type="train"
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| ):
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| if not isinstance(concat_dataset, ConcatDataset):
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| raise ValueError(
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| "concat_dataset must be an instance of ConcatDataset, but got {}".format(
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| type(concat_dataset)
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| )
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| )
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| if not isinstance(batch_size, int):
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| raise ValueError(
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| "batch_size must be an integer, but got {}".format(type(batch_size))
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| )
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| if not isinstance(holistic_shuffle, bool):
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| raise ValueError(
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| "holistic_shuffle must be a boolean, but got {}".format(
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| type(holistic_shuffle)
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| )
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| )
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| self.concat_dataset = concat_dataset
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| self.batch_size = batch_size
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| self.holistic_shuffle = holistic_shuffle
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| affected_dataset_name = []
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| affected_dataset_len = []
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| for dataset in concat_dataset.datasets:
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| dataset_len = len(dataset)
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| dataset_name = dataset.get_dataset_name()
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| if dataset_len < batch_size:
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| affected_dataset_name.append(dataset_name)
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| affected_dataset_len.append(dataset_len)
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| self.type = type
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| for dataset_name, dataset_len in zip(
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| affected_dataset_name, affected_dataset_len
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| ):
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| if not type == "valid":
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| logger.warning(
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| "The {} dataset {} has a length of {}, which is smaller than the batch size {}. This may cause unexpected behavior.".format(
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| type, dataset_name, dataset_len, batch_size
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| )
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| )
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| def __len__(self):
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| num_of_batches = sum(
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| [
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| math.floor(len(dataset) / self.batch_size)
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| for dataset in self.concat_dataset.datasets
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| ]
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| )
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| return num_of_batches * self.batch_size
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| def __iter__(self):
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| iters = []
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| for dataset in self.concat_dataset.datasets:
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| iters.append(
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| SequentialSampler(dataset).__iter__()
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| if self.holistic_shuffle
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| else RandomSampler(dataset).__iter__()
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| )
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| init_indices = [0] + self.concat_dataset.cumulative_sizes[:-1]
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| output_batches = []
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| for dataset_idx in range(len(self.concat_dataset.datasets)):
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| cur_batch = []
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| for idx in iters[dataset_idx]:
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| cur_batch.append(idx + init_indices[dataset_idx])
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| if len(cur_batch) == self.batch_size:
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| output_batches.append(cur_batch)
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| cur_batch = []
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| if self.type == "valid" and len(cur_batch) > 0:
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| output_batches.append(cur_batch)
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| cur_batch = []
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| random.shuffle(output_batches)
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| output_indices = [item for sublist in output_batches for item in sublist]
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| return iter(output_indices)
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| def build_samplers(concat_dataset: Dataset, cfg, logger, type):
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| sampler = ScheduledSampler(
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| concat_dataset,
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| cfg.train.batch_size,
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| cfg.train.sampler.holistic_shuffle,
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| logger,
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| type,
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| )
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| batch_sampler = BatchSampler(
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| sampler,
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| cfg.train.batch_size,
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| cfg.train.sampler.drop_last if not type == "valid" else False,
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| )
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| return sampler, batch_sampler
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|
