"""BSD 3-Clause License
Copyright (c) Soumith Chintala 2016,
All rights reserved.
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions are met:
* Redistributions of source code must retain the above copyright notice, this
list of conditions and the following disclaimer.
* Redistributions in binary form must reproduce the above copyright notice,
this list of conditions and the following disclaimer in the documentation
and/or other materials provided with the distribution.
* Neither the name of the copyright holder nor the names of its
contributors may be used to endorse or promote products derived from
this software without specific prior written permission.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
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"""
from collections import defaultdict
from torch.utils.data.sampler import BatchSampler, Sampler
[docs]class GroupedBatchSampler(BatchSampler):
"""Wraps another sampler to yield a mini-batch of indices.
It enforces that the batch only contain elements from the same group.
It also tries to provide mini-batches which follows an ordering which is
as close as possible to the ordering from the original sampler.
Parameters
----------
sampler: Sampler
Base sampler.
group_ids: list[int]
If the sampler produces indices in range [0, N),
`group_ids` must be a list of `N` ints which contains the group id of each sample.
The group ids must be a continuous set of integers starting from
0, i.e. they must be in the range [0, num_groups).
batch_size: int
Size of mini-batch.
"""
def __init__(self, sampler, group_ids, batch_size):
if not isinstance(sampler, Sampler):
raise ValueError(
"sampler should be an instance of "
"torch.utils.data.Sampler, but got sampler={}".format(sampler)
)
self.sampler = sampler
self.group_ids = group_ids
self.batch_size = batch_size
def __iter__(self):
buffer_per_group = defaultdict(list)
samples_per_group = defaultdict(list)
num_batches = 0
for idx in self.sampler:
group_id = self.group_ids[idx]
buffer_per_group[group_id].append(idx)
samples_per_group[group_id].append(idx)
if len(buffer_per_group[group_id]) == self.batch_size:
yield buffer_per_group[group_id]
num_batches += 1
del buffer_per_group[group_id]
assert len(buffer_per_group[group_id]) < self.batch_size
# now we have run out of elements that satisfy
# the group criteria, let's return the remaining
# elements so that the size of the sampler is
# deterministic
expected_num_batches = len(self)
num_remaining = expected_num_batches - num_batches
if num_remaining > 0:
# for the remaining batches, take first the buffers with largest number
# of elements
for group_id, _ in sorted(buffer_per_group.items(),
key=lambda x: len(x[1]), reverse=True):
remaining = self.batch_size - len(buffer_per_group[group_id])
buffer_per_group[group_id].extend(
samples_per_group[group_id][:remaining])
assert len(buffer_per_group[group_id]) == self.batch_size
yield buffer_per_group[group_id]
num_remaining -= 1
if num_remaining == 0:
break
assert num_remaining == 0
def __len__(self):
return len(self.sampler) // self.batch_size