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# Last modified: 2024-04-18
#
# Copyright 2023 Bingxin Ke, ETH Zurich. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# --------------------------------------------------------------------------
# If you find this code useful, we kindly ask you to cite our paper in your work.
# Please find bibtex at: https://github.com/prs-eth/Marigold#-citation
# If you use or adapt this code, please attribute to https://github.com/prs-eth/marigold.
# More information about the method can be found at https://marigoldmonodepth.github.io
# --------------------------------------------------------------------------
import torch
from torch.utils.data import (
BatchSampler,
RandomSampler,
SequentialSampler,
)
class MixedBatchSampler(BatchSampler):
"""Sample one batch from a selected dataset with given probability.
Compatible with datasets at different resolution
"""
def __init__(
self, src_dataset_ls, batch_size, drop_last, shuffle, prob=None, generator=None
):
self.base_sampler = None
self.batch_size = batch_size
self.shuffle = shuffle
self.drop_last = drop_last
self.generator = generator
self.src_dataset_ls = src_dataset_ls
self.n_dataset = len(self.src_dataset_ls)
# Dataset length
self.dataset_length = [len(ds) for ds in self.src_dataset_ls]
self.cum_dataset_length = [
sum(self.dataset_length[:i]) for i in range(self.n_dataset)
] # cumulative dataset length
# BatchSamplers for each source dataset
if self.shuffle:
self.src_batch_samplers = [
BatchSampler(
sampler=RandomSampler(
ds, replacement=False, generator=self.generator
),
batch_size=self.batch_size,
drop_last=self.drop_last,
)
for ds in self.src_dataset_ls
]
else:
self.src_batch_samplers = [
BatchSampler(
sampler=SequentialSampler(ds),
batch_size=self.batch_size,
drop_last=self.drop_last,
)
for ds in self.src_dataset_ls
]
self.raw_batches = [
list(bs) for bs in self.src_batch_samplers
] # index in original dataset
self.n_batches = [len(b) for b in self.raw_batches]
self.n_total_batch = sum(self.n_batches)
# sampling probability
if prob is None:
# if not given, decide by dataset length
self.prob = torch.tensor(self.n_batches) / self.n_total_batch
else:
self.prob = torch.as_tensor(prob)
def __iter__(self):
"""_summary_
Yields:
list(int): a batch of indics, corresponding to ConcatDataset of src_dataset_ls
"""
for _ in range(self.n_total_batch):
idx_ds = torch.multinomial(
self.prob, 1, replacement=True, generator=self.generator
).item()
# if batch list is empty, generate new list
if 0 == len(self.raw_batches[idx_ds]):
self.raw_batches[idx_ds] = list(self.src_batch_samplers[idx_ds])
# get a batch from list
batch_raw = self.raw_batches[idx_ds].pop()
# shift by cumulative dataset length
shift = self.cum_dataset_length[idx_ds]
batch = [n + shift for n in batch_raw]
yield batch
def __len__(self):
return self.n_total_batch
# Unit test
if "__main__" == __name__:
from torch.utils.data import ConcatDataset, DataLoader, Dataset
class SimpleDataset(Dataset):
def __init__(self, start, len) -> None:
super().__init__()
self.start = start
self.len = len
def __len__(self):
return self.len
def __getitem__(self, index):
return self.start + index
dataset_1 = SimpleDataset(0, 10)
dataset_2 = SimpleDataset(200, 20)
dataset_3 = SimpleDataset(1000, 50)
concat_dataset = ConcatDataset(
[dataset_1, dataset_2, dataset_3]
) # will directly concatenate
mixed_sampler = MixedBatchSampler(
src_dataset_ls=[dataset_1, dataset_2, dataset_3],
batch_size=4,
drop_last=True,
shuffle=False,
prob=[0.6, 0.3, 0.1],
generator=torch.Generator().manual_seed(0),
)
loader = DataLoader(concat_dataset, batch_sampler=mixed_sampler)
for d in loader:
print(d)
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