SegMamba/monai/data/samplers.py

# Copyright (c) MONAI Consortium
# 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.

from __future__ import annotations

from collections.abc import Sequence

import torch
from torch.utils.data import Dataset
from torch.utils.data import DistributedSampler as _TorchDistributedSampler

__all__ = ["DistributedSampler", "DistributedWeightedRandomSampler"]


class DistributedSampler(_TorchDistributedSampler):
    """
    Enhance PyTorch DistributedSampler to support non-evenly divisible sampling.

    Args:
        dataset: Dataset used for sampling.
        even_divisible: if False, different ranks can have different data length.
            for example, input data: [1, 2, 3, 4, 5], rank 0: [1, 3, 5], rank 1: [2, 4].
        num_replicas: number of processes participating in distributed training.
            by default, `world_size` is retrieved from the current distributed group.
        rank: rank of the current process within `num_replicas`. by default,
            `rank` is retrieved from the current distributed group.
        shuffle: if `True`, sampler will shuffle the indices, default to True.
        kwargs: additional arguments for `DistributedSampler` super class, can be `seed` and `drop_last`.

    More information about DistributedSampler, please check:
    https://pytorch.org/docs/stable/data.html#torch.utils.data.distributed.DistributedSampler.

    """

    def __init__(
        self,
        dataset: Dataset,
        even_divisible: bool = True,
        num_replicas: int | None = None,
        rank: int | None = None,
        shuffle: bool = True,
        **kwargs,
    ):
        super().__init__(dataset=dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle, **kwargs)

        if not even_divisible:
            data_len = len(dataset)  # type: ignore
            if data_len < self.num_replicas:
                raise ValueError("the dataset length is less than the number of participating ranks.")
            extra_size = self.total_size - data_len
            if self.rank + extra_size >= self.num_replicas:
                self.num_samples -= 1
            self.total_size = data_len


class DistributedWeightedRandomSampler(DistributedSampler):
    """
    Extend the `DistributedSampler` to support weighted sampling.
    Refer to `torch.utils.data.WeightedRandomSampler`, for more details please check:
    https://pytorch.org/docs/stable/data.html#torch.utils.data.WeightedRandomSampler.

    Args:
        dataset: Dataset used for sampling.
        weights: a sequence of weights, not necessary summing up to one, length should exactly
            match the full dataset.
        num_samples_per_rank: number of samples to draw for every rank, sample from
            the distributed subset of dataset.
            if None, default to the length of dataset split by DistributedSampler.
        generator: PyTorch Generator used in sampling.
        even_divisible: if False, different ranks can have different data length.
            for example, input data: [1, 2, 3, 4, 5], rank 0: [1, 3, 5], rank 1: [2, 4].'
        num_replicas: number of processes participating in distributed training.
            by default, `world_size` is retrieved from the current distributed group.
        rank: rank of the current process within `num_replicas`. by default,
            `rank` is retrieved from the current distributed group.
        kwargs: additional arguments for `DistributedSampler` super class, can be `seed` and `drop_last`.

    """

    def __init__(
        self,
        dataset: Dataset,
        weights: Sequence[float],
        num_samples_per_rank: int | None = None,
        generator: torch.Generator | None = None,
        even_divisible: bool = True,
        num_replicas: int | None = None,
        rank: int | None = None,
        **kwargs,
    ):
        kwargs.setdefault("shuffle", True)
        super().__init__(dataset=dataset, even_divisible=even_divisible, num_replicas=num_replicas, rank=rank, **kwargs)
        self.weights = weights
        self.num_samples_per_rank = num_samples_per_rank if num_samples_per_rank is not None else self.num_samples
        self.generator = generator

    def __iter__(self):
        indices = list(super().__iter__())
        weights = torch.as_tensor([self.weights[i] for i in indices], dtype=torch.double)
        # sample based on the provided weights
        rand_tensor = torch.multinomial(weights, self.num_samples_per_rank, True, generator=self.generator)

        for i in rand_tensor:
            yield indices[i]

    def __len__(self):
        return self.num_samples_per_rank