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Source code for torchgeo.datamodules.fair1m

# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.

"""FAIR1M datamodule."""

from typing import Any

import torch
from torch import Tensor

from ..datasets import FAIR1M
from .geo import NonGeoDataModule


def collate_fn(batch: list[dict[str, Tensor]]) -> dict[str, Any]:
    """Custom object detection collate fn to handle variable boxes.

    Args:
        batch: list of sample dicts return by dataset

    Returns:
        batch dict output

    .. versionadded:: 0.5
    """
    output: dict[str, Any] = {}
    output["image"] = torch.stack([sample["image"] for sample in batch])

    if "boxes" in batch[0]:
        output["boxes"] = [sample["boxes"] for sample in batch]
    if "label" in batch[0]:
        output["label"] = [sample["label"] for sample in batch]

    return output


[docs]class FAIR1MDataModule(NonGeoDataModule): """LightningDataModule implementation for the FAIR1M dataset. .. versionadded:: 0.2 """
[docs] def __init__( self, batch_size: int = 64, num_workers: int = 0, **kwargs: Any ) -> None: """Initialize a new FAIR1MDataModule instance. Args: batch_size: Size of each mini-batch. num_workers: Number of workers for parallel data loading. **kwargs: Additional keyword arguments passed to :class:`~torchgeo.datasets.FAIR1M`. .. versionchanged:: 0.5 Removed *val_split_pct* and *test_split_pct* parameters. """ super().__init__(FAIR1M, batch_size, num_workers, **kwargs) self.collate_fn = collate_fn
[docs] def setup(self, stage: str) -> None: """Set up datasets. Args: stage: Either 'fit', 'validate', 'test', or 'predict'. """ if stage in ["fit"]: self.train_dataset = FAIR1M(split="train", **self.kwargs) if stage in ["fit", "validate"]: self.val_dataset = FAIR1M(split="val", **self.kwargs) if stage in ["predict"]: # Test set labels are not publicly available self.predict_dataset = FAIR1M(split="test", **self.kwargs)

© Copyright 2021, Microsoft Corporation. Revision 0171b1e3.

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