Source code for torchgeo.datamodules.so2sat
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
"""So2Sat datamodule."""
from typing import Any, Dict, Optional, cast
import pytorch_lightning as pl
import torch
from torch.utils.data import DataLoader
from torchvision.transforms import Compose
from ..datasets import So2Sat
# https://github.com/pytorch/pytorch/issues/60979
# https://github.com/pytorch/pytorch/pull/61045
DataLoader.__module__ = "torch.utils.data"
class So2SatDataModule(pl.LightningDataModule):
"""LightningDataModule implementation for the So2Sat dataset.
Uses the train/val/test splits from the dataset.
"""
band_means = torch.tensor( # type: ignore[attr-defined]
[
-3.591224256609313e-05,
-7.658561276843396e-06,
5.9373857475971184e-05,
2.5166231537121083e-05,
0.04420110659759328,
0.25761027084996196,
0.0007556743372573258,
0.0013503466830024448,
0.12375696117681859,
0.1092774636368323,
0.1010855203267882,
0.1142398616114001,
0.1592656692023089,
0.18147236008771792,
0.1745740312291377,
0.19501607349635292,
0.15428468872076637,
0.10905050699570007,
]
).reshape(18, 1, 1)
band_stds = torch.tensor( # type: ignore[attr-defined]
[
0.17555201137417686,
0.17556463274968204,
0.45998793417834255,
0.455988755730148,
2.8559909213125763,
8.324800606439833,
2.4498757382563103,
1.4647352984509094,
0.03958795985905458,
0.047778262752410296,
0.06636616706371974,
0.06358874912497474,
0.07744387147984592,
0.09101635085921553,
0.09218466562387101,
0.10164581233948201,
0.09991773043519253,
0.08780632509122865,
]
).reshape(18, 1, 1)
# this reorders the bands to put S2 RGB first, then remainder of S2, then S1
reindex_to_rgb_first = [
10,
9,
8,
11,
12,
13,
14,
15,
16,
17,
# 0,
# 1,
# 2,
# 3,
# 4,
# 5,
# 6,
# 7,
]
[docs] def __init__(
self,
root_dir: str,
batch_size: int = 64,
num_workers: int = 0,
bands: str = "rgb",
unsupervised_mode: bool = False,
**kwargs: Any,
) -> None:
"""Initialize a LightningDataModule for So2Sat based DataLoaders.
Args:
root_dir: The ``root`` arugment to pass to the So2Sat Dataset classes
batch_size: The batch size to use in all created DataLoaders
num_workers: The number of workers to use in all created DataLoaders
bands: Either "rgb" or "s2"
unsupervised_mode: Makes the train dataloader return imagery from the train,
val, and test sets
"""
super().__init__() # type: ignore[no-untyped-call]
self.root_dir = root_dir
self.batch_size = batch_size
self.num_workers = num_workers
self.bands = bands
self.unsupervised_mode = unsupervised_mode
[docs] def preprocess(self, sample: Dict[str, Any]) -> Dict[str, Any]:
"""Transform a single sample from the Dataset.
Args:
sample: dictionary containing image
Returns:
preprocessed sample
"""
# sample["image"] = (sample["image"] - self.band_means) / self.band_stds
sample["image"] = sample["image"].float()
sample["image"] = sample["image"][self.reindex_to_rgb_first, :, :]
if self.bands == "rgb":
sample["image"] = sample["image"][:3, :, :]
return sample
[docs] def prepare_data(self) -> None:
"""Make sure that the dataset is downloaded.
This method is only called once per run.
"""
So2Sat(self.root_dir, checksum=False)
[docs] def setup(self, stage: Optional[str] = None) -> None:
"""Initialize the main ``Dataset`` objects.
This method is called once per GPU per run.
Args:
stage: stage to set up
"""
train_transforms = Compose([self.preprocess])
val_test_transforms = self.preprocess
if not self.unsupervised_mode:
self.train_dataset = So2Sat(
self.root_dir, split="train", transforms=train_transforms
)
self.val_dataset = So2Sat(
self.root_dir, split="validation", transforms=val_test_transforms
)
self.test_dataset = So2Sat(
self.root_dir, split="test", transforms=val_test_transforms
)
else:
temp_train = So2Sat(
self.root_dir, split="train", transforms=train_transforms
)
self.val_dataset = So2Sat(
self.root_dir, split="validation", transforms=train_transforms
)
self.test_dataset = So2Sat(
self.root_dir, split="test", transforms=train_transforms
)
self.train_dataset = cast(
So2Sat, temp_train + self.val_dataset + self.test_dataset
)
[docs] def train_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for training.
Returns:
training data loader
"""
return DataLoader(
self.train_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=True,
)
[docs] def val_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for validation.
Returns:
validation data loader
"""
return DataLoader(
self.val_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=False,
)
[docs] def test_dataloader(self) -> DataLoader[Any]:
"""Return a DataLoader for testing.
Returns:
testing data loader
"""
return DataLoader(
self.test_dataset,
batch_size=self.batch_size,
num_workers=self.num_workers,
shuffle=False,
)
