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import os
import logging
from typing import Any, Union, Optional
import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from pytorch_lightning import LightningDataModule
from pytorch_lightning.utilities.cli import DATAMODULE_REGISTRY
from terragpu.ai.deep_learning.datasets.segmentation_dataset \
import SegmentationDataset
@DATAMODULE_REGISTRY
class SegmentationDataModule(LightningDataModule):
def __init__(
self,
# Dataset parameters
dataset_dir: str = 'dataset/',
images_regex: str = 'dataset/images/*.tif',
labels_regex: str = 'dataset/labels/*.tif',
generate_dataset: bool = True,
tile_size: int = 256,
max_patches: Union[float, int] = 100,
augment: bool = True,
chunks: dict = {'band': 1, 'x': 2048, 'y': 2048},
input_bands: list = ['CB', 'B', 'G', 'Y', 'R', 'RE', 'N1', 'N2'],
output_bands: list = ['B', 'G', 'R'],
seed: int = 24,
normalize: bool = True,
pytorch: bool = True,
# Datamodule parameters
val_split: float = 0.2,
test_split: float = 0.1,
num_workers: int = os.cpu_count(),
batch_size: int = 32,
shuffle: bool = True,
pin_memory: bool = False,
drop_last: bool = False,
# Inference parameters
raster_regex: str = 'rasters/*.tif',
*args: Any,
**kwargs: Any,
) -> None:
super().__init__(*args, **kwargs)
# Dataset parameters
self.images_regex = images_regex
self.labels_regex = labels_regex
self.dataset_dir = dataset_dir
self.generate_dataset = generate_dataset
self.tile_size = tile_size
self.max_patches = max_patches
self.augment = augment
self.chunks = chunks
self.input_bands = input_bands
self.output_bands = output_bands
self.seed = seed
self.normalize = normalize
self.pytorch = pytorch
self.val_split = val_split
self.test_split = test_split
self.raster_regex = raster_regex
# Performance parameters
self.batch_size = batch_size
self.num_workers = num_workers
self.shuffle = shuffle
self.pin_memory = pin_memory
self.drop_last = drop_last
def prepare_data(self):
if self.generate_dataset:
SegmentationDataset(
images_regex=self.images_regex,
labels_regex=self.labels_regex,
dataset_dir=self.dataset_dir,
generate_dataset=self.generate_dataset,
tile_size=self.tile_size,
max_patches=self.max_patches,
augment=self.augment,
chunks=self.chunks,
input_bands=self.input_bands,
output_bands=self.output_bands,
seed=self.seed,
normalize=self.normalize,
pytorch=self.pytorch,
)
def setup(self, stage: Optional[str] = None):
# Split into train, val, test
segmentation_dataset = SegmentationDataset(
images_regex=self.images_regex,
labels_regex=self.labels_regex,
dataset_dir=self.dataset_dir,
generate_dataset=False,
tile_size=self.tile_size,
max_patches=self.max_patches,
augment=self.augment,
chunks=self.chunks,
input_bands=self.input_bands,
output_bands=self.output_bands,
seed=self.seed,
normalize=self.normalize,
pytorch=self.pytorch,
)
# Split datasets into train, val, and test sets
val_len = round(self.val_split * len(segmentation_dataset))
test_len = round(self.test_split * len(segmentation_dataset))
train_len = len(segmentation_dataset) - val_len - test_len
# Initialize datasets
self.train_set, self.val_set, self.test_set = random_split(
segmentation_dataset, lengths=[train_len, val_len, test_len],
generator=torch.Generator().manual_seed(self.seed)
)
logging.info("Initialized datasets...")
def train_dataloader(self) -> DataLoader:
loader = DataLoader(
self.train_set,
batch_size=self.batch_size,
shuffle=self.shuffle,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory,
)
return loader
def val_dataloader(self) -> DataLoader:
loader = DataLoader(
self.val_set,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory,
)
return loader
def test_dataloader(self) -> DataLoader:
loader = DataLoader(
self.test_set,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
drop_last=self.drop_last,
pin_memory=self.pin_memory,
)
return loader
def predict_dataloader(self) -> DataLoader:
raise NotImplementedError
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