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import kornia as K
import torch
from torchgeo.datasets import CloudCoverDetection
from typing import ClassVar
from collections.abc import Callable, Sequence
from torch import Tensor
from datetime import date
import os
import pandas as pd
import numpy as np
import rasterio
from pyproj import Transformer
from typing import TypeAlias
Path: TypeAlias = str | os.PathLike[str]
class SenBenchCloudS2(CloudCoverDetection):
url = None
all_bands = ('B02', 'B03', 'B04', 'B08')
splits: ClassVar[dict[str, str]] = {'train': 'public', 'val': 'private', 'test': 'private'}
def __init__(
self,
root: Path = 'data',
split: str = 'train',
bands: Sequence[str] = all_bands,
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
) -> None:
#super().__init__(root=root, split=split, bands=bands, transforms=transforms, download=download)
assert split in self.splits
assert set(bands) <= set(self.all_bands)
self.root = root
self.split = split
self.bands = bands
self.transforms = transforms
self.download = download
self.csv = os.path.join(self.root, self.split, f'{self.split}_metadata.csv')
self._verify()
self.metadata = pd.read_csv(self.csv)
self.reference_date = date(1970, 1, 1)
self.patch_area = (16*10)**2 # patchsize 16 pix, gsd 10m
def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Returns a sample from dataset.
Args:
index: index to return
Returns:
data, metadata (lon,lat,days,area) and label at given index
"""
chip_id = self.metadata.iat[index, 0]
date_str = self.metadata.iat[index, 2]
date_obj = date(int(date_str[:4]), int(date_str[5:7]), int(date_str[8:10]))
delta = (date_obj - self.reference_date).days
image, coord = self._load_image(chip_id)
label = self._load_target(chip_id)
meta_info = np.array([coord[0], coord[1], delta, self.patch_area]).astype(np.float32)
sample = {'image': image, 'mask': label, 'meta': torch.from_numpy(meta_info)}
if self.transforms is not None:
sample = self.transforms(sample)
# # add metadata
# sample['meta'] = torch.from_numpy(meta_info)
return sample
def _load_image(self, chip_id: str) -> Tensor:
"""Load all source images for a chip.
Args:
chip_id: ID of the chip.
Returns:
a tensor of stacked source image data, coord (lon,lat)
"""
path = os.path.join(self.root, self.split, f'{self.split}_features', chip_id)
images = []
coords = None
for band in self.bands:
with rasterio.open(os.path.join(path, f'{band}.tif')) as src:
images.append(src.read(1).astype(np.float32))
if coords is None:
cx,cy = src.xy(src.height // 2, src.width // 2)
if src.crs.to_string() != 'EPSG:4326':
crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326', always_xy=True)
lon, lat = crs_transformer.transform(cx,cy)
else:
lon, lat = cx, cy
return torch.from_numpy(np.stack(images, axis=0)), (lon,lat)
class SegDataAugmentation(torch.nn.Module):
def __init__(self, split, size):
super().__init__()
mean = torch.Tensor([0.0])
std = torch.Tensor([1.0])
self.norm = K.augmentation.Normalize(mean=mean, std=std)
if split == "train":
self.transform = K.augmentation.AugmentationSequential(
K.augmentation.Resize(size=size, align_corners=True),
K.augmentation.RandomRotation(degrees=90, p=0.5, align_corners=True),
K.augmentation.RandomHorizontalFlip(p=0.5),
K.augmentation.RandomVerticalFlip(p=0.5),
data_keys=["input", "mask"],
)
else:
self.transform = K.augmentation.AugmentationSequential(
K.augmentation.Resize(size=size, align_corners=True),
data_keys=["input", "mask"],
)
@torch.no_grad()
def forward(self, batch: dict[str,]):
"""Torchgeo returns a dictionary with 'image' and 'label' keys, but engine expects a tuple"""
x,mask = batch["image"], batch["mask"]
x = self.norm(x)
x_out, mask_out = self.transform(x, mask)
return x_out.squeeze(0), mask_out.squeeze(0).squeeze(0), batch["meta"]
class SenBenchCloudS2Dataset:
def __init__(self, config):
self.dataset_config = config
self.img_size = (config.image_resolution, config.image_resolution)
self.root_dir = config.data_path
def create_dataset(self):
train_transform = SegDataAugmentation(split="train", size=self.img_size)
eval_transform = SegDataAugmentation(split="test", size=self.img_size)
dataset_train = SenBenchCloudS2(
root=self.root_dir, split="train", transforms=train_transform
)
dataset_val = SenBenchCloudS2(
root=self.root_dir, split="val", transforms=eval_transform
)
dataset_test = SenBenchCloudS2(
root=self.root_dir, split="test", transforms=eval_transform
)
return dataset_train, dataset_val, dataset_test |