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import kornia.augmentation as K
import torch
from torchgeo.datasets import So2Sat
import os
from collections.abc import Callable, Sequence
from torch import Tensor
import numpy as np
import rasterio
from pyproj import Transformer
import h5py
from typing import TypeAlias, ClassVar
import pathlib
Path: TypeAlias = str | os.PathLike[str]
class SenBenchSo2Sat(So2Sat):
versions = ('4_senbench')
filenames_by_version: ClassVar[dict[str, dict[str, str]]] = {
# '2': {
# 'train': 'training.h5',
# 'validation': 'validation.h5',
# 'test': 'testing.h5',
# },
# '3_random': {'train': 'random/training.h5', 'test': 'random/testing.h5'},
# '3_block': {'train': 'block/training.h5', 'test': 'block/testing.h5'},
# '3_culture_10': {
# 'train': 'culture_10/train-new.h5',
# 'val': 'culture_10/val-new.h5',
# 'test': 'culture_10/test-new.h5',
# },
'4_senbench': {
'train': 'train-new.h5',
'val': 'val-new.h5',
'test': 'test-new.h5'
}
}
classes = (
'Compact high rise',
'Compact mid rise',
'Compact low rise',
'Open high rise',
'Open mid rise',
'Open low rise',
'Lightweight low rise',
'Large low rise',
'Sparsely built',
'Heavy industry',
'Dense trees',
'Scattered trees',
'Bush, scrub',
'Low plants',
'Bare rock or paved',
'Bare soil or sand',
'Water',
)
all_s1_band_names = (
'S1_B1', # VH real
'S1_B2', # VH imaginary
'S1_B3', # VV real
'S1_B4', # VV imaginary
'S1_B5', # VH intensity
'S1_B6', # VV intensity
'S1_B7', # PolSAR covariance matrix off-diagonal real
'S1_B8', # PolSAR covariance matrix off-diagonal imaginary
)
all_s2_band_names = (
'S2_B02',
'S2_B03',
'S2_B04',
'S2_B05',
'S2_B06',
'S2_B07',
'S2_B08',
'S2_B8A',
'S2_B11',
'S2_B12',
)
all_band_names = all_s1_band_names + all_s2_band_names
rgb_bands = ('S2_B04', 'S2_B03', 'S2_B02')
BAND_SETS: ClassVar[dict[str, tuple[str, ...]]] = {
'all': all_band_names,
's1': all_s1_band_names,
's2': all_s2_band_names,
'rgb': rgb_bands,
}
def __init__(
self,
root: Path = 'data',
version: str = '4_senbench', # only supported version now
split: str = 'train',
bands: Sequence[str] = BAND_SETS['s2'], # only supported bands now
transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
download: bool = False,
) -> None:
#h5py = lazy_import('h5py')
assert version in self.versions
assert split in self.filenames_by_version[version]
self._validate_bands(bands)
self.s1_band_indices: np.typing.NDArray[np.int_] = np.array(
[
self.all_s1_band_names.index(b)
for b in bands
if b in self.all_s1_band_names
]
).astype(int)
self.s1_band_names = [self.all_s1_band_names[i] for i in self.s1_band_indices]
self.s2_band_indices: np.typing.NDArray[np.int_] = np.array(
[
self.all_s2_band_names.index(b)
for b in bands
if b in self.all_s2_band_names
]
).astype(int)
self.s2_band_names = [self.all_s2_band_names[i] for i in self.s2_band_indices]
self.bands = bands
self.root = root
self.version = version
self.split = split
self.transforms = transforms
# self.checksum = checksum
self.fn = os.path.join(self.root, self.filenames_by_version[version][split])
# if not self._check_integrity():
# raise DatasetNotFoundError(self)
with h5py.File(self.fn, 'r') as f:
self.size: int = f['label'].shape[0]
self.patch_area = (16*10/1000)**2 # patchsize 16 pix, gsd 10m
def __getitem__(self, index: int) -> dict[str, Tensor]:
"""Return an index within the dataset.
Args:
index: index to return
Returns:
data and label at that index
"""
#h5py = lazy_import('h5py')
with h5py.File(self.fn, 'r') as f:
#s1 = f['sen1'][index].astype(np.float32)
#s1 = np.take(s1, indices=self.s1_band_indices, axis=2)
s2 = f['sen2'][index].astype(np.float32)
s2 = np.take(s2, indices=self.s2_band_indices, axis=2)
# convert one-hot encoding to int64 then torch int
label = torch.tensor(f['label'][index].argmax())
#s1 = np.rollaxis(s1, 2, 0) # convert to CxHxW format
s2 = np.rollaxis(s2, 2, 0) # convert to CxHxW format
#s1 = torch.from_numpy(s1)
s2 = torch.from_numpy(s2)
meta_info = np.array([np.nan, np.nan, np.nan, self.patch_area]).astype(np.float32)
sample = {'image': s2, 'label': label, 'meta': torch.from_numpy(meta_info)}
if self.transforms is not None:
sample = self.transforms(sample)
return sample
class ClsDataAugmentation(torch.nn.Module):
def __init__(self, split, size, band_stats):
super().__init__()
if band_stats is not None:
mean = band_stats['mean']
std = band_stats['std']
else:
mean = [0.0]
std = [1.0]
mean = torch.Tensor(mean)
std = torch.Tensor(std)
if split == "train":
self.transform = torch.nn.Sequential(
K.Normalize(mean=mean, std=std),
K.Resize(size=size, align_corners=True),
#K.RandomResizedCrop(size=size, scale=(0.8,1.0)),
K.RandomHorizontalFlip(p=0.5),
K.RandomVerticalFlip(p=0.5),
)
else:
self.transform = torch.nn.Sequential(
K.Normalize(mean=mean, std=std),
K.Resize(size=size, align_corners=True),
)
@torch.no_grad()
def forward(self, batch: dict[str,]):
"""Torchgeo returns a dictionary with 'image' and 'label' keys, but engine expects a tuple"""
x_out = self.transform(batch["image"]).squeeze(0)
return x_out, batch["label"], batch["meta"]
class SenBenchSo2SatDataset:
def __init__(self, config):
self.dataset_config = config
self.img_size = (config.image_resolution, config.image_resolution)
self.root_dir = config.data_path
self.bands = config.band_names
self.version = config.version
self.band_stats = config.band_stats
def create_dataset(self):
train_transform = ClsDataAugmentation(split="train", size=self.img_size, band_stats=self.band_stats)
eval_transform = ClsDataAugmentation(split="test", size=self.img_size, band_stats=self.band_stats)
dataset_train = SenBenchSo2Sat(
root=self.root_dir, version=self.version, split="train", bands=self.bands, transforms=train_transform
)
dataset_val = SenBenchSo2Sat(
root=self.root_dir, version=self.version, split="val", bands=self.bands, transforms=eval_transform
)
dataset_test = SenBenchSo2Sat(
root=self.root_dir, version=self.version, split="test", bands=self.bands, transforms=eval_transform
)
return dataset_train, dataset_val, dataset_test |