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lc100_s3olci/senbench_lc100s3cls_wrapper.py

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+import kornia.augmentation as K
+import torch
+from torchgeo.datasets.geo import NonGeoDataset
+import os
+from collections.abc import Callable, Sequence
+from torch import Tensor
+import numpy as np
+import rasterio
+import cv2
+from pyproj import Transformer
+from datetime import date
+from typing import TypeAlias, ClassVar
+import pathlib
+
+import logging
+
+logging.getLogger("rasterio").setLevel(logging.ERROR)
+Path: TypeAlias = str | os.PathLike[str]
+
+class SenBenchLC100S3Cls(NonGeoDataset):
+    url = None
+    #base_dir = 'all_imgs'
+    splits = ('train', 'val', 'test')
+    label_filenames = {
+        'train': 'lc100_multilabel-train.csv',
+        'val': 'lc100_multilabel-val.csv',
+        'test': 'lc100_multilabel-test.csv',
+    }
+    static_filenames = {
+        'train': 'static_fnames-train.csv',
+        'val': 'static_fnames-val.csv',
+        'test': 'static_fnames-test.csv',
+    }
+    all_band_names = (
+        'Oa01_radiance', 'Oa02_radiance', 'Oa03_radiance', 'Oa04_radiance', 'Oa05_radiance', 'Oa06_radiance', 'Oa07_radiance',
+        'Oa08_radiance', 'Oa09_radiance', 'Oa10_radiance', 'Oa11_radiance', 'Oa12_radiance', 'Oa13_radiance', 'Oa14_radiance',
+        'Oa15_radiance', 'Oa16_radiance', 'Oa17_radiance', 'Oa18_radiance', 'Oa19_radiance', 'Oa20_radiance', 'Oa21_radiance',
+    )
+    all_band_scale = (
+        0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,
+        0.00876539,0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,
+        0.00526779,0.00530267,0.00493004,0.00549962,0.00502847,0.00326378,0.00324118)
+    rgb_bands = ('Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance')
+
+    LC100_CLSID = {
+        0: 0, # unknown
+        20: 1,
+        30: 2,
+        40: 3,
+        50: 4,
+        60: 5,
+        70: 6,
+        80: 7,
+        90: 8,
+        100: 9,
+        111: 10,
+        112: 11,
+        113: 12,
+        114: 13,
+        115: 14,
+        116: 15,
+        121: 16,
+        122: 17,
+        123: 18,
+        124: 19,
+        125: 20,
+        126: 21,
+        200: 22, # ocean
+    }
+
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: str = 'train',
+        bands: Sequence[str] = all_band_names,
+        mode = 'static',
+        transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
+        download: bool = False,
+    ) -> None:
+
+        self.root = root
+        self.transforms = transforms
+        self.download = download
+        #self.checksum = checksum
+
+        assert split in ['train', 'val', 'test']
+
+        self.bands = bands
+        self.band_indices = [(self.all_band_names.index(b)+1) for b in bands if b in self.all_band_names]
+
+        self.mode = mode
+        self.img_dir = os.path.join(self.root, 's3_olci')
+        self.lc100_cls = os.path.join(self.root, self.label_filenames[split])
+
+        self.pids = []
+        self.labels = []
+        with open(self.lc100_cls, 'r') as f:
+            lines = f.readlines()
+            for line in lines:
+                self.pids.append(line.strip().split(',')[0])
+                self.labels.append(list(map(int, line.strip().split(',')[1:])))
+        
+        if self.mode == 'static':
+            self.static_csv = os.path.join(self.root, self.static_filenames[split])
+            with open(self.static_csv, 'r') as f:
+                lines = f.readlines()
+                self.static_img = {}
+                for line in lines:
+                    pid = line.strip().split(',')[0]
+                    img_fname = line.strip().split(',')[1]
+                    self.static_img[pid] = img_fname
+
+
+        self.reference_date = date(1970, 1, 1)
+        self.patch_area = (16*10/1000)**2 # patchsize 16 pix, gsd 10m
+
+    def __len__(self):
+        return len(self.pids)
+
+    def __getitem__(self, index):
+
+        images, meta_infos = self._load_image(index)
+        #meta_info = np.array([coord[0], coord[1], np.nan, self.patch_area]).astype(np.float32)
+        label = self._load_target(index)
+        if self.mode == 'static':
+            sample = {'image': images[0], 'label': label, 'meta': meta_infos[0]}
+        elif self.mode == 'series':
+            sample = {'image': images, 'label': label, 'meta': meta_infos}
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+
+    def _load_image(self, index):
+
+        pid = self.pids[index]
+        s3_path = os.path.join(self.img_dir, pid)
+        if self.mode == 'static':
+            img_fname = self.static_img[pid]
+            s3_paths = [os.path.join(s3_path, img_fname)]
+        else:
+            img_fnames = os.listdir(s3_path)
+            s3_paths = []
+            for img_fname in img_fnames:
+                s3_paths.append(os.path.join(s3_path, img_fname))
+        
+        imgs = []
+        img_paths = []
+        meta_infos = []
+        for img_path in s3_paths:
+            with rasterio.open(img_path) as src:
+                img = src.read()
+                img[np.isnan(img)] = 0
+                chs = []
+                for b in range(21):
+                    ch = img[b]*self.all_band_scale[b]
+                    ch = cv2.resize(ch, (96,96), interpolation=cv2.INTER_CUBIC)
+                    chs.append(ch)
+                img = np.stack(chs)
+                img = torch.from_numpy(img).float()
+
+                # get lon, lat
+                cx,cy = src.xy(src.height // 2, src.width // 2)
+                if src.crs.to_string() != 'EPSG:4326':
+                    # convert to lon, lat
+                    crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326', always_xy=True)
+                    lon, lat = crs_transformer.transform(cx,cy)
+                else:
+                    lon, lat = cx, cy
+                # get time
+                img_fname = os.path.basename(img_path)
+                date_str = img_fname.split('_')[1][:8]
+                date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
+                delta = (date_obj - self.reference_date).days
+                meta_info = np.array([lon, lat, delta, self.patch_area]).astype(np.float32)
+                meta_info = torch.from_numpy(meta_info)
+
+            imgs.append(img)
+            img_paths.append(img_path)
+            meta_infos.append(meta_info)
+
+        if self.mode == 'series':
+            # pad to 4 images if less than 4
+            while len(imgs) < 4:
+                imgs.append(img)
+                img_paths.append(img_path)
+                meta_infos.append(meta_info)
+
+        return imgs, meta_infos
+
+    def _load_target(self, index):
+
+        label = self.labels[index]
+        labels = torch.zeros(23)
+        # turn into one-hot
+        for l in label:
+            cls_id = self.LC100_CLSID[l]
+            labels[cls_id] = 1
+
+        return labels
+
+
+class ClsDataAugmentation(torch.nn.Module):
+
+    def __init__(self, split, size, bands):
+        super().__init__()
+
+        mean = torch.Tensor([0.0])
+        std = torch.Tensor([1.0])
+
+        if split == "train":
+            self.transform = torch.nn.Sequential(
+                K.Normalize(mean=mean, std=std),
+                K.Resize(size=size, align_corners=True),
+                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 SenBenchLC100S3ClsDataset:
+    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.mode = config.mode
+
+    def create_dataset(self):
+        train_transform = ClsDataAugmentation(split="train", size=self.img_size, bands=self.bands)
+        eval_transform = ClsDataAugmentation(split="test", size=self.img_size, bands=self.bands)
+
+        dataset_train = SenBenchLC100S3Cls(
+            root=self.root_dir, split="train", bands=self.bands, mode=self.mode, transforms=train_transform
+        )
+        dataset_val = SenBenchLC100S3Cls(
+            root=self.root_dir, split="val", bands=self.bands, mode=self.mode, transforms=eval_transform
+        )
+        dataset_test = SenBenchLC100S3Cls(
+            root=self.root_dir, split="test", bands=self.bands, mode=self.mode, transforms=eval_transform
+        )
+
+        return dataset_train, dataset_val, dataset_test

lc100_s3olci/senbench_lc100s3seg_wrapper.py

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+import kornia as K
+import torch
+from torchgeo.datasets.geo import NonGeoDataset
+import os
+from collections.abc import Callable, Sequence
+from torch import Tensor
+import numpy as np
+import rasterio
+import cv2
+from pyproj import Transformer
+from datetime import date
+from typing import TypeAlias, ClassVar
+import pathlib
+
+import logging
+
+logging.getLogger("rasterio").setLevel(logging.ERROR)
+Path: TypeAlias = str | os.PathLike[str]
+
+class SenBenchLC100S3Seg(NonGeoDataset):
+    url = None
+    #base_dir = 'all_imgs'
+    splits = ('train', 'val', 'test')
+    # label_filenames = {
+    #     'train': 'lc100_multilabel-train.csv',
+    #     'val': 'lc100_multilabel-val.csv',
+    #     'test': 'lc100_multilabel-test.csv',
+    # }
+    static_filenames = {
+        'train': 'static_fnames-train.csv',
+        'val': 'static_fnames-val.csv',
+        'test': 'static_fnames-test.csv',
+    }
+    all_band_names = (
+        'Oa01_radiance', 'Oa02_radiance', 'Oa03_radiance', 'Oa04_radiance', 'Oa05_radiance', 'Oa06_radiance', 'Oa07_radiance',
+        'Oa08_radiance', 'Oa09_radiance', 'Oa10_radiance', 'Oa11_radiance', 'Oa12_radiance', 'Oa13_radiance', 'Oa14_radiance',
+        'Oa15_radiance', 'Oa16_radiance', 'Oa17_radiance', 'Oa18_radiance', 'Oa19_radiance', 'Oa20_radiance', 'Oa21_radiance',
+    )
+    all_band_scale = (
+        0.0139465,0.0133873,0.0121481,0.0115198,0.0100953,0.0123538,0.00879161,
+        0.00876539,0.0095103,0.00773378,0.00675523,0.0071996,0.00749684,0.0086512,
+        0.00526779,0.00530267,0.00493004,0.00549962,0.00502847,0.00326378,0.00324118)
+    rgb_bands = ('Oa08_radiance', 'Oa06_radiance', 'Oa04_radiance')
+
+    LC100_CLSID = {
+        0: 0, # unknown
+        20: 1,
+        30: 2,
+        40: 3,
+        50: 4,
+        60: 5,
+        70: 6,
+        80: 7,
+        90: 8,
+        100: 9,
+        111: 10,
+        112: 11,
+        113: 12,
+        114: 13,
+        115: 14,
+        116: 15,
+        121: 16,
+        122: 17,
+        123: 18,
+        124: 19,
+        125: 20,
+        126: 21,
+        200: 22, # ocean
+    }
+
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: str = 'train',
+        bands: Sequence[str] = all_band_names,
+        mode = 'static',
+        transforms: Callable[[dict[str, Tensor]], dict[str, Tensor]] | None = None,
+        download: bool = False,
+    ) -> None:
+
+        self.root = root
+        self.transforms = transforms
+        self.download = download
+        #self.checksum = checksum
+
+        assert split in ['train', 'val', 'test']
+
+        self.bands = bands
+        self.band_indices = [(self.all_band_names.index(b)+1) for b in bands if b in self.all_band_names]
+
+        self.mode = mode
+        self.img_dir = os.path.join(self.root, 's3_olci')
+        #self.lc100_cls = os.path.join(self.root, self.label_filenames[split])
+        self.label_dir = os.path.join(self.root, 'lc100')
+        
+        if self.mode == 'static':
+            self.static_csv = os.path.join(self.root, self.static_filenames[split])
+            with open(self.static_csv, 'r') as f:
+                lines = f.readlines()
+                self.static_img = {}
+                for line in lines:
+                    pid = line.strip().split(',')[0]
+                    img_fname = line.strip().split(',')[1]
+                    self.static_img[pid] = img_fname
+
+        self.pids = list(self.static_img.keys())
+
+        self.reference_date = date(1970, 1, 1)
+        self.patch_area = (16*10/1000)**2 # patchsize 16 pix, gsd 10m
+
+    def __len__(self):
+        return len(self.pids)
+
+    def __getitem__(self, index):
+
+        images, meta_infos = self._load_image(index)
+        #meta_info = np.array([coord[0], coord[1], np.nan, self.patch_area]).astype(np.float32)
+        label = self._load_target(index)
+        if self.mode == 'static':
+            sample = {'image': images[0], 'mask': label, 'meta': meta_infos[0]}
+        elif self.mode == 'series':
+            sample = {'image': images, 'mask': label, 'meta': meta_infos}
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+
+    def _load_image(self, index):
+
+        pid = self.pids[index]
+        s3_path = os.path.join(self.img_dir, pid)
+        if self.mode == 'static':
+            img_fname = self.static_img[pid]
+            s3_paths = [os.path.join(s3_path, img_fname)]
+        else:
+            img_fnames = os.listdir(s3_path)
+            s3_paths = []
+            for img_fname in img_fnames:
+                s3_paths.append(os.path.join(s3_path, img_fname))
+        
+        imgs = []
+        img_paths = []
+        meta_infos = []
+        for img_path in s3_paths:
+            with rasterio.open(img_path) as src:
+                img = src.read()
+                img[np.isnan(img)] = 0
+                chs = []
+                for b in range(21):
+                    ch = img[b]*self.all_band_scale[b]
+                    #ch = cv2.resize(ch, (96,96), interpolation=cv2.INTER_CUBIC)
+                    ch = cv2.resize(ch, (282,282), interpolation=cv2.INTER_CUBIC)
+                    chs.append(ch)
+                img = np.stack(chs)
+                img = torch.from_numpy(img).float()
+
+                # get lon, lat
+                cx,cy = src.xy(src.height // 2, src.width // 2)
+                if src.crs.to_string() != 'EPSG:4326':
+                    # convert to lon, lat
+                    crs_transformer = Transformer.from_crs(src.crs, 'epsg:4326', always_xy=True)
+                    lon, lat = crs_transformer.transform(cx,cy)
+                else:
+                    lon, lat = cx, cy
+                # get time
+                img_fname = os.path.basename(img_path)
+                date_str = img_fname.split('_')[1][:8]
+                date_obj = date(int(date_str[:4]), int(date_str[4:6]), int(date_str[6:8]))
+                delta = (date_obj - self.reference_date).days
+                meta_info = np.array([lon, lat, delta, self.patch_area]).astype(np.float32)
+                meta_info = torch.from_numpy(meta_info)
+
+            imgs.append(img)
+            img_paths.append(img_path)
+            meta_infos.append(meta_info)
+
+        if self.mode == 'series':
+            # pad to 4 images if less than 4
+            while len(imgs) < 4:
+                imgs.append(img)
+                img_paths.append(img_path)
+                meta_infos.append(meta_info)
+
+        return imgs, meta_infos
+
+    def _load_target(self, index):
+
+        pid = self.pids[index]
+        label_path = os.path.join(self.label_dir, pid+'.tif')
+
+        with rasterio.open(label_path) as src:
+            label = src.read(1)
+            label = cv2.resize(label, (282,282), interpolation=cv2.INTER_NEAREST) # 0-650
+            label_new = np.zeros_like(label)
+            for k,v in self.LC100_CLSID.items():
+                label_new[label==k] = v
+            labels = torch.from_numpy(label_new.astype('int64'))
+
+        return labels
+
+
+
+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 SenBenchLC100S3SegDataset:
+    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.mode = config.mode
+
+    def create_dataset(self):
+        train_transform = SegDataAugmentation(split="train", size=self.img_size)
+        eval_transform = SegDataAugmentation(split="test", size=self.img_size)
+
+        dataset_train = SenBenchLC100S3Seg(
+            root=self.root_dir, split="train", bands=self.bands, mode=self.mode, transforms=train_transform
+        )
+        dataset_val = SenBenchLC100S3Seg(
+            root=self.root_dir, split="val", bands=self.bands, mode=self.mode, transforms=eval_transform
+        )
+        dataset_test = SenBenchLC100S3Seg(
+            root=self.root_dir, split="test", bands=self.bands, mode=self.mode, transforms=eval_transform
+        )
+
+        return dataset_train, dataset_val, dataset_test