Upload senbench_airqualitys5p_wrapper.py

airquality_s5p/senbench_airqualitys5p_wrapper.py

@@ -0,0 +1,208 @@
+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
+import pdb
+
+logging.getLogger("rasterio").setLevel(logging.ERROR)
+Path: TypeAlias = str | os.PathLike[str]
+
+class SenBenchAirQualityS5P(NonGeoDataset):
+    url = None
+    splits = ('train', 'val', 'test')
+    split_fnames = {
+        'train': 'train.csv',
+        'val': 'val.csv',
+        'test': 'test.csv',
+    }
+
+    def __init__(
+        self,
+        root: Path = 'data',
+        split: str = 'train',
+        modality = 'no2', # or 'o3'
+        mode = 'annual', # or 'seasonal'
+        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.modality = modality
+        self.mode = mode
+
+        if self.mode == 'annual':
+            mode_dir = 's5p_annual'
+        elif self.mode == 'seasonal':
+            mode_dir = 's5p_seasonal'
+
+        self.img_dir = os.path.join(root, modality, mode_dir)
+        self.label_dir = os.path.join(root, modality, 'label_annual')
+        
+        self.split_csv = os.path.join(self.root, modality, self.split_fnames[split])
+        with open(self.split_csv, 'r') as f:
+            lines = f.readlines()
+            self.pids = []
+            for line in lines:
+                self.pids.append(line.strip())
+
+        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 == 'annual':
+            sample = {'image': images[0], 'groudtruth': label, 'meta': meta_infos[0]}
+        elif self.mode == 'seasonal':
+            sample = {'image': images, 'groudtruth': label, 'meta': meta_infos}
+
+        #pdb.set_trace()
+
+        if self.transforms is not None:
+            sample = self.transforms(sample)
+
+        return sample
+
+
+    def _load_image(self, index):
+
+        pid = self.pids[index]
+        s5p_path = os.path.join(self.img_dir, pid)
+
+        img_fnames = os.listdir(s5p_path)
+        s5p_paths = []
+        for img_fname in img_fnames:
+            s5p_paths.append(os.path.join(s5p_path, img_fname))
+        
+        imgs = []
+        meta_infos = []
+        for img_path in s5p_paths:
+            with rasterio.open(img_path) as src:
+                img = src.read(1)
+                img[np.isnan(img)] = 0
+                img = cv2.resize(img, (56,56), interpolation=cv2.INTER_CUBIC)
+                img = torch.from_numpy(img).float()
+                img = img.unsqueeze(0)
+
+                # 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('_')[0][:10]
+                date_obj = date(int(date_str[:4]), int(date_str[5:7]), int(date_str[8:10]))
+                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)
+            meta_infos.append(meta_info)
+
+        if self.mode == 'seasonal':
+            # pad to 4 images if less than 4
+            while len(imgs) < 4:
+                imgs.append(img)
+                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, (56,56), interpolation=cv2.INTER_NEAREST) # 0-650
+            # label contains -inf
+            #pdb.set_trace()
+            label[label<-1e10] = np.nan
+            label[label>1e10] = np.nan
+            label = torch.from_numpy(label.astype('float32'))
+
+        return label
+
+
+
+class RegDataAugmentation(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", "input"],
+            )
+        else:
+            self.transform = K.augmentation.AugmentationSequential(
+                K.augmentation.Resize(size=size, align_corners=True),
+                data_keys=["input", "input"],
+            )
+
+    @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["groudtruth"]
+        x = self.norm(x)
+        x_out, mask_out = self.transform(x, mask.unsqueeze(0))
+        return x_out.squeeze(0), mask_out.squeeze(0), batch["meta"]
+
+
+class SenBenchAirQualityS5PDataset:
+    def __init__(self, config):
+        self.dataset_config = config
+        self.img_size = (config.image_resolution, config.image_resolution)
+        self.root_dir = config.data_path
+        self.modality = config.modality
+        self.mode = config.mode
+
+    def create_dataset(self):
+        train_transform = RegDataAugmentation(split="train", size=self.img_size)
+        eval_transform = RegDataAugmentation(split="test", size=self.img_size)
+
+        dataset_train = SenBenchAirQualityS5P(
+            root=self.root_dir, split="train", modality=self.modality, mode=self.mode, transforms=train_transform
+        )
+        dataset_val = SenBenchAirQualityS5P(
+            root=self.root_dir, split="val", modality=self.modality, mode=self.mode, transforms=eval_transform
+        )
+        dataset_test = SenBenchAirQualityS5P(
+            root=self.root_dir, split="test", modality=self.modality, mode=self.mode, transforms=eval_transform
+        )
+
+        return dataset_train, dataset_val, dataset_test