Delete example_raw/ssl4eo_s_dataset.py

example_raw/ssl4eo_s_dataset.py

@@ -1,234 +0,0 @@
-import os
-import gzip
-import json
-import numpy as np
-import rasterio
-import re
-from torch.utils.data import Dataset, DataLoader
-import torch
-#from cvtorchvision import cvtransforms
-from kornia.augmentation import AugmentationSequential
-import kornia
-import argparse
-
-
-class SSL4EO_S(Dataset):
-    def __init__(self, fnames_path, root_dir, modality=['s1_grd', 's2_toa', 's3_olci', 's5p_co', 's5p_no2', 's5p_so2', 's5p_o3', 'dem'], transform_s1=None, transform_s2=None, transform_s3=None, transform_s5p=None, transform_dem=None):
-        with gzip.open(fnames_path, 'rt', encoding='utf-8') as gz_file:
-            self.fnames_json = json.load(gz_file)
-        self.grid_ids = list(self.fnames_json.keys())
-        self.root_dir = root_dir
-        self.transform_s1 = transform_s1
-        self.transform_s2 = transform_s2
-        self.transform_s3 = transform_s3
-        self.transform_s5p = transform_s5p
-        self.transform_dem = transform_dem
-        self.modality = modality
-
-    def __len__(self):
-        return len(self.grid_ids)
-
-    def get_s1_s2(self,grid_id,modality):
-        arrays = []
-        meta_data = []
-        local_grids = list(self.fnames_json[grid_id][modality].keys())
-        grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
-        for local_grid in local_grids:
-            local_fpaths = self.fnames_json[grid_id][modality][local_grid]
-            imgs = []
-            meta = []
-            for local_fpath in local_fpaths:
-                with rasterio.open(os.path.join(self.root_dir, local_fpath)) as src:
-                    img = src.read()
-                if modality=='s1_grd' and self.transform_s1:
-                    #img = self.transform_s1(np.transpose(img, (1, 2, 0)))
-                    img = torch.from_numpy(img).unsqueeze(0)
-                    img = self.transform_s1(img).squeeze(0)
-                elif modality=='s2_toa' and self.transform_s2:
-                    #img = self.transform_s2(np.transpose(img.astype(np.int16), (1, 2, 0)))
-                    img = torch.from_numpy(img.astype(np.int16)).unsqueeze(0)
-                    img = self.transform_s2(img.to(torch.float16)).squeeze(0)
-                imgs.append(img)
-                fname = local_fpath.split('/')[-1]
-                date = re.search(r'(\d{8})T', fname).group(1)
-                meta_info = f"{grid_id_coord}/{local_grid}/{date}"
-                meta.append(meta_info)
-            arrays.append(imgs)
-            meta_data.append(meta)
-        return arrays, meta_data
-
-    def get_s3(self,grid_id):
-        arrays = []
-        meta_data = []
-        fpaths = self.fnames_json[grid_id]['s3_olci']
-        grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
-        for fpath in fpaths:
-            with rasterio.open(os.path.join(self.root_dir, fpath)) as src:
-                img = src.read()
-            if self.transform_s3:
-                #img = self.transform_s3(np.transpose(img, (1, 2, 0)))
-                img = torch.from_numpy(img).unsqueeze(0)
-                img = self.transform_s3(img).squeeze(0)
-            arrays.append(img)
-            fname = fpath.split('/')[-1]
-            date = re.search(r'(\d{8})T', fname).group(1)
-            meta_info = f"{grid_id_coord}/{date}"
-            meta_data.append(meta_info)
-        return arrays, meta_data
-
-    def get_s5p(self,grid_id,modality):
-        arrays = []
-        meta_data = []
-        fpaths = self.fnames_json[grid_id][modality]
-        grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
-        for fpath in fpaths:
-            with rasterio.open(os.path.join(self.root_dir, fpath)) as src:
-                img = src.read()
-            if self.transform_s5p:
-                #img = self.transform_s5p(np.transpose(img, (1, 2, 0)))
-                img = torch.from_numpy(img).unsqueeze(0)
-                img = self.transform_s5p(img).squeeze(0)
-            arrays.append(img)
-            fname = fpath.split('/')[-1]
-            match = re.search(r'(\d{4})-(\d{2})-(\d{2})', fname)
-            date = f"{match.group(1)}{match.group(2)}{match.group(3)}"
-            meta_info = f"{grid_id_coord}/{date}"
-            meta_data.append(meta_info)
-        return arrays, meta_data
-
-    def get_dem(self,grid_id):
-        fpath = self.fnames_json[grid_id]['dem'][0]
-        with rasterio.open(os.path.join(self.root_dir, fpath)) as src:
-            img = src.read()
-        if self.transform_dem:
-            #img = self.transform_dem(np.transpose(img, (1, 2, 0)))
-            img = torch.from_numpy(img).unsqueeze(0)
-            img = self.transform_dem(img).squeeze(0)
-        return img
-
-    def __getitem__(self, idx):
-        grid_id = self.grid_ids[idx]
-        grid_id_coord = self.fnames_json[grid_id]['grid_id_coord']
-        sample = {}
-        meta_data = {}
-        # s1
-        if 's1_grd' in self.modality:
-            arr_s1, meta_s1 = self.get_s1_s2(grid_id,'s1_grd')
-            sample['s1_grd'] = arr_s1
-            meta_data['s1_grd'] = meta_s1
-        # s2
-        if 's2_toa' in self.modality:
-            arr_s2, meta_s2 = self.get_s1_s2(grid_id,'s2_toa')
-            sample['s2_toa'] = arr_s2
-            meta_data['s2_toa'] = meta_s2
-        # s3
-        if 's3_olci' in self.modality:
-            arr_s3, meta_s3 = self.get_s3(grid_id)
-            sample['s3_olci'] = arr_s3
-            meta_data['s3_olci'] = meta_s3
-        # s5p_co
-        if 's5p_co' in self.modality:
-            arr_s5p_co, meta_s5p_co = self.get_s5p(grid_id,'s5p_co')
-            sample['s5p_co'] = arr_s5p_co
-            meta_data['s5p_co'] = meta_s5p_co
-        # s5p_no2
-        if 's5p_no2' in self.modality:
-            arr_s5p_no2, meta_s5p_no2 = self.get_s5p(grid_id,'s5p_no2')
-            sample['s5p_no2'] = arr_s5p_no2
-            meta_data['s5p_no2'] = meta_s5p_no2
-        # s5p_o3
-        if 's5p_o3' in self.modality:
-            arr_s5p_o3, meta_s5p_o3 = self.get_s5p(grid_id,'s5p_o3')
-            sample['s5p_o3'] = arr_s5p_o3
-            meta_data['s5p_o3'] = meta_s5p_o3
-        # s5p_so2
-        if 's5p_so2' in self.modality:
-            arr_s5p_so2, meta_s5p_so2 = self.get_s5p(grid_id,'s5p_so2')
-            sample['s5p_so2'] = arr_s5p_so2
-            meta_data['s5p_so2'] = meta_s5p_so2
-        # dem
-        if 'dem' in self.modality:
-            arr_dem = self.get_dem(grid_id)
-            sample['dem'] = arr_dem
-            meta_data['dem'] = grid_id_coord
-
-        return sample, meta_data
-
-
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser()
-    parser.add_argument('--fnames_path', type=str, default='data_loading/fnames.json.gz')
-    parser.add_argument('--root_dir', type=str, default='data_loading/data')
-    args = parser.parse_args()
-
-    # transform_s1 = cvtransforms.Compose([
-    #     cvtransforms.CenterCrop(224),
-    #     cvtransforms.ToTensor()
-    # ])
-    # transform_s2 = cvtransforms.Compose([
-    #     cvtransforms.CenterCrop(224),
-    #     cvtransforms.ToTensor()
-    # ])
-    # transform_s3 = cvtransforms.Compose([
-    #     cvtransforms.CenterCrop(96),
-    #     cvtransforms.ToTensor()
-    # ])
-    # transform_s5p = cvtransforms.Compose([
-    #     cvtransforms.CenterCrop(28),
-    #     cvtransforms.ToTensor()
-    # ])
-    # transform_dem = cvtransforms.Compose([
-    #     cvtransforms.CenterCrop(960),
-    #     cvtransforms.ToTensor()
-    # ])
-    transform_s1 = AugmentationSequential(
-        #kornia.augmentation.SmallestMaxSize(264),
-        kornia.augmentation.CenterCrop(224),
-    )
-    transform_s2 = AugmentationSequential(
-        #kornia.augmentation.SmallestMaxSize(264),
-        kornia.augmentation.CenterCrop(224),
-    )
-    transform_s3 = AugmentationSequential(
-        kornia.augmentation.SmallestMaxSize(96),
-        kornia.augmentation.CenterCrop(96),
-    )
-    transform_s5p = AugmentationSequential(
-        kornia.augmentation.SmallestMaxSize(28),
-        kornia.augmentation.CenterCrop(28),
-    )
-    transform_dem = AugmentationSequential(
-        kornia.augmentation.SmallestMaxSize(960),
-        kornia.augmentation.CenterCrop(960),
-    )
-
-
-    ssl4eo_s = SSL4EO_S(args.fnames_path, args.root_dir, transform_s1=transform_s1, transform_s2=transform_s2, transform_s3=transform_s3, transform_s5p=transform_s5p, transform_dem=transform_dem)
-    dataloader = DataLoader(ssl4eo_s, batch_size=1, shuffle=True, num_workers=0) # batch size can only be 1 because of varying number of images per grid
-
-    for i, (sample, meta_data) in enumerate(dataloader):
-        #print(i)
-        print('Grid ID:', meta_data['dem'][0])
-        print(sample.keys())
-        print(meta_data.keys())
-
-        
-        print('### S1 GRD ###')
-        print('Number of s1 local patches:', len(meta_data['s1_grd']), '  ', 'Number of time stamps for first local patch:', len(meta_data['s1_grd'][0]))
-        print('Example for one image:', sample['s1_grd'][0][0].shape, meta_data['s1_grd'][0][0])
-        print('### S2 TOA ###')
-        print('Number of s2 local patches:', len(meta_data['s2_toa']), '  ', 'Number of time stamps for first local patch:', len(meta_data['s2_toa'][0]))
-        print('Example for one image:', sample['s2_toa'][0][0].shape, meta_data['s2_toa'][0][0])
-        print('### S3 OLCI ###')
-        print('Number of s3 time stamps:', len(meta_data['s3_olci']))
-        print('Example for one image:', sample['s3_olci'][0].shape, meta_data['s3_olci'][0])
-        print('### S5P ###')
-        print('Number of s5p time stamps for CO/NO2/O3/SO2:', len(meta_data['s5p_co']), len(meta_data['s5p_no2']), len(meta_data['s5p_o3']), len(meta_data['s5p_so2']))
-        print('Example for one CO image:', sample['s5p_co'][0].shape, meta_data['s5p_co'][0])
-        print('Example for one NO2 image:', sample['s5p_no2'][0].shape, meta_data['s5p_no2'][0])
-        print('Example for one O3 image:', sample['s5p_o3'][0].shape, meta_data['s5p_o3'][0])
-        print('Example for one SO2 image:', sample['s5p_so2'][0].shape, meta_data['s5p_so2'][0])
-        print('### DEM ###')
-        print('One DEM image for the grid:', sample['dem'].shape, meta_data['dem'][0])
-
-        break