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- {
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- "cells": [
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- {
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- "cell_type": "markdown",
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- "metadata": {},
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- "source": [
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- "### Load from raw data (geotif)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 1,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "import torch\n",
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- "import kornia\n",
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- "import numpy as np\n",
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- "from kornia.augmentation import AugmentationSequential\n",
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- "from torch.utils.data import Dataset, DataLoader\n",
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- "from ssl4eo_s_dataset import SSL4EO_S\n",
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- "import time"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 2,
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- "metadata": {},
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- "outputs": [],
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- "source": [
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- "fnames_path = '../data/example_100_grids_cleaned/fnames_sampled_cleaned.json.gz'\n",
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- "root_dir = '../data/example_100_grids_cleaned/'"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": 4,
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- "metadata": {},
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- "outputs": [
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- {
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- "name": "stdout",
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- "output_type": "stream",
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- "text": [
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- "Grid ID: 0685159_109.75_28.75\n",
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- "Grid ID: 0661743_15.75_24.75\n",
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- "Grid ID: 0644832_108.00_21.75\n",
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- "Grid ID: 0818849_52.25_52.00\n",
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- "Grid ID: 0351810_-67.50_-29.00\n",
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- "Grid ID: 0602947_76.75_14.50\n",
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- "Grid ID: 0906994_128.50_67.25\n",
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- "Grid ID: 0436848_-48.00_-14.25\n",
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- "Grid ID: 0774040_10.00_44.25\n",
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- "Grid ID: 0452591_-72.25_-11.50\n",
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- "Grid ID: 0653390_87.50_23.25\n",
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- "Grid ID: 0485853_-36.75_-5.75\n",
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- "Grid ID: 0421706_126.50_-17.00\n",
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- "Grid ID: 0846377_94.25_56.75\n",
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- "Grid ID: 0828442_-69.50_53.75\n",
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- "Grid ID: 0458494_-36.50_-10.50\n",
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- "Grid ID: 0726869_97.25_36.00\n",
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- "Grid ID: 0419942_45.50_-17.25\n",
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- "Grid ID: 0757070_87.50_41.25\n",
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- "Grid ID: 0536127_-68.25_3.00\n",
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- "Grid ID: 0366272_-52.00_-26.50\n",
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- "Grid ID: 0442952_38.00_-13.25\n",
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- "Grid ID: 0388675_148.75_-22.75\n",
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- "Grid ID: 0711658_-105.50_33.50\n",
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- "Grid ID: 0852399_159.75_57.75\n",
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- "Grid ID: 0786984_6.00_46.50\n",
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- "Grid ID: 0609868_7.00_15.75\n",
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- "Grid ID: 0639046_101.50_20.75\n",
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- "Grid ID: 0693560_50.00_30.25\n",
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- "Grid ID: 0806728_-98.00_50.00\n",
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- "Grid ID: 0715237_69.25_34.00\n",
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- "Grid ID: 0404465_136.25_-20.00\n",
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- "Grid ID: 0765751_97.75_42.75\n",
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- "Grid ID: 0666369_92.25_25.50\n",
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- "Grid ID: 0442523_-69.25_-13.25\n",
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- "Grid ID: 0557752_-62.00_6.75\n",
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- "Grid ID: 0483681_140.25_-6.25\n",
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- "Grid ID: 0864309_-102.75_60.00\n",
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- "Grid ID: 0483549_107.25_-6.25\n",
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- "Grid ID: 0295191_177.75_-39.00\n",
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- "Grid ID: 0706643_80.75_32.50\n",
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- "Grid ID: 0653323_70.75_23.25\n",
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- "Grid ID: 0535351_97.75_2.75\n",
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- "Grid ID: 0847024_-104.00_57.00\n",
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- "Grid ID: 0729643_70.75_36.50\n",
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- "Grid ID: 0772176_-96.00_44.00\n",
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- "Grid ID: 0745484_71.00_39.25\n",
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- "Grid ID: 0881585_-103.75_63.00\n",
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- "Grid ID: 0819002_90.50_52.00\n",
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- "Grid ID: 0679995_-101.25_28.00\n",
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- "Grid ID: 0784419_84.75_46.00\n",
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- "Grid ID: 0572425_6.25_9.25\n",
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- "Grid ID: 0697807_31.75_31.00\n",
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- "Grid ID: 0546588_27.00_4.75\n",
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- "Grid ID: 0772634_18.50_44.00\n",
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- "Grid ID: 0414141_35.25_-18.25\n",
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- "Grid ID: 0680382_-4.50_28.00\n",
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- "Grid ID: 0730483_-79.25_36.75\n",
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- "Grid ID: 0817701_125.25_51.75\n",
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- "Grid ID: 0648708_-3.00_22.50\n",
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- "Grid ID: 0911614_-156.50_68.25\n",
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- "Grid ID: 0734741_-94.75_37.50\n",
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- "Grid ID: 0849078_49.50_57.25\n",
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- "Grid ID: 0445380_-75.00_-12.75\n",
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- "Grid ID: 0805860_45.00_49.75\n",
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- "Grid ID: 0902681_130.25_66.50\n",
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- "Grid ID: 0533572_13.00_2.50\n",
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- "Grid ID: 0680734_83.50_28.00\n",
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- "Grid ID: 0684932_53.00_28.75\n",
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- "Grid ID: 0438282_-49.50_-14.00\n",
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- "Grid ID: 0784237_39.25_46.00\n",
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- "Grid ID: 0320991_147.75_-34.50\n",
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- "Grid ID: 0618779_74.75_17.25\n",
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- "Time: 85.03972792625427\n"
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- ]
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- }
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- ],
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- "source": [
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- "transform_s1 = AugmentationSequential(\n",
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- " #kornia.augmentation.SmallestMaxSize(264),\n",
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- " kornia.augmentation.CenterCrop(224),\n",
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- ")\n",
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- "transform_s2 = AugmentationSequential(\n",
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- " #kornia.augmentation.SmallestMaxSize(264),\n",
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- " kornia.augmentation.CenterCrop(224),\n",
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- ")\n",
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- "transform_s3 = AugmentationSequential(\n",
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- " kornia.augmentation.SmallestMaxSize(96),\n",
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- " kornia.augmentation.CenterCrop(96),\n",
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- ")\n",
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- "transform_s5p = AugmentationSequential(\n",
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- " kornia.augmentation.SmallestMaxSize(28),\n",
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- " kornia.augmentation.CenterCrop(28),\n",
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- ")\n",
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- "transform_dem = AugmentationSequential(\n",
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- " kornia.augmentation.SmallestMaxSize(960),\n",
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- " kornia.augmentation.CenterCrop(960),\n",
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- ")\n",
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- "\n",
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- "ssl4eo_s = SSL4EO_S(fnames_path, root_dir, transform_s1=transform_s1, transform_s2=transform_s2, transform_s3=transform_s3, transform_s5p=transform_s5p, transform_dem=transform_dem)\n",
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- "dataloader = DataLoader(ssl4eo_s, batch_size=1, shuffle=True, num_workers=4) # batch size can only be 1 because of varying number of images per grid\n",
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- "\n",
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- "start_time = time.time()\n",
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- "\n",
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- "for i, (sample, meta_data) in enumerate(dataloader):\n",
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- " pass\n",
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- " #print('Grid ID:', meta_data['dem'][0])\n",
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- " # print(sample.keys())\n",
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- " # print(meta_data.keys())\n",
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- "\n",
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- " \n",
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- " # print('### S1 GRD ###')\n",
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- " # 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]))\n",
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- " # print('Example for one image:', sample['s1_grd'][0][0].shape, meta_data['s1_grd'][0][0])\n",
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- " # print('### S2 TOA ###')\n",
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- " # 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]))\n",
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- " # print('Example for one image:', sample['s2_toa'][0][0].shape, meta_data['s2_toa'][0][0])\n",
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- " # print('### S3 OLCI ###')\n",
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- " # print('Number of s3 time stamps:', len(meta_data['s3_olci']))\n",
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- " # print('Example for one image:', sample['s3_olci'][0].shape, meta_data['s3_olci'][0])\n",
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- " # print('### S5P ###')\n",
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- " # 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']))\n",
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- " # print('Example for one CO image:', sample['s5p_co'][0].shape, meta_data['s5p_co'][0])\n",
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- " # print('Example for one NO2 image:', sample['s5p_no2'][0].shape, meta_data['s5p_no2'][0])\n",
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- " # print('Example for one O3 image:', sample['s5p_o3'][0].shape, meta_data['s5p_o3'][0])\n",
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- " # print('Example for one SO2 image:', sample['s5p_so2'][0].shape, meta_data['s5p_so2'][0])\n",
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- " # print('### DEM ###')\n",
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- " # print('One DEM image for the grid:', sample['dem'].shape, meta_data['dem'][0])\n",
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- " # break\n",
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- "\n",
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- "print('Time:', time.time()-start_time)"
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- ]
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- },
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- {
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- "cell_type": "code",
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- "execution_count": null,
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- "metadata": {},
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- "outputs": [],
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- "source": []
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- }
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- ],
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- "metadata": {
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- "kernelspec": {
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- "display_name": "pytorch",
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- "language": "python",
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- "name": "python3"
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- },
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- "language_info": {
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- "codemirror_mode": {
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- "name": "ipython",
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- "version": 3
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- },
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- "file_extension": ".py",
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- "mimetype": "text/x-python",
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- "name": "python",
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- "nbconvert_exporter": "python",
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- "pygments_lexer": "ipython3",
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- "version": "3.10.13"
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- }
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- },
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- "nbformat": 4,
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- "nbformat_minor": 2
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- }