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import lmdb
import os
import torch
import numpy as np
from torch.utils.data import Dataset, DataLoader
import csv
class SSL4EO_S_lmdb(Dataset):
def __init__(self, lmdb_path, key_path, slurm_job=False, mode=['s1_grd','s2_toa','s3_olci','s5p_co','s5p_no2','s5p_so2','s5p_o3','dem'], s1_transform=None, s2_transform=None, s3_transform=None, s5p_transform=None, dem_transform=None):
self.lmdb_path = lmdb_path
self.key_path = key_path
self.slurm_job = slurm_job
self.mode = mode
self.s1_transform = s1_transform
self.s2_transform = s2_transform
self.s3_transform = s3_transform
self.s5p_transform = s5p_transform
self.dem_transform = dem_transform
if not self.slurm_job:
self.env = lmdb.open(lmdb_path, readonly=True, lock=False, readahead=False, meminit=False)
#self.txn = self.env.begin(write=False) # Q: when to close the txn? #
self.keys = {}
with open(key_path, 'r') as f:
reader = csv.reader(f)
for row in reader:
modality, meta_info = row[0], row[1]
if modality=='s1_grd' or modality=='s2_toa':
_, grid_id, local_grid_id, date = meta_info.split('/')
if grid_id not in self.keys:
self.keys[grid_id] = {}
if modality not in self.keys[grid_id]:
self.keys[grid_id][modality] = {}
if local_grid_id not in self.keys[grid_id][modality]:
self.keys[grid_id][modality][local_grid_id] = []
self.keys[grid_id][modality][local_grid_id].append(meta_info)
elif modality=='s3_olci' or modality=='s5p_co' or modality=='s5p_no2' or modality=='s5p_so2' or modality=='s5p_o3':
_, grid_id, date = meta_info.split('/')
if grid_id not in self.keys:
self.keys[grid_id] = {}
if modality not in self.keys[grid_id]:
self.keys[grid_id][modality] = []
self.keys[grid_id][modality].append(meta_info)
elif modality=='dem':
_, grid_id = meta_info.split('/')
if grid_id not in self.keys:
self.keys[grid_id] = {}
if modality not in self.keys[grid_id]:
self.keys[grid_id][modality] = []
self.keys[grid_id][modality].append(meta_info)
self.indices = list(self.keys.keys())
def __len__(self):
return len(self.indices)
def _init_db(self):
self.env = lmdb.open(self.lmdb_path, max_readers=1, readonly=True, lock=False, readahead=False, meminit=False)
def __getitem__(self, idx):
if self.slurm_job:
# Delay loading LMDB data until after initialization
if self.env is None:
self._init_db()
# get all images in a random local grid in one era5 grid (for batch loading)
grid_id = self.indices[idx]
grid_keys = self.keys[grid_id]
sample = {}
meta_info = {}
with self.env.begin(write=False) as txn:
# s1
if 's1_grd' in self.mode:
sample['s1_grd'] = []
meta_info['s1_grd'] = []
if 's1_grd' in grid_keys:
local_grids = list(grid_keys['s1_grd'].keys()) # list of local grid ids
for local_grid_id in local_grids:
local_keys = grid_keys['s1_grd'][local_grid_id] # list of 4 keys
local_meta_info = []
local_imgs = []
for key in local_keys:
#print(key)
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(264, 264, 2)
if self.s1_transform:
img = self.s1_transform(img)
local_meta_info.append(key)
local_imgs.append(img)
## pad time stamps to 4
#if len(s1_meta_info) < 4:
# s1_meta_info += [s1_meta_info[-1]] * (4 - len(s1_meta_info))
# s1_imgs += [s1_imgs[-1]] * (4 - len(s1_imgs))
sample['s1_grd'].append(local_imgs)
meta_info['s1_grd'].append(local_meta_info)
# s2
if 's2_toa' in self.mode:
sample['s2_toa'] = []
meta_info['s2_toa'] = []
if 's2_toa' in grid_keys:
local_grids = list(grid_keys['s2_toa'].keys())
for local_grid_id in local_grids:
local_keys = grid_keys['s2_toa'][local_grid_id]
local_meta_info = []
local_imgs = []
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.int16).reshape(264, 264, 13)
if self.s2_transform:
img = self.s2_transform(img)
local_meta_info.append(key)
local_imgs.append(img)
sample['s2_toa'].append(local_imgs)
meta_info['s2_toa'].append(local_meta_info)
# s3
if 's3_olci' in self.mode:
sample['s3_olci'] = []
meta_info['s3_olci'] = []
if 's3_olci' in grid_keys:
local_keys = grid_keys['s3_olci']
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(96, 96, 21)
if self.s3_transform:
img = self.s3_transform(img)
meta_info['s3_olci'].append(key)
sample['s3_olci'].append(img)
# s5p
if 's5p_co' in self.mode:
sample['s5p_co'] = []
meta_info['s5p_co'] = []
if 's5p_co' in grid_keys:
local_keys = grid_keys['s5p_co']
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(28, 28, 1)
if self.s5p_transform:
img = self.s5p_transform(img)
meta_info['s5p_co'].append(key)
sample['s5p_co'].append(img)
if 's5p_no2' in self.mode:
sample['s5p_no2'] = []
meta_info['s5p_no2'] = []
if 's5p_no2' in grid_keys:
local_keys = grid_keys['s5p_no2']
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(28, 28, 1)
if self.s5p_transform:
img = self.s5p_transform(img)
meta_info['s5p_no2'].append(key)
sample['s5p_no2'].append(img)
if 's5p_so2' in self.mode:
sample['s5p_so2'] = []
meta_info['s5p_so2'] = []
if 's5p_so2' in grid_keys:
local_keys = grid_keys['s5p_so2']
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(28, 28, 1)
if self.s5p_transform:
img = self.s5p_transform(img)
meta_info['s5p_so2'].append(key)
sample['s5p_so2'].append(img)
if 's5p_o3' in self.mode:
sample['s5p_o3'] = []
meta_info['s5p_o3'] = []
if 's5p_o3' in grid_keys:
local_keys = grid_keys['s5p_o3']
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(28, 28, 1)
if self.s5p_transform:
img = self.s5p_transform(img)
meta_info['s5p_o3'].append(key)
sample['s5p_o3'].append(img)
# dem
if 'dem' in self.mode:
sample['dem'] = []
meta_info['dem'] = []
if 'dem' in grid_keys:
local_keys = grid_keys['dem']
for key in local_keys:
img_bytes = txn.get(key.encode('utf-8'))
img = np.frombuffer(img_bytes, dtype=np.float32).reshape(960,960,1)
if self.dem_transform:
img = self.dem_transform(img)
meta_info['dem'].append(key)
sample['dem'].append(img)
return sample, meta_info |