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# obtain a mask for each pred |
return loss |
# <FILESEP> |
import os |
import sys |
import warnings |
import hydra |
from omegaconf import OmegaConf |
import torch |
import pytorch_lightning as pl |
from pytorch_lightning.loggers import WandbLogger |
ROOT_DIR = os.path.dirname(os.path.abspath(__file__)) |
sys.path.append(ROOT_DIR) |
from model.module import PretrainingModule |
from model.network import create_encoder_network |
from data_utils.PretrainDataset import create_dataloader |
@hydra.main(version_base="1.2", config_path="configs", config_name="pretrain") |
def main(cfg): |
print("******************************** [Config] ********************************") |
print(OmegaConf.to_yaml(cfg)) |
print("******************************** [Config] ********************************") |
pl.seed_everything(cfg.seed) |
logger = WandbLogger( |
name=cfg.name, |
save_dir=cfg.wandb.save_dir, |
project=cfg.wandb.project |
) |
trainer = pl.Trainer( |
logger=logger, |
accelerator='gpu', |
devices=cfg.gpu, |
log_every_n_steps=cfg.log_every_n_steps, |
max_epochs=cfg.training.max_epochs |
) |
dataloader = create_dataloader(cfg.dataset) |
encoder = create_encoder_network(cfg.model.emb_dim) |
model = PretrainingModule( |
cfg=cfg.training, |
encoder=encoder |
) |
model.train() |
trainer.fit(model, dataloader) |
if __name__ == "__main__": |
torch.set_float32_matmul_precision("high") |
torch.autograd.set_detect_anomaly(True) |
torch.cuda.empty_cache() |
torch.multiprocessing.set_sharing_strategy("file_system") |
warnings.simplefilter(action='ignore', category=FutureWarning) |
main() |
# <FILESEP> |
# -*- coding: utf-8 -*- |
from dotenv import load_dotenv |
load_dotenv('.env') |
import logging |
logging.basicConfig(filename='logs/push-2-gee.log', level=logging.INFO) |
import ast |
import glob |
import json |
import os |
import subprocess |
from datetime import datetime |
from dbio import * |
scale_factor = 10000 |
output_path = os.getenv('OUTPUT_PATH') |
final_output = os.getenv('POST_PROCESS_OUTPUT_PATH') |
gdal_path = os.getenv('GDAL_PATH') |
manifest_dir = os.getenv('MANIFESTS_PATH') |
cloud_path = os.getenv('GCS_PATH') |
gee_asset_path = os.getenv('GEE_ASSET_PATH') |
calc = '{0}gdal_calc.py -A %s --calc="A*{1}" --outfile={2}%s --type=UInt16'.format(gdal_path, scale_factor, final_output) |
_cp_to_gs = 'gsutil cp {0}%s {1}'.format(final_output, cloud_path) |
_upload_to_gee = 'earthengine upload image --manifest "{0}%s.json"'.format(manifest_dir) |
properties = ['acquisitiontype', 'lastorbitnumber', 'lastrelativeorbitnumber', 'missiondatatakeid', 'orbitdirection', |
'orbitnumber', 'platformidentifier', 'polarisationmode', 'producttype', 'relativeorbitnumber', |
'sensoroperationalmode', 'swathidentifier'] |
def get_processed_images(table_name): |
query = "SELECT id, title, beginposition, endposition, {} FROM {} WHERE processed={} AND slave={} " \ |
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