text
stringlengths 1
93.6k
|
|---|
# 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={} " \
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.