logging.info("# Downloading data #")
logging.info("####################")
downloader.download_zipfiles()
logging.info("")
if FLAGS.process_tiles:
# Load products to be processed (always load from file to ensure modularity for the downloader and processor)
queried_products_path = (data_dir / 'orders' /
order_id).with_suffix('.pkl')
products_df = pd.read_pickle(queried_products_path)
logging.info("###################")
logging.info("# Processing data #")
logging.info("###################")
processpipeliner = ProcessPipeliner(products_df=products_df,
directory=data_dir)
processpipeliner.process_products()
# preprocessor = PreProcessor(products=products_df, directory=data_dir, overwrite_products=FLAGS.overwrite,
# compress_gtiff=FLAGS.compress, delete_jp2_files=True)
# preprocessor.preprocess_products()
#
# postprocessor = PostProcessor(products=products_df, directory=data_dir,
# overwrite_products=FLAGS.overwrite, compress_gtiff=FLAGS.compress)
# postprocessor.postprocess_products(ndvi=False, vrt=True, coreg=True)
if __name__ == '__main__':
define_flags()
app.run(main)
"""Eval."""
import os
import time
from absl import app
from absl import logging
import config
import network as builder
import tf_util
import waymo_loader
import tensorflow.compat.v2 as tf
tf.enable_v2_behavior()
FLAGS = config.define_flags()
_SUMMARY_TXT = 'validation_summary.txt'
_MIN_SUMMARY_STEPS = 10
def steps_to_run(current_step, steps_per_epoch, steps_per_loop):
"""Calculates steps to run on device."""
if steps_per_loop <= 0:
raise ValueError('steps_per_loop should be positive integer.')
if steps_per_loop == 1:
return steps_per_loop
remainder_in_epoch = current_step % steps_per_epoch
if remainder_in_epoch != 0:
return min(steps_per_epoch - remainder_in_epoch, steps_per_loop)
else:
return steps_per_loop
tf.logging.set_verbosity(tf.logging.ERROR)
import config
import importlib
from idas.utils.utils import Colors, safe_mkdir
from data_interface.utils_acdc.prepare_dataset import *
# ----------------------------------------------------------------------------------- #
# test our model on ACDC test data
EXPERIMENT = 'model_ours_full_acdc'
DATASET_NAME = 'acdc'
TEST_ROOT_DIR = '../DATA/ACDC_testing'
OUT_DIR = './acdc_test_results'
# ----------------------------------------------------------------------------------- #
safe_mkdir(OUT_DIR)
config.define_flags()
# noinspection PyUnresolvedReferences
FLAGS = tf.app.flags.FLAGS
# ----------------------------------------------------------------------------------- #
def parse_info_cfg(filename):
"""
Extracts information contained in the Info.cfg file given as input.
:param filename: path/to/patient/folder/Info.cfg
:return: values for: ed, es
"""
ed, es = None, None
with open(filename, 'r') as f:
for line in f: