import torch
import argparse
import config as cfg
from ipeps.ipeps import *
from ctm.generic.env import *
from ctm.generic import ctmrg
from models import coupledLadders
from optim.ad_optim_lbfgs_mod import optimize_state
from ctm.generic import transferops
import json
import unittest
import logging
log = logging.getLogger(__name__)
# parse command line args and build necessary configuration objects
parser = cfg.get_args_parser()
# additional model-dependent arguments
parser.add_argument("--alpha",
type=float,
default=0.,
help="inter-ladder coupling")
parser.add_argument("--top_freq",
type=int,
default=-1,
help="freuqency of transfer operator spectrum evaluation")
parser.add_argument("--top_n",
type=int,
default=2,
help="number of leading eigenvalues" +
"of transfer operator to compute")
args, unknown_args = parser.parse_known_args()
'n_parameters': n_parameters
}
if args.output_dir and utils.is_main_process():
with (output_dir / "log.txt").open("a") as f:
f.write(json.dumps(log_stats) + "\n")
# for evaluation logs
if coco_evaluator is not None:
(output_dir / 'eval').mkdir(exist_ok=True)
if "bbox" in coco_evaluator.coco_eval:
filenames = ['latest.pth']
if epoch % 50 == 0:
filenames.append(f'{epoch:03}.pth')
for name in filenames:
torch.save(coco_evaluator.coco_eval["bbox"].eval,
output_dir / "eval" / name)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if __name__ == '__main__':
parser = argparse.ArgumentParser('DETR training and evaluation script',
parents=[get_args_parser()])
args = parser.parse_args()
if args.output_dir:
Path(args.output_dir).mkdir(parents=True, exist_ok=True)
main(args)
