/
run_experiments.py
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/
run_experiments.py
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# Licensed under the CC BY-NC 4.0 license (https://creativecommons.org/licenses/by-nc/4.0/)
# Obtained from: https://github.com/lhoyer/HRDA
# Modifications: Add startup test
import argparse
import json
import logging
import os
import subprocess
import uuid
from datetime import datetime
import time
import torch
import mmcv
from experiments import generate_experiment_cfgs
from mmcv import Config, get_git_hash
from tools import train
def run_command(command):
p = subprocess.Popen(
command, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, shell=True)
for line in iter(p.stdout.readline, b''):
print(line.decode('utf-8'), end='')
def rsync(src, dst):
rsync_cmd = f'rsync -a {src} {dst}'
print(rsync_cmd)
run_command(rsync_cmd)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
group = parser.add_mutually_exclusive_group(required=True)
group.add_argument(
'--exp',
type=int,
default=None,
help='Experiment id as defined in experiment.py',
)
group.add_argument(
'--config',
default=None,
help='Path to config file',
)
parser.add_argument(
'--machine', type=str, choices=['local'], default='local')
parser.add_argument(
'--local_rank', type=int, default=0)
parser.add_argument(
'--resume-from',
type=str,
default=None,
help='Path to checkpoint file',
)
parser.add_argument(
'--seed-to-resume-from',
type=int,
default=0,
help='Seed ID of experiment that needs resuming',
)
parser.add_argument('--debug', action='store_true')
parser.add_argument('--startup-test', action='store_true')
args = parser.parse_args()
if 'LOCAL_RANK' not in os.environ:
os.environ['LOCAL_RANK'] = str(args.local_rank)
assert (args.config is None) != (args.exp is None), \
'Either config or exp has to be defined.'
GEN_CONFIG_DIR = 'configs/generated/'
JOB_DIR = 'jobs'
cfgs, config_files = [], []
# Training with Predefined Config
if args.config is not None:
cfg = Config.fromfile(args.config)
# Specify Name and Work Directory
exp_name = f'{args.machine}-{cfg["exp"]}'
unique_name = f'{datetime.now().strftime("%y%m%d_%H%M")}_' \
f'{cfg["name"]}_{str(uuid.uuid4())[:5]}'
child_cfg = {
'_base_': args.config.replace('configs', '../..'),
'name': unique_name,
'work_dir': os.path.join('work_dirs', exp_name, unique_name),
'git_rev': get_git_hash()
}
cfg_out_file = f"{GEN_CONFIG_DIR}/{exp_name}/{child_cfg['name']}.json"
os.makedirs(os.path.dirname(cfg_out_file), exist_ok=True)
assert not os.path.isfile(cfg_out_file)
with open(cfg_out_file, 'w') as of:
json.dump(child_cfg, of, indent=4)
config_files.append(cfg_out_file)
cfgs.append(cfg)
# Training with Generated Configs from experiments.py
if args.exp is not None:
exp_name = f'{args.machine}-exp{args.exp}'
if args.startup_test:
exp_name += '-startup'
cfgs = generate_experiment_cfgs(args.exp)
# Generate Configs
for i, cfg in enumerate(cfgs):
if args.debug:
cfg.setdefault('log_config', {})['interval'] = 10
cfg['evaluation'] = dict(interval=200, metric='mIoU')
if 'dacs' in cfg['name']:
cfg.setdefault('uda', {})['debug_img_interval'] = 10
# cfg.setdefault('uda', {})['print_grad_magnitude'] = True
if args.startup_test:
cfg['log_level'] = logging.ERROR
cfg['runner'] = dict(type='IterBasedRunner', max_iters=2)
cfg['evaluation']['interval'] = 100
cfg['checkpoint_config'] = dict(
by_epoch=False, interval=100, save_last=False)
# Generate Config File
# cfg['runner'] = dict(type='IterBasedRunner', max_iters=2)
# cfg['evaluation'] = dict(interval=2, metric='mIoU', distributed_eval=True, pre_eval=True)
# cfg.setdefault('log_config', {})['interval'] = 1
# cfg['checkpoint_config'] = dict(by_epoch=False, interval=1, max_keep_ckpts=1)
if 'SLURM_ARRAY_TASK_ID' not in os.environ and ('LSB_JOBINDEX' not in os.environ or int(os.environ['LSB_JOBINDEX']) == 0) and i == args.seed_to_resume_from:
cfg['resume_from'] = args.resume_from
cfg['first_run'] = True
# In case of job array, only run the configuration with number that corresponds to the task ID.
if 'SLURM_ARRAY_TASK_ID' in os.environ:
if i != int(os.environ['SLURM_ARRAY_TASK_ID']):
config_files.append([])
continue
else:
cfg['first_run'] = True
cfg['resume_from'] = args.resume_from
if 'LSB_JOBINDEX' in os.environ and int(os.environ['LSB_JOBINDEX']) >= 1:
# LSF only allows one-based task IDs.
if (i + 1) != int(os.environ['LSB_JOBINDEX']):
config_files.append([])
continue
else:
cfg['first_run'] = True
cfg['resume_from'] = args.resume_from
if args.local_rank == 0:
cfg['name'] = f'{datetime.now().strftime("%y%m%d_%H%M")}_' \
f'{cfg["name"]}_{str(uuid.uuid4())[:5]}'
cfg['work_dir'] = os.path.join('work_dirs', exp_name, cfg['name'])
cfg['git_rev'] = get_git_hash()
cfg['_base_'] = ['../../' + e for e in cfg['_base_']]
cfg_out_file = f"{GEN_CONFIG_DIR}/{exp_name}/{cfg['name']}.json"
os.makedirs(os.path.dirname(cfg_out_file), exist_ok=True)
assert not os.path.isfile(cfg_out_file)
with open(cfg_out_file, 'w') as of:
json.dump(cfg, of, indent=4)
name_file = f"{os.path.join('work_dirs', exp_name)}/name_{cfg['name'][:-6]}_{str(i)}.txt"
mmcv.mkdir_or_exist(os.path.abspath(cfg['work_dir']))
with open(name_file, 'w') as of:
of.write(cfg['name'])
print(name_file)
print(cfg['name'])
else:
cfg_name_constant = f'{datetime.now().strftime("%y%m%d_%H%M")}_{cfg["name"]}'
name_file = f"{os.path.join('work_dirs', exp_name)}/name_{cfg_name_constant}_{str(i)}.txt"
print(name_file)
while not os.path.exists(name_file):
time.sleep(5)
with open(name_file, 'r') as f:
cfg['name'] = f.read()
print('Recovered cfg[name]: ', cfg['name'])
cfg['work_dir'] = os.path.join('work_dirs', exp_name, cfg['name'])
cfg['git_rev'] = get_git_hash()
cfg['_base_'] = ['../../' + e for e in cfg['_base_']]
cfg_out_file = f"{GEN_CONFIG_DIR}/{exp_name}/{cfg['name']}.json"
config_files.append(cfg_out_file)
if args.machine == 'local':
for i, cfg in enumerate(cfgs):
if args.startup_test and cfg['seed'] != 0:
continue
if 'SLURM_ARRAY_TASK_ID' not in os.environ and ('LSB_JOBINDEX' not in os.environ or int(os.environ['LSB_JOBINDEX']) == 0) and i < args.seed_to_resume_from:
continue
# In case of job array, only run the configuration with number that corresponds to the task ID.
if 'SLURM_ARRAY_TASK_ID' in os.environ:
if i != int(os.environ['SLURM_ARRAY_TASK_ID']):
continue
if 'LSB_JOBINDEX' in os.environ and int(os.environ['LSB_JOBINDEX']) >= 1:
# LSF only allows one-based task IDs.
if (i + 1) != int(os.environ['LSB_JOBINDEX']):
continue
print('Run job {}'.format(cfg['name']))
train.main([config_files[i]])
torch.cuda.empty_cache()
else:
raise NotImplementedError(args.machine)