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jblitzar
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github-python

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parser.add_argument('--debug', action='store_true', help='debug mode')
FLAGS = parser.parse_args()
# sys.argv = sys.argv[:1] # clean extra argv
# ---------------------------------------------------------------------------- #
# solve env & cfg
# ---------------------------------------------------------------------------- #
assert FLAGS.cfg_path is not None
# load config - config path: config(dir).dataset_name(py).config_name(py_class)
cfg = load_config(cfg_path=FLAGS.cfg_path)
# update config
for arg in ['data_path', 'model_path', 'saving_path', 'mode', 'gpus', 'rand_seed', 'num_threads', 'num_votes', 'debug']:
if getattr(FLAGS, arg) is not None:
setattr(cfg, arg, getattr(FLAGS, arg))
if FLAGS.set:
for arg in FLAGS.set.split(';'):
cfg.update(arg)
# env setting: visible gpu, tf warnings (level = '0'/'3')
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu_devices
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0'
if cfg.mixed_precision:
os.environ['TF_ENABLE_AUTO_MIXED_PRECISION'] = '1'
import tensorflow as tf
if tf.__version__.split('.')[0] == '2':
tf = tf.compat.v1
tf.disable_v2_behavior()
import models, datasets
from utils.tester import ModelTester
from utils.trainer import ModelTrainer
from utils.tf_graph_builder import GraphBuilder
# solve config
if cfg.dataset in ['S3DIS']:
cfg.mode = cfg.mode.replace('test', 'validation')
if cfg.model_path and os.path.isdir(cfg.model_path):
cfg.model_path = get_snap(cfg.model_path, step='last')
if cfg.save_memory: # use gradient-checkpointing to save memory
import utils.memory_saving_gradients
tf.__dict__['gradients'] = utils.memory_saving_gradients.gradients_memory # one from the: gradients_speed, gradients_memory, gradients_collection
if isinstance(cfg.rand_seed, int): # manual set seed
tf.set_random_seed(cfg.rand_seed)
np.random.seed(cfg.rand_seed)
if cfg.debug: # debug mode
cfg.saving_path = 'test'
cfg.log_file = sys.stdout
# ---------------------------------------------------------------------------- #
# training
# ---------------------------------------------------------------------------- #
if 'train' in cfg.mode:
# result dir: results/dataset_name/config_name/Log_time/...
if not cfg.saving_path:
time.sleep(np.random.randint(1, 10)) # random sleep (avoid same log dir)
# dataset_name = '_'.join([i for i in [cfg.dataset.lower(), cfg.version, cfg.validation_split] if i]) # default version / validation_split specified in dataset class
cfg.saving_path = f'results/{cfg.dataset.lower()}/{cfg.name}/' + time.strftime('Log_%Y-%m-%d_%H-%M-%S', time.gmtime())
os.makedirs(cfg.saving_path, exist_ok=True)
if not cfg.log_file:
cfg.log_file = os.path.join(cfg.saving_path, 'log_train.txt')
if isinstance(cfg.log_file, str):
cfg.log_file = open(cfg.log_file, 'w')
log_config(cfg)
log_config(cfg, f_out=cfg.log_file)
# actual training
print_mem('>>> start training', check_time=True)
with redirect_io(cfg.log_file, cfg.debug):
trainer = ModelTrainer(cfg)
trainer.train()
print(flush=True)
print_mem('>>> finished training', check_time=True)
if cfg.gpu_num > 1:
cfg.gpus = 1
if 'test' in cfg.mode or 'val' in cfg.mode:
# find chosen snap (and saving_path if not specified)
log_config(cfg)
if cfg.model_path and 'train' not in cfg.mode: # specified for val/test (not for continue training)
snap_list = [cfg.model_path]
cfg.saving_path = os.path.dirname(cfg.model_path).split(cfg.snap_dir)[0].rstrip('/') # ensure at least is a dir
elif cfg.saving_path:
snap_list = [f[:-5] for f in glob.glob(os.path.join(cfg.saving_path, cfg.snap_dir, f'{cfg.snap_prefix}*.meta'))]
else:
raise ValueError('provide either cfg.model_path (snap) or cfg.saving_path (dir)')
assert len(snap_list) > 0, f'no snap found in saving_path={cfg.saving_path}'
def val_test(snap):
# using the saved model
step = snap.split(f'{cfg.snap_prefix}-')[-1].split('.')[0]
assert len(glob.glob(snap + '*')) > 0 and os.path.isdir(cfg.saving_path), f'err path: chosen_snap = {snap}, saving_path = {cfg.saving_path}'
print('using restored model, chosen_snap =', snap, flush=True)
with tf.Graph().as_default():