def initialize():
tf.compat.v1.disable_eager_execution()
options.initialize_with_logfiles(get_parser())
logging.info(f'-- Starting --')
logging.info(f'Host: {socket.gethostname()}')
logging.info(f'Process id (pid): {os.getpid()}')
if FLAGS.comment:
logging.info(f'Comment: {FLAGS.comment}')
logging.info(f'Raw command: {" ".join(map(shlex.quote, sys.argv))}')
logging.info(f'Parsed flags: {FLAGS}')
tfu.set_data_format(FLAGS.data_format)
tfu.set_dtype(tf.float32 if FLAGS.dtype == 'float32' else tf.float16)
if FLAGS.batch_size_test is None:
FLAGS.batch_size_test = FLAGS.batch_size
if FLAGS.checkpoint_dir is None:
FLAGS.checkpoint_dir = FLAGS.logdir
FLAGS.checkpoint_dir = util.ensure_absolute_path(
FLAGS.checkpoint_dir, root=f'{paths.DATA_ROOT}/experiments')
os.makedirs(FLAGS.checkpoint_dir, exist_ok=True)
if not FLAGS.pred_path:
FLAGS.pred_path = f'predictions_{FLAGS.dataset}.npz'
base = os.path.dirname(
FLAGS.load_path) if FLAGS.load_path else FLAGS.checkpoint_dir
FLAGS.pred_path = util.ensure_absolute_path(FLAGS.pred_path, base)
if FLAGS.bone_length_dataset is None:
FLAGS.bone_length_dataset = FLAGS.dataset
if FLAGS.load_path:
if FLAGS.load_path.endswith('.index') or FLAGS.load_path.endswith(
'.meta'):
FLAGS.load_path = os.path.splitext(FLAGS.load_path)[0]
FLAGS.load_path = util.ensure_absolute_path(FLAGS.load_path,
FLAGS.checkpoint_dir)
# Override the default data format in slim layers
enter_context(
slim.arg_scope([
slim.conv2d, slim.conv3d, slim.conv3d_transpose,
slim.conv2d_transpose, slim.avg_pool2d, slim.separable_conv2d,
slim.max_pool2d, slim.batch_norm, slim.spatial_softmax
],
data_format=tfu.data_format()))
# Override default paddings to SAME
enter_context(
slim.arg_scope([slim.avg_pool2d, slim.max_pool2d], padding='SAME'))
tf.compat.v2.random.set_seed(FLAGS.seed)
if FLAGS.gui:
plt.switch_backend('TkAgg')
def initialize(args=None):
options.initialize_with_logfiles(get_parser(), args)
logger.info(f'-- Starting --')
logger.info(f'Host: {socket.gethostname()}')
logger.info(f'Process id (pid): {os.getpid()}')
if FLAGS.comment:
logger.info(f'Comment: {FLAGS.comment}')
logger.info(f'Raw command: {" ".join(map(shlex.quote, sys.argv))}')
logger.info(f'Parsed flags: {FLAGS}')
tfu.set_data_format(FLAGS.data_format)
tfu.set_dtype(tf.float32 if FLAGS.dtype == 'float32' else tf.float16)
if FLAGS.batch_size_test is None:
FLAGS.batch_size_test = FLAGS.batch_size
if FLAGS.checkpoint_dir is None:
FLAGS.checkpoint_dir = FLAGS.logdir
FLAGS.checkpoint_dir = util.ensure_absolute_path(
FLAGS.checkpoint_dir, root=f'{paths.DATA_ROOT}/experiments')
os.makedirs(FLAGS.checkpoint_dir, exist_ok=True)
if not FLAGS.pred_path:
FLAGS.pred_path = f'predictions_{FLAGS.dataset}.npz'
base = os.path.dirname(
FLAGS.load_path) if FLAGS.load_path else FLAGS.checkpoint_dir
FLAGS.pred_path = util.ensure_absolute_path(FLAGS.pred_path, base)
if FLAGS.bone_length_dataset is None:
FLAGS.bone_length_dataset = FLAGS.dataset
if FLAGS.model_joints is None:
FLAGS.model_joints = FLAGS.dataset
if FLAGS.output_joints is None:
FLAGS.output_joints = FLAGS.dataset
if FLAGS.load_path:
if FLAGS.load_path.endswith('.index') or FLAGS.load_path.endswith(
'.meta'):
FLAGS.load_path = os.path.splitext(FLAGS.load_path)[0]
FLAGS.load_path = util.ensure_absolute_path(FLAGS.load_path,
FLAGS.checkpoint_dir)
tf.random.set_seed(FLAGS.seed)
if FLAGS.viz:
plt.switch_backend('TkAgg')
FLAGS.backbone = FLAGS.backbone.replace('_', '-')
for gpu in tf.config.experimental.list_physical_devices('GPU'):
tf.config.experimental.set_memory_growth(gpu, True)
if FLAGS.dtype == 'float16':
tf.keras.mixed_precision.set_global_policy('mixed_float16')
def init_worker_process_flags(flags):
import os
os.environ['OMP_NUM_THREADS'] = '1'
os.environ['KMP_INIT_AT_FORK'] = 'FALSE'
from options import FLAGS
for key in flags.__dict__:
setattr(FLAGS, key, getattr(flags, key))
import tfu
tfu.set_data_format(FLAGS.data_format)
init_worker_process()
def initialize():
global FLAGS
parse_and_set_global_flags()
setup_logging()
logging.info(f'-- Starting --')
logging.info(f'Host: {socket.gethostname()}')
logging.info(f'Process id (pid): {os.getpid()}')
if FLAGS.comment:
logging.info(f'Comment: {FLAGS.comment}')
logging.info(f'Raw command: {" ".join(map(shlex.quote, sys.argv))}')
logging.info(f'Parsed flags: {FLAGS}')
tfu.set_data_format(FLAGS.data_format)
if FLAGS.dtype == 'float32':
tfu.set_dtype(tf.float32)
elif FLAGS.dtype == 'float16':
tfu.set_dtype(tf.float16)
else:
raise ValueError(f'Training dtype {FLAGS.dtype} not supported, only float16/32.')
# We parallelize on a coarser level already, openmp just makes things slower
# Override the default data format in slim layers
enter_context(slim.arg_scope(
[slim.conv2d, slim.conv3d, slim.conv3d_transpose, slim.conv2d_transpose, slim.avg_pool2d,
slim.separable_conv2d, slim.max_pool2d, slim.batch_norm, slim.spatial_softmax],
data_format=tfu.data_format()))
# Override default paddings to SAME
enter_context(slim.arg_scope([slim.avg_pool2d, slim.max_pool2d], padding='SAME'))
if FLAGS.gui:
plt.switch_backend('TkAgg')
tf.set_random_seed(FLAGS.seed)