exp_vd/eval_sl.py [47:83]:
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try:
  args = vars(parser.parse_args())
except (IOError) as msg:
  parser.error(str(msg))

# set the cuda environment variable for the gpu to use
gpu_id = '' if args['gpu_id'] < 0 else str(args['gpu_id'])
print('Using GPU id: %s' % gpu_id)
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id

# Start the session BEFORE importing tensorflow_fold
# to avoid taking up all GPU memory
tf_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True),
                           allow_soft_placement=False,
                           log_device_placement=False)
sess = tf.Session(config=tf_config)

from models_vd.assembler import Assembler
from models_vd.model import CorefNMN
from loader_vd.data_reader import DataReader
from util import metrics
from util import support

# setting random seeds
np.random.seed(1234)
tf.set_random_seed(1234)

# read the train args from checkpoint
param_path = args['checkpoint'].replace('.tmodel', '_params.json')
with open(param_path, 'r') as file_id:
  saved_args = json.load(file_id)

saved_args.update(args)
args = saved_args
args['preload_feats'] = False
# no supervision is needed
args['supervise_attention'] = False
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exp_vd/visualize_sl.py [50:85]:
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try:
  args = vars(parser.parse_args())
except (IOError) as msg:
  parser.error(str(msg))

# Set the cuda environment variable for the gpu to use.
gpu_id = '' if args['gpu_id'] < 0 else str(args['gpu_id'])
print('Using GPU id: %s' % gpu_id)
os.environ['CUDA_VISIBLE_DEVICES'] = gpu_id

# Start the session BEFORE importing tensorflow_fold
# to avoid taking up all GPU memory
tf_config = tf.ConfigProto(gpu_options=tf.GPUOptions(allow_growth=True),
                           allow_soft_placement=False,
                           log_device_placement=False)
sess = tf.Session(config=tf_config)

from models_vd.assembler import Assembler
from models_vd.model import CorefNMN
from loader_vd.data_reader import DataReader
from util import metrics
from util import support

# setting random seeds
np.random.seed(1234)
tf.set_random_seed(1234)

# read the train args from checkpoint
param_path = args['checkpoint'].replace('.tmodel', '_params.json')
with open(param_path, 'r') as file_id:
  saved_args = json.load(file_id)

saved_args.update(args)
args = saved_args
args['preload_feats'] = False
args['supervise_attention'] = False
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