def main(_):
"""
The main function for training steps
"""
pp.pprint(flags.FLAGS.__flags)
n_per_itr_print_results = 100
kb_work_on_patch = True
# ---------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# Manual Switchs ------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# DATASET PARAMETER : UCSD
# FLAGS.dataset = 'UCSD'
# FLAGS.dataset_address = './dataset/UCSD_Anomaly_Dataset.v1p2/UCSDped2/Train'
nd_input_frame_size = (240, 360)
nd_slice_size = (45, 45)
n_stride = 25
n_fetch_data = 600
# ---------------------------------------------------------------------------------------------
# # DATASET PARAMETER : MNIST
# FLAGS.dataset = 'mnist'
# FLAGS.dataset_address = './dataset/mnist'
# nd_input_frame_size = (28, 28)
# nd_slice_size = (28, 28)
FLAGS.train = True
FLAGS.input_width = nd_slice_size[0]
FLAGS.input_height = nd_slice_size[1]
FLAGS.output_width = nd_slice_size[0]
FLAGS.output_height = nd_slice_size[1]
FLAGS.sample_dir = 'export/' + FLAGS.dataset + '_%d.%d' % (
nd_slice_size[0], nd_slice_size[1])
FLAGS.input_fname_pattern = '*'
check_some_assertions()
# manual handling of GPU
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.4)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
tmp_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
is_training=FLAGS.train,
log_dir=FLAGS.log_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_slice_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size=nd_input_frame_size,
n_fetch_data=n_fetch_data)
# show_all_variables()
if FLAGS.train:
print('Program is on Train Mode')
tmp_model.train(FLAGS)
else:
if not tmp_model.load(FLAGS.checkpoint_dir)[0]:
print('Program is on Test Mode')
raise Exception(
"[!] Train a model first, then run test mode from file test.py"
)
def main(_):
"""
The main function for training steps
"""
n_per_itr_print_results = 100
kb_work_on_patch = True
# ---------------------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# Manual Switchs ------------------------------------------------------------------------------
# ---------------------------------------------------------------------------------------------
# DATASET PARAMETER : data-alocc
FLAGS.dataset = "data-alocc"
FLAGS.dataset_address = "./dataset/data-alocc/train"
kb_work_on_patch = False
nd_input_frame_size = (180, 270)
nd_slice_size = (180, 270)
n_stride = 1
FLAGS.train = True
FLAGS.input_width = nd_slice_size[0]
FLAGS.input_height = nd_slice_size[1]
FLAGS.output_width = nd_slice_size[0]
FLAGS.output_height = nd_slice_size[1]
FLAGS.sample_dir = ("export/" + FLAGS.dataset + "_%d.%d.%f_" %
(nd_slice_size[0], nd_slice_size[1], FLAGS.r_alpha))
FLAGS.input_fname_pattern = "*"
pp.pprint(flags.FLAGS.__flags)
check_some_assertions()
# manual handling of GPU
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)
run_config = tf.ConfigProto(gpu_options=gpu_options)
run_config.gpu_options.allow_growth = True
with tf.Session(config=run_config) as sess:
tmp_model = ALOCC_Model(
sess,
input_width=FLAGS.input_width,
input_height=FLAGS.input_height,
output_width=FLAGS.output_width,
output_height=FLAGS.output_height,
batch_size=FLAGS.batch_size,
sample_num=FLAGS.batch_size,
attention_label=FLAGS.attention_label,
r_alpha=FLAGS.r_alpha,
dataset_name=FLAGS.dataset,
dataset_address=FLAGS.dataset_address,
input_fname_pattern=FLAGS.input_fname_pattern,
checkpoint_dir=FLAGS.checkpoint_dir,
is_training=FLAGS.train,
log_dir=FLAGS.log_dir,
sample_dir=FLAGS.sample_dir,
nd_patch_size=nd_slice_size,
n_stride=n_stride,
n_per_itr_print_results=n_per_itr_print_results,
kb_work_on_patch=kb_work_on_patch,
nd_input_frame_size=nd_input_frame_size,
)
# show_all_variables()
if FLAGS.train:
print("Program is on Train Mode")
tmp_model.train(FLAGS)
else:
if not tmp_model.load(FLAGS.checkpoint_dir)[0]:
print("Program is on Test Mode")
raise Exception(
"[!] Train a model first, then run test mode from file test.py"
)
