tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=tf_config)
tf.global_variables_initializer().run()
model_saver.restore(sess, os.path.join(checkpoint_dir, 'dbg_model_best'))
""" Get test or validation video list
"""
train_dict, val_dict, test_dict = data_loader.getDatasetDict(
video_info_file)
if test_mode == 'validation':
video_dict = val_dict
else:
video_dict = test_dict
""" load test or validation data
"""
batch_video_list = data_loader.getBatchListTest(video_dict, batch_size)
""" init result list
"""
batch_result_xmin = []
batch_result_xmax = []
batch_result_iou = []
batch_result_pstart = []
batch_result_pend = []
""" Run DBG model
"""
print('Runing DBG model ...')
for idx in mmcv.track_iter_progress(range(len(batch_video_list))):
""" Get batch data
"""
batch_anchor_xmin, batch_anchor_xmax, batch_anchor_feature = \
data_loader.getProposalDataTest(
pend = tf.reduce_sum(prop_end, 1) / tf.maximum(tf.reduce_sum(tf_mask, 1), 1)
model_saver = tf.train.Saver()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=tf_config)
tf.global_variables_initializer().run()
model_saver.restore(sess, os.path.join(checkpoint_dir, 'dbg_model_best'))
train_dict, val_dict, test_dict = data_loader.getDatasetDict()
if test_mode == 'validation':
video_dict = val_dict
else:
video_dict = test_dict
batch_video_list = data_loader.getBatchListTest(video_dict, batch_size, shuffle=False)
batch_result_xmin = []
batch_result_xmax = []
batch_result_iou = []
batch_result_pstart = []
batch_result_pend = []
print('Runing DBG model ...')
for idx in tqdm.tqdm(range(len(batch_video_list))):
batch_anchor_xmin, batch_anchor_xmax, batch_anchor_feature = data_loader.getProposalDataTest(
batch_video_list[idx], video_dict)
out_iou, out_start, out_end = sess.run([iou_mat, pstart, pend],
feed_dict={X_feature: batch_anchor_feature})
batch_result_xmin.append(batch_anchor_xmin)
batch_result_xmax.append(batch_anchor_xmax)
pend = tf.reduce_sum(prop_end, 1) / tf.maximum(tf.reduce_sum(tf_mask, 1),
1)
""" Load model weights """
model_saver = tf.train.Saver()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
sess = tf.InteractiveSession(config=tf_config)
tf.global_variables_initializer().run()
model_saver.restore(sess, os.path.join(checkpoint_dir, 'dbg_model_best'))
""" Get test or validation video list
"""
# train_dict, val_dict, test_dict = data_loader.getDatasetDict(video_info_file)
test_dict = getDatasetDict(video_info_file)
""" load test or validation data
"""
batch_video_list = data_loader.getBatchListTest(test_dict, 1)
""" init result list
"""
batch_result_xmin = []
batch_result_xmax = []
batch_result_iou = []
batch_result_pstart = []
batch_result_pend = []
""" Run DBG model
"""
print('Runing DBG model ...')
for idx in tqdm.tqdm(range(len(batch_video_list))):
""" Get batch data
"""
batch_anchor_xmin, batch_anchor_xmax, batch_anchor_feature = \
data_loader.getProposalDataTest(