import tensorflow as tf
sys.path.insert(1, os.path.realpath(os.path.pardir))
import utils.kinetics_i3d_utils as ki3du
import i3d
# import skvideo
import utils.pre_process_rgb_flow as img_tool
#%%
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)
tf_config = tf.ConfigProto()
# tf_config.gpu_options.per_process_gpu_memory_fraction = 0.99
eval_type = 'rgb'
cfg = ki3du.load_config(yml_path='run_config.yml')
ATTACK_CFG = cfg.SINGLE_VIDEO_ATTACK
kinetics_classes = ki3du.load_kinetics_classes(eval_type)
k_i3d = ki3du.kinetics_i3d(ckpt_path=cfg.MODEL.CKPT_PATH, batch_size=ATTACK_CFG.BATCH_SIZE)
if ATTACK_CFG.IMPROVE_ADV_LOSS:
adversarial_loss = k_i3d.improve_adversarial_loss(margin=ATTACK_CFG.PROB_MARGIN,
targeted = ATTACK_CFG.TARGETED_ATTACK,
logits = ATTACK_CFG.USE_LOGITS)
else:
adversarial_loss = k_i3d.ce_adversarial_loss(targeted=ATTACK_CFG.TARGETED_ATTACK)
beta_0_default = tf.constant(1, dtype=tf.float32)
beta_0 = tf.placeholder_with_default(beta_0_default, name='beta_0', shape=beta_0_default.shape)
# dataset = dataset.batch(batch_size)
dataset = dataset.prefetch(buffer_size=1)
# images, labels = dataset.make_one_shot_iterator().get_next()
#
# features = {'images': images}
return dataset
return _input_fn
#%%
kinetics_classes = ki3du.load_kinetics_classes()
if sys.argv.__len__() > 1:
cfg = ki3du.load_config(yml_path=sys.argv[1])
else:
cfg = ki3du.load_config(yml_path='run_config.yml')
ATTACK_CFG = cfg.UNIVERSAL_ATTACK
source_class = ATTACK_CFG.TF_RECORDS_TRAIN_PATH[-1].split('/')[-2]
# assert source_class in kinetics_classes, "Oh no! {} not in kinetics classes".format(source_class)
# NUM_OF_VID_EACH_TF_RECORDS
# NUM_OF_TRAIN_TF_RECORDS
# NUM_OF_VAL_TF_RECORDS
if ATTACK_CFG.FLICKERING_ATTACK:
attack_type = 'FLICKERING_ATTACK'
else:
attack_type = 'SUP_ATTACK'