def test_hash_metric(self, activate_k, k_set):
self.logger.info("Model testing k hash starts")
self.logger.info("Activation k(={}) in buckets(={})".format(
activate_k, self.args.d))
self.regen_session()
test_k_activate = activate_k_2D(self.test_k_hash,
k=activate_k,
session=self.sess) # [ntest, args.d]
if not hasattr(self, 'te_te_distance'):
self.regen_session()
self.te_te_distance = pairwise_distance_euclid_efficient(
input1=self.test_embed,
input2=self.test_embed,
session=self.sess,
batch_size=128)
self.logger.info(
"Calculating pairwise distance from test embeddings")
performance = evaluate_hash_te(test_hash_key=test_k_activate, te_te_distance=self.te_te_distance,\
te_te_query_key=test_k_activate, te_te_query_value=self.test_k_hash,\
test_label=self.test_label, ncls_test=self.ncls_test,\
activate_k=activate_k, k_set=k_set, logger=self.logger)
self.logger.info("Model testing k hash ends")
return performance
def test_vq(self, activate_k, k_set):
self.logger.info("Model testing vq starts")
self.logger.info("Activation k(={}) in buckets(={})".format(
activate_k, self.args.m))
if not hasattr(self, 'test_kmc'):
self.regen_session()
self.test_kmc = KMeansClustering(self.test_embed, self.args.m)
if not hasattr(self, 'te_te_distance'):
self.regen_session()
self.te_te_distance = pairwise_distance_euclid_efficient(
input1=self.test_embed,
input2=self.test_embed,
session=self.sess,
batch_size=128)
self.logger.info(
"Calculating pairwise distance from test embeddings")
te_te_query_value = self.test_kmc.k_hash(
self.test_embed, self.sess) # [ntest, args.d] center test
te_te_query_key = activate_k_2D(te_te_query_value,
k=activate_k,
session=self.sess) # [ntest, args.d]
test_hash_key = te_te_query_key
self.regen_session()
performance = evaluate_hash_te(test_hash_key=test_hash_key, te_te_distance=self.te_te_distance,\
te_te_query_key=te_te_query_key, te_te_query_value=te_te_query_value,\
test_label=self.test_label, ncls_test=self.ncls_test,\
activate_k=activate_k, k_set=k_set, logger=self.logger)
self.logger.info("Model testing vq ends")
return performance
def initialize(self):
'''Initialize uninitialized variables'''
self.logger.info("Model initialization starts")
self.generate_sess()
rest_initializer(self.sess)
self.start_epoch = 0
val_p_dist = pairwise_distance_euclid_efficient(
input1=self.val_embed,
input2=self.val_embed,
session=self.sess,
batch_size=self.args.nbatch)
self.logger.info("Calculating pairwise distance of validation data")
self.val_arg_sort = np.argsort(val_p_dist, axis=1)
self.logger.info("Model initialization ends")
def test_metric(self, k_set):
self.logger.info("Model testing metric starts")
if not hasattr(self, 'te_te_distance'):
self.regen_session()
self.te_te_distance = pairwise_distance_euclid_efficient(
input1=self.test_embed,
input2=self.test_embed,
session=self.sess,
batch_size=128)
self.logger.info(
"Calculating pairwise distance from test embeddings")
performance = evaluate_metric_te(test_label=self.test_label,
te_te_distance=self.te_te_distance,
k_set=k_set,
logger=self.logger)
return performance