def custom_insightface_evaluation(args):
tf.reset_default_graph()
# Read the directory containing images
pairs = read_pairs(args.insightface_pair)
image_list, issame_list = get_paths_with_pairs(
args.insightface_dataset_dir, pairs)
# Evaluate custom dataset with facenet pre-trained model
print("Getting embeddings with facenet pre-trained model")
# Getting batched images by TF dataset
tf_dataset = facenet.tf_gen_dataset(
image_list=image_list,
label_list=None,
nrof_preprocess_threads=args.nrof_preprocess_threads,
image_size=args.insightface_dataset_dir,
method='cache_slices',
BATCH_SIZE=args.batch_size,
repeat_count=1,
to_float32=True,
shuffle=False)
# tf_dataset = facenet.tf_gen_dataset(image_list, label_list, args.nrof_preprocess_threads, args.facenet_image_size, method='cache_slices',
# BATCH_SIZE=args.batch_size, repeat_count=1, shuffle=False)
tf_dataset_iterator = tf_dataset.make_initializable_iterator()
tf_dataset_next_element = tf_dataset_iterator.get_next()
images = tf.placeholder(name='img_inputs',
shape=[
None, args.insightface_image_size,
args.insightface_image_size, 3
],
dtype=tf.float32)
labels = tf.placeholder(name='img_labels', shape=[
None,
], dtype=tf.int64)
dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
net = L_Resnet_E_IR_fix_issue9.get_resnet(images,
args.net_depth,
type='ir',
w_init=w_init_method,
trainable=False,
keep_rate=dropout_rate)
embeddings = net.outputs
# mv_mean = tl.layers.get_variables_with_name('resnet_v1_50/bn0/moving_mean', False, True)[0]
# 3.2 get arcface loss
logit = arcface_loss(embedding=net.outputs,
labels=labels,
w_init=w_init_method,
out_num=args.num_output)
sess = tf.Session()
saver = tf.train.Saver()
feed_dict = {}
feed_dict_flip = {}
path = args.ckpt_file + args.ckpt_index_list[0]
saver.restore(sess, path)
print('ckpt file %s restored!' % args.ckpt_index_list[0])
feed_dict.update(tl.utils.dict_to_one(net.all_drop))
feed_dict_flip.update(tl.utils.dict_to_one(net.all_drop))
feed_dict[dropout_rate] = 1.0
feed_dict_flip[dropout_rate] = 1.0
batch_size = args.batch_size
input_placeholder = images
sess.run(tf_dataset_iterator.initializer)
print('getting embeddings..')
total_time = 0
batch_number = 0
embeddings_array = None
embeddings_array_flip = None
while True:
try:
images = sess.run(tf_dataset_next_element)
data_tmp = images.copy() # fix issues #4
for i in range(data_tmp.shape[0]):
data_tmp[i, ...] -= 127.5
data_tmp[i, ...] *= 0.0078125
data_tmp[i, ...] = cv2.cvtColor(data_tmp[i, ...],
cv2.COLOR_RGB2BGR)
# Getting flip to left_right batched images by TF dataset
data_tmp_flip = images.copy() # fix issues #4
for i in range(data_tmp_flip.shape[0]):
data_tmp_flip[i, ...] = np.fliplr(data_tmp_flip[i, ...])
data_tmp_flip[i, ...] -= 127.5
data_tmp_flip[i, ...] *= 0.0078125
data_tmp_flip[i, ...] = cv2.cvtColor(data_tmp_flip[i, ...],
cv2.COLOR_RGB2BGR)
start_time = time.time()
feed_dict[input_placeholder] = data_tmp
_embeddings = sess.run(embeddings, feed_dict)
feed_dict_flip[input_placeholder] = data_tmp_flip
_embeddings_flip = sess.run(embeddings, feed_dict_flip)
if embeddings_array is None:
embeddings_array = np.zeros(
(len(image_list), _embeddings.shape[1]))
embeddings_array_flip = np.zeros(
(len(image_list), _embeddings_flip.shape[1]))
try:
embeddings_array[batch_number *
batch_size:min((batch_number + 1) *
batch_size, len(image_list)),
...] = _embeddings
embeddings_array_flip[batch_number * batch_size:min(
(batch_number + 1) * batch_size, len(image_list)),
...] = _embeddings_flip
# print('try: ', batch_number * batch_size, min((batch_number + 1) * batch_size, len(image_list)), ...)
except ValueError:
print(
'batch_number*batch_size value is %d min((batch_number+1)*batch_size, len(image_list)) %d,'
' batch_size %d, data.shape[0] %d' %
(batch_number * batch_size,
min((batch_number + 1) * batch_size,
len(image_list)), batch_size, images.shape[0]))
print('except: ', batch_number * batch_size,
min((batch_number + 1) * batch_size, images.shape[0]),
...)
duration = time.time() - start_time
batch_number += 1
total_time += duration
except tf.errors.OutOfRangeError:
print(
'tf.errors.OutOfRangeError, Reinitialize tf_dataset_iterator')
sess.run(tf_dataset_iterator.initializer)
break
print(f"total_time: {total_time}")
_xnorm = 0.0
_xnorm_cnt = 0
for embed in [embeddings_array, embeddings_array_flip]:
for i in range(embed.shape[0]):
_em = embed[i]
_norm = np.linalg.norm(_em)
# print(_em.shape, _norm)
_xnorm += _norm
_xnorm_cnt += 1
_xnorm /= _xnorm_cnt
final_embeddings_output = embeddings_array + embeddings_array_flip
final_embeddings_output = sklearn.preprocessing.normalize(
final_embeddings_output)
print(final_embeddings_output.shape)
tpr, fpr, accuracy, val, val_std, far = verification.evaluate(
final_embeddings_output, issame_list, nrof_folds=10)
acc2, std2 = np.mean(accuracy), np.std(accuracy)
auc = metrics.auc(fpr, tpr)
print('XNorm: %f' % (_xnorm))
print('Accuracy-Flip: %1.5f+-%1.5f' % (acc2, std2))
print('TPR: ', np.mean(tpr), 'FPR: ', np.mean(fpr))
print('Area Under Curve (AUC): %1.3f' % auc)
tpr_lfw, fpr_lfw, accuracy_lfw, val_lfw, val_std_lfw, far_lfw = lfw.evaluate(
final_embeddings_output,
issame_list,
nrof_folds=10,
distance_metric=0,
subtract_mean=False)
print('accuracy_lfw: %2.5f+-%2.5f' %
(np.mean(accuracy_lfw), np.std(accuracy_lfw)))
print(
f"val_lfw: {val_lfw}, val_std_lfw: {val_std_lfw}, far_lfw: {far_lfw}")
print('val_lfw rate: %2.5f+-%2.5f @ FAR=%2.5f' %
(val_lfw, val_std_lfw, far_lfw))
auc_lfw = metrics.auc(fpr_lfw, tpr_lfw)
print('TPR_LFW:', np.mean(tpr_lfw), 'FPR_LFW: ', np.mean(fpr_lfw))
print('Area Under Curve LFW (AUC): %1.3f' % auc_lfw)
sess.close()
return acc2, std2, _xnorm, [embeddings_array, embeddings_array_flip]
def custom_facenet_evaluation(args):
tf.reset_default_graph()
# Read the directory containing images
pairs = read_pairs(args.insightface_pair)
image_list, issame_list = get_paths_with_pairs(args.facenet_dataset_dir,
pairs)
# Evaluate custom dataset with facenet pre-trained model
print("Getting embeddings with facenet pre-trained model")
with tf.Graph().as_default():
# Getting batched images by TF dataset
# image_list = path_list
tf_dataset = facenet.tf_gen_dataset(
image_list=image_list,
label_list=None,
nrof_preprocess_threads=args.nrof_preprocess_threads,
image_size=args.facenet_image_size,
method='cache_slices',
BATCH_SIZE=args.batch_size,
repeat_count=1,
to_float32=True,
shuffle=False)
tf_dataset_iterator = tf_dataset.make_initializable_iterator()
tf_dataset_next_element = tf_dataset_iterator.get_next()
with tf.Session() as sess:
sess.run(tf_dataset_iterator.initializer)
phase_train_placeholder = tf.placeholder(tf.bool,
name='phase_train')
image_batch = tf.placeholder(name='img_inputs',
shape=[
None, args.facenet_image_size,
args.facenet_image_size, 3
],
dtype=tf.float32)
label_batch = tf.placeholder(name='img_labels',
shape=[
None,
],
dtype=tf.int32)
# Load the model
input_map = {
'image_batch': image_batch,
'label_batch': label_batch,
'phase_train': phase_train_placeholder
}
facenet.load_model(args.facenet_model, input_map=input_map)
# Get output tensor
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
batch_size = args.batch_size
input_placeholder = image_batch
print('getting embeddings..')
total_time = 0
batch_number = 0
embeddings_array = None
embeddings_array_flip = None
while True:
try:
images = sess.run(tf_dataset_next_element)
data_tmp = images.copy() # fix issues #4
for i in range(data_tmp.shape[0]):
data_tmp[i, ...] -= 127.5
data_tmp[i, ...] *= 0.0078125
data_tmp[i,
...] = cv2.cvtColor(data_tmp[i, ...],
cv2.COLOR_RGB2BGR)
# Getting flip to left_right batched images by TF dataset
data_tmp_flip = images.copy() # fix issues #4
for i in range(data_tmp_flip.shape[0]):
data_tmp_flip[i, ...] = np.fliplr(data_tmp_flip[i,
...])
data_tmp_flip[i, ...] -= 127.5
data_tmp_flip[i, ...] *= 0.0078125
data_tmp_flip[i, ...] = cv2.cvtColor(
data_tmp_flip[i, ...], cv2.COLOR_RGB2BGR)
start_time = time.time()
mr_feed_dict = {
input_placeholder: data_tmp,
phase_train_placeholder: False
}
mr_feed_dict_flip = {
input_placeholder: data_tmp_flip,
phase_train_placeholder: False
}
_embeddings = sess.run(embeddings, mr_feed_dict)
_embeddings_flip = sess.run(embeddings, mr_feed_dict_flip)
if embeddings_array is None:
embeddings_array = np.zeros(
(len(image_list), _embeddings.shape[1]))
embeddings_array_flip = np.zeros(
(len(image_list), _embeddings_flip.shape[1]))
try:
embeddings_array[batch_number * batch_size:min(
(batch_number + 1) * batch_size, len(image_list)),
...] = _embeddings
embeddings_array_flip[batch_number * batch_size:min(
(batch_number + 1) * batch_size, len(image_list)),
...] = _embeddings_flip
# print('try: ', batch_number * batch_size, min((batch_number + 1) * batch_size, len(image_list)), ...)
except ValueError:
print(
'batch_number*batch_size value is %d min((batch_number+1)*batch_size, len(image_list)) %d,'
' batch_size %d, data.shape[0] %d' %
(batch_number * batch_size,
min((batch_number + 1) * batch_size,
len(image_list)), batch_size,
images.shape[0]))
print(
'except: ', batch_number * batch_size,
min((batch_number + 1) * batch_size,
images.shape[0]), ...)
duration = time.time() - start_time
batch_number += 1
total_time += duration
except tf.errors.OutOfRangeError:
print(
'tf.errors.OutOfRangeError, Reinitialize tf_dataset_iterator'
)
sess.run(tf_dataset_iterator.initializer)
break
print(f"total_time: {total_time}")
_xnorm = 0.0
_xnorm_cnt = 0
for embed in [embeddings_array, embeddings_array_flip]:
for i in range(embed.shape[0]):
_em = embed[i]
_norm = np.linalg.norm(_em)
# print(_em.shape, _norm)
_xnorm += _norm
_xnorm_cnt += 1
_xnorm /= _xnorm_cnt
final_embeddings_output = embeddings_array + embeddings_array_flip
final_embeddings_output = sklearn.preprocessing.normalize(
final_embeddings_output)
print(final_embeddings_output.shape)
tpr, fpr, accuracy, val, val_std, far = verification.evaluate(
final_embeddings_output, issame_list, nrof_folds=10)
acc2, std2 = np.mean(accuracy), np.std(accuracy)
auc = metrics.auc(fpr, tpr)
print('XNorm: %f' % (_xnorm))
print('Accuracy-Flip: %1.5f+-%1.5f' % (acc2, std2))
print('TPR: ', np.mean(tpr), 'FPR: ', np.mean(fpr))
print('Area Under Curve (AUC): %1.3f' % auc)
tpr_lfw, fpr_lfw, accuracy_lfw, val_lfw, val_std_lfw, far_lfw = lfw.evaluate(
final_embeddings_output,
issame_list,
nrof_folds=10,
distance_metric=0,
subtract_mean=False)
print('accuracy_lfw: %2.5f+-%2.5f' %
(np.mean(accuracy_lfw), np.std(accuracy_lfw)))
print(
f"val_lfw: {val_lfw}, val_std_lfw: {val_std_lfw}, far_lfw: {far_lfw}")
print('val_lfw rate: %2.5f+-%2.5f @ FAR=%2.5f' %
(val_lfw, val_std_lfw, far_lfw))
auc_lfw = metrics.auc(fpr_lfw, tpr_lfw)
print('TPR_LFW:', np.mean(tpr_lfw), 'FPR_LFW: ', np.mean(fpr_lfw))
print('Area Under Curve LFW (AUC): %1.3f' % auc_lfw)
return acc2, std2, _xnorm, [embeddings_array, embeddings_array_flip]
def get_insightface_embeddings(args):
tf.reset_default_graph()
# Read the directory containing images
dataset = facenet.get_dataset(args.insightface_dataset_dir)
nrof_classes = len(dataset)
# Evaluate custom dataset with facenet pre-trained model
print("Getting embeddings with facenet pre-trained model")
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(dataset)
assert len(image_list) > 0, 'The dataset should not be empty'
print('Number of classes in dataset: %d' % nrof_classes)
print('Number of examples in dataset: %d' % len(image_list))
# Getting batched images by TF dataset
tf_dataset = facenet.tf_gen_dataset(
image_list=image_list,
label_list=None,
nrof_preprocess_threads=args.nrof_preprocess_threads,
image_size=args.insightface_dataset_dir,
method='cache_slices',
BATCH_SIZE=args.batch_size,
repeat_count=1,
to_float32=True,
shuffle=False)
tf_dataset_iterator = tf_dataset.make_initializable_iterator()
tf_dataset_next_element = tf_dataset_iterator.get_next()
images = tf.placeholder(name='img_inputs',
shape=[
None, args.insightface_image_size,
args.insightface_image_size, 3
],
dtype=tf.float32)
labels = tf.placeholder(name='img_labels', shape=[
None,
], dtype=tf.int64)
dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
net = L_Resnet_E_IR_fix_issue9.get_resnet(images,
args.net_depth,
type='ir',
w_init=w_init_method,
trainable=False,
keep_rate=dropout_rate)
embeddings = net.outputs
# mv_mean = tl.layers.get_variables_with_name('resnet_v1_50/bn0/moving_mean', False, True)[0]
# 3.2 get arcface loss
logit = arcface_loss(embedding=net.outputs,
labels=labels,
w_init=w_init_method,
out_num=args.num_output)
sess = tf.Session()
saver = tf.train.Saver()
feed_dict = {}
feed_dict_flip = {}
path = args.ckpt_file + args.ckpt_index_list[0]
saver.restore(sess, path)
print('ckpt file %s restored!' % args.ckpt_index_list[0])
feed_dict.update(tl.utils.dict_to_one(net.all_drop))
feed_dict_flip.update(tl.utils.dict_to_one(net.all_drop))
feed_dict[dropout_rate] = 1.0
feed_dict_flip[dropout_rate] = 1.0
batch_size = args.batch_size
input_placeholder = images
sess.run(tf_dataset_iterator.initializer)
# sess.run(tf_dataset_iterator_flip.initializer)
print('getting embeddings..')
total_time = 0
batch_number = 0
embeddings_array = None
embeddings_array_flip = None
while True:
try:
images = sess.run(tf_dataset_next_element)
data_tmp = images.copy() # fix issues #4
for i in range(data_tmp.shape[0]):
data_tmp[i, ...] -= 127.5
data_tmp[i, ...] *= 0.0078125
data_tmp[i, ...] = cv2.cvtColor(data_tmp[i, ...],
cv2.COLOR_RGB2BGR)
# Getting flip to left_right batched images by TF dataset
data_tmp_flip = images.copy() # fix issues #4
for i in range(data_tmp_flip.shape[0]):
data_tmp_flip[i, ...] = np.fliplr(data_tmp_flip[i, ...])
data_tmp_flip[i, ...] -= 127.5
data_tmp_flip[i, ...] *= 0.0078125
data_tmp_flip[i, ...] = cv2.cvtColor(data_tmp_flip[i, ...],
cv2.COLOR_RGB2BGR)
start_time = time.time()
feed_dict[input_placeholder] = data_tmp
_embeddings = sess.run(embeddings, feed_dict)
feed_dict_flip[input_placeholder] = data_tmp_flip
_embeddings_flip = sess.run(embeddings, feed_dict_flip)
if embeddings_array is None:
embeddings_array = np.zeros(
(len(image_list), _embeddings.shape[1]))
embeddings_array_flip = np.zeros(
(len(image_list), _embeddings_flip.shape[1]))
try:
embeddings_array[batch_number *
batch_size:min((batch_number + 1) *
batch_size, len(image_list)),
...] = _embeddings
embeddings_array_flip[batch_number * batch_size:min(
(batch_number + 1) * batch_size, len(image_list)),
...] = _embeddings_flip
# print('try: ', batch_number * batch_size, min((batch_number + 1) * batch_size, len(image_list)), ...)
except ValueError:
print(
'batch_number*batch_size value is %d min((batch_number+1)*batch_size, len(image_list)) %d,'
' batch_size %d, data.shape[0] %d' %
(batch_number * batch_size,
min((batch_number + 1) * batch_size,
len(image_list)), batch_size, images.shape[0]))
print('except: ', batch_number * batch_size,
min((batch_number + 1) * batch_size, images.shape[0]),
...)
duration = time.time() - start_time
batch_number += 1
total_time += duration
except tf.errors.OutOfRangeError:
print(
'tf.errors.OutOfRangeError, Reinitialize tf_dataset_iterator')
sess.run(tf_dataset_iterator.initializer)
break
print(f"total_time: {total_time}")
_xnorm = 0.0
_xnorm_cnt = 0
for embed in [embeddings_array, embeddings_array_flip]:
for i in range(embed.shape[0]):
_em = embed[i]
_norm = np.linalg.norm(_em)
# print(_em.shape, _norm)
_xnorm += _norm
_xnorm_cnt += 1
_xnorm /= _xnorm_cnt
final_embeddings_output = embeddings_array + embeddings_array_flip
final_embeddings_output = sklearn.preprocessing.normalize(
final_embeddings_output)
print(final_embeddings_output.shape)
sess.close()
return embeddings_array, embeddings_array_flip, final_embeddings_output, _xnorm
def get_facenet_embeddings(args):
# Read the directory containing images
dataset = facenet.get_dataset(args.facenet_dataset_dir)
nrof_classes = len(dataset)
# Evaluate custom dataset with facenet pre-trained model
print("Getting embeddings with facenet pre-trained model")
with tf.Graph().as_default():
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(dataset)
assert len(image_list) > 0, 'The dataset should not be empty'
print('Number of classes in dataset: %d' % nrof_classes)
print('Number of examples in dataset: %d' % len(image_list))
# Getting batched images by TF dataset
tf_dataset = facenet.tf_gen_dataset(
image_list=image_list,
label_list=None,
nrof_preprocess_threads=args.nrof_preprocess_threads,
image_size=args.facenet_image_size,
method='cache_slices',
BATCH_SIZE=args.batch_size,
repeat_count=1,
to_float32=True,
shuffle=False)
tf_dataset_iterator = tf_dataset.make_initializable_iterator()
tf_dataset_next_element = tf_dataset_iterator.get_next()
# Getting flip to left_right batched images by TF dataset
# tf_dataset_flip = facenet.tf_gen_dataset(image_list, label_list, args.nrof_preprocess_threads, args.facenet_image_size,
# method='cache_slices',
# BATCH_SIZE=args.batch_size, repeat_count=1, flip_left_right=True, shuffle=False)
#
# tf_dataset_iterator_flip = tf_dataset_flip.make_initializable_iterator()
# tf_dataset_next_element_flip = tf_dataset_iterator_flip.get_next()
with tf.Session() as sess:
sess.run(tf_dataset_iterator.initializer)
phase_train_placeholder = tf.placeholder(tf.bool,
name='phase_train')
image_batch = tf.placeholder(name='img_inputs',
shape=[
None, args.facenet_image_size,
args.facenet_image_size, 3
],
dtype=tf.float32)
label_batch = tf.placeholder(name='img_labels',
shape=[
None,
],
dtype=tf.int32)
# Load the model
input_map = {
'image_batch': image_batch,
'label_batch': label_batch,
'phase_train': phase_train_placeholder
}
facenet.load_model(args.facenet_model, input_map=input_map)
# Get output tensor
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
batch_size = args.batch_size
input_placeholder = image_batch
print('getting embeddings..')
total_time = 0
batch_number = 0
embeddings_array = None
embeddings_array_flip = None
while True:
try:
images = sess.run(tf_dataset_next_element)
data_tmp = images.copy() # fix issues #4
for i in range(data_tmp.shape[0]):
data_tmp[i, ...] -= 127.5
data_tmp[i, ...] *= 0.0078125
data_tmp[i,
...] = cv2.cvtColor(data_tmp[i, ...],
cv2.COLOR_RGB2BGR)
# Getting flip to left_right batched images by TF dataset
data_tmp_flip = images.copy() # fix issues #4
for i in range(data_tmp_flip.shape[0]):
data_tmp_flip[i, ...] = np.fliplr(data_tmp_flip[i,
...])
data_tmp_flip[i, ...] -= 127.5
data_tmp_flip[i, ...] *= 0.0078125
data_tmp_flip[i, ...] = cv2.cvtColor(
data_tmp_flip[i, ...], cv2.COLOR_RGB2BGR)
start_time = time.time()
mr_feed_dict = {
input_placeholder: data_tmp,
phase_train_placeholder: False
}
mr_feed_dict_flip = {
input_placeholder: data_tmp_flip,
phase_train_placeholder: False
}
_embeddings = sess.run(embeddings, mr_feed_dict)
_embeddings_flip = sess.run(embeddings, mr_feed_dict_flip)
if embeddings_array is None:
embeddings_array = np.zeros(
(len(image_list), _embeddings.shape[1]))
embeddings_array_flip = np.zeros(
(len(image_list), _embeddings_flip.shape[1]))
try:
embeddings_array[batch_number * batch_size:min(
(batch_number + 1) * batch_size, len(image_list)),
...] = _embeddings
embeddings_array_flip[batch_number * batch_size:min(
(batch_number + 1) * batch_size, len(image_list)),
...] = _embeddings_flip
# print('try: ', batch_number * batch_size, min((batch_number + 1) * batch_size, len(image_list)), ...)
except ValueError:
print(
'batch_number*batch_size value is %d min((batch_number+1)*batch_size, len(image_list)) %d,'
' batch_size %d, data.shape[0] %d' %
(batch_number * batch_size,
min((batch_number + 1) * batch_size,
len(image_list)), batch_size,
images.shape[0]))
print(
'except: ', batch_number * batch_size,
min((batch_number + 1) * batch_size,
images.shape[0]), ...)
duration = time.time() - start_time
batch_number += 1
total_time += duration
except tf.errors.OutOfRangeError:
print(
'tf.errors.OutOfRangeError, Reinitialize tf_dataset_iterator'
)
sess.run(tf_dataset_iterator.initializer)
# sess.run(tf_dataset_iterator_flip.initializer)
break
print(f"total_time: {total_time}")
_xnorm = 0.0
_xnorm_cnt = 0
for embed in [embeddings_array, embeddings_array_flip]:
for i in range(embed.shape[0]):
_em = embed[i]
_norm = np.linalg.norm(_em)
# print(_em.shape, _norm)
_xnorm += _norm
_xnorm_cnt += 1
_xnorm /= _xnorm_cnt
final_embeddings_output = embeddings_array + embeddings_array_flip
final_embeddings_output = sklearn.preprocessing.normalize(
final_embeddings_output)
print(final_embeddings_output.shape)
return embeddings_array, embeddings_array_flip, final_embeddings_output, _xnorm