def get_extractor(args):
# load model
model_path = args.model_path
images = tf.placeholder(
name='img_inputs',
shape=[None, args.image_size[0], args.image_size[1], 3],
dtype=tf.float32)
dropout_rate = tf.placeholder(name='dropout_rate', dtype=tf.float32)
print('Buiding net structure')
w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
# test net because of batch normal layer
tl.layers.set_name_reuse(True)
test_net = get_resnet(images,
args.net_depth,
type='ir',
w_init=w_init_method,
trainable=False,
reuse=tf.AUTO_REUSE,
keep_rate=dropout_rate)
embedding_tensor = test_net.outputs
# 3.10 define sess
#sess = tf.Session()
gpu_config = tf.ConfigProto(allow_soft_placement=True)
gpu_config.gpu_options.allow_growth = True
sess = tf.Session(config=gpu_config)
# 3.13 init all variables
sess.run(tf.global_variables_initializer())
# restore weights
saver = tf.train.Saver()
saver.restore(sess, model_path)
# lfw validate
feed_dict = {images: None, dropout_rate: 1.0}
#feed_dict_test.update(tl.utils.dict_to_one(net.all_drop))
extractor = TensorflowExtractor(sess, embedding_tensor, args.batch_size,
feed_dict, images)
return extractor
# 2.2 prepare validate datasets
ver_list = []
ver_name_list = []
for db in args.eval_datasets:
print('begin db %s convert.' % db) #列出每一個驗證dataset的path，一次迴圈一個
data_set = load_bin(
db, args.image_size,
args) #預設是lfw,image size 27行 112*112，得到dataset BGR [112, 112, 3]
ver_list.append(data_set) #把驗證集加到ver_list裡
ver_name_list.append(db) #把驗證集path加到ver_name_list裡
# 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
# 3.1 inference phase
w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
net = get_resnet(images,
args.net_depth,
type='ir',
w_init=w_init_method,
trainable=True,
keep_rate=dropout_rate)
# 3.2 get arcface loss
logit = arcface_loss(embedding=net.outputs,
labels=labels,
w_init=w_init_method,
out_num=args.num_output)
# test net because of batch normal layer
tl.layers.set_name_reuse(True)
test_net = get_resnet(images,
args.net_depth,
type='ir',
w_init=w_init_method,
trainable=False,
reuse=True,
dropout_keep_rate = tf.placeholder(name='dropout_keep_rate',
dtype=tf.float32)
images_t = tf.placeholder(name='img_inputs_t',
shape=[None, *args.image_size, 3],
dtype=tf.float32)
labels_t = tf.placeholder(name='img_labels_t',
shape=[
None,
],
dtype=tf.int64)
t_model_path = args.teacher_model_path
w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
net_t = get_resnet(images_t,
args.net_depth,
type='ir',
w_init=w_init_method,
trainable=False,
keep_rate=dropout_keep_rate)
embedding_tensor_t = net_t.outputs
logit_t = arcface_loss(embedding=embedding_tensor_t,
labels=labels_t,
var_scope='arcface_loss',
w_init=w_init_method,
out_num=args.num_output)
t_tau = tf.scalar_mul(1.0 / args.tau, logit_t)
pred_t = tf.nn.softmax(logit_t)
acc_t = tf.reduce_mean(
tf.cast(tf.equal(tf.argmax(pred_t, axis=1), labels_t),
dtype=tf.float32))
# labels = sess.graph.get_tensor_by_name("img_labels:0")
# dropout_rate = sess.graph.get_tensor_by_name("dropout_rate:0")
# output = sess.graph.get_tensor_by_name("resnet_v1_50/E_BN2/Identity_2:0")
images = tf.placeholder(name='img_inputs',
shape=[None, 112, 112, 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 = get_resnet(images,
50,
type='ir',
w_init=w_init_method,
trainable=False,
keep_rate=dropout_rate)
output = net.outputs
saver = tf.train.Saver()
saver.restore(sess, './model/InsightFace_iter_best_710000.ckpt')
embedding_array = np.zeros((278, 10, 512))
label_array = np.zeros((278, 1))
file_list = 'D:/Project/face/INSIGHT_FACE/datasets/CROP_0.95'
classes = [
path for path in os.listdir(file_list)
if os.path.isdir(os.path.join(file_list, path))
]
dataset = dataset.shuffle(buffer_size=args.buffer_size)
dataset = dataset.batch(args.batch_size)
iterator = dataset.make_initializable_iterator()
next_element = iterator.get_next()
# 2.2 prepare validate datasets
ver_list = []
ver_name_list = []
for db in args.eval_datasets:
print('begin db %s convert.' % db)
data_set = load_bin(db, args.image_size, args)
ver_list.append(data_set)
ver_name_list.append(db)
# 3. define network, loss, optimize method, learning rate schedule, summary writer, saver
# 3.1 inference phase
w_init_method = tf.contrib.layers.xavier_initializer(uniform=False)
net = get_resnet(images, args.net_depth, type='ir', w_init=w_init_method, trainable=True, keep_rate=dropout_rate)
# 3.2 get arcface loss
logit = arcface_loss(embedding=net.outputs, labels=labels, w_init=w_init_method, out_num=args.num_output)
# test net because of batch normal layer
tl.layers.set_name_reuse(True)
test_net = get_resnet(images, args.net_depth, type='ir', w_init=w_init_method, trainable=False, reuse=True, keep_rate=dropout_rate)
embedding_tensor = test_net.outputs
# 3.3 define the cross entropy
inference_loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logit, labels=labels))
# inference_loss_avg = tf.reduce_mean(inference_loss)
# 3.4 define weight deacy losses
# for var in tf.trainable_variables():
# print(var.name)
# print('##########'*30)
wd_loss = 0
for weights in tl.layers.get_variables_with_name('W_conv2d', True, True):
