def build(self):
self.logger.info("Model building starts")
tf.reset_default_graph()
if self.args.ltype == 'npair':
self.anc_img = tf.placeholder(tf.float32,
shape=[
self.args.nbatch // 2,
self.height, self.width,
self.nchannel
])
self.pos_img = tf.placeholder(tf.float32,
shape=[
self.args.nbatch // 2,
self.height, self.width,
self.nchannel
])
self.istrain = tf.placeholder(tf.bool, shape=[])
self.label = tf.placeholder(tf.int32,
shape=[self.args.nbatch // 2])
else: # triplet
self.img = tf.placeholder(tf.float32,
shape=[
self.args.nbatch, self.height,
self.width, self.nchannel
])
self.istrain = tf.placeholder(tf.bool, shape=[])
self.label = tf.placeholder(tf.int32, shape=[self.args.nbatch])
self.generate_sess()
self.conv_net = CONV_DICT[self.args.dataset][self.args.conv]
if self.args.ltype == 'npair':
self.anc_last, _ = self.conv_net(self.anc_img,
is_training=self.istrain,
reuse=False)
self.pos_last, _ = self.conv_net(self.pos_img,
is_training=self.istrain,
reuse=True)
self.anc_last = tf.nn.relu(self.anc_last)
self.pos_last = tf.nn.relu(self.pos_last)
else: #triplet
self.last, _ = self.conv_net(self.img,
is_training=self.istrain,
reuse=False)
self.last = tf.nn.relu(self.last)
with slim.arg_scope([slim.fully_connected],
activation_fn=None,
weights_regularizer=slim.l2_regularizer(0.0005),
biases_initializer=tf.zeros_initializer()):
if self.args.ltype == 'npair':
with tf.variable_scope('Embed', reuse=False):
self.anc_embed = slim.fully_connected(self.anc_last,
self.args.m,
scope="fc1")
with tf.variable_scope('Embed', reuse=True):
self.pos_embed = slim.fully_connected(self.pos_last,
self.args.m,
scope="fc1")
self.loss = npairs_loss(labels=self.label,
embeddings_anchor=self.anc_embed,
embeddings_positive=self.pos_embed,
reg_lambda=self.args.lamb)
else: #triplet
with tf.variable_scope('Embed', reuse=False):
self.embed = slim.fully_connected(self.last,
self.args.m,
scope="fc1")
self.embed_l2_norm = tf.nn.l2_normalize(
self.embed, dim=-1) # embedding with l2 normalization
def pairwise_distance_c(embeddings):
return pairwise_distance_euclid(embeddings, squared=True)
self.loss = triplet_semihard_loss(
labels=self.label,
embeddings=self.embed_l2_norm,
pairwise_distance=pairwise_distance_c,
margin=self.args.ma)
self.loss += tf.losses.get_regularization_loss()
initialized_variables = get_initialized_vars(self.sess)
self.logger.info("Variables loaded from pretrained network\n{}".format(
vars_info_vl(initialized_variables)))
self.logger.info("Model building ends")
def build(self, pretrain=False):
self.logger.info("Model building starts")
tf.reset_default_graph()
if self.args.hltype == 'npair':
self.anc_img = tf.placeholder(tf.float32,
shape=[
self.args.nbatch // 2,
self.height, self.width,
self.nchannel
])
self.pos_img = tf.placeholder(tf.float32,
shape=[
self.args.nbatch // 2,
self.height, self.width,
self.nchannel
])
self.istrain = tf.placeholder(tf.bool, shape=[])
self.label = tf.placeholder(tf.int32,
shape=[self.args.nbatch // 2])
else: # triplet
self.img = tf.placeholder(tf.float32,
shape=[
self.args.nbatch, self.height,
self.width, self.nchannel
])
self.istrain = tf.placeholder(tf.bool, shape=[])
self.label = tf.placeholder(tf.int32, shape=[self.args.nbatch])
self.generate_sess()
self.conv_net = CONV_DICT[self.args.dataset][self.args.conv]
if self.args.hltype == 'npair':
self.anc_last, _ = self.conv_net(self.anc_img,
is_training=self.istrain,
reuse=False)
self.pos_last, _ = self.conv_net(self.pos_img,
is_training=self.istrain,
reuse=True)
self.anc_last = tf.nn.relu(self.anc_last)
self.pos_last = tf.nn.relu(self.pos_last)
else:
self.last, _ = self.conv_net(self.img,
is_training=self.istrain,
reuse=False)
self.last = tf.nn.relu(self.last)
with slim.arg_scope(
[slim.fully_connected],
activation_fn=None,
weights_regularizer=slim.l2_regularizer(0.0005),
biases_initializer=tf.zeros_initializer(),
weights_initializer=tf.truncated_normal_initializer(0.0,
0.01)):
if self.args.hltype == 'npair':
with tf.variable_scope('Embed', reuse=False):
self.anc_embed = slim.fully_connected(self.anc_last,
self.args.m,
scope="fc1")
with tf.variable_scope('Embed', reuse=True):
self.pos_embed = slim.fully_connected(self.pos_last,
self.args.m,
scope="fc1")
else: #triplet
with tf.variable_scope('Embed', reuse=False):
self.embed = slim.fully_connected(self.last,
self.args.m,
scope="fc1")
initialized_variables = get_initialized_vars(self.sess)
self.logger.info("Variables loaded from pretrained network\n{}".format(
vars_info_vl(initialized_variables)))
self.logger.info("Model building ends")