def configHDML(is_Training, embedding_y_origin, label, J_m):
with tf.name_scope('Javg'):
Javg = tf.placeholder(tf.float32)
with tf.name_scope('Jgen'):
Jgen = tf.placeholder(tf.float32)
#Augmentor
embedding_z_quta = HDML.Pulling(FLAGS.LossType, embedding_z, Javg)
embedding_z_concate = tf.concat([embedding_z, embedding_z_quta], axis=0)
params = (is_Training, embedding_y_origin, label, J_m, Javg, Jgen,
embedding_z_quta, embedding_z_concate)
if FLAGS.LossType == "Triplet":
return configHDMLTriplet(params)
elif FLAGS.LossType == "NpairLoss":
return configHDMLNPair(params)
def main(_):
if not FLAGS.LossType == 'NpairLoss':
print("LossType n-pair-loss is required")
return 0
# placeholders
x_raw = tf.placeholder(
tf.float32,
shape=[None, FLAGS.default_image_size, FLAGS.default_image_size, 3])
label_raw = tf.placeholder(tf.int32, shape=[None, 1])
with tf.name_scope('istraining'):
is_Training = tf.placeholder(tf.bool)
with tf.name_scope('learning_rate'):
lr = tf.placeholder(tf.float32)
with tf.variable_scope('Classifier'):
google_net_model = GoogleNet_Model.GoogleNet_Model()
embedding = google_net_model.forward(x_raw)
if FLAGS.Apply_HDML:
embedding_y_origin = embedding
# Batch Normalization layer 1
embedding = nn_Ops.bn_block(embedding,
normal=FLAGS.normalize,
is_Training=is_Training,
name='BN1')
# FC layer 1
embedding_z = nn_Ops.fc_block(embedding,
in_d=1024,
out_d=FLAGS.embedding_size,
name='fc1',
is_bn=False,
is_relu=False,
is_Training=is_Training)
# Embedding Visualization
assignment, embedding_var = Embedding_Visualization.embedding_assign(
batch_size=256,
embedding=embedding_z,
embedding_size=FLAGS.embedding_size,
name='Embedding_of_fc1')
# conventional Loss function
with tf.name_scope('Loss'):
# wdLoss = layers.apply_regularization(regularizer, weights_list=None)
def exclude_batch_norm(name):
return 'batch_normalization' not in name and 'Generator' not in name and 'Loss' not in name
wdLoss = FLAGS.Regular_factor * tf.add_n([
tf.nn.l2_loss(v)
for v in tf.trainable_variables() if exclude_batch_norm(v.name)
])
# Get the Label
label = tf.reduce_mean(label_raw, axis=1, keep_dims=False)
# For some kinds of Losses, the embedding should be l2 normed
#embedding_l = embedding_z
J_m = Loss_ops.Loss(embedding_z, label, FLAGS.LossType) + wdLoss
# if HNG is applied
if FLAGS.Apply_HDML:
with tf.name_scope('Javg'):
Javg = tf.placeholder(tf.float32)
with tf.name_scope('Jgen'):
Jgen = tf.placeholder(tf.float32)
embedding_z_quta = HDML.Pulling(FLAGS.LossType, embedding_z, Javg)
embedding_z_concate = tf.concat([embedding_z, embedding_z_quta],
axis=0)
# Generator
with tf.variable_scope('Generator'):
# generator fc3
embedding_y_concate = nn_Ops.fc_block(embedding_z_concate,
in_d=FLAGS.embedding_size,
out_d=512,
name='generator1',
is_bn=True,
is_relu=True,
is_Training=is_Training)
# generator fc4
embedding_y_concate = nn_Ops.fc_block(embedding_y_concate,
in_d=512,
out_d=1024,
name='generator2',
is_bn=False,
is_relu=False,
is_Training=is_Training)
embedding_yp, embedding_yq = HDML.npairSplit(embedding_y_concate)
with tf.variable_scope('Classifier'):
embedding_z_quta = nn_Ops.bn_block(embedding_yq,
normal=FLAGS.normalize,
is_Training=is_Training,
name='BN1',
reuse=True)
embedding_z_quta = nn_Ops.fc_block(embedding_z_quta,
in_d=1024,
out_d=FLAGS.embedding_size,
name='fc1',
is_bn=False,
is_relu=False,
reuse=True,
is_Training=is_Training)
embedding_zq_anc = tf.slice(
input_=embedding_z_quta,
begin=[0, 0],
size=[int(FLAGS.batch_size / 2),
int(FLAGS.embedding_size)])
embedding_zq_negtile = tf.slice(
input_=embedding_z_quta,
begin=[int(FLAGS.batch_size / 2), 0],
size=[
int(np.square(FLAGS.batch_size / 2)),
int(FLAGS.embedding_size)
])
with tf.name_scope('Loss'):
J_syn = (1. -
tf.exp(-FLAGS.beta / Jgen)) * Loss_ops.new_npair_loss(
labels=label,
embedding_anchor=embedding_zq_anc,
embedding_positive=embedding_zq_negtile,
equal_shape=False,
reg_lambda=FLAGS.loss_l2_reg)
J_m = (tf.exp(-FLAGS.beta / Jgen)) * J_m
J_metric = J_m + J_syn
cross_entropy, W_fc, b_fc = HDML.cross_entropy(
embedding=embedding_y_origin, label=label)
embedding_yq_anc = tf.slice(input_=embedding_yq,
begin=[0, 0],
size=[int(FLAGS.batch_size / 2), 1024])
embedding_yq_negtile = tf.slice(
input_=embedding_yq,
begin=[int(FLAGS.batch_size / 2), 0],
size=[int(np.square(FLAGS.batch_size / 2)), 1024])
J_recon = (1 - FLAGS._lambda) * tf.reduce_sum(
tf.square(embedding_yp - embedding_y_origin))
J_soft = HDML.genSoftmax(embedding_anc=embedding_yq_anc,
embedding_neg=embedding_yq_negtile,
W_fc=W_fc,
b_fc=b_fc,
label=label)
J_gen = J_recon + J_soft
if FLAGS.Apply_HDML:
c_train_step = nn_Ops.training(loss=J_metric,
lr=lr,
var_scope='Classifier')
g_train_step = nn_Ops.training(loss=J_gen,
lr=FLAGS.lr_gen,
var_scope='Generator')
s_train_step = nn_Ops.training(loss=cross_entropy,
lr=FLAGS.s_lr,
var_scope='Softmax_classifier')
else:
train_step = nn_Ops.training(loss=J_m, lr=lr)
# initialise the session
with tf.Session(config=config) as sess:
# Initial all the variables with the sess
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
# learning rate
_lr = FLAGS.init_learning_rate
# Restore a checkpoint
if FLAGS.load_formalVal:
saver.restore(
sess, FLAGS.log_save_path + FLAGS.dataSet + '/' +
FLAGS.LossType + '/' + FLAGS.formerTimer)
# Training
# epoch_iterator = stream_train.get_epoch_iterator()
# collectors
J_m_loss = nn_Ops.data_collector(tag='Jm', init=1e+6)
J_syn_loss = nn_Ops.data_collector(tag='J_syn', init=1e+6)
J_metric_loss = nn_Ops.data_collector(tag='J_metric', init=1e+6)
J_soft_loss = nn_Ops.data_collector(tag='J_soft', init=1e+6)
J_recon_loss = nn_Ops.data_collector(tag='J_recon', init=1e+6)
J_gen_loss = nn_Ops.data_collector(tag='J_gen', init=1e+6)
cross_entropy_loss = nn_Ops.data_collector(tag='cross_entropy',
init=1e+6)
wd_Loss = nn_Ops.data_collector(tag='weight_decay', init=1e+6)
# max_nmi = 0
# step = 0
#
# bp_epoch = FLAGS.init_batch_per_epoch
# evaluation
print('only eval!')
# nmi_tr, f1_tr, recalls_tr = evaluation.Evaluation(
# stream_train_eval, image_mean, sess, x_raw, label_raw, is_Training, embedding_z, 98, neighbours)
# nmi_te, f1_te, recalls_te = evaluation.Evaluation(
# stream_test, image_mean, sess, x_raw, label_raw, is_Training, embedding_z, FLAGS.num_class_test, neighbours)
embeddings, labels = evaluation.Evaluation_icon2(
stream_train, image_mean, sess, x_raw, label_raw, is_Training,
embedding_z, FLAGS.num_class_test, neighbours)
out_dir = os.path.expanduser(
FLAGS.log_save_path[0:len(FLAGS.log_save_path) - 1] +
'_embeddings')
if not os.path.exists(out_dir):
os.makedirs(out_dir)
for idx, distance in enumerate(embeddings):
save_1Darray(distance,
os.path.expanduser(os.path.join(out_dir, str(idx))))
print('End')
def main(_):
if not FLAGS.LossType == 'NpairLoss':
print("LossType n-pair-loss is required")
return 0
# placeholders
x_raw = tf.placeholder(
tf.float32,
shape=[None, FLAGS.default_image_size, FLAGS.default_image_size, 3])
label_raw = tf.placeholder(tf.int32, shape=[None, 1])
with tf.name_scope('istraining'):
is_Training = tf.placeholder(tf.bool)
with tf.name_scope('learning_rate'):
lr = tf.placeholder(tf.float32)
with tf.variable_scope('Classifier'):
google_net_model = GoogleNet_Model.GoogleNet_Model()
embedding = google_net_model.forward(x_raw)
if FLAGS.Apply_HDML:
embedding_y_origin = embedding
# Batch Normalization layer 1
embedding = nn_Ops.bn_block(embedding,
normal=FLAGS.normalize,
is_Training=is_Training,
name='BN1')
# FC layer 1
embedding_z = nn_Ops.fc_block(embedding,
in_d=1024,
out_d=FLAGS.embedding_size,
name='fc1',
is_bn=False,
is_relu=False,
is_Training=is_Training)
# Embedding Visualization
assignment, embedding_var = Embedding_Visualization.embedding_assign(
batch_size=256,
embedding=embedding_z,
embedding_size=FLAGS.embedding_size,
name='Embedding_of_fc1')
# conventional Loss function
with tf.name_scope('Loss'):
# wdLoss = layers.apply_regularization(regularizer, weights_list=None)
def exclude_batch_norm(name):
return 'batch_normalization' not in name and 'Generator' not in name and 'Loss' not in name
wdLoss = FLAGS.Regular_factor * tf.add_n([
tf.nn.l2_loss(v)
for v in tf.trainable_variables() if exclude_batch_norm(v.name)
])
# Get the Label
label = tf.reduce_mean(label_raw, axis=1, keep_dims=False)
# For some kinds of Losses, the embedding should be l2 normed
#embedding_l = embedding_z
J_m = Loss_ops.Loss(embedding_z, label, FLAGS.LossType) + wdLoss
# if HNG is applied
if FLAGS.Apply_HDML:
with tf.name_scope('Javg'):
Javg = tf.placeholder(tf.float32)
with tf.name_scope('Jgen'):
Jgen = tf.placeholder(tf.float32)
embedding_z_quta = HDML.Pulling(FLAGS.LossType, embedding_z, Javg)
embedding_z_concate = tf.concat([embedding_z, embedding_z_quta],
axis=0)
# Generator
with tf.variable_scope('Generator'):
# generator fc3
embedding_y_concate = nn_Ops.fc_block(embedding_z_concate,
in_d=FLAGS.embedding_size,
out_d=512,
name='generator1',
is_bn=True,
is_relu=True,
is_Training=is_Training)
# generator fc4
embedding_y_concate = nn_Ops.fc_block(embedding_y_concate,
in_d=512,
out_d=1024,
name='generator2',
is_bn=False,
is_relu=False,
is_Training=is_Training)
embedding_yp, embedding_yq = HDML.npairSplit(embedding_y_concate)
with tf.variable_scope('Classifier'):
embedding_z_quta = nn_Ops.bn_block(embedding_yq,
normal=FLAGS.normalize,
is_Training=is_Training,
name='BN1',
reuse=True)
embedding_z_quta = nn_Ops.fc_block(embedding_z_quta,
in_d=1024,
out_d=FLAGS.embedding_size,
name='fc1',
is_bn=False,
is_relu=False,
reuse=True,
is_Training=is_Training)
embedding_zq_anc = tf.slice(
input_=embedding_z_quta,
begin=[0, 0],
size=[int(FLAGS.batch_size / 2),
int(FLAGS.embedding_size)])
embedding_zq_negtile = tf.slice(
input_=embedding_z_quta,
begin=[int(FLAGS.batch_size / 2), 0],
size=[
int(np.square(FLAGS.batch_size / 2)),
int(FLAGS.embedding_size)
])
with tf.name_scope('Loss'):
J_syn = (1. -
tf.exp(-FLAGS.beta / Jgen)) * Loss_ops.new_npair_loss(
labels=label,
embedding_anchor=embedding_zq_anc,
embedding_positive=embedding_zq_negtile,
equal_shape=False,
reg_lambda=FLAGS.loss_l2_reg)
J_m = (tf.exp(-FLAGS.beta / Jgen)) * J_m
J_metric = J_m + J_syn
cross_entropy, W_fc, b_fc = HDML.cross_entropy(
embedding=embedding_y_origin, label=label)
embedding_yq_anc = tf.slice(input_=embedding_yq,
begin=[0, 0],
size=[int(FLAGS.batch_size / 2), 1024])
embedding_yq_negtile = tf.slice(
input_=embedding_yq,
begin=[int(FLAGS.batch_size / 2), 0],
size=[int(np.square(FLAGS.batch_size / 2)), 1024])
J_recon = (1 - FLAGS._lambda) * tf.reduce_sum(
tf.square(embedding_yp - embedding_y_origin))
J_soft = HDML.genSoftmax(embedding_anc=embedding_yq_anc,
embedding_neg=embedding_yq_negtile,
W_fc=W_fc,
b_fc=b_fc,
label=label)
J_gen = J_recon + J_soft
if FLAGS.Apply_HDML:
c_train_step = nn_Ops.training(loss=J_metric,
lr=lr,
var_scope='Classifier')
g_train_step = nn_Ops.training(loss=J_gen,
lr=FLAGS.lr_gen,
var_scope='Generator')
s_train_step = nn_Ops.training(loss=cross_entropy,
lr=FLAGS.s_lr,
var_scope='Softmax_classifier')
else:
train_step = nn_Ops.training(loss=J_m, lr=lr)
# initialise the session
with tf.Session(config=config) as sess:
# Initial all the variables with the sess
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
# learning rate
_lr = FLAGS.init_learning_rate
# Restore a checkpoint
if FLAGS.load_formalVal:
saver.restore(
sess, FLAGS.log_save_path + FLAGS.dataSet + '/' +
FLAGS.LossType + '/' + FLAGS.formerTimer)
# Training
epoch_iterator = stream_train.get_epoch_iterator()
# collectors
J_m_loss = nn_Ops.data_collector(tag='Jm', init=1e+6)
J_syn_loss = nn_Ops.data_collector(tag='J_syn', init=1e+6)
J_metric_loss = nn_Ops.data_collector(tag='J_metric', init=1e+6)
J_soft_loss = nn_Ops.data_collector(tag='J_soft', init=1e+6)
J_recon_loss = nn_Ops.data_collector(tag='J_recon', init=1e+6)
J_gen_loss = nn_Ops.data_collector(tag='J_gen', init=1e+6)
cross_entropy_loss = nn_Ops.data_collector(tag='cross_entropy',
init=1e+6)
wd_Loss = nn_Ops.data_collector(tag='weight_decay', init=1e+6)
max_nmi = 0
step = 0
bp_epoch = FLAGS.init_batch_per_epoch
with tqdm(total=FLAGS.max_steps) as pbar:
for batch in copy.copy(epoch_iterator):
# get images and labels from batch
x_batch_data, Label_raw = nn_Ops.batch_data(batch)
pbar.update(1)
if not FLAGS.Apply_HDML:
train, J_m_var, wd_Loss_var = sess.run(
[train_step, J_m, wdLoss],
feed_dict={
x_raw: x_batch_data,
label_raw: Label_raw,
is_Training: True,
lr: _lr
})
J_m_loss.update(var=J_m_var)
wd_Loss.update(var=wd_Loss_var)
else:
c_train, g_train, s_train, wd_Loss_var, J_metric_var, J_m_var, \
J_syn_var, J_recon_var, J_soft_var, J_gen_var, cross_en_var = sess.run(
[c_train_step, g_train_step, s_train_step, wdLoss,
J_metric, J_m, J_syn, J_recon, J_soft, J_gen, cross_entropy],
feed_dict={x_raw: x_batch_data,
label_raw: Label_raw,
is_Training: True, lr: _lr, Javg: J_m_loss.read(), Jgen: J_gen_loss.read()})
wd_Loss.update(var=wd_Loss_var)
J_metric_loss.update(var=J_metric_var)
J_m_loss.update(var=J_m_var)
J_syn_loss.update(var=J_syn_var)
J_recon_loss.update(var=J_recon_var)
J_soft_loss.update(var=J_soft_var)
J_gen_loss.update(var=J_gen_var)
cross_entropy_loss.update(cross_en_var)
step += 1
# print('learning rate %f' % _lr)
# evaluation
if step % bp_epoch == 0:
print('only eval eval')
# nmi_tr, f1_tr, recalls_tr = evaluation.Evaluation(
# stream_train_eval, image_mean, sess, x_raw, label_raw, is_Training, embedding_z, 98, neighbours)
nmi_te, f1_te, recalls_te = evaluation.Evaluation(
stream_test, image_mean, sess, x_raw, label_raw,
is_Training, embedding_z, FLAGS.num_class_test,
neighbours)
# Summary
eval_summary = tf.Summary()
# eval_summary.value.add(tag='train nmi', simple_value=nmi_tr)
# eval_summary.value.add(tag='train f1', simple_value=f1_tr)
# for i in range(0, np.shape(neighbours)[0]):
# eval_summary.value.add(tag='Recall@%d train' % neighbours[i], simple_value=recalls_tr[i])
eval_summary.value.add(tag='test nmi', simple_value=nmi_te)
eval_summary.value.add(tag='test f1', simple_value=f1_te)
for i in range(0, np.shape(neighbours)[0]):
eval_summary.value.add(tag='Recall@%d test' %
neighbours[i],
simple_value=recalls_te[i])
J_m_loss.write_to_tfboard(eval_summary)
wd_Loss.write_to_tfboard(eval_summary)
eval_summary.value.add(tag='learning_rate',
simple_value=_lr)
if FLAGS.Apply_HDML:
J_syn_loss.write_to_tfboard(eval_summary)
J_metric_loss.write_to_tfboard(eval_summary)
J_soft_loss.write_to_tfboard(eval_summary)
J_recon_loss.write_to_tfboard(eval_summary)
J_gen_loss.write_to_tfboard(eval_summary)
cross_entropy_loss.write_to_tfboard(eval_summary)
summary_writer.add_summary(eval_summary, step)
print('Summary written')
if nmi_te > max_nmi:
max_nmi = nmi_te
print("Saved")
saver.save(sess, os.path.join(LOGDIR,
"modelBest.ckpt"))
saver.save(sess, os.path.join(LOGDIR, "model.ckpt"))
summary_writer.flush()
# if step in [5632, 6848]:
# _lr = _lr * 0.5
if step >= 5000:
bp_epoch = FLAGS.batch_per_epoch
if step >= FLAGS.max_steps:
os._exit()