def testDecovLoss(self):
batch_size = 7
image_size = 4
channels = 3
with tf.Graph().as_default():
xs = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, channels), name='input')
loss = facenet.decov_loss(xs)
sess = tf.Session()
with sess.as_default():
np.random.seed(seed=666)
xs_ = np.random.normal(loc=0.0, scale=0.1, size=(batch_size,image_size,image_size,channels))
xflat = xs_.reshape([batch_size,image_size*image_size*channels])
ui = np.mean(xflat,0)
nd = image_size*image_size*channels
corr_ref = np.zeros((nd,nd))
for i in range(nd):
for j in range(nd):
corr_ref[i,j] = 0.0
for n in range(batch_size):
corr_ref[i,j] += (xflat[n,i]-ui[i]) * (xflat[n,j]-ui[j]) / batch_size
corr_frob_sqr_ref = np.trace(np.matmul(corr_ref.T, corr_ref))
corr_diag_sqr_ref = np.sum(np.square(np.diag(corr_ref)))
loss_ref = 0.5*(corr_frob_sqr_ref - corr_diag_sqr_ref)
loss_ = sess.run(loss, feed_dict={xs:xs_})
testing.assert_almost_equal(loss_ref, loss_, 6,
'Tensorflow implementation gives a different result compared to reference')
def testDecovLoss(self):
batch_size = 7
image_size = 4
channels = 3
with tf.Graph().as_default():
xs = tf.placeholder(tf.float32, shape=(batch_size, image_size, image_size, channels), name='input')
loss = facenet.decov_loss(xs)
sess = tf.Session()
with sess.as_default():
np.random.seed(seed=666)
xs_ = np.random.normal(loc=0.0, scale=0.1, size=(batch_size,image_size,image_size,channels))
xflat = xs_.reshape([batch_size,image_size*image_size*channels])
ui = np.mean(xflat,0)
nd = image_size*image_size*channels
corr_ref = np.zeros((nd,nd))
for i in range(nd):
for j in range(nd):
corr_ref[i,j] = 0.0
for n in range(batch_size):
corr_ref[i,j] += (xflat[n,i]-ui[i]) * (xflat[n,j]-ui[j]) / batch_size
corr_frob_sqr_ref = np.trace(np.matmul(corr_ref.T, corr_ref))
corr_diag_sqr_ref = np.sum(np.square(np.diag(corr_ref)))
loss_ref = 0.5*(corr_frob_sqr_ref - corr_diag_sqr_ref)
loss_ = sess.run(loss, feed_dict={xs:xs_})
testing.assert_almost_equal(loss_ref, loss_, 6,
'Tensorflow implementation gives a different result compared to reference')
def main(args):
network = importlib.import_module(args.model_def, 'inference')
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
if not os.path.isdir(
log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
if not os.path.isdir(
model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
train_set = facenet.get_dataset(args.data_dir)
nrof_classes = len(train_set)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
# Read data and apply label preserving distortions
image_batch, label_batch = facenet.read_and_augument_data(
image_list, label_list, args.image_size, args.batch_size,
args.max_nrof_epochs, args.random_crop, args.random_flip,
args.nrof_preprocess_threads)
print('Total number of classes: %d' % nrof_classes)
print('Total number of examples: %d' % len(image_list))
print('Building training graph')
# Placeholder for the learning rate
learning_rate_placeholder = tf.placeholder(tf.float32,
name='learning_rate')
# Build the inference graph
prelogits, _ = network.inference(image_batch,
args.keep_probability,
phase_train=True,
weight_decay=args.weight_decay)
logits = slim.fully_connected(
prelogits,
len(train_set),
activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
scope='Logits',
reuse=False)
# Add DeCov regularization loss
if args.decov_loss_factor > 0.0:
logits_decov_loss = facenet.decov_loss(
logits) * args.decov_loss_factor
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
logits_decov_loss)
# Add center loss
if args.center_loss_factor > 0.0:
prelogits_center_loss, _ = facenet.center_loss(
prelogits, label_batch, args.center_loss_alfa, nrof_classes)
tf.add_to_collection(
tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_center_loss * args.center_loss_factor)
learning_rate = tf.train.exponential_decay(
learning_rate_placeholder,
global_step,
args.learning_rate_decay_epochs * args.epoch_size,
args.learning_rate_decay_factor,
staircase=True)
tf.scalar_summary('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, label_batch, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# Calculate the total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] + regularization_losses,
name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay,
tf.all_variables(), args.log_histograms)
# Evaluation
print('Building evaluation graph')
lfw_label_list = range(0, len(lfw_paths))
assert (
len(lfw_paths) % args.lfw_batch_size == 0
), "The number of images in the LFW test set need to be divisible by the lfw_batch_size"
eval_image_batch, eval_label_batch = facenet.read_and_augument_data(
lfw_paths,
lfw_label_list,
args.image_size,
args.lfw_batch_size,
None,
False,
False,
args.nrof_preprocess_threads,
shuffle=False)
# Node for input images
eval_image_batch.set_shape((None, args.image_size, args.image_size, 3))
eval_image_batch = tf.identity(eval_image_batch, name='input')
eval_prelogits, _ = network.inference(eval_image_batch,
1.0,
phase_train=False,
weight_decay=0.0,
reuse=True)
eval_embeddings = tf.nn.l2_normalize(eval_prelogits,
1,
1e-10,
name='embeddings')
# Create a saver
saver = tf.train.Saver(tf.all_variables(), max_to_keep=3)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Start running operations on the Graph.
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
sess.run(tf.initialize_all_variables())
sess.run(tf.initialize_local_variables())
summary_writer = tf.train.SummaryWriter(log_dir, sess.graph)
tf.train.start_queue_runners(sess=sess)
with sess.as_default():
if pretrained_model:
print('Restoring pretrained model: %s' % pretrained_model)
saver.restore(sess, pretrained_model)
# Training and validation loop
print('Running training')
epoch = 0
while epoch < args.max_nrof_epochs:
step = sess.run(global_step, feed_dict=None)
epoch = step // args.epoch_size
# Train for one epoch
train(args, sess, epoch, learning_rate_placeholder,
global_step, total_loss, train_op, summary_op,
summary_writer, regularization_losses,
args.learning_rate_schedule_file)
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer,
model_dir, subdir, step)
# Evaluate on LFW
if args.lfw_dir:
evaluate(sess, eval_embeddings, eval_label_batch,
actual_issame, args.lfw_batch_size, args.seed,
args.lfw_nrof_folds, log_dir, step,
summary_writer)
return model_dir
def main(args):
network = importlib.import_module(args.model_def, 'inference')
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
if not os.path.isdir(
log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
if not os.path.isdir(
model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
train_set = facenet.get_dataset(args.data_dir)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
# Read data and apply label preserving distortions
image_batch, label_batch = facenet.read_and_augument_data(
image_list, label_list, args.image_size, args.batch_size,
args.max_nrof_epochs, args.random_crop, args.random_flip,
args.nrof_preprocess_threads)
print('Total number of classes: %d' % len(train_set))
print('Total number of examples: %d' % len(image_list))
# Node for input images
image_batch.set_shape((None, args.image_size, args.image_size, 3))
image_batch = tf.identity(image_batch, name='input')
# Placeholder for the learning rate
learning_rate_placeholder = tf.placeholder(tf.float32,
name='learning_rate')
# Placeholder for phase_train
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
# Build the inference graph
prelogits, _ = network.inference(image_batch,
args.keep_probability,
phase_train=phase_train_placeholder,
weight_decay=args.weight_decay)
with tf.variable_scope('Logits'):
n = int(prelogits.get_shape()[1])
m = len(train_set)
w = tf.get_variable(
'w',
shape=[n, m],
dtype=tf.float32,
initializer=tf.truncated_normal_initializer(stddev=0.1),
regularizer=slim.l2_regularizer(args.weight_decay),
trainable=True)
b = tf.get_variable('b', [m], initializer=None, trainable=True)
logits = tf.matmul(prelogits, w) + b
# Add DeCov regularization loss
if args.decov_loss_factor > 0.0:
logits_decov_loss = facenet.decov_loss(
logits) * args.decov_loss_factor
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
logits_decov_loss)
# Add center loss
update_centers = tf.no_op('update_centers')
if args.center_loss_factor > 0.0:
prelogits_center_loss, update_centers = facenet.center_loss(
prelogits, label_batch, args.center_loss_alfa)
tf.add_to_collection(
tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_center_loss * args.center_loss_factor)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
learning_rate = tf.train.exponential_decay(
learning_rate_placeholder,
global_step,
args.learning_rate_decay_epochs * args.epoch_size,
args.learning_rate_decay_factor,
staircase=True)
tf.scalar_summary('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, label_batch, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# Calculate the total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] + regularization_losses,
name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay,
tf.all_variables(), args.log_histograms)
# Create a saver
save_variables = list(set(tf.all_variables()) - set([w]) - set([b]))
saver = tf.train.Saver(save_variables, max_to_keep=3)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Start running operations on the Graph.
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
sess.run(tf.initialize_all_variables())
sess.run(tf.initialize_local_variables())
summary_writer = tf.train.SummaryWriter(log_dir, sess.graph)
tf.train.start_queue_runners(sess=sess)
with sess.as_default():
if pretrained_model:
saver.restore(sess, pretrained_model)
# Training and validation loop
epoch = 0
while epoch < args.max_nrof_epochs:
step = sess.run(global_step, feed_dict=None)
epoch = step // args.epoch_size
# Train for one epoch
train(args, sess, epoch, phase_train_placeholder,
learning_rate_placeholder, global_step, total_loss,
train_op, summary_op, summary_writer,
regularization_losses, args.learning_rate_schedule_file,
update_centers)
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer,
model_dir, subdir, step)
# Evaluate on LFW
if args.lfw_dir:
start_time = time.time()
_, _, accuracy, val, val_std, far = lfw.validate(
sess,
lfw_paths,
actual_issame,
args.seed,
args.batch_size,
image_batch,
phase_train_placeholder,
embeddings,
nrof_folds=args.lfw_nrof_folds)
print('Accuracy: %1.3f+-%1.3f' %
(np.mean(accuracy), np.std(accuracy)))
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' %
(val, val_std, far))
lfw_time = time.time() - start_time
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='lfw/accuracy',
simple_value=np.mean(accuracy))
summary.value.add(tag='lfw/val_rate', simple_value=val)
summary.value.add(tag='time/lfw', simple_value=lfw_time)
summary_writer.add_summary(summary, step)
with open(os.path.join(log_dir, 'lfw_result.txt'),
'at') as f:
f.write('%d\t%.5f\t%.5f\n' %
(step, np.mean(accuracy), val))
return model_dir
def main(args):
network = importlib.import_module(args.model_def, 'inference')
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
# 创建模型文件夹
if not os.path.isdir(log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
if not os.path.isdir(model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
train_set = facenet.get_dataset(args.data_dir)
nrof_classes = len(train_set)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
if args.baihe_pack_file:
print('load baihe dataset')
lfw_paths, actual_issame = msgpack_numpy.load(open(args.baihe_pack_file))
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
# 迭代轮数, 不同的轮数可以使用不同的学习率
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
# Read data and apply label preserving distortions
image_batch, label_batch = facenet.read_and_augment_data(image_list, label_list, args.image_size,
args.batch_size, args.max_nrof_epochs, args.random_crop, args.random_flip, args.random_rotate,
args.nrof_preprocess_threads)
print('Total number of classes: %d' % nrof_classes)
print('Total number of examples: %d' % len(image_list))
print('Building training graph')
# Placeholder for the learning rate
learning_rate_placeholder = tf.placeholder(tf.float32, name='learning_rate')
# Build the inference graph, 返回的是网络结构
prelogits, _ = network.inference(image_batch, args.keep_probability, phase_train=True,
weight_decay=args.weight_decay)
# 初始化采用截断的正态分布噪声, 标准差为0.1
# tf.truncated_normal_initializer(stddev=0.1)
logits = slim.fully_connected(prelogits, len(train_set), activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
scope='Logits', reuse=False)
# Add DeCov regularization loss
if args.decov_loss_factor > 0.0:
logits_decov_loss = facenet.decov_loss(logits) * args.decov_loss_factor
# 将decov_loss加入到名字为tf.GraphKeys.REGULARIZATION_LOSSES的集合当中来
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, logits_decov_loss)
# Add center loss (center_loss作为一个正则项加入到collections)
if args.center_loss_factor > 0.0:
prelogits_center_loss, _ = facenet.center_loss(prelogits, label_batch, args.center_loss_alfa, nrof_classes)
# 将center加入到名字为tf.GraphKeys.REGULARIZATION_LOSSES的集合当中来
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, prelogits_center_loss * args.center_loss_factor)
# 对学习率进行指数衰退
learning_rate = tf.train.exponential_decay(learning_rate_placeholder, global_step,
args.learning_rate_decay_epochs*args.epoch_size, args.learning_rate_decay_factor, staircase=True)
tf.scalar_summary('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
# 将softmax和交叉熵一起做,得到最后的损失函数,提高效率
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
logits, label_batch, name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy, name='cross_entropy')
tf.add_to_collection('losses', cross_entropy_mean)
# Calculate the total losses
# 获取正则loss
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] + regularization_losses, name='total_loss')
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay, tf.all_variables(), args.log_histograms)
# Evaluation
print('Building evaluation graph')
lfw_label_list = range(0, len(lfw_paths))
assert (len(lfw_paths) % args.lfw_batch_size == 0), \
"The number of images in the LFW test set need to be divisible by the lfw_batch_size"
eval_image_batch, eval_label_batch = facenet.read_and_augment_data(lfw_paths, lfw_label_list, args.image_size,
args.lfw_batch_size, None, False, False,
False, args.nrof_preprocess_threads,
shuffle=False)
# Node for input images
eval_image_batch.set_shape((None, args.image_size, args.image_size, 3))
eval_image_batch = tf.identity(eval_image_batch, name='input')
eval_prelogits, _ = network.inference(eval_image_batch, 1.0,
phase_train=False, weight_decay=0.0, reuse=True)
eval_embeddings = tf.nn.l2_normalize(eval_prelogits, 1, 1e-10, name='embeddings')
# Create a saver
saver = tf.train.Saver(tf.all_variables(), max_to_keep=10)
# saver = tf.train.Saver(tf.global_variables(), max_to_keep=10)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.merge_all_summaries()
# Start running operations on the Graph.
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
# sess.run(tf.global_variables_initializer())
# sess.run(tf.local_variables_initializer())
sess.run(tf.initialize_all_variables())
sess.run(tf.initialize_local_variables())
summary_writer = tf.train.SummaryWriter(log_dir, sess.graph)
tf.train.start_queue_runners(sess=sess)
# 将队列runner启动，队列就开始运行，返回启动的线程
# 注意input_queue是先入列，再出列，由于入列的时候输入是place holder，因此到后的线程的时候，会阻塞，
# 直到下train中sess run （enqueue_op）的时候， 会向队列中载入值，后面的出列才有对象，才在各自的队列中开始执行
with sess.as_default():
if pretrained_model:
print('Restoring pretrained model: %s' % pretrained_model)
saver.restore(sess, pretrained_model)
# Training and validation loop
print('Running training')
epoch = 0
while epoch < args.max_nrof_epochs:
try:
step = sess.run(global_step, feed_dict=None)
epoch = step // args.epoch_size
# Train for one epoch
train(args, sess, epoch, learning_rate_placeholder, global_step, total_loss, train_op, summary_op,
summary_writer, regularization_losses, args.learning_rate_schedule_file)
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer, model_dir, subdir, step)
# Evaluate on LFW
if args.lfw_dir:
evaluate(sess, eval_embeddings, eval_label_batch, actual_issame, args.lfw_batch_size, args.seed,
args.lfw_nrof_folds, log_dir, step, summary_writer)
# Evaluate on baihe_data
if args.baihe_pack_file:
evaluate(sess, eval_embeddings, eval_label_batch, actual_issame, args.lfw_batch_size, args.seed,
args.lfw_nrof_folds, log_dir, step, summary_writer)
except:
traceback.print_exc()
continue
return model_dir
def main(args):
#network = importlib.import_module(args.model_def, 'inception_v3')
network = importlib.import_module(args.model_def)
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir), subdir)
if not os.path.isdir(
log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
model_dir = os.path.join(os.path.expanduser(args.models_base_dir), subdir)
if not os.path.isdir(
model_dir): # Create the model directory if it doesn't exist
os.makedirs(model_dir)
with open(os.path.join(model_dir, 'args.txt'), 'w') as f:
for arg in vars(args):
f.write(arg + ' ' + str(getattr(args, arg)) + '\n')
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
facenet.store_revision_info(src_path, log_dir, ' '.join(sys.argv))
np.random.seed(seed=args.seed)
#train_set = facenet.get_dataset(args.data_dir)
train_set = facenet.get_dataset_with_enhanced(args.data_dir)
nrof_classes = len(train_set)
print('Model directory: %s' % model_dir)
print('Log directory: %s' % log_dir)
pretrained_model = None
if args.pretrained_model:
pretrained_model = os.path.expanduser(args.pretrained_model)
print('Pre-trained model: %s' % pretrained_model)
if args.lfw_dir:
print('LFW directory: %s' % args.lfw_dir)
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
# Get the paths for the corresponding images
lfw_paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Get a list of image paths and their labels
image_list, label_list = facenet.get_image_paths_and_labels(train_set)
# Read data and apply label preserving distortions
image_batch, label_batch = facenet.read_and_augument_data(
image_list, label_list, args.image_size, args.batch_size,
args.max_nrof_epochs, args.random_rotate, args.random_crop,
args.random_flip, args.nrof_preprocess_threads, args.padding_size,
args.patch_type)
#print('Total number of classes: %d' % len(train_set))
print('Total number of examples: %d' % len(image_list))
# Node for input images
image_batch = tf.identity(image_batch, name='input')
# Placeholder for the learning rate
learning_rate_placeholder = tf.placeholder(tf.float32,
name='learning_rate')
# Placeholder for phase_train
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
# Placeholder for keep probability
keep_probability_placeholder = tf.placeholder(tf.float32,
name='keep_prob')
# Build the inference graph
# prelogits = network.inference(image_batch, keep_probability_placeholder,
# phase_train=phase_train_placeholder, weight_decay=args.weight_decay)
batch_norm_params = {
# Decay for the moving averages
'decay': 0.995,
# epsilon to prevent 0s in variance
'epsilon': 0.001,
# force in-place updates of mean and variance estimates
'updates_collections': None,
# Moving averages ends up in the trainable variables collection
'variables_collections': [tf.GraphKeys.TRAINABLE_VARIABLES],
# Only update statistics during training mode
'is_training': phase_train_placeholder
}
#prelogits, _ = network.inception_v3(image_batch, num_classes=len(train_set),is_training=True)
prelogits, _ = network.inference(image_batch,
args.keep_probability,
phase_train=phase_train_placeholder,
weight_decay=args.weight_decay)
#prelogits = tf.identity(prelogits, name="prelogits")
bottleneck = _fully_connected(prelogits,
args.embedding_size,
name='pre_embedding')
#bottleneck = tf.nn.l2_normalize(bottleneck, dim=1,name='embedding')
logits = _fully_connected_classifier(bottleneck,
len(train_set),
name='logits')
"""
bottleneck = slim.fully_connected(prelogits, args.embedding_size, activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
normalizer_fn=slim.batch_norm,
normalizer_params=batch_norm_params,
scope='Bottleneck', reuse=False)
logits = slim.fully_connected(bottleneck, len(train_set), activation_fn=None,
weights_initializer=tf.truncated_normal_initializer(stddev=0.1),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
scope='Logits', reuse=False)
logits = tf.identity(logits, name="logits")
"""
# Add DeCov regularization loss
if args.decov_loss_factor > 0.0:
logits_decov_loss = facenet.decov_loss(
logits) * args.decov_loss_factor
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
logits_decov_loss)
# Add center loss
update_centers = tf.no_op('update_centers')
if args.center_loss_factor > 0.0:
prelogits_center_loss, update_centers = facenet.center_loss(
bottleneck, label_batch, args.center_loss_alfa, nrof_classes)
tf.add_to_collection(
tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_center_loss * args.center_loss_factor)
#embeddings = tf.nn.l2_normalize(bottleneck, 1, 1e-10, name='embeddings')
learning_rate = tf.train.exponential_decay(
learning_rate_placeholder,
global_step,
args.learning_rate_decay_epochs * args.epoch_size,
args.learning_rate_decay_factor,
staircase=True)
tf.summary.scalar('learning_rate', learning_rate)
# Calculate the average cross entropy loss across the batch
cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(
labels=label_batch,
logits=logits,
name='cross_entropy_per_example')
cross_entropy_mean = tf.reduce_mean(cross_entropy,
name='cross_entropy')
"""
# Multi-label loss: sigmoid loss
sigmoid_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=label_batch, logits=logits, name='sigmoid_loss_per_example')
sigmoid_loss_mean = tf.reduce_mean(sigmoid_loss, name='sigmoid_loss')
"""
tf.add_to_collection('losses', cross_entropy_mean)
# Calculate the total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([cross_entropy_mean] + regularization_losses,
name='total_loss')
#total_loss = tf.add_n([cross_entropy_mean], name='total_loss')
# prediction
prediction = tf.argmax(logits, axis=1, name='prediction')
acc = slim.metrics.accuracy(predictions=tf.cast(prediction,
dtype=tf.int32),
labels=tf.cast(label_batch,
dtype=tf.int32))
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = facenet.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay,
tf.global_variables())
# Create a saver
# save_variables = list(set(tf.all_variables())-set([w])-set([b]))
save_variables = tf.trainable_variables()
saver = tf.train.Saver(save_variables, max_to_keep=3)
# Build the summary operation based on the TF collection of Summaries.
summary_op = tf.summary.merge_all()
# Start running operations on the Graph.
# gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=args.gpu_memory_fraction)
# sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options, log_device_placement=False))
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
tf.train.start_queue_runners(sess=sess)
with sess.as_default():
if pretrained_model:
saver.restore(sess, pretrained_model)
# Training and validation loop
epoch = 0
while epoch < args.max_nrof_epochs:
step = sess.run(global_step, feed_dict=None)
epoch = step // args.epoch_size
# Train for one epoch
train(args, sess, epoch, phase_train_placeholder,
learning_rate_placeholder, keep_probability_placeholder,
global_step, total_loss, acc, train_op, summary_op,
summary_writer, regularization_losses,
args.learning_rate_schedule_file, update_centers)
# Evaluate on LFW
if args.lfw_dir:
start_time = time.time()
_, _, accuracy, val, val_std, far = lfw.validate(
sess,
lfw_paths,
actual_issame,
args.seed,
args.batch_size,
image_batch,
phase_train_placeholder,
keep_probability_placeholder,
embeddings,
nrof_folds=args.lfw_nrof_folds)
print('Accuracy: %1.3f+-%1.3f' %
(np.mean(accuracy), np.std(accuracy)))
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' %
(val, val_std, far))
lfw_time = time.time() - start_time
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='lfw/accuracy',
simple_value=np.mean(accuracy))
summary.value.add(tag='lfw/val_rate', simple_value=val)
summary.value.add(tag='time/lfw', simple_value=lfw_time)
summary_writer.add_summary(summary, step)
with open(os.path.join(log_dir, 'lfw_result.txt'),
'at') as f:
f.write('%d\t%.5f\t%.5f\n' %
(step, np.mean(accuracy), val))
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer,
model_dir, subdir, step)
return model_dir
