def main(args):
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.mkdir(output_dir)
# Store some git revision info in a text file in the output directory
src_path, _ = os.path.split(os.path.realpath(__file__))
fc.store_revision_info(src_path, output_dir, " ".join(sys.argv))
i = 0
for f in args.tsv_files:
for line in f:
fields = line.split("\t")
class_dir = fields[0]
img_name = fields[1] + "-" + fields[4] + "." + args.output_format
img_string = fields[5]
img_dec_string = base64.b64decode(img_string)
img_data = np.fromstring(img_dec_string, dtype=np.uint8)
img = cv2.imdecode(img_data, cv2.IMREAD_COLOR) # pylint: disable=maybe-no-member
if args.size:
img = misc.imresize(img, (args.size, args.size),
interp="bilinear")
full_class_dir = os.path.join(output_dir, class_dir)
if not os.path.exists(full_class_dir):
os.mkdir(full_class_dir)
full_path = os.path.join(full_class_dir,
img_name.replace("/", "_"))
cv2.imwrite(full_path, img) # pylint: disable=maybe-no-member
print("%8d: %s" % (i, full_path))
i += 1
def main(args):
sleep(random.random())
output_dir = os.path.expanduser(args.output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_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, output_dir, ' '.join(sys.argv))
dataset = facenet.get_dataset(args.input_dir)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
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))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
# Add a random key to the filename to allow alignment using multiple processes
random_key = np.random.randint(0, high=99999)
bounding_boxes_filename = os.path.join(
output_dir, 'bounding_boxes_%05d.txt' % random_key)
with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
if args.random_order:
random.shuffle(dataset)
for cls in dataset:
output_class_dir = os.path.join(output_dir, cls.name)
if not os.path.exists(output_class_dir):
os.makedirs(output_class_dir)
if args.random_order:
random.shuffle(cls.image_paths)
for image_path in cls.image_paths:
nrof_images_total += 1
filename = os.path.splitext(os.path.split(image_path)[1])[0]
output_filename = os.path.join(output_class_dir,
filename + '.png')
print(image_path)
if not os.path.exists(output_filename):
try:
img = misc.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = facenet.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = align.detect_face.detect_face(
img, minsize, pnet, rnet, onet, threshold, factor)
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
det_arr = []
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
if args.detect_multiple_faces:
for i in range(nrof_faces):
det_arr.append(np.squeeze(det[i]))
else:
bounding_box_size = (
det[:, 2] - det[:, 0]) * (det[:, 3] -
det[:, 1])
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 -
img_center[1],
(det[:, 1] + det[:, 3]) / 2 -
img_center[0]
])
offset_dist_squared = np.sum(
np.power(offsets, 2.0), 0)
index = np.argmax(
bounding_box_size -
offset_dist_squared * 2.0
) # some extra weight on the centering
det_arr.append(det[index, :])
else:
det_arr.append(np.squeeze(det))
for i, det in enumerate(det_arr):
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - args.margin / 2, 0)
bb[1] = np.maximum(det[1] - args.margin / 2, 0)
bb[2] = np.minimum(det[2] + args.margin / 2,
img_size[1])
bb[3] = np.minimum(det[3] + args.margin / 2,
img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
scaled = misc.imresize(
cropped,
(args.image_size, args.image_size),
interp='bilinear')
nrof_successfully_aligned += 1
filename_base, file_extension = os.path.splitext(
output_filename)
if args.detect_multiple_faces:
output_filename_n = "{}_{}{}".format(
filename_base, i, file_extension)
else:
output_filename_n = "{}{}".format(
filename_base, file_extension)
misc.imsave(output_filename_n, scaled)
text_file.write('%s %d %d %d %d\n' %
(output_filename_n, bb[0],
bb[1], bb[2], bb[3]))
else:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned)
def main(args):
network = importlib.import_module(args.model_def)
image_size = (args.image_size, args.image_size)
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)
stat_file_name = os.path.join(log_dir, "stat.h5")
# Write arguments to a text file
fc.write_arguments_to_file(args, os.path.join(log_dir, "arguments.txt"))
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
fc.store_revision_info(src_path, log_dir, " ".join(sys.argv))
np.random.seed(seed=args.seed)
random.seed(args.seed)
dataset = fc.get_dataset(args.data_dir)
if args.filter_filename:
dataset = filter_dataset(
dataset,
os.path.expanduser(args.filter_filename),
args.filter_percentile,
args.filter_min_nrof_images_per_class,
)
if args.validation_set_split_ratio > 0.0:
train_set, val_set = fc.split_dataset(
dataset, args.validation_set_split_ratio,
args.min_nrof_val_images_per_class, "SPLIT_IMAGES")
else:
train_set, val_set = dataset, []
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)
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 = fc.get_image_paths_and_labels(train_set)
assert len(image_list) > 0, "The training set should not be empty"
val_image_list, val_label_list = fc.get_image_paths_and_labels(val_set)
# Create a queue that produces indices into the image_list and label_list
labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
range_size = array_ops.shape(labels)[0]
index_queue = tf.train.range_input_producer(range_size,
num_epochs=None,
shuffle=True,
seed=None,
capacity=32)
index_dequeue_op = index_queue.dequeue_many(
args.batch_size * args.epoch_size, "index_dequeue")
learning_rate_placeholder = tf.placeholder(tf.float32,
name="learning_rate")
batch_size_placeholder = tf.placeholder(tf.int32, name="batch_size")
phase_train_placeholder = tf.placeholder(tf.bool, name="phase_train")
image_paths_placeholder = tf.placeholder(tf.string,
shape=(None, 1),
name="image_paths")
labels_placeholder = tf.placeholder(tf.int32,
shape=(None, 1),
name="labels")
control_placeholder = tf.placeholder(tf.int32,
shape=(None, 1),
name="control")
nrof_preprocess_threads = 4
input_queue = data_flow_ops.FIFOQueue(
capacity=2000000,
dtypes=[tf.string, tf.int32, tf.int32],
shapes=[(1, ), (1, ), (1, )],
shared_name=None,
name=None,
)
enqueue_op = input_queue.enqueue_many(
[image_paths_placeholder, labels_placeholder, control_placeholder],
name="enqueue_op")
image_batch, label_batch = fc.create_input_pipeline(
input_queue, image_size, nrof_preprocess_threads,
batch_size_placeholder)
image_batch = tf.identity(image_batch, "image_batch")
image_batch = tf.identity(image_batch, "input")
label_batch = tf.identity(label_batch, "label_batch")
print("Number of classes in training set: %d" % nrof_classes)
print("Number of examples in training set: %d" % len(image_list))
print("Number of classes in validation set: %d" % len(val_set))
print("Number of examples in validation set: %d" % len(val_image_list))
print("Building training graph")
# Build the inference graph
prelogits, _ = network.inference(
image_batch,
args.keep_probability,
phase_train=phase_train_placeholder,
bottleneck_layer_size=args.embedding_size,
weight_decay=args.weight_decay,
)
logits = slim.fully_connected(
prelogits,
len(train_set),
activation_fn=None,
weights_initializer=slim.initializers.xavier_initializer(),
weights_regularizer=slim.l2_regularizer(args.weight_decay),
scope="Logits",
reuse=False,
)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name="embeddings")
# Norm for the prelogits
eps = 1e-4
prelogits_norm = tf.reduce_mean(
tf.norm(tf.abs(prelogits) + eps, ord=args.prelogits_norm_p,
axis=1))
tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES,
prelogits_norm * args.prelogits_norm_loss_factor)
# Add center loss
prelogits_center_loss, _ = fc.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.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")
tf.add_to_collection("losses", cross_entropy_mean)
correct_prediction = tf.cast(
tf.equal(tf.argmax(logits, 1), tf.cast(label_batch, tf.int64)),
tf.float32)
accuracy = tf.reduce_mean(correct_prediction)
# 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 = fc.train(
total_loss,
global_step,
args.optimizer,
learning_rate,
args.moving_average_decay,
tf.global_variables(),
args.log_histograms,
)
# Create a saver
saver = tf.train.Saver(tf.trainable_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))
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord, 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")
nrof_steps = args.max_nrof_epochs * args.epoch_size
nrof_val_samples = int(
math.ceil(args.max_nrof_epochs / args.validate_every_n_epochs)
) # Validate every validate_every_n_epochs as well as in the last epoch
stat = {
"loss":
np.zeros((nrof_steps, ), np.float32),
"center_loss":
np.zeros((nrof_steps, ), np.float32),
"reg_loss":
np.zeros((nrof_steps, ), np.float32),
"xent_loss":
np.zeros((nrof_steps, ), np.float32),
"prelogits_norm":
np.zeros((nrof_steps, ), np.float32),
"accuracy":
np.zeros((nrof_steps, ), np.float32),
"val_loss":
np.zeros((nrof_val_samples, ), np.float32),
"val_xent_loss":
np.zeros((nrof_val_samples, ), np.float32),
"val_accuracy":
np.zeros((nrof_val_samples, ), np.float32),
"lfw_accuracy":
np.zeros((args.max_nrof_epochs, ), np.float32),
"lfw_valrate":
np.zeros((args.max_nrof_epochs, ), np.float32),
"learning_rate":
np.zeros((args.max_nrof_epochs, ), np.float32),
"time_train":
np.zeros((args.max_nrof_epochs, ), np.float32),
"time_validate":
np.zeros((args.max_nrof_epochs, ), np.float32),
"time_evaluate":
np.zeros((args.max_nrof_epochs, ), np.float32),
"prelogits_hist":
np.zeros((args.max_nrof_epochs, 1000), np.float32),
}
for epoch in range(1, args.max_nrof_epochs + 1):
step = sess.run(global_step, feed_dict=None)
# Train for one epoch
t = time.time()
cont = train(
args,
sess,
epoch,
image_list,
label_list,
index_dequeue_op,
enqueue_op,
image_paths_placeholder,
labels_placeholder,
learning_rate_placeholder,
phase_train_placeholder,
batch_size_placeholder,
control_placeholder,
global_step,
total_loss,
train_op,
summary_op,
summary_writer,
regularization_losses,
args.learning_rate_schedule_file,
stat,
cross_entropy_mean,
accuracy,
learning_rate,
prelogits,
prelogits_center_loss,
args.random_rotate,
args.random_crop,
args.random_flip,
prelogits_norm,
args.prelogits_hist_max,
args.use_fixed_image_standardization,
)
stat["time_train"][epoch - 1] = time.time() - t
if not cont:
break
t = time.time()
if len(val_image_list) > 0 and (
(epoch - 1) % args.validate_every_n_epochs
== args.validate_every_n_epochs - 1
or epoch == args.max_nrof_epochs):
validate(
args,
sess,
epoch,
val_image_list,
val_label_list,
enqueue_op,
image_paths_placeholder,
labels_placeholder,
control_placeholder,
phase_train_placeholder,
batch_size_placeholder,
stat,
total_loss,
regularization_losses,
cross_entropy_mean,
accuracy,
args.validate_every_n_epochs,
args.use_fixed_image_standardization,
)
stat["time_validate"][epoch - 1] = time.time() - t
# Save variables and the metagraph if it doesn't exist already
save_variables_and_metagraph(sess, saver, summary_writer,
model_dir, subdir, epoch)
# Evaluate on LFW
t = time.time()
if args.lfw_dir:
evaluate(
sess,
enqueue_op,
image_paths_placeholder,
labels_placeholder,
phase_train_placeholder,
batch_size_placeholder,
control_placeholder,
embeddings,
label_batch,
lfw_paths,
actual_issame,
args.lfw_batch_size,
args.lfw_nrof_folds,
log_dir,
step,
summary_writer,
stat,
epoch,
args.lfw_distance_metric,
args.lfw_subtract_mean,
args.lfw_use_flipped_images,
args.use_fixed_image_standardization,
)
stat["time_evaluate"][epoch - 1] = time.time() - t
print("Saving statistics")
with h5py.File(stat_file_name, "w") as f:
for key, value in stat.iteritems():
f.create_dataset(key, data=value)
return model_dir
def main(args):
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)
# Write arguments to a text file
fc.write_arguments_to_file(args, os.path.join(log_dir, "arguments.txt"))
# Store some git revision info in a text file in the log directory
src_path, _ = os.path.split(os.path.realpath(__file__))
fc.store_revision_info(src_path, log_dir, " ".join(sys.argv))
np.random.seed(seed=args.seed)
train_set = fc.get_dataset(args.data_dir)
print("Model directory: %s" % model_dir)
print("Log directory: %s" % log_dir)
if args.pretrained_model:
print("Pre-trained model: %s" %
os.path.expanduser(args.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)
with tf.Graph().as_default():
tf.set_random_seed(args.seed)
global_step = tf.Variable(0, trainable=False)
# Placeholder for the learning rate
learning_rate_placeholder = tf.placeholder(tf.float32,
name="learning_rate")
batch_size_placeholder = tf.placeholder(tf.int32, name="batch_size")
phase_train_placeholder = tf.placeholder(tf.bool, name="phase_train")
image_paths_placeholder = tf.placeholder(tf.string,
shape=(None, 3),
name="image_paths")
labels_placeholder = tf.placeholder(tf.int64,
shape=(None, 3),
name="labels")
input_queue = data_flow_ops.FIFOQueue(capacity=100000,
dtypes=[tf.string, tf.int64],
shapes=[(3, ), (3, )],
shared_name=None,
name=None)
enqueue_op = input_queue.enqueue_many(
[image_paths_placeholder, labels_placeholder])
nrof_preprocess_threads = 4
images_and_labels = []
for _ in range(nrof_preprocess_threads):
filenames, label = input_queue.dequeue()
images = []
for filename in tf.unstack(filenames):
file_contents = tf.read_file(filename)
image = tf.image.decode_image(file_contents, channels=3)
if args.random_crop:
image = tf.random_crop(
image, [args.image_size, args.image_size, 3])
else:
image = tf.image.resize_image_with_crop_or_pad(
image, args.image_size, args.image_size)
if args.random_flip:
image = tf.image.random_flip_left_right(image)
# pylint: disable=no-member
image.set_shape((args.image_size, args.image_size, 3))
images.append(tf.image.per_image_standardization(image))
images_and_labels.append([images, label])
image_batch, labels_batch = tf.train.batch_join(
images_and_labels,
batch_size=batch_size_placeholder,
shapes=[(args.image_size, args.image_size, 3), ()],
enqueue_many=True,
capacity=4 * nrof_preprocess_threads * args.batch_size,
allow_smaller_final_batch=True,
)
image_batch = tf.identity(image_batch, "image_batch")
image_batch = tf.identity(image_batch, "input")
labels_batch = tf.identity(labels_batch, "label_batch")
# Build the inference graph
prelogits, _ = network.inference(
image_batch,
args.keep_probability,
phase_train=phase_train_placeholder,
bottleneck_layer_size=args.embedding_size,
weight_decay=args.weight_decay,
)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name="embeddings")
# Split embeddings into anchor, positive and negative and calculate triplet loss
anchor, positive, negative = tf.unstack(
tf.reshape(embeddings, [-1, 3, args.embedding_size]), 3, 1)
triplet_loss = fc.triplet_loss(anchor, positive, negative, args.alpha)
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 total losses
regularization_losses = tf.get_collection(
tf.GraphKeys.REGULARIZATION_LOSSES)
total_loss = tf.add_n([triplet_loss] + regularization_losses,
name="total_loss")
# Build a Graph that trains the model with one batch of examples and updates the model parameters
train_op = fc.train(total_loss, global_step, args.optimizer,
learning_rate, args.moving_average_decay,
tf.global_variables())
# Create a saver
saver = tf.train.Saver(tf.trainable_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))
# Initialize variables
sess.run(tf.global_variables_initializer(),
feed_dict={phase_train_placeholder: True})
sess.run(tf.local_variables_initializer(),
feed_dict={phase_train_placeholder: True})
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord, sess=sess)
with sess.as_default():
if args.pretrained_model:
print("Restoring pretrained model: %s" % args.pretrained_model)
saver.restore(sess, os.path.expanduser(args.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,
train_set,
epoch,
image_paths_placeholder,
labels_placeholder,
labels_batch,
batch_size_placeholder,
learning_rate_placeholder,
phase_train_placeholder,
enqueue_op,
input_queue,
global_step,
embeddings,
total_loss,
train_op,
summary_op,
summary_writer,
args.learning_rate_schedule_file,
args.embedding_size,
anchor,
positive,
negative,
triplet_loss,
)
# 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,
lfw_paths,
embeddings,
labels_batch,
image_paths_placeholder,
labels_placeholder,
batch_size_placeholder,
learning_rate_placeholder,
phase_train_placeholder,
enqueue_op,
actual_issame,
args.batch_size,
args.lfw_nrof_folds,
log_dir,
step,
summary_writer,
args.embedding_size,
)
return model_dir
def main(args):
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)
# Write arguments to a text file
facenet.write_arguments_to_file(args, os.path.join(log_dir,
'arguments.txt'))
# 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)
random.seed(args.seed)
train_set = facenet.get_dataset(args.data_dir)
if args.filter_filename:
train_set = filter_dataset(train_set,
os.path.expanduser(args.filter_filename),
args.filter_percentile,
args.filter_min_nrof_images_per_class)
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)
# Removed lfw dir. Using custom dataset
# 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)
assert len(image_list) > 0, 'The dataset should not be empty'
# Create a queue that produces indices into the image_list and label_list
labels = ops.convert_to_tensor(label_list, dtype=tf.int32)
range_size = array_ops.shape(labels)[0]
index_queue = tf.train.range_input_producer(range_size,
num_epochs=None,
shuffle=True,
seed=None,
capacity=32)
index_dequeue_op = index_queue.dequeue_many(
args.batch_size * args.epoch_size, 'index_dequeue')
learning_rate_placeholder = tf.placeholder(tf.float32,
name='learning_rate')
batch_size_placeholder = tf.placeholder(tf.int32, name='batch_size')
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
image_paths_placeholder = tf.placeholder(tf.string,
shape=(None, 1),
name='image_paths')
labels_placeholder = tf.placeholder(tf.int64,
shape=(None, 1),
name='labels')
input_queue = data_flow_ops.FIFOQueue(capacity=100000,
dtypes=[tf.string, tf.int64],
shapes=[(1, ), (1, )],
shared_name=None,
name=None)
enqueue_op = input_queue.enqueue_many(
[image_paths_placeholder, labels_placeholder], name='enqueue_op')
nrof_preprocess_threads = 4
images_and_labels = []
for _ in range(nrof_preprocess_threads):
filenames, label = input_queue.dequeue()
images = []
for filename in tf.unstack(filenames):
file_contents = tf.read_file(filename)
image = tf.image.decode_image(file_contents, channels=3)
if args.random_rotate:
image = tf.py_func(facenet.random_rotate_image, [image],
tf.uint8)
if args.random_crop:
image = tf.random_crop(
image, [args.image_size, args.image_size, 3])
else:
image = tf.image.resize_image_with_crop_or_pad(
image, args.image_size, args.image_size)
if args.random_flip:
image = tf.image.random_flip_left_right(image)
#pylint: disable=no-member
image.set_shape((args.image_size, args.image_size, 3))
images.append(tf.image.per_image_standardization(image))
images_and_labels.append([images, label])
image_batch, label_batch = tf.train.batch_join(
images_and_labels,
batch_size=batch_size_placeholder,
shapes=[(args.image_size, args.image_size, 3), ()],
enqueue_many=True,
capacity=4 * nrof_preprocess_threads * args.batch_size,
allow_smaller_final_batch=True)
image_batch = tf.identity(image_batch, 'image_batch')
image_batch = tf.identity(image_batch, 'input')
label_batch = tf.identity(label_batch, 'label_batch')
print('Total number of classes: %d' % nrof_classes)
print('Total number of examples: %d' % len(image_list))
print('Building training graph')
# Build the inference graph
prelogits, _ = network.inference(
image_batch,
args.keep_probability,
phase_train=phase_train_placeholder,
bottleneck_layer_size=args.embedding_size,
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)
embeddings = tf.nn.l2_normalize(prelogits, 1, 1e-10, name='embeddings')
# 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.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')
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.global_variables(), args.log_histograms)
# Create a saver
saver = tf.train.Saver(tf.trainable_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))
sess.run(tf.global_variables_initializer())
sess.run(tf.local_variables_initializer())
summary_writer = tf.summary.FileWriter(log_dir, sess.graph)
coord = tf.train.Coordinator()
tf.train.start_queue_runners(coord=coord, sess=sess)
with sess.as_default():
if pretrained_model:
print('Restoring pretrained model: %s' % pretrained_model)
# saver.restore(sess, pretrained_model)
facenet.load_model(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, image_list, label_list,
index_dequeue_op, enqueue_op, image_paths_placeholder,
labels_placeholder, learning_rate_placeholder,
phase_train_placeholder, batch_size_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, enqueue_op, image_paths_placeholder, labels_placeholder, phase_train_placeholder, batch_size_placeholder,
# embeddings, label_batch, lfw_paths, actual_issame, args.lfw_batch_size, args.lfw_nrof_folds, log_dir, step, summary_writer)
return model_dir
def align(input_dir='database',
output_dir='database_aligned',
image_size=182,
margin=44,
gpu_memory_fraction=1,
random_order=False):
sleep(random.random())
output_dir = os.path.expanduser(output_dir)
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Store some git revision info in a text file in the log directory
# src_path, _ = os.path.split(os.path.realpath(__file__))
cwd = os.getcwd()
facenet.store_revision_info(cwd, output_dir, ' '.join(sys.argv))
dataset = facenet.get_dataset(input_dir)
print(input_dir)
print('Creating networks and loading parameters')
with tf.Graph().as_default():
gpu_options = tf.GPUOptions(
per_process_gpu_memory_fraction=gpu_memory_fraction) # noqa: E501
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options,
log_device_placement=False))
with sess.as_default():
pnet, rnet, onet = detect_face.create_mtcnn(sess, None)
minsize = 20 # minimum size of face
threshold = [0.6, 0.7, 0.7] # three steps's threshold
factor = 0.709 # scale factor
# Add a random key to the filename to allow alignment using multiple
# processes
random_key = np.random.randint(0, high=99999)
bounding_boxes_filename = os.path.join(
output_dir, 'bounding_boxes_%05d.txt' % random_key) # noqa: E501
with open(bounding_boxes_filename, "w") as text_file:
nrof_images_total = 0
nrof_successfully_aligned = 0
if random_order:
random.shuffle(dataset)
for cls in dataset:
output_class_dir = os.path.join(output_dir, cls.name)
if not os.path.exists(output_class_dir):
os.makedirs(output_class_dir)
if random_order:
random.shuffle(cls.image_paths)
for image_path in cls.image_paths:
nrof_images_total += 1
filename = os.path.splitext(os.path.split(image_path)[1])[0]
output_filename = os.path.join(output_class_dir,
filename + '.png')
print(image_path)
if not os.path.exists(output_filename):
try:
img = imageio.imread(image_path)
except (IOError, ValueError, IndexError) as e:
errorMessage = '{}: {}'.format(image_path, e)
print(errorMessage)
else:
if img.ndim < 2:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
continue
if img.ndim == 2:
img = facenet.to_rgb(img)
img = img[:, :, 0:3]
bounding_boxes, _ = detect_face.detect_face(
img, minsize, pnet, rnet, onet, threshold,
factor) # noqa: E501
nrof_faces = bounding_boxes.shape[0]
if nrof_faces > 0:
det = bounding_boxes[:, 0:4]
img_size = np.asarray(img.shape)[0:2]
if nrof_faces > 1:
bounding_box_size = (det[:, 2] - det[:, 0]) * (
det[:, 3] - det[:, 1]) # noqa: E501
img_center = img_size / 2
offsets = np.vstack([
(det[:, 0] + det[:, 2]) / 2 -
img_center[1],
(det[:, 1] + det[:, 3]) / 2 - img_center[0]
]) # noqa: E501
offset_dist_squared = np.sum(
np.power(offsets, 2.0), 0) # noqa: E501
index = np.argmax(
bounding_box_size -
offset_dist_squared * 2.0
) # noqa: E501 # some extra weight on the centering
det = det[index, :]
det = np.squeeze(det)
bb = np.zeros(4, dtype=np.int32)
bb[0] = np.maximum(det[0] - margin / 2, 0)
bb[1] = np.maximum(det[1] - margin / 2, 0)
bb[2] = np.minimum(det[2] + margin / 2,
img_size[1])
bb[3] = np.minimum(det[3] + margin / 2,
img_size[0])
cropped = img[bb[1]:bb[3], bb[0]:bb[2], :]
scaled = resize(cropped, (image_size, image_size),
mode='constant')
nrof_successfully_aligned += 1
# convert image to uint8 before saving
imageio.imwrite(output_filename,
img_as_ubyte(scaled))
text_file.write('%s %d %d %d %d\n' %
(output_filename, bb[0], bb[1],
bb[2], bb[3])) # noqa: E501
else:
print('Unable to align "%s"' % image_path)
text_file.write('%s\n' % (output_filename))
print('Total number of images: %d' % nrof_images_total)
print('Number of successfully aligned images: %d' %
nrof_successfully_aligned) # noqa: E501