def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# 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
paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
print('Loading model "%s"' % args.model_file)
facenet.load_model(args.model_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
tpr, fpr, accuracy, val, val_std, far = lfw.validate(
sess, paths, actual_issame, args.seed, 60, images_placeholder,
phase_train_placeholder, embeddings)
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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main(args):
with tf.Graph().as_default():
images_placeholder = tf.placeholder(tf.float32, shape=(None,args.image_size,args.image_size,3), name='input')
with tf.Session() as sess:
print('Loading graphdef: %s' % args.model_file)
with gfile.FastGFile(os.path.expanduser(args.model_file),'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
return_elements = ['phase_train:0', 'embeddings:0']
phase_train_placeholder, embeddings = tf.import_graph_def(graph_def, input_map={'input':images_placeholder},
return_elements=return_elements, name='import')
# 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
paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Evaluate
tpr, fpr, accuracy, val, val_std, far = lfw.validate(sess, paths,
actual_issame, args.seed, 60,
images_placeholder, 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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# 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
paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
print('Loading model "%s"' % args.model_file)
facenet.load_model(args.model_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
tpr, fpr, accuracy, val, val_std, far = lfw.validate(sess,
paths, actual_issame, args.seed, 60,
images_placeholder, 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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# 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
paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
print('Model directory: %s' % args.model_dir)
meta_file, ckpt_file = facenet.get_model_filenames(
os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
facenet.load_model(args.model_dir, meta_file, ckpt_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {images_placeholder: images}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(
emb_array,
args.seed,
actual_issame,
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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main():
pairs = read_pairs(os.path.expanduser(FLAGS.lfw_pairs))
paths, actual_issame = get_paths(os.path.expanduser(FLAGS.lfw_dir), pairs)
with tf.Graph().as_default():
# Creates graph from saved GraphDef
# NOTE: This does not work at the moment. Needs tensorflow to store variables in the graph_def.
# graphdef_filename = os.path.join(os.path.expanduser(FLAGS.model_dir), 'graphdef', 'graph_def.pb')
# with gfile.FastGFile(graphdef_filename, 'rb') as f:
# graph_def = tf.GraphDef()
# graph_def.ParseFromString(f.read())
# images_placeholder, phase_train_placeholder, embeddings = tf.import_graph_def(graph_def, return_elements = ['input', 'phase_train', 'embeddings'], name='')
# Placeholder for input images
images_placeholder = tf.placeholder(tf.float32, shape=(FLAGS.batch_size, FLAGS.image_size, FLAGS.image_size, 3), name='input')
# Placeholder for phase_train
phase_train_placeholder = tf.placeholder(tf.bool, name='phase_train')
# Build the inference graph
embeddings = facenet.inference_nn4_max_pool_96(images_placeholder, FLAGS.pool_type, FLAGS.use_lrn,
1.0, phase_train=phase_train_placeholder)
# Create a saver for restoring variable averages
ema = tf.train.ExponentialMovingAverage(1.0)
saver = tf.train.Saver(ema.variables_to_restore())
with tf.Session() as sess:
ckpt = tf.train.get_checkpoint_state(os.path.expanduser(FLAGS.model_dir))
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
else:
raise ValueError('Checkpoint not found')
nrof_images = len(paths)
nrof_batches = int(nrof_images / FLAGS.batch_size) # Run forward pass on the remainder in the last batch
emb_list = []
for i in range(nrof_batches):
start_time = time.time()
paths_batch = paths[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size]
images = facenet.load_data(paths_batch, False, False, FLAGS.image_size)
feed_dict = { images_placeholder: images, phase_train_placeholder: False }
emb_list += sess.run([embeddings], feed_dict=feed_dict)
duration = time.time() - start_time
print('Calculated embeddings for batch %d of %d: time=%.3f seconds' % (i+1,nrof_batches, duration))
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), FLAGS.seed)
print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
facenet.plot_roc(fpr, tpr, 'NN4')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# 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
paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
print('Model directory: %s' % args.model_dir)
meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
facenet.load_model(args.model_dir, meta_file, ckpt_file)
# Get input and output tensors
image_paths_placeholder = tf.get_default_graph().get_tensor_by_name("image_paths:0")
labels_placeholder = tf.get_default_graph().get_tensor_by_name("labels:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
batch_size_placeholder = tf.get_default_graph().get_tensor_by_name("batch_size:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
enqueue_op = tf.get_default_graph().get_operation_by_name("enqueue_op")
label_batch = tf.get_default_graph().get_tensor_by_name("label_batch:0")
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
# Enqueue one epoch of image paths and labels
labels_array = np.expand_dims(np.arange(0,len(paths)),1)
image_paths_array = np.expand_dims(np.array(paths),1)
sess.run(enqueue_op, {image_paths_placeholder: image_paths_array, labels_placeholder: labels_array})
embedding_size = embeddings.get_shape()[1]
nrof_images = len(actual_issame)*2
nrof_batches = nrof_images // args.lfw_batch_size
emb_array = np.zeros((nrof_images, embedding_size))
lab_array = np.zeros((nrof_images,))
for _ in range(nrof_batches):
feed_dict = {phase_train_placeholder:False, batch_size_placeholder:args.lfw_batch_size}
emb, lab = sess.run([embeddings, label_batch], feed_dict=feed_dict)
lab_array[lab] = lab
emb_array[lab] = emb
assert np.array_equal(lab_array, np.arange(nrof_images))==True, 'Wrong labels used for evaluation, possibly caused by training examples left in the input pipeline'
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array,
actual_issame, 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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main():
pairs = read_pairs(os.path.expanduser(FLAGS.lfw_pairs))
paths, actual_issame = get_paths(os.path.expanduser(FLAGS.lfw_dir), pairs)
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
print('Loading model "%s"' % FLAGS.model_file)
load_model(FLAGS.model_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
image_size = images_placeholder.get_shape()[1]
# Run forward pass to calculate embeddings
nrof_images = len(paths)
nrof_batches = int(nrof_images / FLAGS.batch_size)
emb_list = []
for i in range(nrof_batches):
start_time = time.time()
paths_batch = paths[i * FLAGS.batch_size:(i + 1) *
FLAGS.batch_size]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_list += sess.run([embeddings], feed_dict=feed_dict)
duration = time.time() - start_time
print(
'Calculated embeddings for batch %d of %d: time=%.3f seconds'
% (i + 1, nrof_batches, duration))
emb_array = np.vstack(
emb_list
) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(
thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), FLAGS.seed)
print('Accuracy: %1.3f+-%1.3f' %
(np.mean(accuracy), np.std(accuracy)))
facenet.plot_roc(fpr, tpr, 'NN4')
def main():
pairs = read_pairs(os.path.expanduser(FLAGS.lfw_pairs))
paths, actual_issame = get_paths(os.path.expanduser(FLAGS.lfw_dir), pairs)
with tf.Graph().as_default():
with tf.Session() as sess:
print('Reading model "%s"' % FLAGS.model_file)
new_saver = tf.train.import_meta_graph(os.path.expanduser(FLAGS.model_file+'.meta'))
all_vars_dict = {var.name: var for var in tf.all_variables()}
restore_vars = {}
for var_name in all_vars_dict:
if '/ExponentialMovingAverage' in var_name:
param_name = var_name.replace('/ExponentialMovingAverage', '')
if param_name in all_vars_dict:
param = all_vars_dict[param_name]
print('%s\t%s' % (var_name, param))
restore_vars[var_name] = param
saver = tf.train.Saver(restore_vars, saver_def=new_saver.as_saver_def())
saver.restore(sess, os.path.expanduser(FLAGS.model_file))
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
image_size = images_placeholder.get_shape()[1]
nrof_images = len(paths)
nrof_batches = int(nrof_images / FLAGS.batch_size) # Run forward pass on the remainder in the last batch
emb_list = []
for i in range(nrof_batches):
start_time = time.time()
paths_batch = paths[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder: images, phase_train_placeholder: False }
emb_list += sess.run([embeddings], feed_dict=feed_dict)
duration = time.time() - start_time
print('Calculated embeddings for batch %d of %d: time=%.3f seconds' % (i+1,nrof_batches, duration))
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), FLAGS.seed)
print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
facenet.plot_roc(fpr, tpr, 'NN4')
def validate(sess, dataset, epoch, images_placeholder, phase_train_placeholder,
global_step, embeddings, loss, train_op, summary_op, summary_writer):
people_per_batch = FLAGS.people_per_batch * 4
print('Loading validation data')
# Sample people and load new data
image_paths, num_per_class = facenet.sample_people(dataset, people_per_batch, FLAGS.images_per_person)
image_data = facenet.load_data(image_paths)
print('Selecting random triplets for validation')
triplets, nrof_triplets = facenet.select_validation_triplets(num_per_class, people_per_batch, image_data)
start_time = time.time()
anchor_list = []
positive_list = []
negative_list = []
triplet_loss_list = []
# Run a forward pass for the sampled images
print('Running forward pass on validation set')
nrof_batches_per_epoch = nrof_triplets * 3 // FLAGS.batch_size
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_triplet_batch(triplets, i)
feed_dict = {images_placeholder: batch, phase_train_placeholder: False}
emb, triplet_loss, step = sess.run([embeddings, loss, global_step], feed_dict=feed_dict)
nrof_batch_triplets = emb.shape[0] / 3
anchor_list.append(emb[(0 * nrof_batch_triplets):(1 * nrof_batch_triplets), :])
positive_list.append(emb[(1 * nrof_batch_triplets):(2 * nrof_batch_triplets), :])
negative_list.append(emb[(2 * nrof_batch_triplets):(3 * nrof_batch_triplets), :])
triplet_loss_list.append(triplet_loss)
anchor = np.vstack(anchor_list)
positive = np.vstack(positive_list)
negative = np.vstack(negative_list)
duration = time.time() - start_time
thresholds = np.arange(0, 4, 0.01)
embeddings1 = np.vstack([anchor, anchor])
embeddings2 = np.vstack([positive, negative])
actual_issame = np.asarray([True] * anchor.shape[0] + [False] * anchor.shape[0])
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, actual_issame)
print('Epoch: [%d]\tTime %.3f\ttripErr %2.3f\taccuracy %1.3f%c%1.3f' % (
epoch, duration, np.mean(triplet_loss_list), np.mean(accuracy), u"\u00B1", np.std(accuracy)))
# Add validation loss and accuracy to summary
summary = tf.Summary()
summary.value.add(tag='validation_loss', simple_value=np.mean(triplet_loss_list).astype(float))
summary.value.add(tag='accuracy', simple_value=np.mean(accuracy))
summary_writer.add_summary(summary, step)
if False:
facenet.plot_roc(fpr, tpr, 'NN4')
def validate(sess, dataset, epoch, images_placeholder, phase_train_placeholder,
global_step, embeddings, loss, prefix_str, summary_writer):
nrof_people = FLAGS.people_per_batch * 4
print('Loading %s data' % prefix_str)
# Sample people and load new data
image_paths, num_per_class = facenet.sample_people(dataset, nrof_people, FLAGS.images_per_person)
image_data = facenet.load_data(image_paths, False, False, FLAGS.image_size)
print('Selecting random triplets from %s set' % prefix_str)
triplets, nrof_triplets = facenet.select_validation_triplets(num_per_class, nrof_people, image_data, FLAGS.batch_size)
start_time = time.time()
anchor_list = []
positive_list = []
negative_list = []
triplet_loss_list = []
# Run a forward pass for the sampled images
print('Running forward pass on %s set' % prefix_str)
nrof_batches_per_epoch = nrof_triplets * 3 // FLAGS.batch_size
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_triplet_batch(triplets, i, FLAGS.batch_size)
feed_dict = {images_placeholder: batch, phase_train_placeholder: False}
emb, triplet_loss, step = sess.run([embeddings, loss, global_step], feed_dict=feed_dict)
nrof_batch_triplets = emb.shape[0] / 3
anchor_list.append(emb[(0 * nrof_batch_triplets):(1 * nrof_batch_triplets), :])
positive_list.append(emb[(1 * nrof_batch_triplets):(2 * nrof_batch_triplets), :])
negative_list.append(emb[(2 * nrof_batch_triplets):(3 * nrof_batch_triplets), :])
triplet_loss_list.append(triplet_loss)
anchor = np.vstack(anchor_list)
positive = np.vstack(positive_list)
negative = np.vstack(negative_list)
duration = time.time() - start_time
thresholds = np.arange(0, 4, 0.01)
embeddings1 = np.vstack([anchor, anchor])
embeddings2 = np.vstack([positive, negative])
actual_issame = np.asarray([True] * anchor.shape[0] + [False] * anchor.shape[0])
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, actual_issame, FLAGS.seed)
print('Epoch: [%d]\tTime %.3f\ttripErr %2.3f\t%sAccuracy %1.3f+-%1.3f' % (
epoch, duration, np.mean(triplet_loss_list), prefix_str, np.mean(accuracy), np.std(accuracy)))
# Add validation loss and accuracy to summary
summary = tf.Summary()
summary.value.add(tag='{}_loss'.format(prefix_str), simple_value=np.mean(triplet_loss_list).astype(float))
summary.value.add(tag='{}_accuracy'.format(prefix_str), simple_value=np.mean(accuracy))
summary_writer.add_summary(summary, step)
if False:
facenet.plot_roc(fpr, tpr, 'NN4')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# 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
paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
print('Model directory: %s' % args.model_dir)
meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
facenet.load_model(args.model_dir, meta_file, ckpt_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder:images }
emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array,
args.seed, actual_issame, 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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main(argv=None):
pairs = read_pairs(os.path.expanduser(FLAGS.lfw_pairs))
paths, actual_issame = get_paths(os.path.expanduser(FLAGS.lfw_dir), pairs)
with tf.Graph().as_default():
with tf.Session() as sess:
# Load the model
print('Loading model "%s"' % FLAGS.model_file)
facenet.load_model(FLAGS.model_file)
# Get input and output tensors
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
image_size = images_placeholder.get_shape()[1]
# Run forward pass to calculate embeddings
nrof_images = len(paths)
nrof_batches = int(nrof_images / FLAGS.batch_size)
emb_list = []
for i in range(nrof_batches):
start_time = time.time()
paths_batch = paths[i*FLAGS.batch_size:(i+1)*FLAGS.batch_size]
images = facenet.load_data(paths_batch, False, False, image_size)
feed_dict = { images_placeholder: images, phase_train_placeholder: False }
emb_list += sess.run([embeddings], feed_dict=feed_dict)
duration = time.time() - start_time
print('Calculated embeddings for batch %d of %d: time=%.3f seconds' % (i+1,nrof_batches, duration))
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Calculate evaluation metrics
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), FLAGS.seed)
print('Accuracy: %1.3f+-%1.3f' % (np.mean(accuracy), np.std(accuracy)))
thresholds = np.arange(0, 4, 0.001)
val, val_std, far = facenet.calculate_val(thresholds, embeddings1, embeddings2, np.asarray(actual_issame), 1e-3, FLAGS.seed)
print('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f' % (val, val_std, far))
facenet.plot_roc(fpr, tpr, 'NN4')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# Read the file containing the pairs used for testing
pairs = lfw.read_pairs(os.path.expanduser(args.lfw_pairs))
np.random.shuffle(pairs)
# Get the paths for the corresponding images
paths, actual_issame = lfw.get_paths(
os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
#actual_issame= actual_issame[0:args.lfw_batch_size]
# Load the model
print('Model directory: %s' % args.model_dir)
meta_file, ckpt_file = facenet.get_model_filenames(
os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
facenet.load_model(args.model_dir, meta_file, ckpt_file)
# Get input and output tensors
#images_placeholder = tf.get_default_graph().get_tensor_by_name("image_batch:0")
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
#keep_probability_placeholder = tf.get_default_graph().get_tensor_by_name('keep_probability:0')
#weight_decay_placeholder = tf.get_default_graph().get_tensor_by_name('weight_decay:0')
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name('phase_train:0')
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0 * nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
#emb_array = np.zeros((2*batch_size, embedding_size))
for i in range(nrof_batches):
print("Test batch:%d/%d\n" % (i, nrof_batches))
start_index = i * batch_size
end_index = min((i + 1) * batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False,
image_size)
#feed_dict = {phase_train_placeholder: False, images_placeholder:images, keep_probability_placeholder:1.0, weight_decay_placeholder:0.0}
feed_dict = {
phase_train_placeholder: False,
images_placeholder: images
}
emb_array[start_index:end_index, :] = sess.run(
embeddings, feed_dict=feed_dict)
print('Evaluate_model: %s' % args.evaluate_mode)
if args.evaluate_mode == 'Euclidian':
tpr, fpr, accuracy, val, val_std, far, fp_idx, fn_idx, best_threshold, val_threshold = lfw.evaluate(
emb_array, actual_issame, nrof_folds=args.lfw_nrof_folds)
if args.evaluate_mode == 'similarity':
#pca = PCA(whiten=True)
pca = PCA(n_components=128)
pca.fit(emb_array)
emb_array_pca = pca.transform(emb_array)
tpr, fpr, accuracy, val, val_std, far, fp_idx, fn_idx, best_threshold, val_threshold = lfw.evaluate_cosine(
emb_array_pca,
actual_issame,
nrof_folds=args.lfw_nrof_folds)
################### edit by mzh 12012017: select the false positive/negative images ####################
nrof_test_paths = nrof_batches * batch_size
nrof_test_tp_pairs = sum(actual_issame[0:int(nrof_test_paths / 2)])
nrof_test_tn_pairs = len(
actual_issame[0:int(nrof_test_paths / 2)]) - nrof_test_tp_pairs
paths_pairs = [
paths[0:nrof_test_paths:2], paths[1:nrof_test_paths:2]
] # paths_pairs shape: [2, number of pairs], each column is corresponding to a pair of images
paths_pairs_array = np.array(paths_pairs)
fp_images_paths = paths_pairs_array[:, fp_idx]
fn_images_paths = paths_pairs_array[:, fn_idx]
_, nrof_fp_pairs = fp_images_paths.shape
_, nrof_fn_pairs = fn_images_paths.shape
################### edit by mzh 12012017: select the false positive/negative images ####################
print('Accuracy: %1.3f+-%1.3f @ Best threshold %f\n' %
(np.mean(accuracy), np.std(accuracy), best_threshold))
print(
'Validation rate: %2.5f+-%2.5f @ FAR=%2.5f @val_threshold %f\n'
% (val, val_std, far, val_threshold))
auc = metrics.auc(fpr, tpr)
print('Area Under Curve (AUC): %1.3f\n' % auc)
eer = brentq(lambda x: 1. - x - interpolate.interp1d(fpr, tpr)(x),
0., 1.)
print('Equal Error Rate (EER): %1.3f\n' % eer)
subdir = datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S')
log_dir = os.path.join(os.path.expanduser(args.logs_base_dir),
subdir)
print('log_dir: %s\n' % log_dir)
if not os.path.isdir(
log_dir): # Create the log directory if it doesn't exist
os.makedirs(log_dir)
with open(os.path.join(log_dir, 'validation_on_dataset.txt'),
'at') as f:
print('Saving the evaluation results...\n')
f.write('arguments: %s\n--------------------\n' %
' '.join(sys.argv))
f.write('Accuracy: %1.3f+-%1.3f\n' %
(np.mean(accuracy), np.std(accuracy)))
f.write('Validation rate: %2.5f+-%2.5f @ FAR=%2.5f\n' %
(val, val_std, far))
f.write('Best threshold: %2.5f \n' % (best_threshold))
f.write('Validation threshold: %2.5f \n' % (val_threshold))
f.write('Area Under Curve (AUC): %1.3f\n' % auc)
f.write('Equal Error Rate (EER): %1.3f\n' % eer)
print('Saving the False positive pairs images ...\n ')
f.write(
'False positive pairs: %d / %d ---------------------------------------------\n'
% (nrof_fp_pairs, nrof_test_tp_pairs))
for i in range(nrof_fp_pairs):
f.write('%d %s\n' % (i, fp_images_paths[:, i]))
print('Saving the False negative pairs images ...\n ')
f.write(
'False negative pairs: %d / %d ---------------------------------------------\n'
% (nrof_fn_pairs, nrof_test_tn_pairs))
for i in range(nrof_fn_pairs):
f.write('%d %s\n' % (i, fn_images_paths[:, i]))
################### edit by mzh 12012017: write the false positive/negative images to the file ####################
false_images_list = os.path.join(log_dir,
'validation_on_dataset.txt')
save_dir = log_dir
save_false_images.save_false_images(false_images_list, save_dir)
with open(os.path.join(log_dir, 'validation_on_dataset.txt'),
'at') as f:
print('Saving the tpr, fpr of ROC ...\n ')
f.write(
'ROC: tpr, fpr --------------------------------------------------------------\n'
)
for tp, fp in zip(tpr, fpr):
f.write('tpr/fpr: %f/%f\n' % (tp, fp))
facenet.plot_roc(fpr, tpr, 'ROC')
def main(args):
with tf.Graph().as_default():
with tf.Session() as sess:
# 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
paths, actual_issame = lfw.get_paths(os.path.expanduser(args.lfw_dir), pairs, args.lfw_file_ext)
# Load the model
print('Model directory: %s' % args.model_dir)
meta_file, ckpt_file = facenet.get_model_filenames(os.path.expanduser(args.model_dir))
print('Metagraph file: %s' % meta_file)
print('Checkpoint file: %s' % ckpt_file)
facenet.load_model(args.model_dir, meta_file, ckpt_file)
# Get input and output tensors
#images_placeholder = tf.get_default_graph().get_tensor_by_name("image_batch:0")
images_placeholder = tf.get_default_graph().get_tensor_by_name("input:0")
embeddings = tf.get_default_graph().get_tensor_by_name("embeddings:0")
#keep_probability_placeholder = tf.get_default_graph().get_tensor_by_name('keep_probability:0')
#weight_decay_placeholder = tf.get_default_graph().get_tensor_by_name('weight_decay:0')
phase_train_placeholder = tf.get_default_graph().get_tensor_by_name('phase_train:0')
image_size = images_placeholder.get_shape()[1]
embedding_size = embeddings.get_shape()[1]
# Run forward pass to calculate embeddings
print('Runnning forward pass on LFW images')
batch_size = args.lfw_batch_size
nrof_images = len(paths)
nrof_batches = int(math.ceil(1.0*nrof_images / batch_size))
emb_array = np.zeros((nrof_images, embedding_size))
for i in range(nrof_batches):
start_index = i*batch_size
end_index = min((i+1)*batch_size, nrof_images)
paths_batch = paths[start_index:end_index]
images = facenet.load_data(paths_batch, False, False, image_size)
#feed_dict = {phase_train_placeholder: False, images_placeholder:images, keep_probability_placeholder:1.0, weight_decay_placeholder:0.0}
feed_dict = {phase_train_placeholder: False, images_placeholder: images}
emb_array[start_index:end_index,:] = sess.run(embeddings, feed_dict=feed_dict)
print('Evaluate_model: %s' % args.evaluate_mode)
if args.evaluate_mode == 'Euclidian':
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate(emb_array,
actual_issame, nrof_folds=args.lfw_nrof_folds)
if args.evaluate_mode == 'similarity':
#pca = PCA(whiten=True)
pca = PCA(n_components=128)
pca.fit(emb_array)
emb_array_pca = pca.transform(emb_array)
tpr, fpr, accuracy, val, val_std, far = lfw.evaluate_cosine(emb_array_pca,
actual_issame, 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))
facenet.plot_roc(fpr, tpr, 'NN4')
def main():
pairs = read_pairs(os.path.expanduser(FLAGS.lfw_pairs))
paths, actual_issame = get_paths(os.path.expanduser(FLAGS.lfw_dir), pairs)
with tf.Graph().as_default():
with tf.Session() as sess:
print('Reading model "%s"' % FLAGS.model_file)
new_saver = tf.train.import_meta_graph(
os.path.expanduser(FLAGS.model_file + '.meta'))
all_vars_dict = {var.name: var for var in tf.all_variables()}
restore_vars = {}
for var_name in all_vars_dict:
if '/ExponentialMovingAverage' in var_name:
param_name = var_name.replace('/ExponentialMovingAverage',
'')
if param_name in all_vars_dict:
param = all_vars_dict[param_name]
print('%s\t%s' % (var_name, param))
restore_vars[var_name] = param
saver = tf.train.Saver(restore_vars,
saver_def=new_saver.as_saver_def())
saver.restore(sess, os.path.expanduser(FLAGS.model_file))
images_placeholder = tf.get_default_graph().get_tensor_by_name(
"input:0")
phase_train_placeholder = tf.get_default_graph(
).get_tensor_by_name("phase_train:0")
embeddings = tf.get_default_graph().get_tensor_by_name(
"embeddings:0")
image_size = images_placeholder.get_shape()[1]
nrof_images = len(paths)
nrof_batches = int(
nrof_images / FLAGS.batch_size
) # Run forward pass on the remainder in the last batch
emb_list = []
for i in range(nrof_batches):
start_time = time.time()
paths_batch = paths[i * FLAGS.batch_size:(i + 1) *
FLAGS.batch_size]
images = facenet.load_data(paths_batch, False, False,
image_size)
feed_dict = {
images_placeholder: images,
phase_train_placeholder: False
}
emb_list += sess.run([embeddings], feed_dict=feed_dict)
duration = time.time() - start_time
print(
'Calculated embeddings for batch %d of %d: time=%.3f seconds'
% (i + 1, nrof_batches, duration))
emb_array = np.vstack(
emb_list
) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
thresholds = np.arange(0, 4, 0.01)
embeddings1 = emb_array[0::2]
embeddings2 = emb_array[1::2]
tpr, fpr, accuracy = facenet.calculate_roc(
thresholds, embeddings1, embeddings2,
np.asarray(actual_issame), FLAGS.seed)
print('Accuracy: %1.3f+-%1.3f' %
(np.mean(accuracy), np.std(accuracy)))
facenet.plot_roc(fpr, tpr, 'NN4')