def train(sess, dataset, epoch, images_placeholder, phase_train_placeholder,
global_step, embeddings, loss, train_op, summary_op, summary_writer):
batch_number = 0
while batch_number < FLAGS.epoch_size:
print('Loading training data')
# Sample people and load new data
image_paths, num_per_class = facenet.sample_people(
dataset, FLAGS.people_per_batch, FLAGS.images_per_person)
image_data = facenet.load_data(image_paths, FLAGS.random_crop,
FLAGS.random_flip, FLAGS.image_size)
print('Selecting suitable triplets for training')
start_time = time.time()
emb_list = []
# Run a forward pass for the sampled images
nrof_examples_per_epoch = FLAGS.people_per_batch * FLAGS.images_per_person
nrof_batches_per_epoch = int(
np.floor(nrof_examples_per_epoch / FLAGS.batch_size))
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_batch(image_data, FLAGS.batch_size, i)
feed_dict = {
images_placeholder: batch,
phase_train_placeholder: True
}
emb_list += sess.run([embeddings], feed_dict=feed_dict)
emb_array = np.vstack(
emb_list
) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Select triplets based on the embeddings
triplets, nrof_random_negs, nrof_triplets = facenet.select_training_triplets(
emb_array, num_per_class, image_data, FLAGS.people_per_batch,
FLAGS.alpha)
duration = time.time() - start_time
print(
'(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' %
(nrof_random_negs, nrof_triplets, duration))
# Perform training on the selected triplets
i = 0
while i * FLAGS.batch_size < nrof_triplets * 3 and batch_number < FLAGS.epoch_size:
start_time = time.time()
batch = facenet.get_triplet_batch(triplets, i, FLAGS.batch_size)
feed_dict = {
images_placeholder: batch,
phase_train_placeholder: True
}
err, _, step = sess.run([loss, train_op, global_step],
feed_dict=feed_dict)
if (batch_number % 20 == 0):
summary_str, step = sess.run([summary_op, global_step],
feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=step)
duration = time.time() - start_time
print('Epoch: [%d][%d/%d]\tTime %.3f\ttripErr %2.3f' %
(epoch, batch_number, FLAGS.epoch_size, duration, err))
batch_number += 1
i += 1
return step
def train(args, sess, dataset, epoch, images_placeholder, phase_train_placeholder,
global_step, embeddings, loss, train_op, summary_op, summary_writer):
batch_number = 0
while batch_number < args.epoch_size:
print('Loading training data')
# Sample people and load new data
start_time = time.time()
image_paths, num_per_class = facenet.sample_people(dataset, args.people_per_batch, args.images_per_person)
image_data = facenet.load_data(image_paths, args.random_crop, args.random_flip, args.image_size)
load_time = time.time() - start_time
print('Loaded %d images in %.2f seconds' % (image_data.shape[0], load_time))
print('Selecting suitable triplets for training')
start_time = time.time()
emb_list = []
# Run a forward pass for the sampled images
nrof_examples_per_epoch = args.people_per_batch * args.images_per_person
nrof_batches_per_epoch = int(np.floor(nrof_examples_per_epoch / args.batch_size))
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_batch(image_data, args.batch_size, i)
feed_dict = {images_placeholder: batch, phase_train_placeholder: True}
emb_list += sess.run([embeddings], feed_dict=feed_dict)
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Select triplets based on the embeddings
triplets, nrof_random_negs, nrof_triplets = facenet.select_triplets(
emb_array, num_per_class, image_data, args.people_per_batch, args.alpha)
selection_time = time.time() - start_time
print('(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' % (
nrof_random_negs, nrof_triplets, selection_time))
# Perform training on the selected triplets
train_time = 0
i = 0
while i * args.batch_size < nrof_triplets * 3 and batch_number < args.epoch_size:
start_time = time.time()
batch = facenet.get_triplet_batch(triplets, i, args.batch_size)
feed_dict = {images_placeholder: batch, phase_train_placeholder: True}
err, _, step = sess.run([loss, train_op, global_step], feed_dict=feed_dict)
if (batch_number % 20 == 0):
summary_str, step = sess.run([summary_op, global_step], feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=step)
duration = time.time() - start_time
print('Epoch: [%d][%d/%d]\tTime %.3f\ttripErr %2.3f' %
(epoch, batch_number, args.epoch_size, duration, err))
batch_number += 1
i += 1
train_time += duration
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='time/load', simple_value=load_time)
summary.value.add(tag='time/selection', simple_value=selection_time)
summary.value.add(tag='time/train', simple_value=train_time)
summary.value.add(tag='time/total', simple_value=load_time+selection_time+train_time)
summary_writer.add_summary(summary, step)
return step
def train(args, sess, dataset, epoch, images_placeholder, phase_train_placeholder,
global_step, embeddings, loss, train_op, summary_op, summary_writer):
batch_number = 0
while batch_number < args.epoch_size:
print('Loading training data')
# Sample people and load new data
start_time = time.time()
image_paths, num_per_class = facenet.sample_people(dataset, args.people_per_batch, args.images_per_person)
image_data = facenet.load_data(image_paths, args.random_crop, args.random_flip, args.image_size)
load_time = time.time() - start_time
print('Loaded %d images in %.2f seconds' % (image_data.shape[0], load_time))
print('Selecting suitable triplets for training')
start_time = time.time()
emb_list = []
# Run a forward pass for the sampled images
nrof_examples_per_epoch = args.people_per_batch * args.images_per_person
nrof_batches_per_epoch = int(np.floor(nrof_examples_per_epoch / args.batch_size))
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_batch(image_data, args.batch_size, i)
feed_dict = {images_placeholder: batch, phase_train_placeholder: True}
emb_list += sess.run([embeddings], feed_dict=feed_dict)
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Select triplets based on the embeddings
triplets, nrof_random_negs, nrof_triplets = facenet.select_triplets(
emb_array, num_per_class, image_data, args.people_per_batch, args.alpha)
selection_time = time.time() - start_time
print('(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' % (
nrof_random_negs, nrof_triplets, selection_time))
# Perform training on the selected triplets
train_time = 0
i = 0
while i * args.batch_size < nrof_triplets * 3 and batch_number < args.epoch_size:
start_time = time.time()
batch = facenet.get_triplet_batch(triplets, i, args.batch_size)
feed_dict = {images_placeholder: batch, phase_train_placeholder: True}
err, _, step = sess.run([loss, train_op, global_step], feed_dict=feed_dict)
if (batch_number % 20 == 0):
summary_str, step = sess.run([summary_op, global_step], feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=step)
duration = time.time() - start_time
print('Epoch: [%d][%d/%d]\tTime %.3f\ttripErr %2.3f' %
(epoch, batch_number, args.epoch_size, duration, err))
batch_number += 1
i += 1
train_time += duration
# Add validation loss and accuracy to summary
summary = tf.Summary()
summary.value.add(tag='time/load', simple_value=load_time)
summary.value.add(tag='time/selection', simple_value=selection_time)
summary.value.add(tag='time/train', simple_value=train_time)
summary.value.add(tag='time/total', simple_value=load_time+selection_time+train_time)
summary_writer.add_summary(summary, step)
return step
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 train(sess, dataset, epoch, images_placeholder, phase_train_placeholder,
global_step, embeddings, loss, train_op, summary_op, summary_writer):
batch_number = 0
while batch_number < FLAGS.epoch_size:
print('Loading training data')
# Sample people and load new data
image_paths, num_per_class = facenet.sample_people(dataset, FLAGS.people_per_batch, FLAGS.images_per_person)
image_data = facenet.load_data(image_paths, FLAGS.random_crop, FLAGS.random_flip, FLAGS.image_size)
print('Selecting suitable triplets for training')
start_time = time.time()
emb_list = []
# Run a forward pass for the sampled images
nrof_examples_per_epoch = FLAGS.people_per_batch * FLAGS.images_per_person
nrof_batches_per_epoch = int(np.floor(nrof_examples_per_epoch / FLAGS.batch_size))
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_batch(image_data, FLAGS.batch_size, i)
feed_dict = {images_placeholder: batch, phase_train_placeholder: True}
emb_list += sess.run([embeddings], feed_dict=feed_dict)
emb_array = np.vstack(emb_list) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Select triplets based on the embeddings
triplets, nrof_random_negs, nrof_triplets = facenet.select_training_triplets(emb_array, num_per_class,
image_data, FLAGS.people_per_batch,
FLAGS.alpha)
duration = time.time() - start_time
print('(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' % (
nrof_random_negs, nrof_triplets, duration))
# Perform training on the selected triplets
i = 0
while i * FLAGS.batch_size < nrof_triplets * 3 and batch_number < FLAGS.epoch_size:
start_time = time.time()
batch = facenet.get_triplet_batch(triplets, i, FLAGS.batch_size)
feed_dict = {images_placeholder: batch, phase_train_placeholder: True}
err, _, step = sess.run([loss, train_op, global_step], feed_dict=feed_dict)
if (batch_number % 20 == 0):
summary_str, step = sess.run([summary_op, global_step], feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=step)
duration = time.time() - start_time
print('Epoch: [%d][%d/%d]\tTime %.3f\ttripErr %2.3f' %
(epoch, batch_number, FLAGS.epoch_size, duration, err))
batch_number += 1
i += 1
return step
def train(args, sess, dataset, epoch, images_placeholder,
phase_train_placeholder, learning_rate_placeholder, global_step,
embeddings, loss, train_op, summary_op, summary_writer): #each epoch
batch_number = 0
if args.learning_rate > 0.0:
lr = args.learning_rate
else:
lr = get_learning_rate_from_file('../data/learning_rate_schedule.txt',
epoch)
while batch_number < args.epoch_size: #this batch_number means batch_num in train, not in getting feature
print('Loading training data')
# Sample people and load new data
start_time = time.time()
image_paths, num_per_class = facenet.sample_people(
dataset, args.people_per_epoch, args.images_per_person)
image_data = facenet.load_data(image_paths, args.random_crop,
args.random_flip, args.image_size)
load_time = time.time() - start_time
print('Loaded %d images in %.2f seconds' %
(image_data.shape[0], load_time))
print('Selecting suitable triplets for training')
start_time = time.time()
emb_list = []
# Run a forward pass for the sampled images
nrof_examples_per_epoch = args.people_per_epoch * args.images_per_person #have no connection with epoch_size
nrof_batches_per_epoch = int(
np.floor(nrof_examples_per_epoch / args.batch_size)
) #the variable is only for batched in getting feature
for i in xrange(nrof_batches_per_epoch):
batch = facenet.get_batch(image_data, args.batch_size, i)
feed_dict = {
images_placeholder: batch,
phase_train_placeholder: True,
learning_rate_placeholder: lr
}
emb_list += sess.run(
[embeddings], feed_dict=feed_dict
) #step 1:get the feature of all image in a epoch
emb_array = np.vstack(
emb_list
) # Stack the embeddings to a nrof_examples_per_epoch x 128 matrix
# Select triplets based on the embeddings
# the data from lfw dataset makes the embeddings'size different from each other
triplets, nrof_random_negs, nrof_triplets = facenet.select_triplets(
emb_array, num_per_class, image_data, args.people_per_epoch,
args.alpha
) #Here the information from embedding and information from sample_people()
#print("num_triplets: "+str(len(triplets)))
selection_time = time.time() - start_time
print(
'(nrof_random_negs, nrof_triplets) = (%d, %d): time=%.3f seconds' %
(nrof_random_negs, nrof_triplets, selection_time))
# Perform training on the selected triplets
train_time = 0
i = 0
#if tripplets is less than batch_size, go back to the start of the epoch
while i * args.batch_size < nrof_triplets * 3 and batch_number < args.epoch_size:
#Why not refresh the triplet every batch
start_time = time.time()
batch = facenet.get_triplet_batch(triplets, i, args.batch_size)
feed_dict = {
images_placeholder: batch,
phase_train_placeholder: True,
learning_rate_placeholder: lr
}
err, _, step = sess.run([loss, train_op, global_step],
feed_dict=feed_dict)
if (batch_number % 20 == 1):
#does the line below waste time?
summary_str, step = sess.run([summary_op, global_step],
feed_dict=feed_dict)
summary_writer.add_summary(summary_str, global_step=step)
duration = time.time() - start_time
print('Epoch: [%d][%d/%d]\tTime %.3f\ttripErr %2.3f' %
(epoch, batch_number, args.epoch_size, duration, err))
batch_number += 1
i += 1
train_time += duration
# Add validation loss and accuracy to summary
summary = tf.Summary()
#pylint: disable=maybe-no-member
summary.value.add(tag='time/load', simple_value=load_time)
summary.value.add(tag='time/selection', simple_value=selection_time)
summary.value.add(tag='time/train', simple_value=train_time)
summary.value.add(tag='time/total',
simple_value=load_time + selection_time + train_time)
summary_writer.add_summary(summary, step)
return step
