def main():
cfg = TrainConfig().parse()
print(cfg.name)
result_dir = os.path.join(
cfg.result_root,
cfg.name + '_' + datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
utils.write_configure_to_file(cfg, result_dir)
np.random.seed(seed=cfg.seed)
# prepare dataset
val_session = cfg.val_session
val_set = prepare_dataset(cfg.feature_root, val_session, cfg.feat,
cfg.label_root)
# construct the graph
with tf.Graph().as_default():
tf.set_random_seed(cfg.seed)
global_step = tf.Variable(0, trainable=False)
lr_ph = tf.placeholder(tf.float32, name='learning_rate')
with tf.variable_scope("test"):
# load backbone model
if cfg.network == "tsn":
model_emb = networks.TSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model_emb = networks.RTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "convtsn":
model_emb = networks.ConvTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model_emb = networks.ConvRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
# multitask loss (verification)
#model_ver = networks.PairSim2(n_input=cfg.emb_dim)
model_ver = networks.PairSim(n_input=cfg.emb_dim)
var_list = {}
for v in tf.global_variables():
if v.op.name.startswith("test"):
var_list[v.op.name.replace("test/", "")] = v
restore_saver = tf.train.Saver(var_list)
# get the embedding
if cfg.feat == "sensors":
input_ph = tf.placeholder(tf.float32,
shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet":
input_ph = tf.placeholder(
tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
model_emb.forward(input_ph, dropout_ph)
if cfg.normalized:
embedding = tf.nn.l2_normalize(model_emb.hidden,
axis=-1,
epsilon=1e-10)
else:
embedding = model_emb.hidden
# split embedding into anchor, positive and negative and calculate distance
anchor, positive, negative = tf.unstack(
tf.reshape(embedding, [-1, 3, cfg.emb_dim]), 3, 1)
dist = tf.concat([
tf.reshape(utils.cdist_tf(anchor - positive), [-1, 1]),
tf.reshape(utils.cdist_tf(anchor - negative), [-1, 1])
],
axis=1)
# verification
pos_pairs = tf.concat(
[tf.expand_dims(anchor, axis=1),
tf.expand_dims(positive, axis=1)],
axis=1)
pos_label = tf.ones((tf.shape(pos_pairs)[0], ), tf.int32)
neg_pairs = tf.concat(
[tf.expand_dims(anchor, axis=1),
tf.expand_dims(negative, axis=1)],
axis=1)
neg_label = tf.zeros((tf.shape(neg_pairs)[0], ), tf.int32)
ver_pairs = tf.concat([pos_pairs, neg_pairs], axis=0)
ver_label = tf.concat([pos_label, neg_label], axis=0)
model_ver.forward(ver_pairs, dropout_ph)
prob = tf.reshape(model_ver.prob[:, 1], (-1, 1))
sim = tf.concat(
[prob[:tf.shape(pos_pairs)[0]], prob[tf.shape(pos_pairs)[0]:]],
axis=1)
pred = tf.argmax(model_ver.logits, -1)
# Start running the graph
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
with sess.as_default():
sess.run(tf.global_variables_initializer())
print("Restoring pretrained model: %s" % cfg.model_path)
restore_saver.restore(sess, cfg.model_path)
fout = open(
os.path.join(os.path.dirname(cfg.model_path),
'val_pairsim.txt'), 'w')
for i, session in enumerate(val_set):
session_id = os.path.basename(session[1]).split('_')[0]
print("{0} / {1}: {2}".format(i, len(val_set), session_id))
eve_batch, lab_batch, _ = load_data_and_label(
session[0], session[1], model_emb.prepare_input_test
) # use prepare_input_test for testing time
emb = sess.run(embedding,
feed_dict={
input_ph: eve_batch,
dropout_ph: 1.0
})
triplet_per_batch = 10
triplet_input_idx, negative_count = select_triplets(
lab_batch, emb, triplet_per_batch, 0.2)
triplet_input = eve_batch[triplet_input_idx]
dist_batch, sim_batch, pred_batch = sess.run([dist, sim, pred],
feed_dict={
input_ph:
triplet_input,
dropout_ph:
1.0
})
pdb.set_trace()
batch_label = np.hstack(
(np.ones((triplet_input.shape[0] // 3, ), dtype='int32'),
np.zeros((triplet_input.shape[0] // 3, ), dtype='int32')))
acc = accuracy_score(batch_label, pred_batch)
fout.write("{}: acc = {}\n".format(session_id, acc))
for i in range(dist_batch.shape[0]):
fout.write("{}\t{}\t{}\t{}\n".format(
dist_batch[i, 0], dist_batch[i, 1], sim_batch[i, 0],
sim_batch[i, 1]))
fout.close()
def main():
# Load configurations and write to config.txt
cfg = TrainConfig().parse()
print(cfg.name)
result_dir = os.path.join(
cfg.result_root,
cfg.name + '_' + datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
utils.write_configure_to_file(cfg, result_dir)
np.random.seed(seed=cfg.seed)
# prepare dataset
train_session = cfg.train_session
train_set = prepare_dataset(cfg.feature_root, train_session, cfg.feat,
cfg.label_root)
train_set = train_set[:cfg.label_num]
batch_per_epoch = len(train_set) // cfg.sess_per_batch
val_session = cfg.val_session
val_set = prepare_dataset(cfg.feature_root, val_session, cfg.feat,
cfg.label_root)
# construct the graph
with tf.Graph().as_default():
tf.set_random_seed(cfg.seed)
global_step = tf.Variable(0, trainable=False)
label_ph = tf.placeholder(tf.int32, shape=[None], name="label")
lr_ph = tf.placeholder(tf.float32, name='learning_rate')
####################### Define model here ########################
# Load embedding model
if cfg.network == "tsn":
model_emb = networks.TSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model_emb = networks.RTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "convtsn":
model_emb = networks.ConvTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model_emb = networks.ConvRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim,
n_h=cfg.n_h,
n_w=cfg.n_w,
n_C=cfg.n_C,
n_input=cfg.n_input)
elif cfg.network == "convbirtsn":
model_emb = networks.ConvBiRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
# get the embedding
if cfg.feat == "sensors" or cfg.feat == "segment":
input_ph = tf.placeholder(tf.float32,
shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet" or cfg.feat == "segment_down":
input_ph = tf.placeholder(
tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
model_emb.forward(input_ph, dropout_ph)
if cfg.normalized:
embedding = tf.nn.l2_normalize(model_emb.hidden,
axis=-1,
epsilon=1e-10)
else:
embedding = model_emb.hidden
# Use tensorflow implementation for loss functions
if cfg.loss == 'triplet':
metric_loss, active_count = loss_tf.triplet_semihard_loss(
labels=label_ph, embeddings=embedding, margin=cfg.alpha)
elif cfg.loss == 'lifted':
metric_loss, active_count = loss_tf.lifted_struct_loss(
labels=label_ph, embeddings=embedding, margin=cfg.alpha)
else:
raise NotImplementedError
regularization_loss = tf.reduce_sum(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
total_loss = metric_loss + regularization_loss * cfg.lambda_l2
tf.summary.scalar('learning_rate', lr_ph)
train_op = utils.optimize(total_loss, global_step, cfg.optimizer,
lr_ph, tf.global_variables())
####################### Define data loader ############################
# session iterator for session sampling
feat_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
label_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
train_data = session_generator(feat_paths_ph,
label_paths_ph,
sess_per_batch=cfg.sess_per_batch,
num_threads=2,
shuffled=False,
preprocess_func=model_emb.prepare_input)
train_sess_iterator = train_data.make_initializable_iterator()
next_train = train_sess_iterator.get_next()
# Prepare validation data
val_sess = []
val_feats = []
val_labels = []
val_boundaries = []
for session in val_set:
session_id = os.path.basename(session[1]).split('_')[0]
eve_batch, lab_batch, boundary = load_data_and_label(
session[0], session[-1], model_emb.prepare_input_test
) # use prepare_input_test for testing time
val_feats.append(eve_batch)
val_labels.append(lab_batch)
val_sess.extend([session_id] * eve_batch.shape[0])
val_boundaries.extend(boundary)
val_feats = np.concatenate(val_feats, axis=0)
val_labels = np.concatenate(val_labels, axis=0)
print("Shape of val_feats: ", val_feats.shape)
# generate metadata.tsv for visualize embedding
with open(os.path.join(result_dir, 'metadata_val.tsv'), 'w') as fout:
fout.write('id\tlabel\tsession_id\tstart\tend\n')
for i in range(len(val_sess)):
fout.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format(
i, val_labels[i, 0], val_sess[i], val_boundaries[i][0],
val_boundaries[i][1]))
# Variable for visualizing the embeddings
emb_var = tf.Variable(tf.zeros([val_feats.shape[0], cfg.emb_dim]),
name='embeddings')
set_emb = tf.assign(emb_var, embedding, validate_shape=False)
# calculated for monitoring all-pair embedding distance
diffs = utils.all_diffs_tf(embedding, embedding)
all_dist = utils.cdist_tf(diffs)
tf.summary.histogram('embedding_dists', all_dist)
summary_op = tf.summary.merge_all()
saver = tf.train.Saver(max_to_keep=10)
#########################################################################
# Start running the graph
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
summary_writer = tf.summary.FileWriter(result_dir, sess.graph)
with sess.as_default():
sess.run(tf.global_variables_initializer())
# load pretrain model, if needed
if cfg.model_path:
print("Restoring pretrained model: %s" % cfg.model_path)
saver.restore(sess, cfg.model_path)
################## Training loop ##################
epoch = -1
while epoch < cfg.max_epochs - 1:
step = sess.run(global_step, feed_dict=None)
epoch = step // batch_per_epoch
# learning rate schedule, reference: "In defense of Triplet Loss"
if epoch < cfg.static_epochs:
learning_rate = cfg.learning_rate
else:
learning_rate = cfg.learning_rate * \
0.01**((epoch-cfg.static_epochs)/(cfg.max_epochs-cfg.static_epochs))
# prepare data for this epoch
random.shuffle(train_set)
feat_paths = [path[0] for path in train_set]
label_paths = [path[1] for path in train_set]
# reshape a list to list of list
# interesting hacky code from: https://stackoverflow.com/questions/10124751/convert-a-flat-list-to-list-of-list-in-python
feat_paths = list(zip(*[iter(feat_paths)] *
cfg.sess_per_batch))
label_paths = list(
zip(*[iter(label_paths)] * cfg.sess_per_batch))
sess.run(train_sess_iterator.initializer,
feed_dict={
feat_paths_ph: feat_paths,
label_paths_ph: label_paths
})
# for each epoch
batch_count = 1
while True:
try:
# Get a batch
start_time_select = time.time()
eve, se, lab = sess.run(next_train)
# for memory concern, cfg.event_per_batch events are used in maximum
if eve.shape[0] > cfg.event_per_batch:
idx = np.random.permutation(
eve.shape[0])[:cfg.event_per_batch]
eve = eve[idx]
se = se[idx]
lab = lab[idx]
select_time = time.time() - start_time_select
start_time_train = time.time()
# perform training on the batch
err, _, step, summ = sess.run(
[total_loss, train_op, global_step, summary_op],
feed_dict={
input_ph: eve,
label_ph: np.squeeze(lab),
dropout_ph: cfg.keep_prob,
lr_ph: learning_rate
})
train_time = time.time() - start_time_train
print ("%s\tEpoch: [%d][%d/%d]\tEvent num: %d\tSelect_time: %.3f\tTrain_time: %.3f\tLoss %.4f" % \
(cfg.name, epoch+1, batch_count, batch_per_epoch, eve.shape[0], select_time, train_time, err))
summary = tf.Summary(value=[
tf.Summary.Value(tag="train_loss",
simple_value=err),
])
summary_writer.add_summary(summary, step)
summary_writer.add_summary(summ, step)
batch_count += 1
except tf.errors.OutOfRangeError:
print("Epoch %d done!" % (epoch + 1))
break
# validation on val_set
print("Evaluating on validation set...")
val_embeddings, _ = sess.run([embedding, set_emb],
feed_dict={
input_ph: val_feats,
dropout_ph: 1.0
})
mAP, mPrec, recall = utils.evaluate_simple(
val_embeddings, val_labels)
summary = tf.Summary(value=[
tf.Summary.Value(tag="Valiation mAP", simple_value=mAP),
tf.Summary.Value(tag="Validation Recall@1",
simple_value=recall),
tf.Summary.Value(tag="Validation [email protected]",
simple_value=mPrec)
])
summary_writer.add_summary(summary, step)
print("Epoch: [%d]\tmAP: %.4f\tmPrec: %.4f" %
(epoch + 1, mAP, mPrec))
# config for embedding visualization
config = projector.ProjectorConfig()
visual_embedding = config.embeddings.add()
visual_embedding.tensor_name = emb_var.name
visual_embedding.metadata_path = os.path.join(
result_dir, 'metadata_val.tsv')
projector.visualize_embeddings(summary_writer, config)
# save model
saver.save(sess,
os.path.join(result_dir, cfg.name + '.ckpt'),
global_step=step)
def main():
cfg = TrainConfig().parse()
print(cfg.name)
result_dir = os.path.join(
cfg.result_root,
cfg.name + '_' + datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
utils.write_configure_to_file(cfg, result_dir)
np.random.seed(seed=cfg.seed)
# prepare dataset
train_session = cfg.train_session
train_set = prepare_dataset(cfg.feature_root, train_session, cfg.feat,
cfg.label_root)
batch_per_epoch = len(train_set) // cfg.sess_per_batch
val_session = cfg.val_session
val_set = prepare_dataset(cfg.feature_root, val_session, cfg.feat,
cfg.label_root)
# construct the graph
with tf.Graph().as_default():
tf.set_random_seed(cfg.seed)
global_step = tf.Variable(0, trainable=False)
lr_ph = tf.placeholder(tf.float32, name='learning_rate')
# load backbone model
if cfg.network == "tsn":
model_emb = networks.TSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model_emb = networks.RTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "convtsn":
model_emb = networks.ConvTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model_emb = networks.ConvRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
# multitask loss (verification)
model_ver = networks.PairSim2(n_input=cfg.emb_dim)
#model_ver = networks.PairSim(n_input=cfg.emb_dim)
# get the embedding
if cfg.feat == "sensors":
input_ph = tf.placeholder(tf.float32,
shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet":
input_ph = tf.placeholder(
tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
model_emb.forward(input_ph, dropout_ph)
if cfg.normalized:
embedding = tf.nn.l2_normalize(model_emb.hidden,
axis=-1,
epsilon=1e-10)
else:
embedding = model_emb.hidden
# variable for visualizing the embeddings
emb_var = tf.Variable([0.0], name='embeddings')
set_emb = tf.assign(emb_var, embedding, validate_shape=False)
# calculated for monitoring all-pair embedding distance
diffs = utils.all_diffs_tf(embedding, embedding)
all_dist = utils.cdist_tf(diffs)
tf.summary.histogram('embedding_dists', all_dist)
# split embedding into anchor, positive and negative and calculate triplet loss
anchor, positive, negative = tf.unstack(
tf.reshape(embedding, [-1, 3, cfg.emb_dim]), 3, 1)
metric_loss = networks.triplet_loss(anchor, positive, negative,
cfg.alpha)
# verification loss
pos_pairs = tf.concat(
[tf.expand_dims(anchor, axis=1),
tf.expand_dims(positive, axis=1)],
axis=1)
pos_label = tf.ones((tf.shape(pos_pairs)[0], ), tf.int32)
neg_pairs = tf.concat(
[tf.expand_dims(anchor, axis=1),
tf.expand_dims(negative, axis=1)],
axis=1)
neg_label = tf.zeros((tf.shape(neg_pairs)[0], ), tf.int32)
ver_pairs = tf.concat([pos_pairs, neg_pairs], axis=0)
ver_label = tf.concat([pos_label, neg_label], axis=0)
model_ver.forward(ver_pairs, dropout_ph)
logits = model_ver.logits
pred = tf.argmax(logits, -1)
ver_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=ver_label,
logits=logits))
regularization_loss = tf.reduce_sum(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
total_loss = metric_loss + cfg.lambda_ver * ver_loss + regularization_loss * cfg.lambda_l2
tf.summary.scalar('learning_rate', lr_ph)
train_op = utils.optimize(total_loss, global_step, cfg.optimizer,
lr_ph, tf.global_variables())
saver = tf.train.Saver(max_to_keep=10)
summary_op = tf.summary.merge_all()
# session iterator for session sampling
feat_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
label_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
train_data = session_generator(feat_paths_ph,
label_paths_ph,
sess_per_batch=cfg.sess_per_batch,
num_threads=2,
shuffled=False,
preprocess_func=model_emb.prepare_input)
train_sess_iterator = train_data.make_initializable_iterator()
next_train = train_sess_iterator.get_next()
# prepare validation data
val_sess = []
val_feats = []
val_labels = []
val_boundaries = []
for session in val_set:
session_id = os.path.basename(session[1]).split('_')[0]
eve_batch, lab_batch, boundary = load_data_and_label(
session[0], session[1], model_emb.prepare_input_test
) # use prepare_input_test for testing time
val_feats.append(eve_batch)
val_labels.append(lab_batch)
val_sess.extend([session_id] * eve_batch.shape[0])
val_boundaries.extend(boundary)
val_feats = np.concatenate(val_feats, axis=0)
val_labels = np.concatenate(val_labels, axis=0)
print("Shape of val_feats: ", val_feats.shape)
# generate metadata.tsv for visualize embedding
with open(os.path.join(result_dir, 'metadata_val.tsv'), 'w') as fout:
fout.write('id\tlabel\tsession_id\tstart\tend\n')
for i in range(len(val_sess)):
fout.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format(
i, val_labels[i, 0], val_sess[i], val_boundaries[i][0],
val_boundaries[i][1]))
# Start running the graph
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
summary_writer = tf.summary.FileWriter(result_dir, sess.graph)
with sess.as_default():
sess.run(tf.global_variables_initializer())
# load pretrain model, if needed
if cfg.model_path:
print("Restoring pretrained model: %s" % cfg.model_path)
saver.restore(sess, cfg.model_path)
################## Training loop ##################
epoch = -1
while epoch < cfg.max_epochs - 1:
step = sess.run(global_step, feed_dict=None)
epoch = step // batch_per_epoch
# learning rate schedule, reference: "In defense of Triplet Loss"
if epoch < cfg.static_epochs:
learning_rate = cfg.learning_rate
else:
learning_rate = cfg.learning_rate * \
0.001**((epoch-cfg.static_epochs)/(cfg.max_epochs-cfg.static_epochs))
# prepare data for this epoch
random.shuffle(train_set)
feat_paths = [path[0] for path in train_set]
label_paths = [path[1] for path in train_set]
# reshape a list to list of list
# interesting hacky code from: https://stackoverflow.com/questions/10124751/convert-a-flat-list-to-list-of-list-in-python
feat_paths = list(zip(*[iter(feat_paths)] *
cfg.sess_per_batch))
label_paths = list(
zip(*[iter(label_paths)] * cfg.sess_per_batch))
sess.run(train_sess_iterator.initializer,
feed_dict={
feat_paths_ph: feat_paths,
label_paths_ph: label_paths
})
# for each epoch
batch_count = 1
while True:
try:
# Hierarchical sampling (same as fast rcnn)
start_time_select = time.time()
# First, sample sessions for a batch
eve, se, lab = sess.run(next_train)
select_time1 = time.time() - start_time_select
# Get the embeddings of all events
eve_embedding = np.zeros((eve.shape[0], cfg.emb_dim),
dtype='float32')
for start, end in zip(
range(0, eve.shape[0], cfg.batch_size),
range(cfg.batch_size,
eve.shape[0] + cfg.batch_size,
cfg.batch_size)):
end = min(end, eve.shape[0])
emb = sess.run(embedding,
feed_dict={
input_ph: eve[start:end],
dropout_ph: 1.0
})
eve_embedding[start:end] = emb
# Second, sample triplets within sampled sessions
triplet_input, negative_count = select_triplets_facenet(
eve,
lab,
eve_embedding,
cfg.triplet_per_batch,
cfg.alpha,
metric=cfg.metric)
select_time2 = time.time(
) - start_time_select - select_time1
if triplet_input is not None:
start_time_train = time.time()
# perform training on the selected triplets
err, metric_err, ver_err, y_pred, _, step, summ = sess.run(
[
total_loss, metric_loss, ver_loss, pred,
train_op, global_step, summary_op
],
feed_dict={
input_ph: triplet_input,
dropout_ph: cfg.keep_prob,
lr_ph: learning_rate
})
train_time = time.time() - start_time_train
# calculate accuracy
batch_label = np.hstack(
(np.ones((triplet_input.shape[0] // 3, ),
dtype='int32'),
np.zeros((triplet_input.shape[0] // 3, ),
dtype='int32')))
acc = accuracy_score(batch_label, y_pred)
print ("%s\tEpoch: [%d][%d/%d]\tEvent num: %d\tTriplet num: %d\tSelect_time1: %.3f\tSelect_time2: %.3f\tTrain_time: %.3f\tLoss %.4f" % \
(cfg.name, epoch+1, batch_count, batch_per_epoch, eve.shape[0], triplet_input.shape[0], select_time1, select_time2, train_time, err))
summary = tf.Summary(value=[
tf.Summary.Value(tag="train_loss",
simple_value=err),
tf.Summary.Value(tag="metric_loss",
simple_value=metric_err),
tf.Summary.Value(tag="ver_loss",
simple_value=ver_err),
tf.Summary.Value(tag="acc", simple_value=acc),
tf.Summary.Value(tag="negative_count",
simple_value=negative_count)
])
summary_writer.add_summary(summary, step)
summary_writer.add_summary(summ, step)
batch_count += 1
except tf.errors.OutOfRangeError:
print("Epoch %d done!" % (epoch + 1))
break
# validation on val_set
print("Evaluating on validation set...")
val_embeddings, _ = sess.run([embedding, set_emb],
feed_dict={
input_ph: val_feats,
dropout_ph: 1.0
})
mAP, mPrec = utils.evaluate_simple(val_embeddings, val_labels)
summary = tf.Summary(value=[
tf.Summary.Value(tag="Valiation mAP", simple_value=mAP),
tf.Summary.Value(tag="Validation [email protected]",
simple_value=mPrec)
])
summary_writer.add_summary(summary, step)
# config for embedding visualization
config = projector.ProjectorConfig()
visual_embedding = config.embeddings.add()
visual_embedding.tensor_name = emb_var.name
visual_embedding.metadata_path = os.path.join(
result_dir, 'metadata_val.tsv')
projector.visualize_embeddings(summary_writer, config)
# save model
saver.save(sess,
os.path.join(result_dir, cfg.name + '.ckpt'),
global_step=step)
def main():
cfg = TrainConfig().parse()
print(cfg.name)
result_dir = os.path.join(
cfg.result_root,
cfg.name + '_' + datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
utils.write_configure_to_file(cfg, result_dir)
np.random.seed(seed=cfg.seed)
# prepare dataset
train_session = cfg.train_session
train_set = prepare_dataset(cfg.feature_root, train_session, cfg.feat,
cfg.label_root)
batch_per_epoch = len(train_set) // cfg.sess_per_batch
val_session = cfg.val_session[:3]
val_set = prepare_dataset(cfg.feature_root, val_session, cfg.feat,
cfg.label_root)
# construct the graph
with tf.Graph().as_default():
tf.set_random_seed(cfg.seed)
global_step = tf.Variable(0, trainable=False)
# subtract global_step by 1 if needed (for hard negative mining, keep global_step unchanged)
subtract_global_step_op = tf.assign(global_step, global_step - 1)
lr_ph = tf.placeholder(tf.float32, name='learning_rate')
# load backbone model
if cfg.network == "tsn":
model_emb = networks.TSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model_emb = networks.RTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "convtsn":
model_emb = networks.ConvTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model_emb = networks.ConvRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
model_ver = networks.PairSim(n_input=cfg.emb_dim)
# get the embedding
if cfg.feat == "sensors":
input_ph = tf.placeholder(tf.float32,
shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet":
input_ph = tf.placeholder(
tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
label_ph = tf.placeholder(tf.int32, shape=[None])
model_emb.forward(input_ph, dropout_ph)
embedding = model_emb.hidden
# split embedding into A and B
emb_A, emb_B = tf.unstack(tf.reshape(embedding, [-1, 2, cfg.emb_dim]),
2, 1)
pairs = tf.stack([emb_A, emb_B], axis=1)
model_ver.forward(pairs, dropout_ph)
logits = model_ver.logits
prob = model_ver.prob
pred = tf.argmax(logits, -1)
ver_loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=label_ph,
logits=logits))
regularization_loss = tf.reduce_sum(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
total_loss = ver_loss + regularization_loss * cfg.lambda_l2
tf.summary.scalar('learning_rate', lr_ph)
train_op = utils.optimize(total_loss, global_step, cfg.optimizer,
lr_ph, tf.global_variables())
saver = tf.train.Saver(max_to_keep=10)
summary_op = tf.summary.merge_all()
# session iterator for session sampling
feat_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
label_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
train_data = session_generator(feat_paths_ph,
label_paths_ph,
sess_per_batch=cfg.sess_per_batch,
num_threads=2,
shuffled=False,
preprocess_func=model_emb.prepare_input)
train_sess_iterator = train_data.make_initializable_iterator()
next_train = train_sess_iterator.get_next()
# prepare validation data
val_sess = []
val_feats = []
val_labels = []
val_boundaries = []
for session in val_set:
session_id = os.path.basename(session[1]).split('_')[0]
eve_batch, lab_batch, boundary = load_data_and_label(
session[0], session[1], model_emb.prepare_input_test
) # use prepare_input_test for testing time
val_feats.append(eve_batch)
val_labels.append(lab_batch)
val_sess.extend([session_id] * eve_batch.shape[0])
val_boundaries.extend(boundary)
val_feats = np.concatenate(val_feats, axis=0)
val_labels = np.concatenate(val_labels, axis=0)
# generate metadata.tsv for visualize embedding
with open(os.path.join(result_dir, 'metadata_val.tsv'), 'w') as fout:
fout.write('id\tlabel\tsession_id\tstart\tend\n')
for i in range(len(val_sess)):
fout.write('{0}\t{1}\t{2}\t{3}\t{4}\n'.format(
i, val_labels[i, 0], val_sess[i], val_boundaries[i][0],
val_boundaries[i][1]))
val_idx, val_labels = random_pairs(val_labels, 1000000, test=True)
val_feats = val_feats[val_idx]
val_labels = np.asarray(val_labels, dtype='int32')
print("Shape of val_feats: ", val_feats.shape)
# Start running the graph
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
summary_writer = tf.summary.FileWriter(result_dir, sess.graph)
with sess.as_default():
sess.run(tf.global_variables_initializer())
# load pretrain model, if needed
if cfg.model_path:
print("Restoring pretrained model: %s" % cfg.model_path)
saver.restore(sess, cfg.model_path)
################## Training loop ##################
epoch = -1
while epoch < cfg.max_epochs - 1:
step = sess.run(global_step, feed_dict=None)
epoch = step // batch_per_epoch
# learning rate schedule, reference: "In defense of Triplet Loss"
if epoch < cfg.static_epochs:
learning_rate = cfg.learning_rate
else:
learning_rate = cfg.learning_rate * \
0.001**((epoch-cfg.static_epochs)/(cfg.max_epochs-cfg.static_epochs))
# prepare data for this epoch
random.shuffle(train_set)
feat_paths = [path[0] for path in train_set]
label_paths = [path[1] for path in train_set]
# reshape a list to list of list
# interesting hacky code from: https://stackoverflow.com/questions/10124751/convert-a-flat-list-to-list-of-list-in-python
feat_paths = list(zip(*[iter(feat_paths)] *
cfg.sess_per_batch))
label_paths = list(
zip(*[iter(label_paths)] * cfg.sess_per_batch))
sess.run(train_sess_iterator.initializer,
feed_dict={
feat_paths_ph: feat_paths,
label_paths_ph: label_paths
})
# for each epoch
batch_count = 1
while True:
try:
# Hierarchical sampling (same as fast rcnn)
start_time_select = time.time()
# First, sample sessions for a batch
eve, se, lab = sess.run(next_train)
select_time1 = time.time() - start_time_select
# select pairs for training
pair_idx, train_labels = random_pairs(
lab, cfg.batch_size, cfg.num_negative)
train_input = eve[pair_idx]
train_labels = np.asarray(train_labels, dtype='int32')
select_time2 = time.time(
) - start_time_select - select_time1
start_time_train = time.time()
# perform training on the selected pairs
err, y_pred, y_prob, _, step, summ = sess.run(
[
total_loss, pred, prob, train_op, global_step,
summary_op
],
feed_dict={
input_ph: train_input,
label_ph: train_labels,
dropout_ph: cfg.keep_prob,
lr_ph: learning_rate
})
acc = accuracy_score(train_labels, y_pred)
negative_count = 0
if epoch >= cfg.negative_epochs:
hard_idx, hard_labels, negative_count = hard_pairs(
train_labels, y_prob, 0.5)
if negative_count > 0:
hard_input = train_input[hard_idx]
hard_labels = np.asarray(hard_labels,
dtype='int32')
step = sess.run(subtract_global_step_op)
hard_err, y_pred, _, step = sess.run(
[total_loss, pred, train_op, global_step],
feed_dict={
input_ph: hard_input,
label_ph: hard_labels,
dropout_ph: cfg.keep_prob,
lr_ph: learning_rate
})
train_time = time.time() - start_time_train
print ("%s\tEpoch: [%d][%d/%d]\tEvent num: %d\tSelect_time1: %.3f\tSelect_time2: %.3f\tTrain_time: %.3f\tLoss: %.4f" % \
(cfg.name, epoch+1, batch_count, batch_per_epoch, eve.shape[0], select_time1, select_time2, train_time, err))
summary = tf.Summary(value=[
tf.Summary.Value(tag="train_loss",
simple_value=err),
tf.Summary.Value(tag="acc", simple_value=acc),
tf.Summary.Value(tag="negative_count",
simple_value=negative_count)
])
summary_writer.add_summary(summary, step)
summary_writer.add_summary(summ, step)
batch_count += 1
except tf.errors.OutOfRangeError:
print("Epoch %d done!" % (epoch + 1))
break
# validation on val_set
print("Evaluating on validation set...")
val_err, val_pred, val_prob = sess.run(
[total_loss, pred, prob],
feed_dict={
input_ph: val_feats,
label_ph: val_labels,
dropout_ph: 1.0
})
val_acc = accuracy_score(val_labels, val_pred)
summary = tf.Summary(value=[
tf.Summary.Value(tag="Valiation acc",
simple_value=val_acc),
tf.Summary.Value(tag="Validation loss",
simple_value=val_err)
])
summary_writer.add_summary(summary, step)
# save model
saver.save(sess,
os.path.join(result_dir, cfg.name + '.ckpt'),
global_step=step)
# print log for analysis
with open(os.path.join(result_dir, 'val_results.txt'), 'w') as fout:
fout.write("acc = %.4f\n" % val_acc)
fout.write("label\tprob_0\tprob_1\tA_idx\tB_idx\n")
for i in range(val_prob.shape[0]):
fout.write("%d\t%.4f\t%.4f\t%d\t%d\n" %
(val_labels[i], val_prob[i, 0], val_prob[i, 1],
val_idx[2 * i], val_idx[2 * i + 1]))
def main():
cfg = EvalConfig().parse()
print ("Evaluate the model: {}".format(os.path.basename(cfg.model_path)))
np.random.seed(seed=cfg.seed)
test_session = cfg.test_session
test_set = prepare_dataset(cfg.feature_root, test_session, cfg.feat, cfg.label_root, cfg.label_type)
# load backbone model
if cfg.network == "tsn":
model = networks.TSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model = networks.RTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim, n_input=cfg.n_input)
elif cfg.network == "convtsn":
model = networks.ConvTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model = networks.ConvRTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim, n_h=cfg.n_h, n_w=cfg.n_w, n_C=cfg.n_C, n_input=cfg.n_input)
elif cfg.network == "seq2seqtsn":
model = networks.Seq2seqTSN(n_seg=cfg.num_seg, n_input=n_input, emb_dim=cfg.emb_dim, reverse=cfg.reverse)
elif cfg.network == "convbirtsn":
model = networks.ConvBiRTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
# get the embedding
if cfg.feat == "sensors" or cfg.feat == "segment":
input_ph = tf.placeholder(tf.float32, shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet" or cfg.feat == "segment_down":
input_ph = tf.placeholder(tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
model.forward(input_ph, dropout_ph)
embedding = tf.nn.l2_normalize(model.hidden, axis=1, epsilon=1e-10, name='embedding')
# Testing
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
# restore variables
var_list = {}
for v in tf.global_variables():
var_list[cfg.variable_name+v.op.name] = v
saver = tf.train.Saver(var_list)
with sess.as_default():
sess.run(tf.global_variables_initializer())
# load the model (note that model_path already contains snapshot number
saver.restore(sess, cfg.model_path)
duration = 0.0
eve_embeddings = []
labels = []
for i, session in enumerate(test_set):
session_id = os.path.basename(session[1]).split('_')[0]
print ("{0} / {1}: {2}".format(i, len(test_set), session_id))
# eve_batch, lab_batch, _ = load_data_and_label(session[0], session[1], mean_pool_input, transfer=cfg.transfer) # use prepare_input_test for testing time
eve_batch, lab_batch, _ = load_data_and_label(session[0], session[1], model.prepare_input_test, transfer=cfg.transfer) # use prepare_input_test for testing time
start_time = time.time()
emb = sess.run(embedding, feed_dict={input_ph: eve_batch, dropout_ph: 1.0})
# emb = eve_batch
duration += time.time() - start_time
eve_embeddings.append(emb)
labels.append(lab_batch)
eve_embeddings = np.concatenate(eve_embeddings, axis=0)
labels = np.concatenate(labels, axis=0)
# evaluate the results
mAP, mAP_event, mPrec, confusion, count, recall = evaluate(eve_embeddings, np.squeeze(labels))
mAP_macro = 0.0
for key in mAP_event:
mAP_macro += mAP_event[key]
mAP_macro /= len(list(mAP_event.keys()))
print ("%d events with dim %d for evaluation, run time: %.3f." % (labels.shape[0], eve_embeddings.shape[1], duration))
print ("mAP = {:.4f}".format(mAP))
print ("mAP_macro = {:.4f}".format(mAP_macro))
print ("[email protected] = {:.4f}".format(mPrec))
print ("Recall@1 = {:.4f}".format(recall[0]))
print ("Recall@2 = {:.4f}".format(recall[1]))
print ("Recall@4 = {:.4f}".format(recall[2]))
print ("Recall@8 = {:.4f}".format(recall[3]))
print ("Recall@16 = {:.4f}".format(recall[4]))
print ("Recall@32 = {:.4f}".format(recall[5]))
if cfg.label_type == 'goal':
num2labels = honda_num2labels
elif cfg.label_type == 'stimuli':
num2labels = stimuli_num2labels
keys = confusion['labels']
for i, key in enumerate(keys):
if key not in mAP_event:
continue
print ("Event {0}: {1}, ratio = {2:.4f}, mAP = {3:.4f}, [email protected] = {4:.4f}".format(
key,
num2labels[key],
float(count[i]) / np.sum(count),
mAP_event[key],
confusion['confusion_matrix'][i, i]))
# store results
pkl.dump({"mAP": mAP,
"mAP_macro": mAP_macro,
"mAP_event": mAP_event,
"mPrec": mPrec,
"confusion": confusion,
"count": count,
"recall": recall},
open(os.path.join(os.path.dirname(cfg.model_path), "results.pkl"), 'wb'))
def main():
cfg = TrainConfig().parse()
print (cfg.name)
np.random.seed(seed=cfg.seed)
# prepare dataset
val_session = cfg.val_session
val_set = prepare_dataset(cfg.feature_root, val_session, cfg.feat, cfg.label_root)
# construct the graph
with tf.Graph().as_default():
tf.set_random_seed(cfg.seed)
# load backbone model
if cfg.network == "tsn":
model_emb = networks.TSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model_emb = networks.RTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim, n_input=cfg.n_input)
elif cfg.network == "convtsn":
model_emb = networks.ConvTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model_emb = networks.ConvRTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim, n_h=cfg.n_h, n_w=cfg.n_w, n_C=cfg.n_C, n_input=cfg.n_input)
elif cfg.network == "convbirtsn":
model_emb = networks.ConvBiRTSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
model_ver = networks.PDDM(n_input=cfg.emb_dim)
# get the embedding
if cfg.feat == "sensors" or cfg.feat == "segment":
input_ph = tf.placeholder(tf.float32, shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet" or cfg.feat == "segment_down":
input_ph = tf.placeholder(tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
model_emb.forward(input_ph, dropout_ph)
if cfg.normalized:
embedding = tf.nn.l2_normalize(model_emb.hidden, axis=-1, epsilon=1e-10)
else:
embedding = model_emb.hidden
# split the embedding
emb_A = embedding[:(tf.shape(embedding)[0]//2)]
emb_B = embedding[(tf.shape(embedding)[0]//2):]
model_ver.forward(tf.stack((emb_A, emb_B), axis=1))
pddm = model_ver.prob
restore_saver = tf.train.Saver()
# prepare validation data
val_sess = []
val_feats = []
val_labels = []
val_boundaries = []
for session in val_set:
session_id = os.path.basename(session[1]).split('_')[0]
eve_batch, lab_batch, boundary = load_data_and_label(session[0], session[-1], model_emb.prepare_input_test) # use prepare_input_test for testing time
val_feats.append(eve_batch)
val_labels.append(lab_batch)
val_sess.extend([session_id]*eve_batch.shape[0])
val_boundaries.extend(boundary)
val_feats = np.concatenate(val_feats, axis=0)
val_labels = np.concatenate(val_labels, axis=0)
print ("Shape of val_feats: ", val_feats.shape)
# Start running the graph
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
with sess.as_default():
sess.run(tf.global_variables_initializer())
print ("Restoring pretrained model: %s" % cfg.model_path)
restore_saver.restore(sess, cfg.model_path)
fout_fp = open(os.path.join(os.path.dirname(cfg.model_path), 'val_fp.txt'), 'w')
fout_fn = open(os.path.join(os.path.dirname(cfg.model_path), 'val_fn.txt'), 'w')
fout_fp.write('id_A\tid_B\tlabel_A\tlabel_B\tprob_0\tprob_1\n')
fout_fn.write('id_A\tid_B\tlabel_A\tlabel_B\tprob_0\tprob_1\n')
count = 0
count_high = 0 # high confidence (0.9)
count_fp = 0
count_fn = 0
for i in range(val_feats.shape[0]):
print ("%d/%d" % (i,val_feats.shape[0]))
if val_labels[i] == 0:
continue
A_input = np.tile(val_feats[i], (val_feats.shape[0]-i,1,1))
AB_input = np.vstack((A_input, val_feats[i:])) # concatenate along axis 0
temp_prob = sess.run(pddm, feed_dict={input_ph: AB_input, dropout_ph:1.0})
count += temp_prob.shape[0]
threshold = 0.8
for j in range(temp_prob.shape[0]):
if temp_prob[j, 0] > threshold or temp_prob[j, 1] > threshold:
count_high += 1
if val_labels[i] == val_labels[i+j] and temp_prob[j, 0]>threshold:
count_fn += 1
fout_fn.write("{}\t{}\t{}\t{}\t{:.4f}\t{:.4f}\n".format(i,i+j,val_labels[i,0],val_labels[i+j,0],temp_prob[j,0],temp_prob[j,1]))
elif val_labels[i] != val_labels[i+j] and temp_prob[j,1] > threshold:
count_fp += 1
fout_fp.write("{}\t{}\t{}\t{}\t{:.4f}\t{:.4f}\n".format(i,i+j,val_labels[i,0],val_labels[i+j,0],temp_prob[j,0],temp_prob[j,1]))
fout_fp.close()
fout_fn.close()
print ("High confidence (%f) pairs ratio: %.4f" % (threshold, float(count_high)/count))
print ("Consistent pairs ratio: %.4f" % (float(count_high-count_fp-count_fn)/count_high))
print ("False positive pairs ratio: %.4f" % (float(count_fp)/count_high))
print ("False negative pairs ratio: %.4f" % (float(count_fn)/count_high))
def main():
cfg = TrainConfig().parse()
print(cfg.name)
result_dir = os.path.join(
cfg.result_root,
cfg.name + '_' + datetime.strftime(datetime.now(), '%Y%m%d-%H%M%S'))
if not os.path.isdir(result_dir):
os.makedirs(result_dir)
utils.write_configure_to_file(cfg, result_dir)
np.random.seed(seed=cfg.seed)
# prepare dataset
train_session = cfg.train_session
train_set = prepare_dataset(cfg.feature_root, train_session, cfg.feat,
cfg.label_root)
train_set = train_set[:cfg.label_num]
batch_per_epoch = len(train_set) // cfg.sess_per_batch
val_session = cfg.val_session
val_set = prepare_dataset(cfg.feature_root, val_session, cfg.feat,
cfg.label_root)
# construct the graph
with tf.Graph().as_default():
tf.set_random_seed(cfg.seed)
global_step = tf.Variable(0, trainable=False)
lr_ph = tf.placeholder(tf.float32, name='learning_rate')
# load backbone model
if cfg.network == "tsn":
model_emb = networks.TSN(n_seg=cfg.num_seg, emb_dim=cfg.emb_dim)
elif cfg.network == "rtsn":
model_emb = networks.RTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim,
n_input=cfg.n_input)
elif cfg.network == "convtsn":
model_emb = networks.ConvTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
elif cfg.network == "convrtsn":
model_emb = networks.ConvRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim,
n_h=cfg.n_h,
n_w=cfg.n_w,
n_C=cfg.n_C,
n_input=cfg.n_input)
elif cfg.network == "convbirtsn":
model_emb = networks.ConvBiRTSN(n_seg=cfg.num_seg,
emb_dim=cfg.emb_dim)
else:
raise NotImplementedError
model_ver = networks.PDDM(n_input=cfg.emb_dim)
# get the embedding
if cfg.feat == "sensors" or cfg.feat == "segment":
input_ph = tf.placeholder(tf.float32,
shape=[None, cfg.num_seg, None])
elif cfg.feat == "resnet" or cfg.feat == "segment_down":
input_ph = tf.placeholder(
tf.float32, shape=[None, cfg.num_seg, None, None, None])
dropout_ph = tf.placeholder(tf.float32, shape=[])
model_emb.forward(input_ph, dropout_ph)
if cfg.normalized:
embedding = tf.nn.l2_normalize(model_emb.hidden,
axis=-1,
epsilon=1e-10)
else:
embedding = model_emb.hidden
# variable for visualizing the embeddings
emb_var = tf.Variable([0.0], name='embeddings')
set_emb = tf.assign(emb_var, embedding, validate_shape=False)
# calculated for monitoring all-pair embedding distance
diffs = utils.all_diffs_tf(embedding, embedding)
all_dist = utils.cdist_tf(diffs)
tf.summary.histogram('embedding_dists', all_dist)
# split embedding into anchor, positive and negative and calculate triplet loss
anchor, positive, negative = tf.unstack(
tf.reshape(embedding, [-1, 3, cfg.emb_dim]), 3, 1)
metric_loss = networks.triplet_loss(anchor, positive, negative,
cfg.alpha)
model_ver.forward(tf.stack((anchor, positive), axis=1))
pddm_ap = model_ver.prob[:, 0]
model_ver.forward(tf.stack((anchor, negative), axis=1))
pddm_an = model_ver.prob[:, 0]
pddm_loss = tf.reduce_mean(
tf.maximum(tf.add(tf.subtract(pddm_ap, pddm_an), 0.6), 0.0), 0)
regularization_loss = tf.reduce_sum(
tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES))
total_loss = pddm_loss + 0.5 * metric_loss + regularization_loss * cfg.lambda_l2
tf.summary.scalar('learning_rate', lr_ph)
train_op = utils.optimize(total_loss, global_step, cfg.optimizer,
lr_ph, tf.global_variables())
saver = tf.train.Saver(max_to_keep=10)
summary_op = tf.summary.merge_all()
# session iterator for session sampling
feat_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
label_paths_ph = tf.placeholder(tf.string,
shape=[None, cfg.sess_per_batch])
train_data = session_generator(feat_paths_ph,
label_paths_ph,
sess_per_batch=cfg.sess_per_batch,
num_threads=2,
shuffled=False,
preprocess_func=model_emb.prepare_input)
train_sess_iterator = train_data.make_initializable_iterator()
next_train = train_sess_iterator.get_next()
# prepare validation data
val_feats = []
val_labels = []
for session in val_set:
eve_batch, lab_batch, _ = load_data_and_label(
session[0], session[1], model_emb.prepare_input_test
) # use prepare_input_test for testing time
val_feats.append(eve_batch)
val_labels.append(lab_batch)
val_feats = np.concatenate(val_feats, axis=0)
val_labels = np.concatenate(val_labels, axis=0)
print("Shape of val_feats: ", val_feats.shape)
# generate metadata.tsv for visualize embedding
with open(os.path.join(result_dir, 'metadata_val.tsv'), 'w') as fout:
for v in val_labels:
fout.write('%d\n' % int(v))
# Start running the graph
if cfg.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = cfg.gpu
gpu_options = tf.GPUOptions(allow_growth=True)
sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))
summary_writer = tf.summary.FileWriter(result_dir, sess.graph)
with sess.as_default():
sess.run(tf.global_variables_initializer())
# load pretrain model, if needed
if cfg.model_path:
print("Restoring pretrained model: %s" % cfg.model_path)
saver.restore(sess, cfg.model_path)
################## Training loop ##################
epoch = -1
while epoch < cfg.max_epochs - 1:
step = sess.run(global_step, feed_dict=None)
epoch = step // batch_per_epoch
# learning rate schedule, reference: "In defense of Triplet Loss"
if epoch < cfg.static_epochs:
learning_rate = cfg.learning_rate
else:
learning_rate = cfg.learning_rate * \
0.001**((epoch-cfg.static_epochs)/(cfg.max_epochs-cfg.static_epochs))
# prepare data for this epoch
random.shuffle(train_set)
feat_paths = [path[0] for path in train_set]
label_paths = [path[1] for path in train_set]
# reshape a list to list of list
# interesting hacky code from: https://stackoverflow.com/questions/10124751/convert-a-flat-list-to-list-of-list-in-python
feat_paths = list(zip(*[iter(feat_paths)] *
cfg.sess_per_batch))
label_paths = list(
zip(*[iter(label_paths)] * cfg.sess_per_batch))
sess.run(train_sess_iterator.initializer,
feed_dict={
feat_paths_ph: feat_paths,
label_paths_ph: label_paths
})
# for each epoch
batch_count = 1
while True:
try:
# Hierarchical sampling (same as fast rcnn)
start_time_select = time.time()
# First, sample sessions for a batch
eve, se, lab = sess.run(next_train)
select_time1 = time.time() - start_time_select
# Get the similarity of all events
sim_prob = np.zeros((eve.shape[0], eve.shape[0]),
dtype='float32') * np.nan
comb = list(
itertools.combinations(range(eve.shape[0]), 2))
for start, end in zip(
range(0, len(comb), cfg.batch_size),
range(cfg.batch_size,
len(comb) + cfg.batch_size,
cfg.batch_size)):
end = min(end, len(comb))
comb_idx = []
for c in comb[start:end]:
comb_idx.extend([c[0], c[1], c[1]])
emb = sess.run(pddm_ap,
feed_dict={
input_ph: eve[comb_idx],
dropout_ph: 1.0
})
for i in range(emb.shape[0]):
sim_prob[comb[start + i][0],
comb[start + i][1]] = emb[i]
sim_prob[comb[start + i][1],
comb[start + i][0]] = emb[i]
# Second, sample triplets within sampled sessions
triplet_selected, active_count = utils.select_triplets_facenet(
lab, sim_prob, cfg.triplet_per_batch, cfg.alpha)
select_time2 = time.time(
) - start_time_select - select_time1
start_time_train = time.time()
triplet_input_idx = [
idx for triplet in triplet_selected
for idx in triplet
]
triplet_input = eve[triplet_input_idx]
# perform training on the selected triplets
err, _, step, summ = sess.run(
[total_loss, train_op, global_step, summary_op],
feed_dict={
input_ph: triplet_input,
dropout_ph: cfg.keep_prob,
lr_ph: learning_rate
})
train_time = time.time() - start_time_train
print ("%s\tEpoch: [%d][%d/%d]\tEvent num: %d\tTriplet num: %d\tSelect_time1: %.3f\tSelect_time2: %.3f\tTrain_time: %.3f\tLoss %.4f" % \
(cfg.name, epoch+1, batch_count, batch_per_epoch, eve.shape[0], triplet_input.shape[0]//3, select_time1, select_time2, train_time, err))
summary = tf.Summary(value=[
tf.Summary.Value(tag="train_loss",
simple_value=err),
tf.Summary.Value(tag="active_count",
simple_value=active_count),
tf.Summary.Value(
tag="triplet_num",
simple_value=triplet_input.shape[0] // 3)
])
summary_writer.add_summary(summary, step)
summary_writer.add_summary(summ, step)
batch_count += 1
except tf.errors.OutOfRangeError:
print("Epoch %d done!" % (epoch + 1))
break
# validation on val_set
print("Evaluating on validation set...")
val_embeddings, _ = sess.run([embedding, set_emb],
feed_dict={
input_ph: val_feats,
dropout_ph: 1.0
})
mAP, mPrec = utils.evaluate_simple(val_embeddings, val_labels)
val_sim_prob = np.zeros(
(val_feats.shape[0], val_feats.shape[0]),
dtype='float32') * np.nan
val_comb = list(
itertools.combinations(range(val_feats.shape[0]), 2))
for start, end in zip(
range(0, len(val_comb), cfg.batch_size),
range(cfg.batch_size,
len(val_comb) + cfg.batch_size, cfg.batch_size)):
end = min(end, len(val_comb))
comb_idx = []
for c in val_comb[start:end]:
comb_idx.extend([c[0], c[1], c[1]])
emb = sess.run(pddm_ap,
feed_dict={
input_ph: val_feats[comb_idx],
dropout_ph: 1.0
})
for i in range(emb.shape[0]):
val_sim_prob[val_comb[start + i][0],
val_comb[start + i][1]] = emb[i]
val_sim_prob[val_comb[start + i][1],
val_comb[start + i][0]] = emb[i]
mAP_PDDM = 0.0
count = 0
for i in range(val_labels.shape[0]):
if val_labels[i] > 0:
temp_labels = np.delete(val_labels, i, 0)
temp = np.delete(val_sim_prob, i, 1)
mAP_PDDM += average_precision_score(
np.squeeze(temp_labels == val_labels[i, 0]),
np.squeeze(1 - temp[i]))
count += 1
mAP_PDDM /= count
summary = tf.Summary(value=[
tf.Summary.Value(tag="Validation mAP", simple_value=mAP),
tf.Summary.Value(tag="Validation mAP_PDDM",
simple_value=mAP_PDDM),
tf.Summary.Value(tag="Validation [email protected]",
simple_value=mPrec)
])
summary_writer.add_summary(summary, step)
print("Epoch: [%d]\tmAP: %.4f\tmPrec: %.4f\tmAP_PDDM: %.4f" %
(epoch + 1, mAP, mPrec, mAP_PDDM))
# config for embedding visualization
config = projector.ProjectorConfig()
visual_embedding = config.embeddings.add()
visual_embedding.tensor_name = emb_var.name
visual_embedding.metadata_path = os.path.join(
result_dir, 'metadata_val.tsv')
projector.visualize_embeddings(summary_writer, config)
# save model
saver.save(sess,
os.path.join(result_dir, cfg.name + '.ckpt'),
global_step=step)