From 35180690b232bcb116db9ff949a70ce391b1e3ca Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Thu, 5 Oct 2017 15:18:13 +0200
Subject: [PATCH] Implemented center loss
---
bob/learn/tensorflow/loss/BaseLoss.py | 54 ++++++++-----------
.../tensorflow/trainers/SiameseTrainer.py | 1 -
bob/learn/tensorflow/trainers/Trainer.py | 49 ++++++++++-------
3 files changed, 51 insertions(+), 53 deletions(-)
diff --git a/bob/learn/tensorflow/loss/BaseLoss.py b/bob/learn/tensorflow/loss/BaseLoss.py
index e71f0b73..8c27710d 100644
--- a/bob/learn/tensorflow/loss/BaseLoss.py
+++ b/bob/learn/tensorflow/loss/BaseLoss.py
@@ -61,7 +61,7 @@ class MeanSoftMaxLossCenterLoss(object):
Mean softmax loss. Basically it wrapps the function tf.nn.sparse_softmax_cross_entropy_with_logits.
"""
- def __init__(self, name="loss", add_regularization_losses=True, alpha=0.9, factor=0.01, n_classes=10):
+ def __init__(self, name="loss", alpha=0.9, factor=0.01, n_classes=10):
"""
Constructor
@@ -73,46 +73,36 @@ class MeanSoftMaxLossCenterLoss(object):
"""
self.name = name
- self.add_regularization_losses = add_regularization_losses
self.n_classes = n_classes
self.alpha = alpha
self.factor = factor
- def append_center_loss(self, features, label):
- nrof_features = features.get_shape()[1]
-
- centers = tf.get_variable('centers', [self.n_classes, nrof_features], dtype=tf.float32,
- initializer=tf.constant_initializer(0), trainable=False)
-
- label = tf.reshape(label, [-1])
- centers_batch = tf.gather(centers, label)
- diff = (1 - self.alpha) * (centers_batch - features)
- centers = tf.scatter_sub(centers, label, diff)
- loss = tf.reduce_mean(tf.square(features - centers_batch))
-
- return loss
-
-
- def __call__(self, logits_prelogits, label):
-
- #TODO: Test the dictionary
-
- logits = logits_prelogits['logits']
-
+ def __call__(self, logits, prelogits, label):
# Cross entropy
- loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
- logits=logits, labels=label), name=self.name)
+ with tf.variable_scope('cross_entropy_loss'):
+ loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
+ logits=logits, labels=label), name=self.name)
- # Appending center loss
- prelogits = logits_prelogits['prelogits']
- center_loss = self.append_center_loss(prelogits, label)
- tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, center_loss * self.factor)
+ # Appending center loss
+ with tf.variable_scope('center_loss'):
+ n_features = prelogits.get_shape()[1]
+
+ centers = tf.get_variable('centers', [self.n_classes, n_features], dtype=tf.float32,
+ initializer=tf.constant_initializer(0), trainable=False)
+
+ label = tf.reshape(label, [-1])
+ centers_batch = tf.gather(centers, label)
+ diff = (1 - self.alpha) * (centers_batch - prelogits)
+ centers = tf.scatter_sub(centers, label, diff)
+ center_loss = tf.reduce_mean(tf.square(prelogits - centers_batch))
+ tf.add_to_collection(tf.GraphKeys.REGULARIZATION_LOSSES, center_loss * self.factor)
# Adding the regularizers in the loss
- if self.add_regularization_losses:
+ with tf.variable_scope('total_loss'):
regularization_losses = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES)
- loss = tf.add_n([loss] + regularization_losses, name='total_loss')
+ total_loss = tf.add_n([loss] + regularization_losses, name='total_loss')
- return loss
+ return total_loss, centers
+
diff --git a/bob/learn/tensorflow/trainers/SiameseTrainer.py b/bob/learn/tensorflow/trainers/SiameseTrainer.py
index be16b4fe..300b8a06 100644
--- a/bob/learn/tensorflow/trainers/SiameseTrainer.py
+++ b/bob/learn/tensorflow/trainers/SiameseTrainer.py
@@ -219,7 +219,6 @@ class SiameseTrainer(Trainer):
return feed_dict
def fit(self, step):
-
feed_dict = self.get_feed_dict(self.train_data_shuffler)
_, l, bt_class, wt_class, lr, summary = self.session.run([
self.optimizer,
diff --git a/bob/learn/tensorflow/trainers/Trainer.py b/bob/learn/tensorflow/trainers/Trainer.py
index 6631ca7b..8b7ebc2e 100644
--- a/bob/learn/tensorflow/trainers/Trainer.py
+++ b/bob/learn/tensorflow/trainers/Trainer.py
@@ -177,7 +177,7 @@ class Trainer(object):
self.compute_validation(step)
# Taking snapshot
- if step % self.snapshot == 0:
+ if step % self.snapshot == 0:
logger.info("Taking snapshot")
path = os.path.join(self.temp_dir, 'model_snapshot{0}.ckp'.format(step))
self.saver.save(self.session, path, global_step=step)
@@ -214,6 +214,7 @@ class Trainer(object):
# Learning rate
learning_rate=None,
+ prelogits=None
):
"""
@@ -229,7 +230,6 @@ class Trainer(object):
learning_rate: Learning rate
"""
-
# Getting the pointer to the placeholders
self.data_ph = self.train_data_shuffler("data", from_queue=True)
self.label_ph = self.train_data_shuffler("label", from_queue=True)
@@ -237,8 +237,13 @@ class Trainer(object):
self.graph = graph
self.loss = loss
- # Attaching the loss in the graph
- self.predictor = self.loss(self.graph, self.label_ph)
+ # TODO: SPECIFIC HACK FOR THE CENTER LOSS. I NEED TO FIND A CLEAN SOLUTION FOR THAT
+ self.centers = None
+ if prelogits is not None:
+ tf.add_to_collection("prelogits", prelogits)
+ self.predictor, self.centers = self.loss(self.graph, prelogits, self.label_ph)
+ else:
+ self.predictor = self.loss(self.graph, self.label_ph)
self.optimizer_class = optimizer
self.learning_rate = learning_rate
@@ -257,11 +262,8 @@ class Trainer(object):
# SAving some variables
tf.add_to_collection("global_step", self.global_step)
- if isinstance(self.graph, dict):
- tf.add_to_collection("graph", self.graph['logits'])
- tf.add_to_collection("prelogits", self.graph['prelogits'])
- else:
- tf.add_to_collection("graph", self.graph)
+
+ tf.add_to_collection("graph", self.graph)
tf.add_to_collection("predictor", self.predictor)
@@ -273,6 +275,10 @@ class Trainer(object):
tf.add_to_collection("summaries_train", self.summaries_train)
+ # Appending histograms for each trainable variables
+ for var in tf.trainable_variables():
+ tf.summary.histogram(var.op.name, var)
+
# Same business with the validation
if self.validation_data_shuffler is not None:
self.validation_data_ph = self.validation_data_shuffler("data", from_queue=True)
@@ -280,9 +286,9 @@ class Trainer(object):
self.validation_graph = validation_graph
- if self.validate_with_embeddings:
+ if self.validate_with_embeddings:
self.validation_predictor = self.validation_graph
- else:
+ else:
self.validation_predictor = self.loss(self.validation_graph, self.validation_label_ph)
self.summaries_validation = self.create_general_summary(self.validation_predictor, self.validation_graph, self.validation_label_ph)
@@ -318,13 +324,13 @@ class Trainer(object):
self.saver = tf.train.import_meta_graph(file_name, clear_devices=clear_devices)
self.saver.restore(self.session, tf.train.latest_checkpoint(os.path.dirname(file_name)))
- def load_variables_from_external_model(self, file_name, var_list):
+ def load_variables_from_external_model(self, checkpoint_path, var_list):
"""
Load a set of variables from a given model and update them in the current one
** Parameters **
- file_name:
+ checkpoint_path:
Name of the tensorflow model to be loaded
var_list:
List of variables to be loaded. A tensorflow exception will be raised in case the variable does not exists
@@ -338,7 +344,7 @@ class Trainer(object):
tf_varlist += tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope=v)
saver = tf.train.Saver(tf_varlist)
- saver.restore(self.session, file_name)
+ saver.restore(self.session, tf.train.latest_checkpoint(checkpoint_path))
def create_network_from_file(self, file_name, clear_devices=True):
"""
@@ -406,8 +412,14 @@ class Trainer(object):
"""
if self.train_data_shuffler.prefetch:
- _, l, lr, summary = self.session.run([self.optimizer, self.predictor,
- self.learning_rate, self.summaries_train])
+ # TODO: SPECIFIC HACK FOR THE CENTER LOSS. I NEED TO FIND A CLEAN SOLUTION FOR THAT
+ if self.centers is None:
+ _, l, lr, summary = self.session.run([self.optimizer, self.predictor,
+ self.learning_rate, self.summaries_train])
+ else:
+ _, l, lr, summary, _ = self.session.run([self.optimizer, self.predictor,
+ self.learning_rate, self.summaries_train, self.centers])
+
else:
feed_dict = self.get_feed_dict(self.train_data_shuffler)
_, l, lr, summary = self.session.run([self.optimizer, self.predictor,
@@ -473,10 +485,7 @@ class Trainer(object):
tf.summary.scalar('lr', self.learning_rate)
# Computing accuracy
- if isinstance(output, dict):
- correct_prediction = tf.equal(tf.argmax(output['logits'], 1), label)
- else:
- correct_prediction = tf.equal(tf.argmax(output, 1), label)
+ correct_prediction = tf.equal(tf.argmax(output, 1), label)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
tf.summary.scalar('accuracy', accuracy)
--
GitLab