diff --git a/bob/learn/tensorflow/datashuffler/OnlineSampling.py b/bob/learn/tensorflow/datashuffler/OnlineSampling.py
index 0f630999918191572dbdc642ea5314e84f64572f..daf989de045050087075ea2e4602642fd74ec3df 100644
--- a/bob/learn/tensorflow/datashuffler/OnlineSampling.py
+++ b/bob/learn/tensorflow/datashuffler/OnlineSampling.py
@@ -42,9 +42,14 @@ class OnLineSampling(object):
# Feeding the placeholder
if self.feature_placeholder is None:
- self.feature_placeholder = tf.placeholder(tf.float32, shape=data.shape, name="feature")
+ shape = tuple([None] + list(data.shape[1:]))
+ self.feature_placeholder = tf.placeholder(tf.float32, shape=shape, name="feature")
self.graph = self.feature_extractor.compute_graph(self.feature_placeholder, self.feature_extractor.default_feature_layer,
training=False)
+ #if self.feature_placeholder.get_shape().as_list() != list(data.shape):
+ #tf.reshape(self.feature_placeholder, tf.pack([data.shape]))
+ #self.feature_placeholder.set_shape(data.shape)
+
feed_dict = {self.feature_placeholder: data}
return self.session.run([self.graph], feed_dict=feed_dict)[0]
\ No newline at end of file
diff --git a/bob/learn/tensorflow/datashuffler/TripletWithSelectionDisk.py b/bob/learn/tensorflow/datashuffler/TripletWithSelectionDisk.py
index 7691c9282b3e95c0920f13a57d519311fdff992c..0b1fffff54c53d4a86b00e2d3807b7257e169040 100644
--- a/bob/learn/tensorflow/datashuffler/TripletWithSelectionDisk.py
+++ b/bob/learn/tensorflow/datashuffler/TripletWithSelectionDisk.py
@@ -11,6 +11,9 @@ from .Triplet import Triplet
from .OnlineSampling import OnLineSampling
from scipy.spatial.distance import euclidean
+import logging
+logger = logging.getLogger("bob.learn.tensorflow")
+
class TripletWithSelectionDisk(Triplet, Disk, OnLineSampling):
"""
@@ -110,29 +113,42 @@ class TripletWithSelectionDisk(Triplet, Disk, OnLineSampling):
# Selecting the classes used in the selection
indexes = numpy.random.choice(len(self.possible_labels), self.total_identities, replace=False)
- samples_per_identity = self.batch_size/self.total_identities
+ samples_per_identity = numpy.ceil(self.batch_size/float(self.total_identities))
anchor_labels = numpy.ones(samples_per_identity) * self.possible_labels[indexes[0]]
+
for i in range(1, self.total_identities):
anchor_labels = numpy.hstack((anchor_labels,numpy.ones(samples_per_identity) * self.possible_labels[indexes[i]]))
anchor_labels = anchor_labels[0:self.batch_size]
+
+
+
data_a = numpy.zeros(shape=self.shape, dtype='float32')
data_p = numpy.zeros(shape=self.shape, dtype='float32')
data_n = numpy.zeros(shape=self.shape, dtype='float32')
+ #logger.info("Fetching anchor")
# Fetching the anchors
for i in range(self.shape[0]):
data_a[i, ...] = self.get_anchor(anchor_labels[i])
features_a = self.project(data_a)
for i in range(self.shape[0]):
+ #logger.info("*********Anchor {0}".format(i))
+
label = anchor_labels[i]
#anchor = self.get_anchor(label)
+ #logger.info("********* Positives")
positive, distance_anchor_positive = self.get_positive(label, features_a[i])
+ #logger.info("********* Negatives")
negative = self.get_negative(label, features_a[i], distance_anchor_positive)
+ #logger.info("********* Appending")
+
data_p[i, ...] = positive
data_n[i, ...] = negative
+
+ #logger.info("#################")
# Scaling
#if self.scale:
# data_a *= self.scale_value
@@ -164,27 +180,37 @@ class TripletWithSelectionDisk(Triplet, Disk, OnLineSampling):
The best positive sample for the anchor is the farthest from the anchor
"""
+ #logger.info("****************** numpy.where")
indexes = numpy.where(self.labels == label)[0]
+
numpy.random.shuffle(indexes)
indexes = indexes[
0:self.batch_size] # Limiting to the batch size, otherwise the number of comparisons will explode
distances = []
-
- data_p = numpy.zeros(shape=self.shape, dtype='float32')
- for i in range(self.shape[0]):
+ shape = tuple([len(indexes)] + list(self.shape[1:]))
+ data_p = numpy.zeros(shape=shape, dtype='float32')
+ #logger.info("****************** search")
+ for i in range(shape[0]):
+ #logger.info("****************** fetch")
file_name = self.data[indexes[i], ...]
+ #logger.info("****************** load")
data_p[i, ...] = self.load_from_file(str(file_name))
- if self.scale:
- data_p *= self.scale_value
+
+ if self.scale:
+ data_p *= self.scale_value
+
+ #logger.info("****************** project")
positive_features = self.project(data_p)
+ #logger.info("****************** distances")
# Projecting the positive instances
for p in positive_features:
distances.append(euclidean(anchor_feature, p))
# Geting the max
index = numpy.argmax(distances)
- return self.data[indexes[index], ...], distances[index]
+ #logger.info("****************** return")
+ return data_p[index, ...], distances[index]
def get_negative(self, label, anchor_feature, distance_anchor_positive):
"""
@@ -194,20 +220,29 @@ class TripletWithSelectionDisk(Triplet, Disk, OnLineSampling):
#anchor_feature = self.feature_extractor(self.reshape_for_deploy(anchor), session=self.session)
# Selecting the negative samples
+ #logger.info("****************** numpy.where")
indexes = numpy.where(self.labels != label)[0]
numpy.random.shuffle(indexes)
indexes = indexes[
- 0:self.batch_size] # Limiting to the batch size, otherwise the number of comparisons will explode
+ 0:self.batch_size*3] # Limiting to the batch size, otherwise the number of comparisons will explode
- data_n = numpy.zeros(shape=self.shape, dtype='float32')
- for i in range(self.shape[0]):
+ shape = tuple([len(indexes)] + list(self.shape[1:]))
+ data_n = numpy.zeros(shape=shape, dtype='float32')
+ #logger.info("****************** search")
+ for i in range(shape[0]):
+ #logger.info("****************** fetch")
file_name = self.data[indexes[i], ...]
+ #logger.info("****************** load")
data_n[i, ...] = self.load_from_file(str(file_name))
- if self.scale:
- data_n *= self.scale_value
+
+ if self.scale:
+ data_n *= self.scale_value
+
+ #logger.info("****************** project")
negative_features = self.project(data_n)
distances = []
+ #logger.info("****************** distances")
for n in negative_features:
d = euclidean(anchor_feature, n)
@@ -222,6 +257,7 @@ class TripletWithSelectionDisk(Triplet, Disk, OnLineSampling):
# if the semi-hardest is inf take the first
if numpy.isinf(distances[index]):
+ logger.info("SEMI-HARD negative not found, took the first one")
index = 0
-
- return self.data[indexes[index], ...]
+ #logger.info("****************** return")
+ return data_n[index, ...]
diff --git a/bob/learn/tensorflow/datashuffler/__init__.py b/bob/learn/tensorflow/datashuffler/__init__.py
index 55d3b2232033f1745dc707a5fb317ccc876b6169..2c5751fad9b8b2a7887eaeedf843e41d703da060 100644
--- a/bob/learn/tensorflow/datashuffler/__init__.py
+++ b/bob/learn/tensorflow/datashuffler/__init__.py
@@ -15,5 +15,8 @@ from .SiameseDisk import SiameseDisk
from .TripletDisk import TripletDisk
from .TripletWithSelectionDisk import TripletWithSelectionDisk
+from .DataAugmentation import DataAugmentation
+from .ImageAugmentation import ImageAugmentation
+
# gets sphinx autodoc done right - don't remove it
__all__ = [_ for _ in dir() if not _.startswith('_')]
diff --git a/bob/learn/tensorflow/layers/FullyConnected.py b/bob/learn/tensorflow/layers/FullyConnected.py
index 1277bf88b3c1861d312beef51e557d2c2d5280bd..992162e0b591fe882105e1b8fec6d34a00ce475e 100644
--- a/bob/learn/tensorflow/layers/FullyConnected.py
+++ b/bob/learn/tensorflow/layers/FullyConnected.py
@@ -61,7 +61,9 @@ class FullyConnected(Layer):
if len(self.input_layer.get_shape()) == 4:
shape = self.input_layer.get_shape().as_list()
- fc = tf.reshape(self.input_layer, [shape[0], shape[1] * shape[2] * shape[3]])
+ #fc = tf.reshape(self.input_layer, [shape[0], shape[1] * shape[2] * shape[3]])
+ fc = tf.reshape(self.input_layer, [-1, shape[1] * shape[2] * shape[3]])
+
else:
fc = self.input_layer
diff --git a/bob/learn/tensorflow/script/train_siamese_casia_webface.py b/bob/learn/tensorflow/script/train_siamese_casia_webface.py
index 9506fb4b682f95f234902400dd2837988072d381..80ff95390a040319932f12f4ad2f007e6e88381a 100644
--- a/bob/learn/tensorflow/script/train_siamese_casia_webface.py
+++ b/bob/learn/tensorflow/script/train_siamese_casia_webface.py
@@ -22,10 +22,10 @@ from docopt import docopt
import tensorflow as tf
from .. import util
SEED = 10
-from bob.learn.tensorflow.data import MemoryDataShuffler, TextDataShuffler
+from bob.learn.tensorflow.datashuffler import TripletDisk, TripletWithSelectionDisk
from bob.learn.tensorflow.network import Lenet, MLP, LenetDropout, VGG, Chopra, Dummy
-from bob.learn.tensorflow.trainers import SiameseTrainer
-from bob.learn.tensorflow.loss import ContrastiveLoss
+from bob.learn.tensorflow.trainers import SiameseTrainer, TripletTrainer
+from bob.learn.tensorflow.loss import ContrastiveLoss, TripletLoss
import numpy
@@ -56,9 +56,9 @@ def main():
extension="")
for o in train_objects]
- train_data_shuffler = TextDataShuffler(train_file_names, train_labels,
- input_shape=[125, 125, 3],
- batch_size=BATCH_SIZE)
+ train_data_shuffler = TripletWithSelectionDisk(train_file_names, train_labels,
+ input_shape=[125, 125, 3],
+ batch_size=BATCH_SIZE)
# Preparing train set
directory = "/idiap/temp/tpereira/DEEP_FACE/CASIA/preprocessed"
@@ -70,20 +70,27 @@ def main():
extension=".hdf5")
for o in validation_objects]
- validation_data_shuffler = TextDataShuffler(validation_file_names, validation_labels,
- input_shape=[125, 125, 3],
- batch_size=VALIDATION_BATCH_SIZE)
+ validation_data_shuffler = TripletDisk(validation_file_names, validation_labels,
+ input_shape=[125, 125, 3],
+ batch_size=VALIDATION_BATCH_SIZE)
# Preparing the architecture
# LENET PAPER CHOPRA
architecture = Chopra(seed=SEED)
- loss = ContrastiveLoss(contrastive_margin=50.)
- optimizer = tf.train.GradientDescentOptimizer(0.00001)
- trainer = SiameseTrainer(architecture=architecture,
- loss=loss,
+ #loss = ContrastiveLoss(contrastive_margin=50.)
+ #optimizer = tf.train.GradientDescentOptimizer(0.00001)
+ #trainer = SiameseTrainer(architecture=architecture,
+ # loss=loss,
+ # iterations=ITERATIONS,
+ # snapshot=VALIDATION_TEST,
+ # optimizer=optimizer)
+
+ loss = TripletLoss(margin=4.)
+ trainer = TripletTrainer(architecture=architecture, loss=loss,
iterations=ITERATIONS,
- snapshot=VALIDATION_TEST,
- optimizer=optimizer)
+ prefetch=False,
+ temp_dir="./LOGS_CASIA/triplet-cnn")
+
trainer.train(train_data_shuffler, validation_data_shuffler)
#trainer.train(train_data_shuffler)