diff --git a/bob/learn/tensorflow/analyzers/Analizer.py b/bob/learn/tensorflow/analyzers/Analizer.py
index 08ba2abf7dd4dd20963ac6be0d1598e0ee93576d..cf1499685f67f0ef311bf1ac5bd6438fb21218b0 100644
--- a/bob/learn/tensorflow/analyzers/Analizer.py
+++ b/bob/learn/tensorflow/analyzers/Analizer.py
@@ -49,6 +49,8 @@ class Analizer:
enroll_features = self.machine(enroll_data, session=self.session)
del enroll_data
+ #import ipdb; ipdb.set_trace();
+
# Extracting features for probing
probe_data, probe_labels = self.data_shuffler.get_batch(train_dataset=False)
probe_features = self.machine(probe_data, session=self.session)
@@ -56,22 +58,23 @@ class Analizer:
# Creating models
models = []
- for i in range(self.data_shuffler.total_labels):
- indexes_model = numpy.where(enroll_labels == i)[0]
+ for i in range(len(self.data_shuffler.possible_labels)):
+ indexes_model = numpy.where(enroll_labels == self.data_shuffler.possible_labels[i])[0]
models.append(numpy.mean(enroll_features[indexes_model, :], axis=0))
# Probing
positive_scores = numpy.zeros(shape=0)
negative_scores = numpy.zeros(shape=0)
- for i in range(self.data_shuffler.total_labels):
+ for i in range(len(self.data_shuffler.possible_labels)):
+ #for i in self.data_shuffler.possible_labels:
# Positive scoring
- indexes = probe_labels == i
+ indexes = probe_labels == self.data_shuffler.possible_labels[i]
positive_data = probe_features[indexes, :]
p = [cosine(models[i], positive_data[j]) for j in range(positive_data.shape[0])]
positive_scores = numpy.hstack((positive_scores, p))
# negative scoring
- indexes = probe_labels != i
+ indexes = probe_labels != self.data_shuffler.possible_labels[i]
negative_data = probe_features[indexes, :]
n = [cosine(models[i], negative_data[j]) for j in range(negative_data.shape[0])]
negative_scores = numpy.hstack((negative_scores, n))
diff --git a/bob/learn/tensorflow/data/BaseDataShuffler.py b/bob/learn/tensorflow/data/BaseDataShuffler.py
index c6e53c7c4f12f899d37e86ccea2074e955fdb255..cc60caaef48837761e5498f32b719b48668a8565 100644
--- a/bob/learn/tensorflow/data/BaseDataShuffler.py
+++ b/bob/learn/tensorflow/data/BaseDataShuffler.py
@@ -8,7 +8,13 @@ import tensorflow as tf
class BaseDataShuffler(object):
- def __init__(self, data, labels, input_shape, perc_train=0.9, scale=True, train_batch_size=1, validation_batch_size=300):
+ def __init__(self, data, labels,
+ input_shape,
+ input_dtype="float64",
+ perc_train=0.9,
+ scale=True,
+ train_batch_size=1,
+ validation_batch_size=300):
"""
The class provide base functionoalies to shuffle the data
@@ -23,6 +29,7 @@ class BaseDataShuffler(object):
self.scale = scale
self.scale_value = 0.00390625
+ self.input_dtype = input_dtype
# TODO: Check if the bacth size is higher than the input data
self.train_batch_size = train_batch_size
@@ -34,9 +41,9 @@ class BaseDataShuffler(object):
# TODO: Check if the labels goes from O to N-1
self.labels = labels
- self.total_labels = max(labels) + 1
+ self.possible_labels = list(set(self.labels))
- # Spliting in train and validation
+ # Computing the data samples fro train and validation
self.n_samples = len(self.labels)
self.n_train_samples = int(round(self.n_samples * perc_train))
self.n_validation_samples = self.n_samples - self.n_train_samples
@@ -45,6 +52,15 @@ class BaseDataShuffler(object):
self.indexes = numpy.array(range(self.n_samples))
numpy.random.shuffle(self.indexes)
+ # Spliting the data between train and validation
+ self.train_data = self.data[self.indexes[0:self.n_train_samples], ...]
+ self.train_labels = self.labels[self.indexes[0:self.n_train_samples]]
+
+ self.validation_data = self.data[self.indexes[self.n_train_samples:
+ self.n_train_samples + self.n_validation_samples], ...]
+ self.validation_labels = self.labels[self.indexes[self.n_train_samples:
+ self.n_train_samples + self.n_validation_samples]]
+
def get_placeholders_forprefetch(self, name="", train_dataset=True):
"""
Returns a place holder with the size of your batch
@@ -66,3 +82,35 @@ class BaseDataShuffler(object):
labels = tf.placeholder(tf.int64, shape=shape[0])
return data, labels
+
+ def get_genuine_or_not(self, input_data, input_labels, genuine=True):
+ if genuine:
+ # Getting a client
+ index = numpy.random.randint(len(self.possible_labels))
+ index = self.possible_labels[index]
+
+ # Getting the indexes of the data from a particular client
+ indexes = numpy.where(input_labels == index)[0]
+ numpy.random.shuffle(indexes)
+
+ # Picking a pair
+ data = input_data[indexes[0], ...]
+ data_p = input_data[indexes[1], ...]
+
+ else:
+ # Picking a pair of labels from different clients
+ index = numpy.random.choice(len(self.possible_labels), 2, replace=False)
+ index[0] = self.possible_labels[index[0]]
+ index[1] = self.possible_labels[index[1]]
+
+ # Getting the indexes of the two clients
+ indexes = numpy.where(input_labels == index[0])[0]
+ indexes_p = numpy.where(input_labels == index[1])[0]
+ numpy.random.shuffle(indexes)
+ numpy.random.shuffle(indexes_p)
+
+ # Picking a pair
+ data = input_data[indexes[0], ...]
+ data_p = input_data[indexes_p[0], ...]
+
+ return data, data_p
diff --git a/bob/learn/tensorflow/data/MemoryDataShuffler.py b/bob/learn/tensorflow/data/MemoryDataShuffler.py
index 36b671f966c9d8487429d5e6e68da457e42af0b8..5bd4945915ada4688cac7f44b5a87b338731869d 100644
--- a/bob/learn/tensorflow/data/MemoryDataShuffler.py
+++ b/bob/learn/tensorflow/data/MemoryDataShuffler.py
@@ -16,7 +16,14 @@ def scale_mean_norm(data, scale=0.00390625):
class MemoryDataShuffler(BaseDataShuffler):
- def __init__(self, data, labels, input_shape, perc_train=0.9, scale=True, train_batch_size=1, validation_batch_size=300):
+
+ def __init__(self, data, labels,
+ input_shape,
+ input_dtype="float64",
+ perc_train=0.9,
+ scale=True,
+ train_batch_size=1,
+ validation_batch_size=300):
"""
Shuffler that deal with memory datasets
@@ -33,23 +40,19 @@ class MemoryDataShuffler(BaseDataShuffler):
data=data,
labels=labels,
input_shape=input_shape,
+ input_dtype=input_dtype,
perc_train=perc_train,
scale=scale,
train_batch_size=train_batch_size,
validation_batch_size=validation_batch_size
)
- # Spliting between train and test
- self.train_data = self.data[self.indexes[0:self.n_train_samples], ...]
- self.train_labels = self.labels[self.indexes[0:self.n_train_samples]]
+ self.train_data = self.train_data.astype(input_dtype)
+ self.validation_data = self.validation_data.astype(input_dtype)
- self.validation_data = self.data[self.indexes[self.n_train_samples:
- self.n_train_samples + self.n_validation_samples], ...]
- self.validation_labels = self.labels[self.indexes[self.n_train_samples:
- self.n_train_samples + self.n_validation_samples]]
if self.scale:
- self.train_data, self.mean = scale_mean_norm(self.train_data)
- self.validation_data = (self.validation_data - self.mean) * self.scale_value
+ self.train_data *= self.scale_value
+ self.validation_data *= self.scale_value
def get_batch(self, train_dataset=True):
@@ -70,3 +73,37 @@ class MemoryDataShuffler(BaseDataShuffler):
selected_labels = label[indexes[0:n_samples]]
return selected_data.astype("float32"), selected_labels
+
+ def get_pair(self, train_dataset=True, zero_one_labels=True):
+ """
+ Get a random pair of samples
+
+ **Parameters**
+ is_target_set_train: Defining the target set to get the batch
+
+ **Return**
+ """
+
+ if train_dataset:
+ target_data = self.train_data
+ target_labels = self.train_labels
+ shape = self.train_shape
+ else:
+ target_data = self.validation_data
+ target_labels = self.validation_labels
+ shape = self.validation_shape
+
+ data = numpy.zeros(shape=shape, dtype='float32')
+ data_p = numpy.zeros(shape=shape, dtype='float32')
+ labels_siamese = numpy.zeros(shape=shape[0], dtype='float32')
+
+ genuine = True
+ for i in range(shape[0]):
+ data[i, ...], data_p[i, ...] = self.get_genuine_or_not(target_data, target_labels, genuine=genuine)
+ if zero_one_labels:
+ labels_siamese[i] = not genuine
+ else:
+ labels_siamese[i] = -1 if genuine else +1
+ genuine = not genuine
+
+ return data, data_p, labels_siamese
diff --git a/bob/learn/tensorflow/data/TextDataShuffler.py b/bob/learn/tensorflow/data/TextDataShuffler.py
index e5472d682de9db4c9271c24c473752e133f14aed..2bcec45f7e7e756dba345a0bb2bfc2fba3a97d4b 100644
--- a/bob/learn/tensorflow/data/TextDataShuffler.py
+++ b/bob/learn/tensorflow/data/TextDataShuffler.py
@@ -18,7 +18,13 @@ from .BaseDataShuffler import BaseDataShuffler
class TextDataShuffler(BaseDataShuffler):
- def __init__(self, data, labels, input_shape, perc_train=0.9, scale=0.00390625, train_batch_size=1, validation_batch_size=300):
+ def __init__(self, data, labels,
+ input_shape,
+ input_dtype="float64",
+ perc_train=0.9,
+ scale=True,
+ train_batch_size=1,
+ validation_batch_size=300):
"""
Shuffler that deal with file list
@@ -31,30 +37,32 @@ class TextDataShuffler(BaseDataShuffler):
validation_batch_size:
"""
+ if isinstance(data, list):
+ data = numpy.array(data)
+
+ if isinstance(labels, list):
+ labels = numpy.array(labels)
+
super(TextDataShuffler, self).__init__(
data=data,
labels=labels,
input_shape=input_shape,
+ input_dtype=input_dtype,
perc_train=perc_train,
scale=scale,
train_batch_size=train_batch_size,
validation_batch_size=validation_batch_size
)
- if isinstance(self.data, list):
- self.data = numpy.array(self.data)
-
- if isinstance(self.labels, list):
- self.labels = numpy.array(self.labels)
-
- # Spliting between train and test
- self.train_data = self.data[self.indexes[0:self.n_train_samples]]
- self.train_labels = self.labels[self.indexes[0:self.n_train_samples]]
+ def load_from_file(self, file_name, shape):
+ d = bob.io.base.load(file_name)
+ if len(d.shape) == 2:
+ data = numpy.zeros(shape=tuple(shape[1:]))
+ data[:, :, 0] = d
+ else:
+ data = d
- self.validation_data = self.data[self.indexes[self.n_train_samples:
- self.n_train_samples + self.n_validation_samples]]
- self.validation_labels = self.labels[self.indexes[self.n_train_samples:
- self.n_train_samples + self.n_validation_samples]]
+ return data
def get_batch(self, train_dataset=True):
@@ -77,20 +85,54 @@ class TextDataShuffler(BaseDataShuffler):
for i in range(batch_size):
file_name = files_names[indexes[i]]
-
- d = bob.io.base.load(file_name)
- if len(d.shape) == 2:
- data = numpy.zeros(shape=tuple(shape[1:]))
- data[:, :, 0] = d
- else:
- data = d
+ data = self.load_from_file(file_name, shape)
selected_data[i, ...] = data
- if self.scale is not None:
- selected_data[i, ...] *= self.scale
-
-
+ if self.scale:
+ selected_data[i, ...] *= self.scale_value
selected_labels = label[indexes[0:batch_size]]
return selected_data.astype("float32"), selected_labels
+
+ def get_pair(self, train_dataset=True, zero_one_labels=True):
+ """
+ Get a random pair of samples
+
+ **Parameters**
+ is_target_set_train: Defining the target set to get the batch
+
+ **Return**
+ """
+
+ if train_dataset:
+ target_data = self.train_data
+ target_labels = self.train_labels
+ shape = self.train_shape
+ else:
+ target_data = self.validation_data
+ target_labels = self.validation_labels
+ shape = self.validation_shape
+
+ data = numpy.zeros(shape=shape, dtype='float32')
+ data_p = numpy.zeros(shape=shape, dtype='float32')
+ labels_siamese = numpy.zeros(shape=shape[0], dtype='float32')
+
+ genuine = True
+ for i in range(shape[0]):
+ file_name, file_name_p = self.get_genuine_or_not(target_data, target_labels, genuine=genuine)
+ data[i, ...] = self.load_from_file(str(file_name), shape)
+ data_p[i, ...] = self.load_from_file(str(file_name_p), shape)
+
+ if zero_one_labels:
+ labels_siamese[i] = not genuine
+ else:
+ labels_siamese[i] = -1 if genuine else +1
+ genuine = not genuine
+
+ if self.scale:
+ data *= self.scale_value
+ data_p *= self.scale_value
+
+ return data, data_p, labels_siamese
+
diff --git a/bob/learn/tensorflow/script/train_mnist.py b/bob/learn/tensorflow/script/train_mnist.py
index 04b139c19b3f8bbe2da5d875bc2645b3fbdfdcfd..5a1683069976033a0d27efc762965952af2d87f6 100644
--- a/bob/learn/tensorflow/script/train_mnist.py
+++ b/bob/learn/tensorflow/script/train_mnist.py
@@ -39,26 +39,28 @@ def main():
perc_train = 0.9
# Loading data
- data, labels = util.load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
- data = numpy.reshape(data, (data.shape[0], 28, 28, 1))
- data_shuffler = MemoryDataShuffler(data, labels,
- input_shape=[28, 28, 1],
- train_batch_size=BATCH_SIZE,
- validation_batch_size=BATCH_SIZE*100)
-
-
- #db = bob.db.mobio.Database()
- #objects = db.objects(protocol="male")
-
- #labels = [o.client_id for o in objects]
- #file_names = [o.make_path(
- # directory="/remote/lustre/2/temp/tpereira/FACEREC_EXPERIMENTS/mobio_male/lda/preprocessed",
- # extension=".hdf5")
- # for o in objects]
- #data_shuffler = TextDataShuffler(file_names, labels,
- # input_shape=[80, 64, 1],
- # train_batch_size=BATCH_SIZE,
- # validation_batch_size=BATCH_SIZE*100)
+ #data, labels = util.load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
+ #data = numpy.reshape(data, (data.shape[0], 28, 28, 1))
+
+ #data_shuffler = MemoryDataShuffler(data, labels,
+ # input_shape=[28, 28, 1],
+ # train_batch_size=BATCH_SIZE,
+ # validation_batch_size=BATCH_SIZE*100)
+
+
+ db = bob.db.mobio.Database()
+ objects = db.objects(protocol="male")
+
+ labels = [o.client_id for o in objects]
+ file_names = [o.make_path(
+ directory="/remote/lustre/2/temp/tpereira/FACEREC_EXPERIMENTS/mobio_male/lda/preprocessed",
+ extension=".hdf5")
+ for o in objects]
+
+ data_shuffler = TextDataShuffler(file_names, labels,
+ input_shape=[80, 64, 1],
+ train_batch_size=BATCH_SIZE,
+ validation_batch_size=BATCH_SIZE*100)
# Preparing the architecture
lenet = Lenet()
diff --git a/bob/learn/tensorflow/script/train_mnist_siamese.py b/bob/learn/tensorflow/script/train_mnist_siamese.py
index c4085a948a390204f9ab0fa84c76ead36bd73f85..7bb54b34ac419361a6ab91e694a1cc72c576b8ae 100644
--- a/bob/learn/tensorflow/script/train_mnist_siamese.py
+++ b/bob/learn/tensorflow/script/train_mnist_siamese.py
@@ -21,7 +21,7 @@ from docopt import docopt
import tensorflow as tf
from .. import util
SEED = 10
-from bob.learn.tensorflow.data import MemoryPairDataShuffler, TextDataShuffler
+from bob.learn.tensorflow.data import MemoryDataShuffler, TextDataShuffler
from bob.learn.tensorflow.network import Lenet
from bob.learn.tensorflow.trainers import SiameseTrainer
from bob.learn.tensorflow.loss import ContrastiveLoss
@@ -40,11 +40,11 @@ def main():
# Loading data
data, labels = util.load_mnist(data_dir="./src/bob.db.mnist/bob/db/mnist/")
data = numpy.reshape(data, (data.shape[0], 28, 28, 1))
- data_shuffler = MemoryPairDataShuffler(data, labels,
- input_shape=[28, 28, 1],
- train_batch_size=BATCH_SIZE,
- validation_batch_size=BATCH_SIZE*1000
- )
+ data_shuffler = MemoryDataShuffler(data, labels,
+ input_shape=[28, 28, 1],
+ scale=True,
+ train_batch_size=BATCH_SIZE,
+ validation_batch_size=BATCH_SIZE*1000)
#db = bob.db.mobio.Database()
#objects = db.objects(protocol="male")
@@ -54,11 +54,11 @@ def main():
# directory="/remote/lustre/2/temp/tpereira/FACEREC_EXPERIMENTS/mobio_male/lda/preprocessed",
# extension=".hdf5")
# for o in objects]
+
#data_shuffler = TextDataShuffler(file_names, labels,
# input_shape=[80, 64, 1],
# train_batch_size=BATCH_SIZE,
- # validation_batch_size=BATCH_SIZE*100)
-
+ # validation_batch_size=BATCH_SIZE*500)
# Preparing the architecture
lenet = Lenet(default_feature_layer="fc2")