From 76fdee9769e5e8772ff2740e6281da883023e169 Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Fri, 13 Oct 2017 17:24:54 +0200
Subject: [PATCH] Implemented cdist with l2norm using tensors and accuracy
computation using embeddings
---
bob/learn/tensorflow/test/test_utils.py | 26 ++++-
bob/learn/tensorflow/trainers/__init__.py | 1 +
bob/learn/tensorflow/utils/util.py | 133 +++++++++-------------
3 files changed, 81 insertions(+), 79 deletions(-)
diff --git a/bob/learn/tensorflow/test/test_utils.py b/bob/learn/tensorflow/test/test_utils.py
index c256f717..966fd66c 100755
--- a/bob/learn/tensorflow/test/test_utils.py
+++ b/bob/learn/tensorflow/test/test_utils.py
@@ -3,7 +3,9 @@
# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
import numpy
-from bob.learn.tensorflow.utils import compute_embedding_accuracy
+from bob.learn.tensorflow.utils import compute_embedding_accuracy, cdist, compute_embedding_accuracy_tensors
+
+import tensorflow as tf
"""
Some unit tests for the datashuffler
@@ -33,4 +35,26 @@ def test_embedding_accuracy():
labels = numpy.concatenate((labels, noise_labels))
assert compute_embedding_accuracy(data, labels) == 10 / 15.
+
+
+def test_embedding_accuracy_tensors():
+
+ numpy.random.seed(10)
+ samples_per_class = 5
+
+ class_a = numpy.random.normal(loc=0, scale=0.1, size=(samples_per_class, 2))
+ labels_a = numpy.zeros(samples_per_class)
+ class_b = numpy.random.normal(loc=10, scale=0.1, size=(samples_per_class, 2))
+ labels_b = numpy.ones(samples_per_class)
+
+ data = numpy.vstack((class_a, class_b))
+ labels = numpy.concatenate((labels_a, labels_b))
+
+ data = tf.convert_to_tensor(data.astype("float32"))
+ labels = tf.convert_to_tensor(labels.astype("int64"))
+
+ sess = tf.Session()
+ accuracy = sess.run(compute_embedding_accuracy_tensors(data, labels))
+ assert accuracy == 1.
+
diff --git a/bob/learn/tensorflow/trainers/__init__.py b/bob/learn/tensorflow/trainers/__init__.py
index ee0a9819..6f22f211 100755
--- a/bob/learn/tensorflow/trainers/__init__.py
+++ b/bob/learn/tensorflow/trainers/__init__.py
@@ -3,6 +3,7 @@ from .Trainer import Trainer
from .SiameseTrainer import SiameseTrainer
from .TripletTrainer import TripletTrainer
from .learning_rate import exponential_decay, constant
+from .LogitsTrainer import LogitsTrainer
import numpy
diff --git a/bob/learn/tensorflow/utils/util.py b/bob/learn/tensorflow/utils/util.py
index 982bcb56..7aae42fa 100755
--- a/bob/learn/tensorflow/utils/util.py
+++ b/bob/learn/tensorflow/utils/util.py
@@ -37,95 +37,37 @@ def load_mnist(perc_train=0.9):
n_train = int(perc_train*indexes.shape[0])
n_validation = total_samples - n_train
- train_data = data[0:n_train, :]
+ train_data = data[0:n_train, :].astype("float32") * 0.00390625
train_labels = labels[0:n_train]
- validation_data = data[n_train:n_train+n_validation, :]
+ validation_data = data[n_train:n_train+n_validation, :].astype("float32") * 0.00390625
validation_labels = labels[n_train:n_train+n_validation]
return train_data, train_labels, validation_data, validation_labels
-def plot_embedding_pca(features, labels):
- """
-
- Trains a PCA using bob, reducing the features to dimension 2 and plot it the possible clusters
-
- :param features:
- :param labels:
- :return:
- """
-
- import bob.learn.linear
- import matplotlib.pyplot as mpl
-
- colors = ['#FF0000', '#FFFF00', '#FF00FF', '#00FFFF', '#000000',
- '#AA0000', '#AAAA00', '#AA00AA', '#00AAAA', '#330000']
-
- # Training PCA
- trainer = bob.learn.linear.PCATrainer()
- machine, lamb = trainer.train(features.astype("float64"))
-
- # Getting the first two most relevant features
- projected_features = machine(features.astype("float64"))[:, 0:2]
-
- # Plotting the classes
- n_classes = max(labels)+1
- fig = mpl.figure()
-
- for i in range(n_classes):
- indexes = numpy.where(labels == i)[0]
-
- selected_features = projected_features[indexes,:]
- mpl.scatter(selected_features[:, 0], selected_features[:, 1],
- marker='.', c=colors[i], linewidths=0, label=str(i))
- mpl.legend()
- return fig
-
-def plot_embedding_lda(features, labels):
- """
-
- Trains a LDA using bob, reducing the features to dimension 2 and plot it the possible clusters
-
- :param features:
- :param labels:
- :return:
- """
-
- import bob.learn.linear
- import matplotlib.pyplot as mpl
-
- colors = ['#FF0000', '#FFFF00', '#FF00FF', '#00FFFF', '#000000',
- '#AA0000', '#AAAA00', '#AA00AA', '#00AAAA', '#330000']
- n_classes = max(labels)+1
+def create_mnist_tfrecord(tfrecords_filename, data, labels, n_samples=6000):
- # Training PCA
- trainer = bob.learn.linear.FisherLDATrainer(use_pinv=True)
- lda_features = []
- for i in range(n_classes):
- indexes = numpy.where(labels == i)[0]
- lda_features.append(features[indexes, :].astype("float64"))
-
- machine, lamb = trainer.train(lda_features)
-
- #import ipdb; ipdb.set_trace();
-
-
- # Getting the first two most relevant features
- projected_features = machine(features.astype("float64"))[:, 0:2]
+ def _bytes_feature(value):
+ return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
- # Plotting the classes
- fig = mpl.figure()
+ def _int64_feature(value):
+ return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
- for i in range(n_classes):
- indexes = numpy.where(labels == i)[0]
+ writer = tf.python_io.TFRecordWriter(tfrecords_filename)
- selected_features = projected_features[indexes,:]
- mpl.scatter(selected_features[:, 0], selected_features[:, 1],
- marker='.', c=colors[i], linewidths=0, label=str(i))
- mpl.legend()
- return fig
+ for i in range(n_samples):
+ img = data[i]
+ img_raw = img.tostring()
+
+ feature = {'train/data': _bytes_feature(img_raw),
+ 'train/label': _int64_feature(labels[i])
+ }
+
+ example = tf.train.Example(features=tf.train.Features(feature=feature))
+ writer.write(example.SerializeToString())
+ writer.close()
def compute_eer(data_train, labels_train, data_validation, labels_validation, n_classes):
@@ -209,6 +151,41 @@ def debug_embbeding(image, architecture, embbeding_dim=2, feature_layer="fc3"):
return embeddings
+
+
+def cdist(A):
+ with tf.variable_scope('Pairwisedistance'):
+ p1 = tf.matmul(
+ tf.expand_dims(tf.reduce_sum(tf.square(A), 1), 1),
+ tf.ones(shape=(1, A.shape.as_list()[0]))
+ )
+ p2 = tf.transpose(tf.matmul(
+ tf.reshape(tf.reduce_sum(tf.square(A), 1), shape=[-1, 1]),
+ tf.ones(shape=(A.shape.as_list()[0], 1)),
+ transpose_b=True
+ ))
+
+ return tf.sqrt(tf.add(p1, p2) - 2 * tf.matmul(A, A, transpose_b=True))
+
+
+def compute_embedding_accuracy_tensors(embedding, labels):
+ """
+ Compute the accuracy through exhaustive comparisons between the embeddings using tensors
+ """
+
+ distances = cdist(embedding)
+
+ # Fitting the main diagonal with infs (removing comparisons with the same sample)
+ inf = numpy.ones(10)*numpy.inf
+ inf = inf.astype("float32")
+
+ distances = cdist(embedding)
+ distances = tf.matrix_set_diag(distances, inf)
+ indexes = tf.argmin(distances, axis=1)
+
+ matching = [ tf.equal(labels[i],labels[j]) for i,j in zip(range(indexes.get_shape().as_list()[0]), tf.unstack(indexes))]
+ return tf.reduce_sum(tf.cast(matching, tf.uint8))/indexes.get_shape().as_list()[0]
+
def compute_embedding_accuracy(embedding, labels):
"""
@@ -224,7 +201,7 @@ def compute_embedding_accuracy(embedding, labels):
# Fitting the main diagonal with infs (removing comparisons with the same sample)
numpy.fill_diagonal(distances, numpy.inf)
- indexes = distances.argmin(axis=1)
+ indexes = distances.argmin(axis=1)
# Computing the argmin excluding comparisons with the same samples
# Basically, we are excluding the main diagonal
--
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