From 21fbd15b429f7a46255361140af4ae3dd969e80e Mon Sep 17 00:00:00 2001
From: Tiago Freitas Pereira <tiagofrepereira@gmail.com>
Date: Mon, 29 Aug 2016 18:06:58 +0200
Subject: [PATCH] Worked in the milestone #3
---
bob/learn/tensorflow/analyzers/Analizer.py | 22 +++----
bob/learn/tensorflow/layers/Conv2D.py | 2 -
.../tensorflow/network/SequenceNetwork.py | 61 +++++++++++++++++--
.../tensorflow/script/train_mnist_siamese.py | 7 ++-
.../tensorflow/trainers/SiameseTrainer.py | 20 ++++--
5 files changed, 85 insertions(+), 27 deletions(-)
diff --git a/bob/learn/tensorflow/analyzers/Analizer.py b/bob/learn/tensorflow/analyzers/Analizer.py
index 70f54f29..0e0bf410 100644
--- a/bob/learn/tensorflow/analyzers/Analizer.py
+++ b/bob/learn/tensorflow/analyzers/Analizer.py
@@ -21,7 +21,7 @@ class Analizer:
"""
- def __init__(self, data_shuffler, machine, feature_placeholder, session):
+ def __init__(self, data_shuffler, machine, session):
"""
Use the CNN as feature extractor for a n-class classification
@@ -34,7 +34,6 @@ class Analizer:
self.data_shuffler = data_shuffler
self.machine = machine
- self.feature_placeholder = feature_placeholder
self.session = session
# Statistics
@@ -43,17 +42,17 @@ class Analizer:
self.far100 = []
self.far1000 = []
- def extract_features(self):
- data, labels = self.data_shuffler.get_batch(train_dataset=False)
- feed_dict = {self.feature_placeholder: data}
-
- return self.machine(feed_dict, self.session)
-
def __call__(self):
- # Extracting features
- enroll_features, enroll_labels = self.extract_features()
- probe_features, probe_labels = self.extract_features()
+ # Extracting features for enrollment
+ enroll_data, enroll_labels = self.data_shuffler.get_batch(train_dataset=False)
+ enroll_features = self.machine(enroll_data, self.session)
+ del enroll_data
+
+ # Extracting features for probing
+ probe_data, probe_labels = self.data_shuffler.get_batch(train_dataset=False)
+ probe_features = self.machine(probe_data, self.session)
+ del probe_data
# Creating models
models = []
@@ -64,7 +63,6 @@ class Analizer:
# Probing
positive_scores = numpy.zeros(shape=0)
negative_scores = numpy.zeros(shape=0)
-
for i in range(self.data_shuffler.total_labels):
# Positive scoring
indexes = probe_labels == i
diff --git a/bob/learn/tensorflow/layers/Conv2D.py b/bob/learn/tensorflow/layers/Conv2D.py
index a66e6c92..a0b86348 100644
--- a/bob/learn/tensorflow/layers/Conv2D.py
+++ b/bob/learn/tensorflow/layers/Conv2D.py
@@ -45,11 +45,9 @@ class Conv2D(Layer):
self.input_layer = input_layer
# TODO: Do an assert here
-
if len(input_layer.get_shape().as_list()) != 4:
raise ValueError("The input as a convolutional layer must have 4 dimensions, "
"but {0} were provided".format(len(input_layer.get_shape().as_list())))
-
n_channels = input_layer.get_shape().as_list()[3]
if self.W is None:
diff --git a/bob/learn/tensorflow/network/SequenceNetwork.py b/bob/learn/tensorflow/network/SequenceNetwork.py
index 93452b01..6c87c8bd 100644
--- a/bob/learn/tensorflow/network/SequenceNetwork.py
+++ b/bob/learn/tensorflow/network/SequenceNetwork.py
@@ -7,13 +7,13 @@
Class that creates the lenet architecture
"""
-from ..util import *
import tensorflow as tf
import abc
import six
+import os
from collections import OrderedDict
-from bob.learn.tensorflow.layers import Layer
+from bob.learn.tensorflow.layers import Layer, MaxPooling
class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
@@ -31,6 +31,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
self.sequence_net = OrderedDict()
self.feature_layer = feature_layer
+ self.saver = None
def add(self, layer):
"""
@@ -47,7 +48,7 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
**Parameter**
input_data:
- cut:
+ cut: Name of the layer that you want to cut.
"""
input_offset = input_data
@@ -64,6 +65,54 @@ class SequenceNetwork(six.with_metaclass(abc.ABCMeta, object)):
def compute_projection_graph(self, placeholder):
return self.compute_graph(placeholder, cut=True)
- def __call__(self, feed_dict, session):
- #placeholder
- return session.run([self.graph], feed_dict=feed_dict)[0]
+ def __call__(self, data, session=None):
+
+ if session is None:
+ session = tf.Session()
+
+ batch_size = data.shape[0]
+ width = data.shape[1]
+ height = data.shape[2]
+ channels = data.shape[3]
+
+ # Feeding the placeholder
+ feature_placeholder = tf.placeholder(tf.float32, shape=(batch_size, width, height, channels), name="feature")
+ feed_dict = {feature_placeholder: data}
+
+ return session.run([self.compute_projection_graph(feature_placeholder)], feed_dict=feed_dict)[0]
+
+ def dump_variables(self):
+
+ variables = {}
+ for k in self.sequence_net:
+ # TODO: IT IS NOT SMART TESTING ALONG THIS PAGE
+ if not isinstance(self.sequence_net[k], MaxPooling):
+ variables[self.sequence_net[k].W.name] = self.sequence_net[k].W
+ variables[self.sequence_net[k].b.name] = self.sequence_net[k].b
+
+ return variables
+
+ def save(self, session, path, step=None):
+
+ if self.saver is None:
+ self.saver = tf.train.Saver(self.dump_variables())
+
+ if step is None:
+ return self.saver.save(session, os.path.join(path, "model.ckpt"))
+ else:
+ return self.saver.save(session, os.path.join(path, "model" + str(step) + ".ckpt"))
+
+ def load(self, path, shape, session=None):
+
+ if session is None:
+ session = tf.Session()
+
+ # Loading variables
+ place_holder = tf.placeholder(tf.float32, shape=shape, name="load")
+ self.compute_graph(place_holder)
+ tf.initialize_all_variables().run(session=session)
+
+ if self.saver is None:
+ self.saver = tf.train.Saver(self.dump_variables())
+
+ self.saver.restore(session, path)
diff --git a/bob/learn/tensorflow/script/train_mnist_siamese.py b/bob/learn/tensorflow/script/train_mnist_siamese.py
index 98a83bc2..2a3e9afa 100644
--- a/bob/learn/tensorflow/script/train_mnist_siamese.py
+++ b/bob/learn/tensorflow/script/train_mnist_siamese.py
@@ -47,7 +47,12 @@ def main():
lenet = Lenet(feature_layer="fc2")
loss = ContrastiveLoss()
- trainer = SiameseTrainer(architecture=lenet, loss=loss, iterations=ITERATIONS, base_lr=0.00001)
+ trainer = SiameseTrainer(architecture=lenet,
+ loss=loss,
+ iterations=ITERATIONS,
+ base_lr=0.00001,
+ save_intermediate=False,
+ snapshot=VALIDATION_TEST)
trainer.train(data_shuffler)
diff --git a/bob/learn/tensorflow/trainers/SiameseTrainer.py b/bob/learn/tensorflow/trainers/SiameseTrainer.py
index a6629d06..d1a00240 100644
--- a/bob/learn/tensorflow/trainers/SiameseTrainer.py
+++ b/bob/learn/tensorflow/trainers/SiameseTrainer.py
@@ -8,6 +8,8 @@ logger = logging.getLogger("bob.learn.tensorflow")
import tensorflow as tf
from ..analyzers import Analizer
from ..network import SequenceNetwork
+import bob.io.base
+import os
class SiameseTrainer(object):
@@ -17,6 +19,8 @@ class SiameseTrainer(object):
architecture=None,
use_gpu=False,
loss=None,
+ temp_dir="",
+ save_intermediate=False,
###### training options ##########
convergence_threshold = 0.01,
@@ -31,6 +35,8 @@ class SiameseTrainer(object):
self.loss = loss
self.loss_instance = None
self.optimizer = None
+ self.temp_dir = temp_dir
+ self.save_intermediate = save_intermediate
self.architecture = architecture
self.use_gpu = use_gpu
@@ -42,16 +48,16 @@ class SiameseTrainer(object):
self.weight_decay = weight_decay
self.convergence_threshold = convergence_threshold
-
def train(self, data_shuffler):
"""
Do the loop forward --> backward --|
^--------------------|
"""
+ bob.io.base.create_directories_safe(os.path.join(self.temp_dir, 'OUTPUT'))
train_placeholder_left_data, train_placeholder_labels = data_shuffler.get_placeholders(name="train_left")
train_placeholder_right_data, _ = data_shuffler.get_placeholders(name="train_right")
- feature_placeholder, _ = data_shuffler.get_placeholders(name="feature", train_dataset=False)
+ # feature_placeholder, _ = data_shuffler.get_placeholders(name="feature", train_dataset=False)
#validation_placeholder_data, validation_placeholder_labels = data_shuffler.get_placeholders(name="validation",
# train_dataset=False)
@@ -87,10 +93,9 @@ class SiameseTrainer(object):
print("Initializing !!")
# Training
with tf.Session() as session:
- analizer = Analizer(data_shuffler, self.architecture, feature_placeholder, session)
+ analizer = Analizer(data_shuffler, self.architecture, session)
- train_writer = tf.train.SummaryWriter('./LOGS/train',
- session.graph)
+ train_writer = tf.train.SummaryWriter(os.path.join(self.temp_dir, 'LOGS'), session.graph)
# Tensorboard data
tf.scalar_summary('loss', loss_train)
@@ -113,7 +118,10 @@ class SiameseTrainer(object):
if step % self.snapshot == 0:
analizer()
- print str(step) + " - " + str(analizer.eer[-1])
+ if self.save_intermediate:
+ self.architecture.save(session, os.path.join(self.temp_dir, 'OUTPUT'), step)
+ print str(step) + " - " + str(analizer.eer[-1])
+ self.architecture.save(session, os.path.join(self.temp_dir, 'OUTPUT'))
train_writer.close()
--
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