[architecture] Created a test case for the inception net issue #17

[architecture] Created a test case for the inception net issue #17

bob/learn/tensorflow/test/test_inception.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+# @date: Thu 13 Oct 2016 13:35 CEST
+
+import numpy
+from bob.learn.tensorflow.datashuffler import Disk, ScaleFactor, TripletDisk
+from bob.learn.tensorflow.loss import BaseLoss, ContrastiveLoss, TripletLoss
+from bob.learn.tensorflow.trainers import Trainer, SiameseTrainer, TripletTrainer, constant
+import shutil
+import tensorflow as tf
+from tensorflow.contrib.slim.python.slim.nets import inception
+
+from test_datashuffler import get_dummy_files
+
+"""
+Some unit tests for the datashuffler
+"""
+
+iterations = 5
+seed = 10
+
+
+def test_inception_trainer():
+    directory = "./temp/inception"
+
+    # Loading data
+    train_data, train_labels = get_dummy_files()
+    batch_shape = [None, 224, 224, 3]
+
+    train_data_shuffler = Disk(train_data, train_labels,
+                               input_shape=batch_shape,
+                               batch_size=2)
+
+    # Loss for the softmax
+    loss = BaseLoss(tf.nn.sparse_softmax_cross_entropy_with_logits, tf.reduce_mean)
+
+    # Creating inception model
+    inputs = train_data_shuffler("data", from_queue=False)
+    graph = inception.inception_v1(inputs)[0]
+
+    # One graph trainer
+    trainer = Trainer(train_data_shuffler,
+                      iterations=iterations,
+                      analizer=None,
+                      temp_dir=directory
+                      )
+    trainer.create_network_from_scratch(graph=graph,
+                                        loss=loss,
+                                        learning_rate=constant(0.01, name="regular_lr"),
+                                        optimizer=tf.train.GradientDescentOptimizer(0.01),
+                                        )
+    trainer.train()
+    shutil.rmtree(directory)
+
+
+def test_inception_triplet_trainer():
+    directory = "./temp/inception"
+
+    # Loading data
+    train_data, train_labels = get_dummy_files()
+    batch_shape = [None, 224, 224, 3]
+
+    train_data_shuffler = TripletDisk(train_data, train_labels,
+                                      input_shape=batch_shape,
+                                      batch_size=2)
+
+    # Loss for the softmax
+    loss = TripletLoss()
+
+    # Creating inception model
+    inputs = train_data_shuffler("data", from_queue=False)
+
+    graph = dict()
+    graph['anchor'] = inception.inception_v1(inputs['anchor'])[0]
+    graph['positive'] = inception.inception_v1(inputs['positive'], reuse=True)[0]
+    graph['negative'] = inception.inception_v1(inputs['negative'], reuse=True)[0]
+
+    # One graph trainer
+    trainer = TripletTrainer(train_data_shuffler,
+                             iterations=iterations,
+                             analizer=None,
+                             temp_dir=directory
+                      )
+    trainer.create_network_from_scratch(graph=graph,
+                                        loss=loss,
+                                        learning_rate=constant(0.01, name="regular_lr"),
+                                        optimizer=tf.train.GradientDescentOptimizer(0.01)
+                                        )
+    trainer.train()
+    shutil.rmtree(directory)