Created a dataset to fetch directories

bob/learn/tensorflow/test/test_image_dataset.py

+#!/usr/bin/env python
+# vim: set fileencoding=utf-8 :
+# @author: Tiago de Freitas Pereira <tiago.pereira@idiap.ch>
+
+import tensorflow as tf
+
+from bob.learn.tensorflow.network import dummy
+from bob.learn.tensorflow.trainers import LogitsTrainer, LogitsCenterLossTrainer
+
+from bob.learn.tensorflow.dataset.image import shuffle_data_and_labels_image_augmentation
+import pkg_resources
+
+from bob.learn.tensorflow.dataset import append_image_augmentation
+from bob.learn.tensorflow.utils import load_mnist, create_mnist_tfrecord
+from bob.learn.tensorflow.utils.hooks import LoggerHookEstimator
+from bob.learn.tensorflow.utils import reproducible
+from bob.learn.tensorflow.loss import mean_cross_entropy_loss
+
+import numpy
+
+import shutil
+import os
+
+model_dir = "./temp"
+
+learning_rate = 0.1
+data_shape = (250, 250, 3)  # size of atnt images
+data_type = tf.float32
+batch_size = 16
+validation_batch_size = 250
+epochs = 1
+steps = 5000
+
+
+def test_logitstrainer_images():
+
+    # Trainer logits
+    try:
+        embedding_validation = False
+        trainer = LogitsTrainer(model_dir=model_dir,
+                                architecture=dummy,
+                                optimizer=tf.train.GradientDescentOptimizer(learning_rate),
+                                n_classes=10,
+                                loss_op=mean_cross_entropy_loss,
+                                embedding_validation=embedding_validation,
+                                validation_batch_size=validation_batch_size)
+        run_logitstrainer_images(trainer)
+    finally:
+        try:
+            os.unlink(tfrecord_train)
+            os.unlink(tfrecord_validation)
+            shutil.rmtree(model_dir, ignore_errors=True)
+        except Exception:
+            pass        
+
+def run_logitstrainer_images(trainer):
+    # Cleaning up
+    tf.reset_default_graph()
+    assert len(tf.global_variables()) == 0
+    
+    filenames = [pkg_resources.resource_filename(__name__, 'data/dummy_image_database/m301_01_p01_i0_0.png'),
+                  pkg_resources.resource_filename(__name__, 'data/dummy_image_database/m301_01_p02_i0_0.png'),
+                  pkg_resources.resource_filename(__name__, 'data/dummy_image_database/m304_01_p01_i0_0.png'),
+                  pkg_resources.resource_filename(__name__, 'data/dummy_image_database/m304_02_f12_i0_0.png')]
+    labels = [0, 0, 1, 1]
+    
+    def input_fn():
+
+        return shuffle_data_and_labels_image_augmentation(filenames,labels, data_shape, data_type, batch_size, epochs=epochs)
+        
+
+    def input_fn_validation():
+        return shuffle_data_and_labels_image_augmentation(filenames, labels, data_shape, data_type,
+                                                          validation_batch_size, epochs=1000)
+    
+    hooks = [LoggerHookEstimator(trainer, 16, 300),
+
+             tf.train.SummarySaverHook(save_steps=1000,
+                                       output_dir=model_dir,
+                                       scaffold=tf.train.Scaffold(),
+                                       summary_writer=tf.summary.FileWriter(model_dir) )]
+
+    trainer.train(input_fn, steps=steps, hooks=hooks)
+
+    if not trainer.embedding_validation:
+        acc = trainer.evaluate(input_fn_validation)
+        assert acc['accuracy'] > 0.80
+    else:
+        acc = trainer.evaluate(input_fn_validation)
+        assert acc['accuracy'] > 0.80
+
+    # Cleaning up
+    tf.reset_default_graph()
+    assert len(tf.global_variables()) == 0
+
+