def testVariablesSetDevice(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) # Force all Variables to reside on the device. with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): inception.inception_v3(inputs, num_classes) with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): inception.inception_v3(inputs, num_classes) for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): self.assertDeviceEqual(v.device, '/cpu:0') for v in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): self.assertDeviceEqual(v.device, '/gpu:0')
def testTrainEvalWithReuse(self): train_batch_size = 5 eval_batch_size = 2 height, width = 150, 150 num_classes = 1000 with self.test_session() as sess: train_inputs = tf.random_uniform((train_batch_size, height, width, 3)) inception.inception_v3(train_inputs, num_classes) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform((eval_batch_size, height, width, 3)) logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.initialize_all_variables()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size,))
def testBuildLogits(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes])
def testTrainEvalWithReuse(self): train_batch_size = 5 eval_batch_size = 2 height, width = 150, 150 num_classes = 1000 with self.test_session() as sess: train_inputs = tf.random_uniform( (train_batch_size, height, width, 3)) inception.inception_v3(train_inputs, num_classes) tf.get_variable_scope().reuse_variables() eval_inputs = tf.random_uniform( (eval_batch_size, height, width, 3)) logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.initialize_all_variables()) output = sess.run(predictions) self.assertEquals(output.shape, (eval_batch_size, ))
def testEvaluation(self): batch_size = 2 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.initialize_all_variables()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size,))
def testHalfSizeImages(self): batch_size = 5 height, width = 150, 150 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) logits, end_points = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('logits')) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['mixed_8x8x2048b'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 3, 3, 2048])
def testEvaluation(self): batch_size = 2 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: eval_inputs = tf.random_uniform((batch_size, height, width, 3)) logits, _ = inception.inception_v3(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.initialize_all_variables()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size, ))
def testUnknowBatchSize(self): batch_size = 1 height, width = 299, 299 num_classes = 1000 with self.test_session() as sess: inputs = tf.placeholder(tf.float32, (None, height, width, 3)) logits, _ = inception.inception_v3(inputs, num_classes) self.assertTrue(logits.op.name.startswith('logits')) self.assertListEqual(logits.get_shape().as_list(), [None, num_classes]) images = tf.random_uniform((batch_size, height, width, 3)) sess.run(tf.initialize_all_variables()) output = sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, num_classes))
def testBuildEndPoints(self): batch_size = 5 height, width = 299, 299 num_classes = 1000 with self.test_session(): inputs = tf.random_uniform((batch_size, height, width, 3)) _, end_points = inception.inception_v3(inputs, num_classes) self.assertTrue('logits' in end_points) logits = end_points['logits'] self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertTrue('aux_logits' in end_points) aux_logits = end_points['aux_logits'] self.assertListEqual(aux_logits.get_shape().as_list(), [batch_size, num_classes]) pre_pool = end_points['mixed_8x8x2048b'] self.assertListEqual(pre_pool.get_shape().as_list(), [batch_size, 8, 8, 2048])