def testVariablesSetDeviceMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) tf.train.create_global_step() # Force all Variables to reside on the device. with tf.variable_scope('on_cpu'), tf.device('/cpu:0'): with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): nasnet.build_nasnet_mobile(inputs, num_classes) with tf.variable_scope('on_gpu'), tf.device('/gpu:0'): with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): nasnet.build_nasnet_mobile(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 testBuildPreLogitsMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = None inputs = tf.random_uniform((batch_size, height, width, 3)) tf.train.create_global_step() with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): net, end_points = nasnet.build_nasnet_mobile(inputs, num_classes) self.assertFalse('AuxLogits' in end_points) self.assertFalse('Predictions' in end_points) self.assertTrue(net.op.name.startswith('final_layer/Mean')) self.assertListEqual(net.get_shape().as_list(), [batch_size, 1056])
def testOverrideHParamsMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) tf.train.create_global_step() config = nasnet.mobile_imagenet_config() config.set_hparam('data_format', 'NCHW') with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): _, end_points = nasnet.build_nasnet_mobile(inputs, num_classes, config=config) self.assertListEqual(end_points['Stem'].shape.as_list(), [batch_size, 88, 28, 28])
def testEvaluationMobileModel(self): batch_size = 2 height, width = 224, 224 num_classes = 1000 with self.test_session() as sess: eval_inputs = tf.random_uniform((batch_size, height, width, 3)) with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): logits, _ = nasnet.build_nasnet_mobile(eval_inputs, num_classes, is_training=False) predictions = tf.argmax(logits, 1) sess.run(tf.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size, ))
def testUnknownBatchSizeMobileModel(self): batch_size = 1 height, width = 224, 224 num_classes = 1000 with self.test_session() as sess: inputs = tf.placeholder(tf.float32, (None, height, width, 3)) with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): logits, _ = nasnet.build_nasnet_mobile(inputs, num_classes) self.assertListEqual(logits.get_shape().as_list(), [None, num_classes]) images = tf.random_uniform((batch_size, height, width, 3)) sess.run(tf.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, num_classes))
def testNoAuxHeadMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 for use_aux_head in (True, False): tf.reset_default_graph() inputs = tf.random_uniform((batch_size, height, width, 3)) tf.train.create_global_step() config = nasnet.mobile_imagenet_config() config.set_hparam('use_aux_head', int(use_aux_head)) with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): _, end_points = nasnet.build_nasnet_mobile(inputs, num_classes, config=config) self.assertEqual('AuxLogits' in end_points, use_aux_head)
def testBuildLogitsMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) tf.train.create_global_step() with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): logits, end_points = nasnet.build_nasnet_mobile( inputs, num_classes) auxlogits = end_points['AuxLogits'] predictions = end_points['Predictions'] self.assertListEqual(auxlogits.get_shape().as_list(), [batch_size, num_classes]) self.assertListEqual(logits.get_shape().as_list(), [batch_size, num_classes]) self.assertListEqual(predictions.get_shape().as_list(), [batch_size, num_classes])
def testAllEndPointsShapesMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random_uniform((batch_size, height, width, 3)) tf.train.create_global_step() with slim.arg_scope(nasnet.nasnet_mobile_arg_scope()): _, end_points = nasnet.build_nasnet_mobile(inputs, num_classes) endpoints_shapes = { 'Stem': [batch_size, 28, 28, 88], 'Cell_0': [batch_size, 28, 28, 264], 'Cell_1': [batch_size, 28, 28, 264], 'Cell_2': [batch_size, 28, 28, 264], 'Cell_3': [batch_size, 28, 28, 264], 'Cell_4': [batch_size, 14, 14, 528], 'Cell_5': [batch_size, 14, 14, 528], 'Cell_6': [batch_size, 14, 14, 528], 'Cell_7': [batch_size, 14, 14, 528], 'Cell_8': [batch_size, 7, 7, 1056], 'Cell_9': [batch_size, 7, 7, 1056], 'Cell_10': [batch_size, 7, 7, 1056], 'Cell_11': [batch_size, 7, 7, 1056], 'Reduction_Cell_0': [batch_size, 14, 14, 352], 'Reduction_Cell_1': [batch_size, 7, 7, 704], 'global_pool': [batch_size, 1056], # Logits and predictions 'AuxLogits': [batch_size, num_classes], 'Logits': [batch_size, num_classes], 'Predictions': [batch_size, num_classes] } self.assertItemsEqual(endpoints_shapes.keys(), end_points.keys()) for endpoint_name in endpoints_shapes: tf.logging.info('Endpoint name: {}'.format(endpoint_name)) expected_shape = endpoints_shapes[endpoint_name] self.assertTrue(endpoint_name in end_points) self.assertListEqual( end_points[endpoint_name].get_shape().as_list(), expected_shape)
def pnasnet_mobile_arg_scope(weight_decay=4e-5, batch_norm_decay=0.9997, batch_norm_epsilon=0.001): """Default arg scope for the PNASNet Mobile ImageNet model.""" return nasnet.nasnet_mobile_arg_scope(weight_decay, batch_norm_decay, batch_norm_epsilon)