# Copyright 2017 The TensorFlow Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== """Tests for slim.nasnet.""" from __future__ import absolute_import from __future__ import division from __future__ import print_function import tensorflow as tf from tensorflow.contrib import slim as contrib_slim from nets.nasnet import nasnet slim = contrib_slim class NASNetTest(tf.test.TestCase): def testBuildLogitsCifarModel(self): batch_size = 5 height, width = 32, 32 num_classes = 10 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): logits, end_points = nasnet.build_nasnet_cifar(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 testBuildLogitsMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.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 testBuildLogitsLargeModel(self): batch_size = 5 height, width = 331, 331 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_large_arg_scope()): logits, end_points = nasnet.build_nasnet_large(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 testBuildPreLogitsCifarModel(self): batch_size = 5 height, width = 32, 32 num_classes = None inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): net, end_points = nasnet.build_nasnet_cifar(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, 768]) def testBuildPreLogitsMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = None inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.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 testBuildPreLogitsLargeModel(self): batch_size = 5 height, width = 331, 331 num_classes = None inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_large_arg_scope()): net, end_points = nasnet.build_nasnet_large(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, 4032]) def testAllEndPointsShapesCifarModel(self): batch_size = 5 height, width = 32, 32 num_classes = 10 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): _, end_points = nasnet.build_nasnet_cifar(inputs, num_classes) endpoints_shapes = {'Stem': [batch_size, 32, 32, 96], 'Cell_0': [batch_size, 32, 32, 192], 'Cell_1': [batch_size, 32, 32, 192], 'Cell_2': [batch_size, 32, 32, 192], 'Cell_3': [batch_size, 32, 32, 192], 'Cell_4': [batch_size, 32, 32, 192], 'Cell_5': [batch_size, 32, 32, 192], 'Cell_6': [batch_size, 16, 16, 384], 'Cell_7': [batch_size, 16, 16, 384], 'Cell_8': [batch_size, 16, 16, 384], 'Cell_9': [batch_size, 16, 16, 384], 'Cell_10': [batch_size, 16, 16, 384], 'Cell_11': [batch_size, 16, 16, 384], 'Cell_12': [batch_size, 8, 8, 768], 'Cell_13': [batch_size, 8, 8, 768], 'Cell_14': [batch_size, 8, 8, 768], 'Cell_15': [batch_size, 8, 8, 768], 'Cell_16': [batch_size, 8, 8, 768], 'Cell_17': [batch_size, 8, 8, 768], 'Reduction_Cell_0': [batch_size, 16, 16, 256], 'Reduction_Cell_1': [batch_size, 8, 8, 512], 'global_pool': [batch_size, 768], # 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.compat.v1.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 testNoAuxHeadCifarModel(self): batch_size = 5 height, width = 32, 32 num_classes = 10 for use_aux_head in (True, False): tf.compat.v1.reset_default_graph() inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() config = nasnet.cifar_config() config.set_hparam('use_aux_head', int(use_aux_head)) with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): _, end_points = nasnet.build_nasnet_cifar(inputs, num_classes, config=config) self.assertEqual('AuxLogits' in end_points, use_aux_head) def testAllEndPointsShapesMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.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.compat.v1.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 testNoAuxHeadMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 for use_aux_head in (True, False): tf.compat.v1.reset_default_graph() inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.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 testAllEndPointsShapesLargeModel(self): batch_size = 5 height, width = 331, 331 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_large_arg_scope()): _, end_points = nasnet.build_nasnet_large(inputs, num_classes) endpoints_shapes = {'Stem': [batch_size, 42, 42, 336], 'Cell_0': [batch_size, 42, 42, 1008], 'Cell_1': [batch_size, 42, 42, 1008], 'Cell_2': [batch_size, 42, 42, 1008], 'Cell_3': [batch_size, 42, 42, 1008], 'Cell_4': [batch_size, 42, 42, 1008], 'Cell_5': [batch_size, 42, 42, 1008], 'Cell_6': [batch_size, 21, 21, 2016], 'Cell_7': [batch_size, 21, 21, 2016], 'Cell_8': [batch_size, 21, 21, 2016], 'Cell_9': [batch_size, 21, 21, 2016], 'Cell_10': [batch_size, 21, 21, 2016], 'Cell_11': [batch_size, 21, 21, 2016], 'Cell_12': [batch_size, 11, 11, 4032], 'Cell_13': [batch_size, 11, 11, 4032], 'Cell_14': [batch_size, 11, 11, 4032], 'Cell_15': [batch_size, 11, 11, 4032], 'Cell_16': [batch_size, 11, 11, 4032], 'Cell_17': [batch_size, 11, 11, 4032], 'Reduction_Cell_0': [batch_size, 21, 21, 1344], 'Reduction_Cell_1': [batch_size, 11, 11, 2688], 'global_pool': [batch_size, 4032], # 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.compat.v1.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 testNoAuxHeadLargeModel(self): batch_size = 5 height, width = 331, 331 num_classes = 1000 for use_aux_head in (True, False): tf.compat.v1.reset_default_graph() inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() config = nasnet.large_imagenet_config() config.set_hparam('use_aux_head', int(use_aux_head)) with slim.arg_scope(nasnet.nasnet_large_arg_scope()): _, end_points = nasnet.build_nasnet_large(inputs, num_classes, config=config) self.assertEqual('AuxLogits' in end_points, use_aux_head) def testVariablesSetDeviceMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() # Force all Variables to reside on the device. with tf.compat.v1.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.compat.v1.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.compat.v1.get_collection( tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='on_cpu'): self.assertDeviceEqual(v.device, '/cpu:0') for v in tf.compat.v1.get_collection( tf.compat.v1.GraphKeys.GLOBAL_VARIABLES, scope='on_gpu'): self.assertDeviceEqual(v.device, '/gpu:0') def testUnknownBatchSizeMobileModel(self): batch_size = 1 height, width = 224, 224 num_classes = 1000 with self.test_session() as sess: inputs = tf.compat.v1.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.compat.v1.global_variables_initializer()) output = sess.run(logits, {inputs: images.eval()}) self.assertEquals(output.shape, (batch_size, num_classes)) 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(input=logits, axis=1) sess.run(tf.compat.v1.global_variables_initializer()) output = sess.run(predictions) self.assertEquals(output.shape, (batch_size,)) def testOverrideHParamsCifarModel(self): batch_size = 5 height, width = 32, 32 num_classes = 10 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() config = nasnet.cifar_config() config.set_hparam('data_format', 'NCHW') with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): _, end_points = nasnet.build_nasnet_cifar( inputs, num_classes, config=config) self.assertListEqual( end_points['Stem'].shape.as_list(), [batch_size, 96, 32, 32]) def testOverrideHParamsMobileModel(self): batch_size = 5 height, width = 224, 224 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.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 testOverrideHParamsLargeModel(self): batch_size = 5 height, width = 331, 331 num_classes = 1000 inputs = tf.random.uniform((batch_size, height, width, 3)) tf.compat.v1.train.create_global_step() config = nasnet.large_imagenet_config() config.set_hparam('data_format', 'NCHW') with slim.arg_scope(nasnet.nasnet_large_arg_scope()): _, end_points = nasnet.build_nasnet_large( inputs, num_classes, config=config) self.assertListEqual( end_points['Stem'].shape.as_list(), [batch_size, 336, 42, 42]) def testCurrentStepCifarModel(self): batch_size = 5 height, width = 32, 32 num_classes = 10 inputs = tf.random.uniform((batch_size, height, width, 3)) global_step = tf.compat.v1.train.create_global_step() with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): logits, end_points = nasnet.build_nasnet_cifar(inputs, num_classes, current_step=global_step) 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 testUseBoundedAcitvationCifarModel(self): batch_size = 1 height, width = 32, 32 num_classes = 10 for use_bounded_activation in (True, False): tf.compat.v1.reset_default_graph() inputs = tf.random.uniform((batch_size, height, width, 3)) config = nasnet.cifar_config() config.set_hparam('use_bounded_activation', use_bounded_activation) with slim.arg_scope(nasnet.nasnet_cifar_arg_scope()): _, _ = nasnet.build_nasnet_cifar( inputs, num_classes, config=config) for node in tf.compat.v1.get_default_graph().as_graph_def().node: if node.op.startswith('Relu'): self.assertEqual(node.op == 'Relu6', use_bounded_activation) if __name__ == '__main__': tf.test.main()