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 = pnasnet.mobile_imagenet_config() config.set_hparam('data_format', 'NCHW') with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()): _, end_points = pnasnet.build_pnasnet_mobile( inputs, num_classes, config=config) self.assertListEqual(end_points['Stem'].shape.as_list(), [batch_size, 135, 28, 28])
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 = pnasnet.mobile_imagenet_config() config.set_hparam('data_format', 'NCHW') with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()): _, end_points = pnasnet.build_pnasnet_mobile( inputs, num_classes, config=config) self.assertListEqual(end_points['Stem'].shape.as_list(), [batch_size, 135, 28, 28])
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 = pnasnet.mobile_imagenet_config() config.set_hparam('use_aux_head', int(use_aux_head)) with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()): _, end_points = pnasnet.build_pnasnet_mobile( inputs, num_classes, config=config) self.assertEqual('AuxLogits' in end_points, use_aux_head)
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 = pnasnet.mobile_imagenet_config() config.set_hparam('use_aux_head', int(use_aux_head)) with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()): _, end_points = pnasnet.build_pnasnet_mobile( inputs, num_classes, config=config) self.assertEqual('AuxLogits' in end_points, use_aux_head)
def testUseBoundedAcitvationMobileModel(self): batch_size = 1 height, width = 224, 224 num_classes = 1000 for use_bounded_activation in (True, False): tf.reset_default_graph() inputs = tf.random_uniform((batch_size, height, width, 3)) config = pnasnet.mobile_imagenet_config() config.set_hparam('use_bounded_activation', use_bounded_activation) with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()): _, _ = pnasnet.build_pnasnet_mobile( inputs, num_classes, config=config) for node in tf.get_default_graph().as_graph_def().node: if node.op.startswith('Relu'): self.assertEqual(node.op == 'Relu6', use_bounded_activation)
def testUseBoundedAcitvationMobileModel(self): batch_size = 1 height, width = 224, 224 num_classes = 1000 for use_bounded_activation in (True, False): tf.reset_default_graph() inputs = tf.random_uniform((batch_size, height, width, 3)) config = pnasnet.mobile_imagenet_config() config.set_hparam('use_bounded_activation', use_bounded_activation) with slim.arg_scope(pnasnet.pnasnet_mobile_arg_scope()): _, _ = pnasnet.build_pnasnet_mobile( inputs, num_classes, config=config) for node in tf.get_default_graph().as_graph_def().node: if node.op.startswith('Relu'): self.assertEqual(node.op == 'Relu6', use_bounded_activation)