Ejemplo n.º 1
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 def testBuildPreLogitsLargeModel(self):
     batch_size = 5
     height, width = 331, 331
     num_classes = None
     inputs = tf.random_uniform((batch_size, height, width, 3))
     tf.train.create_global_step()
     with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
         net, end_points = pnasnet.build_pnasnet_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, 4320])
Ejemplo n.º 2
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 def testOverrideHParamsLargeModel(self):
     batch_size = 5
     height, width = 331, 331
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     tf.train.create_global_step()
     config = pnasnet.large_imagenet_config()
     config.set_hparam('data_format', 'NCHW')
     with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
         _, end_points = pnasnet.build_pnasnet_large(inputs,
                                                     num_classes,
                                                     config=config)
     self.assertListEqual(end_points['Stem'].shape.as_list(),
                          [batch_size, 540, 42, 42])
Ejemplo n.º 3
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 def testNoAuxHeadLargeModel(self):
     batch_size = 5
     height, width = 331, 331
     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.large_imagenet_config()
         config.set_hparam('use_aux_head', int(use_aux_head))
         with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
             _, end_points = pnasnet.build_pnasnet_large(inputs,
                                                         num_classes,
                                                         config=config)
         self.assertEqual('AuxLogits' in end_points, use_aux_head)
Ejemplo n.º 4
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 def testBuildLogitsLargeModel(self):
     batch_size = 5
     height, width = 331, 331
     num_classes = 1000
     inputs = tf.random_uniform((batch_size, height, width, 3))
     tf.train.create_global_step()
     with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
         logits, end_points = pnasnet.build_pnasnet_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])
Ejemplo n.º 5
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    def testAllEndPointsShapesLargeModel(self):
        batch_size = 5
        height, width = 331, 331
        num_classes = 1000
        inputs = tf.random_uniform((batch_size, height, width, 3))
        tf.train.create_global_step()
        with slim.arg_scope(pnasnet.pnasnet_large_arg_scope()):
            _, end_points = pnasnet.build_pnasnet_large(inputs, num_classes)

        endpoints_shapes = {
            'Stem': [batch_size, 42, 42, 540],
            'Cell_0': [batch_size, 42, 42, 1080],
            'Cell_1': [batch_size, 42, 42, 1080],
            'Cell_2': [batch_size, 42, 42, 1080],
            'Cell_3': [batch_size, 42, 42, 1080],
            'Cell_4': [batch_size, 21, 21, 2160],
            'Cell_5': [batch_size, 21, 21, 2160],
            'Cell_6': [batch_size, 21, 21, 2160],
            'Cell_7': [batch_size, 21, 21, 2160],
            'Cell_8': [batch_size, 11, 11, 4320],
            'Cell_9': [batch_size, 11, 11, 4320],
            'Cell_10': [batch_size, 11, 11, 4320],
            'Cell_11': [batch_size, 11, 11, 4320],
            'global_pool': [batch_size, 4320],
            # Logits and predictions
            'AuxLogits': [batch_size, 1000],
            'Predictions': [batch_size, 1000],
            'Logits': [batch_size, 1000],
        }
        self.assertEqual(len(end_points), 17)
        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.assertIn(endpoint_name, end_points)
            self.assertListEqual(
                end_points[endpoint_name].get_shape().as_list(),
                expected_shape)