Method testBatchNormalization

caffe2/python/layers_test.py:1037–1113

(self, X)

        X=hu.arrays(dims=[2, 5]),
    )
    def testBatchNormalization(self, X):
        input_record = self.new_record(schema.Scalar((np.float32, (5,))))
        schema.FeedRecord(input_record, [X])
        bn_output = self.model.BatchNormalization(input_record)
        self.assertEqual(schema.Scalar((np.float32, (5,))), bn_output)
        self.model.output_schema = schema.Struct()

        train_init_net, train_net = self.get_training_nets()

        init_ops = self.assertNetContainOps(
            train_init_net,
            [
                OpSpec("ConstantFill", None, None),
                OpSpec("ConstantFill", None, None),
                OpSpec("ConstantFill", None, None),
                OpSpec("ConstantFill", None, None),
            ]
        )

        input_blob = input_record.field_blobs()[0]
        output_blob = bn_output.field_blobs()[0]

        expand_dims_spec = OpSpec(
            "ExpandDims",
            [input_blob],
            None,
        )

        train_bn_spec = OpSpec(
            "SpatialBN",
            [None, init_ops[0].output[0], init_ops[1].output[0],
                init_ops[2].output[0], init_ops[3].output[0]],
            [output_blob, init_ops[2].output[0], init_ops[3].output[0], None, None],
            {'is_test': 0, 'order': 'NCHW', 'momentum': 0.9},
        )

        test_bn_spec = OpSpec(
            "SpatialBN",
            [None, init_ops[0].output[0], init_ops[1].output[0],
                init_ops[2].output[0], init_ops[3].output[0]],
            [output_blob],
            {'is_test': 1, 'order': 'NCHW', 'momentum': 0.9},
        )

        squeeze_spec = OpSpec(
            "Squeeze",
            [output_blob],
            [output_blob],
        )

        self.assertNetContainOps(
            train_net,
            [expand_dims_spec, train_bn_spec, squeeze_spec]
        )

        eval_net = self.get_eval_net()

        self.assertNetContainOps(