e17f56584ef661ef052f4dc21c933be1a6fa25fc,coremltools/models/neural_network/builder.py,NeuralNetworkBuilder,add_reduce_max,#NeuralNetworkBuilder#Any#Any#Any#Any#Any#Any#,6306

Before Change

        spec_layer_params.keepDims = keepdims
        spec_layer_params.reduceAll = reduce_all

        if keepdims:
            self.rank_dict[output_name] = self.get_rank(input_name)
        else:
            if reduce_all:
                self.rank_dict[output_name] = 1
            else:
                self.rank_dict[output_name] = self.get_rank(input_name) - 1
        return spec_layer

    def add_reduce_min(self, name, input_name, output_name,
                       axes=None, keepdims=True, reduce_all=False):

After Change

        self.set_rank_for_reduce_op(input_name, output_name, keepdims, reduce_all)
        return spec_layer

    def add_reduce_max(self, name, input_name, output_name,
                       axes=None, keepdims=True, reduce_all=False):
        
        Add a reduce_max layer to the model that reduces the input tensor
        using ``max(elements across given dimensions)``.
        Refer to the **ReduceMaxLayerParams** message in specification
        (NeuralNetwork.proto) for more details.

        Parameters
        ----------
        name: str
            The name of this layer.
        input_name: str
            The input blob name of this layer.
        output_name: str
            The output blob name of this layer.
        axes: list of int or tuple of int, optional
            List of dimensions for the reduce operations.
            Each should be in range [-rank(input), rank(input)), default: None (reduce_all)
        keepdims: bool, optional
            Whether or not to retain the reduced dimensions with length 1, default: true.
        reduce_all: bool, optional
            Whether or not to reduce on all axes, default: false.

        See Also
        --------
        add_reduce_l1, add_reduce_l2, add_reduce_sum, add_reduce_min, add_reduce_prod
        add_reduce_mean, add_reduce_logsum, add_reduce_logsumexp, add_reduce_sumsquare
        

        spec_layer = self._add_generic_layer(name, [input_name], [output_name])
        spec_layer_params = spec_layer.reduceMax

        if axes and len(axes) != 0:
            spec_layer_params.axes.extend(map(int, axes))
        else:
            reduce_all = True

        spec_layer_params.keepDims = keepdims
        spec_layer_params.reduceAll = reduce_all

        self.set_rank_for_reduce_op(input_name, output_name, keepdims, reduce_all)
        return spec_layer

    def add_reduce_min(self, name, input_name, output_name,
                       axes=None, keepdims=True, reduce_all=False):

Instances