argmax.py

""" a custom layer for 'argmax', maybe we should implement this in standard way.
    more info can be found here: http://caffe.berkeleyvision.org/tutorial/layers/argmax.html
"""
from .register import register


def import_fluid():
    import paddle.fluid as fluid
    return fluid


def argmax_shape(input_shape, out_max_val=False, top_k=1, axis=-1):
    """ calculate the output shape of this layer using input shape

    Args:
        @input_shape (list of num): a list of number which represents the input shape
        @out_max_val (bool): parameter from caffe's ArgMax layer
        @top_k (int): parameter from caffe's ArgMax layer
        @axis (int): parameter from caffe's ArgMax layer

    Returns:
        @output_shape (list of num): a list of numbers represent the output shape
    """
    input_shape = list(input_shape)

    if axis < 0:
        axis += len(input_shape)

    assert (axis + 1 == len(input_shape)
            ), 'only can be applied on the last dimension[axis:%d, %s] now,'\
                    'make sure you have set axis param in xxx.prototxt file' \
                    % (axis, str(input_shape))

    output_shape = input_shape
    output_shape[-1] = top_k
    if out_max_val is True:
        output_shape[-1] *= 2

    return output_shape


def argmax_layer(input, name, out_max_val=False, top_k=1, axis=-1):
    """ build a layer of type 'ArgMax' using fluid

    Args:
        @input (variable): input fluid variable for this layer
        @name (str): name for this layer
        @out_max_val (bool): parameter from caffe's ArgMax layer
        @top_k (int): parameter from caffe's ArgMax layer
        @axis (int): parameter from caffe's ArgMax layer

    Returns:
        output (variable): output variable for this layer
    """

    fluid = import_fluid()

    if axis < 0:
        axis += len(input.shape)

    if out_max_val is True:
        topk_var, index_var = fluid.layers.topk(input=input, k=top_k)
        index_var = fluid.layers.cast(index_var, dtype=topk_var.dtype)
        output = fluid.layers.concat(
            [index_var, topk_var], axis=axis, name=name)
    else:
        topk_var, index_var = fluid.layers.topk(input=input, k=top_k, name=name)
        output = index_var

    return output


register(kind='ArgMax', shape=argmax_shape, layer=argmax_layer)