bof.py 文件源码

def get_output_for(self, input, **kwargs):
        distances = conv_pairwise_distance(input, self.V)
        similarities = T.exp(-distances / T.abs_(self.gamma))
        norm = T.sum(similarities, 1).reshape((similarities.shape[0], 1, similarities.shape[2], similarities.shape[3]))
        membership = similarities / (norm + self.eps)

        histogram = T.mean(membership, axis=(2, 3))
        if self.spatial_level == 1:
            pivot1, pivot2 = membership.shape[2] / 2, membership.shape[3] / 2
            h1 = T.mean(membership[:, :, :pivot1, :pivot2], axis=(2, 3))
            h2 = T.mean(membership[:, :, :pivot1, pivot2:], axis=(2, 3))
            h3 = T.mean(membership[:, :, pivot1:, :pivot2], axis=(2, 3))
            h4 = T.mean(membership[:, :, pivot1:, pivot2:], axis=(2, 3))
            # Pyramid is not used in the paper
            # histogram = T.horizontal_stack(h1, h2, h3, h4)
            histogram = T.horizontal_stack(histogram, h1, h2, h3, h4)
        return histogram