from .register import register from x2paddle.core.util import * def priorbox_shape(input_shape, max_size=None, aspect_ratio=None): fc_shape = input_shape[0] N = 1 if not max_size == None: N += 1 if not aspect_ratio == None: N += 2 * len(aspect_ratio) N_bbx = fc_shape[2] * fc_shape[3] * N output_shape = [1, 2, 4 * N_bbx] return [output_shape] def priorbox_layer(inputs, step=0.0, offset=0.5, min_size=None, max_size=[], aspect_ratio=[1.0], flip=False, clip=False, variance=[0.1, 0.1, 0.2, 0.2], input_shape=None, name=None): input = inputs[0] image = inputs[1] steps = tuple(step) if type(step) is list or type(step) is tuple else (step, step) box, variance_ = fluid.layers.prior_box(input, image, min_sizes=min_size, max_sizes=max_size, aspect_ratios=aspect_ratio, variance=variance, flip=flip, clip=clip, steps=steps, offset=offset, name=name, min_max_aspect_ratios_order=True) box = fluid.layers.reshape(box, [1, 1, -1]) variance_ = fluid.layers.reshape(variance_, [1, 1, -1]) out = fluid.layers.concat([box, variance_], axis=1) return out def priorbox_weights(name, data=None): weights_name = [] return weights_name register(kind='PriorBox', shape=priorbox_shape, layer=priorbox_layer, weights=priorbox_weights)