def _compute_multibox_prior(attrs, inputs, _): """Compute definition of multibox_prior""" sizes = get_float_tuple(attrs.sizes) ratios = get_float_tuple(attrs.ratios) steps = get_float_tuple(attrs.steps) offsets = get_float_tuple(attrs.offsets) clip = bool(get_const_int(attrs.clip)) return [topi_compute(inputs[0], sizes, ratios, steps, offsets, clip)]
def compute_decode_BBox(attrs, inputs, _, target): """Compute definition of proposal""" bbox_mean = get_float_tuple(attrs.bbox_mean) bbox_std = get_float_tuple(attrs.bbox_std) class_agnostic = bool(get_const_int(attrs.class_agnostic)) with target: return [ topi.vision.rcnn.decode_BBox(inputs[0], inputs[1], inputs[2], bbox_mean, bbox_std, class_agnostic) ]
def compute_multibox_prior(attrs, inputs, _, target): """Compute definition of multibox_prior""" sizes = get_float_tuple(attrs.sizes) ratios = get_float_tuple(attrs.ratios) steps = get_float_tuple(attrs.steps) offsets = get_float_tuple(attrs.offsets) clip = bool(get_const_int(attrs.clip)) return [ topi.vision.ssd.multibox_prior(inputs[0], sizes, ratios, steps, offsets, clip) ]
def _compute_proposal(attrs, inputs, out_type): scales = get_float_tuple(attrs.scales) ratios = get_float_tuple(attrs.ratios) feature_stride = attrs.feature_stride threshold = attrs.threshold rpn_pre_nms_top_n = attrs.rpn_pre_nms_top_n rpn_post_nms_top_n = attrs.rpn_post_nms_top_n rpn_min_size = attrs.rpn_min_size iou_loss = bool(get_const_int(attrs.iou_loss)) return [topi_compute(inputs[0], inputs[1], inputs[2], scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss)]
def compute_multibox_transform_loc(attrs, inputs, _, target): """Compute definition of multibox_detection""" clip = bool(get_const_int(attrs.clip)) threshold = get_const_float(attrs.threshold) variances = get_float_tuple(attrs.variances) return topi.vision.ssd.multibox_transform_loc( inputs[0], inputs[1], inputs[2], clip, threshold, variances)
def compute_proposal(attrs, inputs, _, target): """Compute definition of proposal""" scales = get_float_tuple(attrs.scales) ratios = get_float_tuple(attrs.ratios) feature_stride = attrs.feature_stride threshold = attrs.threshold rpn_pre_nms_top_n = attrs.rpn_pre_nms_top_n rpn_post_nms_top_n = attrs.rpn_post_nms_top_n rpn_min_size = attrs.rpn_min_size iou_loss = bool(get_const_int(attrs.iou_loss)) with target: return [ topi.vision.rcnn.proposal(inputs[0], inputs[1], inputs[2], scales, ratios, feature_stride, threshold, rpn_pre_nms_top_n, rpn_post_nms_top_n, rpn_min_size, iou_loss) ]