def extract(cls, node): variance = onnx_attr(node, 'variance', 'floats', default=[], dst_type=lambda x: np.array(x, dtype=np.float32)) if len(variance) == 0: variance = [0.1] update_attrs = { 'aspect_ratio': onnx_attr(node, 'aspect_ratio', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)), 'min_size': onnx_attr(node, 'min_size', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)), 'max_size': onnx_attr(node, 'max_size', 'floats', dst_type=lambda x: np.array(x, dtype=np.float32)), 'flip': onnx_attr(node, 'flip', 'i', default=0), 'clip': onnx_attr(node, 'clip', 'i', default=0), 'variance': list(variance), 'img_size': onnx_attr(node, 'img_size', 'i', default=0), 'img_h': onnx_attr(node, 'img_h', 'i', default=0), 'img_w': onnx_attr(node, 'img_w', 'i', default=0), 'step': onnx_attr(node, 'step', 'f', default=0.0), 'step_h': onnx_attr(node, 'step_h', 'f', default=0.0), 'step_w': onnx_attr(node, 'step_w', 'f', default=0.0), 'offset': onnx_attr(node, 'offset', 'f', default=0.0), } # update the attributes of the node PriorBoxOp.update_node_stat(node, update_attrs) return cls.enabled
def extract(cls, node): proto_layer = node.pb param = proto_layer.prior_box_param variance = param.variance if len(variance) == 0: variance = [0.1] update_attrs = { 'aspect_ratio': np.array(param.aspect_ratio), 'min_size': np.array(param.min_size), 'max_size': np.array(param.max_size), 'flip': int(param.flip), 'clip': int(param.clip), 'variance': list(variance), 'img_size': param.img_size, 'img_h': param.img_h, 'img_w': param.img_w, 'step': param.step, 'step_h': param.step_h, 'step_w': param.step_w, 'offset': param.offset, } # these params can be omitted in caffe.proto and in param as consequence, # so check if it is set or set to default fields = [field[0].name for field in param.ListFields()] if 'density' in fields: update_attrs['density'] = np.array(param.density) if 'fixed_size' in fields: update_attrs['fixed_size'] = np.array(param.fixed_size) if 'fixed_ratio' in fields: update_attrs['fixed_ratio'] = np.array(param.fixed_ratio) mapping_rule = merge_attrs(param, update_attrs) mapping_rule.update(layout_attrs()) # update the attributes of the node PriorBoxOp.update_node_stat(node, mapping_rule) return cls.enabled