def get_vgg16_test(system_dict): from symnet.symbol_vgg import get_vgg_test return get_vgg_test(anchor_scales=system_dict["rpn_anchor_scales"], anchor_ratios=system_dict["rpn_anchor_ratios"], rpn_feature_stride=system_dict["rpn_feat_stride"], rpn_pre_topk=system_dict["rpn_pre_nms_topk"], rpn_post_topk=system_dict["rpn_post_nms_topk"], rpn_nms_thresh=system_dict["rpn_nms_thresh"], rpn_min_size=system_dict["rpn_min_size"], num_classes=system_dict["rcnn_num_classes"], rcnn_feature_stride=system_dict["rcnn_feat_stride"], rcnn_pooled_size=system_dict["rcnn_pooled_size"], rcnn_batch_size=system_dict["rcnn_batch_size"])
def get_vgg16_test(args): from symnet.symbol_vgg import get_vgg_test if not args.params: args.params = 'model/vgg16-0010.params' args.img_pixel_means = (123.68, 116.779, 103.939) args.img_pixel_stds = (1.0, 1.0, 1.0) args.net_fixed_params = ['conv1', 'conv2'] args.rpn_feat_stride = 16 args.rcnn_feat_stride = 16 args.rcnn_pooled_size = (7, 7) return get_vgg_test(anchor_scales=args.rpn_anchor_scales, anchor_ratios=args.rpn_anchor_ratios, rpn_feature_stride=args.rpn_feat_stride, rpn_pre_topk=args.rpn_pre_nms_topk, rpn_post_topk=args.rpn_post_nms_topk, rpn_nms_thresh=args.rpn_nms_thresh, rpn_min_size=args.rpn_min_size, num_classes=args.rcnn_num_classes, rcnn_feature_stride=args.rcnn_feat_stride, rcnn_pooled_size=args.rcnn_pooled_size, rcnn_batch_size=args.rcnn_batch_size)
def get_vgg16_test(args, config): from symnet.symbol_vgg import get_vgg_test if not args.params: args.params = 'model/vgg16-0010.params' # config = Config('configs/vgg_step_{}.yml'.format(args.step)) return get_vgg_test(anchor_scales=config.rpn['rpn_anchor_scales'], anchor_ratios=config.rpn['rpn_anchor_ratios'], rpn_feature_stride=config.rpn['rpn_feat_stride'], rpn_pre_topk=config.rpn['rpn_pre_nms_topk'], rpn_post_topk=config.rpn['rpn_post_nms_topk'], rpn_nms_thresh=config.rpn['rpn_nms_thresh'], rpn_min_size=config.rpn['rpn_min_size'], num_classes=config.rcnn['rcnn_num_classes'], rcnn_feature_stride=config.rcnn['rcnn_feat_stride'], rcnn_pooled_size=config.rcnn['rcnn_pooled_size'], rcnn_batch_size=args.rcnn_batch_size, isBin=config.train_param['is_rcnn_top_bin'], step=args.step)
def get_vgg16_test(args): from symnet.symbol_vgg import get_vgg_test if not args.params: args.params = 'model/vgg16-0010.params' args.rpn_feat_stride = 16 args.rcnn_feat_stride = 16 args.rcnn_pooled_size = (7, 7) return get_vgg_test(anchor_scales=args.rpn_anchor_scales, anchor_ratios=args.rpn_anchor_ratios, rpn_feature_stride=args.rpn_feat_stride, rpn_pre_topk=args.rpn_pre_nms_topk, rpn_post_topk=args.rpn_post_nms_topk, rpn_nms_thresh=args.rpn_nms_thresh, rpn_min_size=args.rpn_min_size, num_classes=args.rcnn_num_classes, rcnn_feature_stride=args.rcnn_feat_stride, rcnn_pooled_size=args.rcnn_pooled_size, rcnn_batch_size=args.rcnn_batch_size, isBin=args.is_bin_new, step=args.step_new)
def get_vgg16_test(system_dict): ''' Internal function: Select vgg16 params Args: system_dict (dict): Dictionary of all the parameters selected for training Returns: mxnet model: Vgg16 model ''' from symnet.symbol_vgg import get_vgg_test return get_vgg_test(anchor_scales=system_dict["rpn_anchor_scales"], anchor_ratios=system_dict["rpn_anchor_ratios"], rpn_feature_stride=system_dict["rpn_feat_stride"], rpn_pre_topk=system_dict["rpn_pre_nms_topk"], rpn_post_topk=system_dict["rpn_post_nms_topk"], rpn_nms_thresh=system_dict["rpn_nms_thresh"], rpn_min_size=system_dict["rpn_min_size"], num_classes=system_dict["rcnn_num_classes"], rcnn_feature_stride=system_dict["rcnn_feat_stride"], rcnn_pooled_size=system_dict["rcnn_pooled_size"], rcnn_batch_size=system_dict["rcnn_batch_size"])