def get_resnet101_train(args): from symnet.symbol_resnet import get_resnet_train if not args.pretrained: args.pretrained = 'model/resnet-101-0000.params' if not args.save_prefix: args.save_prefix = 'model/resnet101' args.img_pixel_means = (0.0, 0.0, 0.0) args.img_pixel_stds = (1.0, 1.0, 1.0) args.net_fixed_params = ['conv0', 'stage1', 'gamma', 'beta'] args.rpn_feat_stride = 16 args.rcnn_feat_stride = 16 args.rcnn_pooled_size = (14, 14) return get_resnet_train(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, rpn_batch_rois=args.rpn_batch_rois, 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, rcnn_batch_rois=args.rcnn_batch_rois, rcnn_fg_fraction=args.rcnn_fg_fraction, rcnn_fg_overlap=args.rcnn_fg_overlap, rcnn_bbox_stds=args.rcnn_bbox_stds, units=(3, 4, 23, 3), filter_list=(256, 512, 1024, 2048))
def get_resnet50_train(system_dict): ''' Internal function: Select resnet50 params Args: system_dict (dict): Dictionary of all the parameters selected for training Returns: mxnet model: Resnet50 model ''' from symnet.symbol_resnet import get_resnet_train return get_resnet_train( 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"], rpn_batch_rois=system_dict["rpn_batch_rois"], 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"], rcnn_batch_rois=system_dict["rcnn_batch_rois"], rcnn_fg_fraction=system_dict["rcnn_fg_fraction"], rcnn_fg_overlap=system_dict["rcnn_fg_overlap"], rcnn_bbox_stds=system_dict["rcnn_bbox_stds"], units=(3, 4, 6, 3), filter_list=(256, 512, 1024, 2048))
def get_resnet101_train(args, config): from symnet.symbol_resnet import get_resnet_train if not args.pretrained: args.pretrained = 'model/resnet-101-0000.params' if not args.save_prefix: args.save_prefix = 'model/resnet101' return get_resnet_train( 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'], rpn_batch_rois=args.rpn_batch_rois, 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, rcnn_batch_rois=args.rcnn_batch_rois, rcnn_fg_fraction=config.rcnn['rcnn_fg_fraction'], rcnn_fg_overlap=config.rcnn['rcnn_fg_overlap'], rcnn_bbox_stds=config.rcnn['rcnn_bbox_stds'], units=(3, 4, 23, 3), filter_list=(256, 512, 1024, 2048), step=args.step)
def get_resnet50_train(system_dict): from symnet.symbol_resnet import get_resnet_train return get_resnet_train(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"], rpn_batch_rois=system_dict["rpn_batch_rois"], 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"], rcnn_batch_rois=system_dict["rcnn_batch_rois"], rcnn_fg_fraction=system_dict["rcnn_fg_fraction"], rcnn_fg_overlap=system_dict["rcnn_fg_overlap"], rcnn_bbox_stds=system_dict["rcnn_bbox_stds"], units=(3, 4, 6, 3), filter_list=(256, 512, 1024, 2048))