def _build_detection_graph(output_collection_name, graph_hook_fn): """Build the detection graph.""" net = mobilenetv1_ssh() # net = vgg16_ssh() net.create_architecture("TEST", 2, tag='default', anchor_scales={ "M1": [1, 2], "M2": [4, 8], "M3": [16, 32] }) placeholder_tensor = net._image outputs = net._predictions outputs = _add_output_tensor_nodes(net, outputs, output_collection_name) # Add global step to the graph. # slim.get_or_create_global_step() if graph_hook_fn: graph_hook_fn() return outputs, placeholder_tensor
tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True # init session sess = tf.Session(config=tfconfig) # load network if args.backbone == 'vgg16': net = vgg16_ssh() elif args.backbone == 'res50': net = resnetv1_ssh(num_layers=50) elif args.backbone == 'res101': net = resnetv1_ssh(num_layers=101) elif args.backbone == 'res152': net = resnetv1_ssh(num_layers=152) elif args.backbone == 'mobile': net = mobilenetv1_ssh() elif args.backbone == 'mobile_v2': net = mobilenetv2_ssh() else: raise NotImplementedError # load model net.create_architecture("TEST", imdb.num_classes, tag=tag, anchor_scales=cfg.ANCHOR_SCALES, anchor_ratios=cfg.ANCHOR_RATIOS) if args.model: print(('Loading model check point from {:s}').format(args.model)) saver = tf.train.Saver() saver.restore(sess, args.model) print('Loaded.')