if not os.path.isfile(tfmodel + '.meta'): print(tfmodel) raise IOError( ('{:s} not found.\nDid you download the proper networks from ' 'our server and place them properly?').format(tfmodel + '.meta')) # set config tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True # init session sess = tf.Session(config=tfconfig) # load network if demonet == 'vgg16': net = vgg16(batch_size=1) # elif demonet == 'res101': # net = resnetv1(batch_size=1, num_layers=101) else: raise NotImplementedError n_classes = len(CLASSES) # create the structure of the net having a certain shape (which depends on the number of classes) net.create_architecture(sess, "TEST", 2, tag='default', anchor_scales=[8, 16, 32]) saver = tf.train.Saver() saver.restore(sess, tfmodel)
# if has model, get the name from it # if does not, then just use the initialization weights filename = 'default/' + args.network_name imdb = get_imdb(test_dataset_name) #配置Session参数 tfconfig = tf.ConfigProto(allow_soft_placement=True) tfconfig.gpu_options.allow_growth = True # init session sess = tf.Session(config=tfconfig) # load network if args.network_name == 'vgg16': net = vgg16() elif args.network_name == 'res50': net = resnetv1(num_layers=50) elif args.network_name == 'res101': net = resnetv1(num_layers=101) elif args.network_name == 'res152': net = resnetv1(num_layers=152) elif args.network_name == 'mobile': net = mobilenetv1() else: raise NotImplementedError # load model net.create_architecture("TEST", imdb.num_classes, tag='default', anchor_scales=cfg.ANCHOR_SCALES,