def create_bbox_plotter(self): self.bbox_plotter = SVHNBBoxPlotter( self.load_image(self.lines[0][0]), os.path.join(self.args.model_dir, "eval_bboxes"), self.target_shape, self ) self.lines = self.lines[:self.args.num_rois]
model_snapshotter = (extensions.snapshot_object( net, 'model_{.updater.iteration}.npz'), (args.snapshot_interval, 'iteration')) # bbox plotter test if not args.test_image: test_image = validation_dataset.get_example(0)[0] else: test_image = train_dataset.load_image(args.test_image) bbox_plotter = (SVHNBBoxPlotter( test_image, os.path.join(log_dir, 'boxes'), target_shape, metrics, send_bboxes=args.send_bboxes, upstream_port=args.port, visualization_anchors=[["localization_net", "vis_anchor"], ["recognition_net", "vis_anchor"]]), (1, 'iteration')) trainer = get_trainer( net, updater, log_dir, fields_to_print, epochs=args.epochs, snapshot_interval=args.snapshot_interval, print_interval=args.log_interval, extra_extensions=( evaluator,