问多gpu推理tensorflow

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 model = modellib.MaskRCNN(mode="inference", model_dir=MODEL_DIR, config=config)

 # Load weights trained on MS-COCO
 model.load_weights(COCO_MODEL_PATH, by_name=True)

 # COCO Class names
 # Index of the class in the list is its ID. For example, to get ID of
 # the teddy bear class, use: class_names.index('teddy bear')
 class_names = ['BG', 'person', 'bicycle', 'car', 'motorcycle', 'airplane',
                'bus', 'train', 'truck', 'boat', 'traffic light',
                'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird',
                'cat', 'dog', 'horse', 'sheep', 'cow', 'elephant', 'bear',
                'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie',
                'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball',
                'kite', 'baseball bat', 'baseball glove', 'skateboard',
                'surfboard', 'tennis racket', 'bottle', 'wine glass', 'cup',
                'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
                'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza',
                'donut', 'cake', 'chair', 'couch', 'potted plant', 'bed',
                'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
                'keyboard', 'cell phone', 'microwave', 'oven', 'toaster',
                'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors',
                'teddy bear', 'hair drier', 'toothbrush']


 # Load a random image from the images folder
 file_names = next(os.walk(IMAGE_DIR))[2]
 image = skimage.io.imread(os.path.join(IMAGE_DIR, random.choice(file_names)))

 # Run detection
 results = model.detect([image], verbose=1)

 # Visualize results
 r = results[0]