Python 使用coco数据集运行mask r-cnn时出现Jetson nano错误

我下面是在jetson nano 4gb上使用coco数据集的mask r-cnn的基本教程。然而，当我运行程序时，我得到以下错误。这是内存问题吗？GPU内存不足吗？我怎么修理它？是否有另一个教程可以实现没有tensorflow的Mask R-CNN

File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1365, in _do_call
 return fn(*args)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1350, in _run_fn
 target_list, run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1443, in _call_tf_sessionrun
 run_metadata)
tensorflow.python.framework.errors_impl.InternalError: 2 root error(s) found.
(0) Internal: Dst tensor is not initialized.
  [[{{node _arg_Placeholder_658_0_619}}]]
(1) Internal: Dst tensor is not initialized.
  [[{{node _arg_Placeholder_658_0_619}}]]
  [[Assign_658/_3999]]
0 successful operations.
0 derived errors ignored.

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "test2.py", line 30, in <module>
 model.load_weights('mask_rcnn_coco.h5', by_name=True)
File "/usr/local/lib/python3.6/dist-packages/mask_rcnn-2.1-py3.6.egg/mrcnn/model.py", line 2130, in load_weights
File "/usr/local/lib/python3.6/dist-packages/keras/engine/saving.py", line 1154, in load_weights_from_hdf5_group_by_name
 K.batch_set_value(weight_value_tuples)
File "/usr/local/lib/python3.6/dist-packages/keras/backend/tensorflow_backend.py", line 2470, in batch_set_value
 get_session().run(assign_ops, feed_dict=feed_dict)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 956, in run
 run_metadata_ptr)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1180, in _run
 feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1359, in _do_run
 run_metadata)
File "/usr/local/lib/python3.6/dist-packages/tensorflow_core/python/client/session.py", line 1384, in _do_call
 raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InternalError: 2 root error(s) found.
(0) Internal: Dst tensor is not initialized.
  [[{{node _arg_Placeholder_658_0_619}}]]
(1) Internal: Dst tensor is not initialized.
  [[{{node _arg_Placeholder_658_0_619}}]]
  [[Assign_658/_3999]]
0 successful operations.
0 derived errors ignored.

编辑 这是本教程的链接

---

你能链接你正在使用的教程吗？@LouisLac我在编辑中添加了链接

import sys
import random
import math
import numpy as np
import skimage.io
import matplotlib
import matplotlib.pyplot as plt
from mrcnn import utils
import mrcnn.model as modellib
from mrcnn import visualize
import coco

COCO_MODEL_PATH = "/home/jetson/Desktop/pano_l515/Mask_RCNN/mask_rcnn_coco.h5"

class InferenceConfig(coco.CocoConfig):
    # Set batch size to 1 since we'll be running inference on
    # one image at a time. Batch size = GPU_COUNT * IMAGES_PER_GPU
    GPU_COUNT = 1
    IMAGES_PER_GPU = 1

config = InferenceConfig()
config.display()


# Create model object in inference mode.
model = modellib.MaskRCNN(mode="inference", model_dir='mask_rcnn_coco.hy', config=config)

# Load weights trained on MS-COCO
model.load_weights('mask_rcnn_coco.h5', by_name=True)

# COCO Class names
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
image = skimage.io.imread('test_images.jpg')

# original image
plt.figure(figsize=(12,10))
skimage.io.imshow(image)

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

# Visualize results
r = results[0]
visualize.display_instances(image, r['rois'], r['masks'], r['class_ids'], class_names, r['scores'])