I am following a basic tutorial for mask r-cnn using coco dataset on a jetson nano 4gb. however when i run the program, im getting the following error. Is it a memory issue ? Is the GPU out of memory? How do i fix it? Is there an another tutorial which can implement Mask R-CNN without tensorflow?
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.
This is the code of the program
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'])
EDIT this is the link for the tutorial https://medium.com/analytics-vidhya/computer-vision-tutorial-implementing-mask-r-cnn-for-image-segmentation-with-python-code-fe34da5b99cd
