我试图运行该程序，该程序利用基于Tensorflow的object_detection_tutorial.ipynb的对象检测。我尝试导入自己的模型，而不是从Internet下载，但是我一直收到表示ResourceExhaustedError的错误（请参见上文的追溯） ）：分配形状为[500,17,17,4032]的张量并在分配器GPU_0_bfc上的/ job：localhost / replica：0 / task：0 / device：GPU：0上键入float时，OOM。
import numpy as np
import os
import sys
import tensorflow as tf
import six.moves.urllib as urllib
import tarfile
import zipfile
from distutils.version import StrictVersion
from collections import defaultdict
from io import StringIO
import matplotlib.pyplot as plt
from PIL import Image
import cv2
print("Initializing Tensorflow")
# This is needed since the notebook is stored in the object_detection folder.
sys.path.append("..")
from object_detection.utils import ops as utils_ops
if StrictVersion(tf.__version__) < StrictVersion('1.12.0'):
raise ImportError('Please upgrade your TensorFlow installation to v1.12.*.')
# ## Env setup
# This is needed to display the images.
#get_ipython().magic(u'matplotlib inline')
# ## Object detection imports
# Here are the imports from the object detection module.
from utils import label_map_util
from utils import visualization_utils as vis_util
print("Preparing Object Detection Model")
# # Model preparation
# ## Variables
#
# Any model exported using the `export_inference_graph.py` tool can be loaded here simply by changing `PATH_TO_FROZEN_GRAPH` to point to a new .pb file.
#
# By default we use an "SSD with Mobilenet" model here. See the [detection model zoo](https://github.com/tensorflow/models/blob/master/research/object_detection/g3doc/detection_model_zoo.md) for a list of other models that can be run out-of-the-box with varying speeds and accuracies.
MODEL_NAME = 'output_inference_graph_v1.pb'
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_FROZEN_GRAPH = MODEL_NAME + '/frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = os.path.join('annotations', 'label_map.pbtxt')
"""
opener = urllib.request.URLopener()
opener.retrieve(DOWNLOAD_BASE + MODEL_FILE, MODEL_FILE)
tar_file = tarfile.open(MODEL_FILE)
for file in tar_file.getmembers():
file_name = os.path.basename(file.name)
if 'frozen_inference_graph.pb' in file_name:
tar_file.extract(file, os.getcwd())
"""
# ## Load a (frozen) Tensorflow model into memory.
print("Importing Label Map")
detection_graph = tf.Graph()
with detection_graph.as_default():
od_graph_def = tf.GraphDef()
with tf.gfile.GFile(PATH_TO_FROZEN_GRAPH, 'rb') as fid:
serialized_graph = fid.read()
od_graph_def.ParseFromString(serialized_graph)
tf.import_graph_def(od_graph_def, name='')
# ## Loading label map
# Label maps map indices to category names, so that when our convolution network predicts `5`, we know that this corresponds to `airplane`. Here we use internal utility functions, but anything that returns a dictionary mapping integers to appropriate string labels would be fine
category_index = label_map_util.create_category_index_from_labelmap(PATH_TO_LABELS, use_display_name=True)
def load_image_into_numpy_array(image):
(im_width, im_height) = image.size
return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)
# For the sake of simplicity we will use only 2 images:
# image1.jpg
# image2.jpg
# If you want to test the code with your images, just add path to the images to the TEST_IMAGE_PATHS.
print("Organizing Images")
PATH_TO_TEST_IMAGES_DIR = 'test_images'
TEST_IMAGE_PATHS = [ os.path.join(PATH_TO_TEST_IMAGES_DIR, '{}.jpg'.format(i)) for i in range(1, 92) ]
# Size, in inches, of the output images.
IMAGE_SIZE = (12, 8)
def run_inference_for_single_image(image, graph):
with graph.as_default():
with tf.Session() as sess:
# Get handles to input and output tensors
ops = tf.get_default_graph().get_operations()
all_tensor_names = {output.name for op in ops for output in op.outputs}
tensor_dict = {}
for key in ['num_detections', 'detection_boxes', 'detection_scores', 'detection_classes', 'detection_masks']:
tensor_name = key + ':0'
if tensor_name in all_tensor_names:
tensor_dict[key] = tf.get_default_graph().get_tensor_by_name(tensor_name)
if 'detection_masks' in tensor_dict:
# The following processing is only for single image
detection_boxes = tf.squeeze(tensor_dict['detection_boxes'], [0])
detection_masks = tf.squeeze(tensor_dict['detection_masks'], [0])
# Reframe is required to translate mask from box coordinates to image coordinates and fit the image size.
real_num_detection = tf.cast(tensor_dict['num_detections'][0], tf.int32)
detection_boxes = tf.slice(detection_boxes, [0, 0], [real_num_detection, -1])
detection_masks = tf.slice(detection_masks, [0, 0, 0], [real_num_detection, -1, -1])
detection_masks_reframed = utils_ops.reframe_box_masks_to_image_masks(detection_masks, detection_boxes, image.shape[1], image.shape[2])
detection_masks_reframed = tf.cast(tf.greater(detection_masks_reframed, 0.5), tf.uint8)
# Follow the convention by adding back the batch dimension
tensor_dict['detection_masks'] = tf.expand_dims(detection_masks_reframed, 0)
image_tensor = tf.get_default_graph().get_tensor_by_name('image_tensor:0')
# Run inference
output_dict = sess.run(tensor_dict,feed_dict={image_tensor: image})
# all outputs are float32 numpy arrays, so convert types as appropriate
output_dict['num_detections'] = int(output_dict['num_detections'][0])
output_dict['detection_classes'] = output_dict['detection_classes'][0].astype(np.int64)
output_dict['detection_boxes'] = output_dict['detection_boxes'][0]
output_dict['detection_scores'] = output_dict['detection_scores'][0]
if 'detection_masks' in output_dict:
output_dict['detection_masks'] = output_dict['detection_masks'][0]
return output_dict
counter = 1
for image_path in TEST_IMAGE_PATHS:
print("Opening Image {}".format(counter))
image = Image.open(image_path)
# the array based representation of the image will be used later in order to prepare the
# result image with boxes and labels on it.
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
image_np_expanded = np.expand_dims(image_np, axis=0)
# Actual detection.
print("Running Object Detection on Image {}".format(counter))
output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
# Visualization of the results of a detection.
vis_util.visualize_boxes_and_labels_on_image_array(image_np,output_dict['detection_boxes'],output_dict['detection_classes'],output_dict['detection_scores'],category_index,instance_masks=output_dict.get('detection_masks'),use_normalized_coordinates=True,line_thickness=8)
print("Saving as OUT-IMAGE-{}".format(counter))
cv2.imwrite("OUT-IMAGE-"+str(counter)+".jpg", image_np)
counter+=1
我得到的错误如下所示：
2019-07-03 12:12:05.269630: I tensorflow/core/common_runtime/bfc_allocator.cc:647] Stats:
Limit: 1433901465
InUse: 542523392
MaxInUse: 1157243648
NumAllocs: 2262
MaxAllocSize: 206440448
2019-07-03 12:12:05.286124: W tensorflow/core/common_runtime/bfc_allocator.cc:271] *****************************__*******xxx___________________________________________________________
2019-07-03 12:12:05.301527: W tensorflow/core/framework/op_kernel.cc:1273] OP_REQUIRES failed at crop_and_resize_op.cc:173 : Resource exhausted: OOM when allocating tensor with shape[500,17,17,4032] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
Traceback (most recent call last):
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 1334, in _do_call
return fn(*args)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 1319, in _run_fn
options, feed_dict, fetch_list, target_list, run_metadata)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 1407, in _call_tf_sessionrun
run_metadata)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[500,17,17,4032] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[{{node CropAndResize/CropAndResize}} = CropAndResize[T=DT_FLOAT, extrapolation_value=0, method="bilinear", _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/concat-1-0-TransposeNCHWToNHWC-LayoutOptimizer, CropAndResize/Reshape, CropAndResize/Reshape_1/_171, CropAndResize/CropAndResize/crop_size)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[{{node SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/unstack/_249}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4101_...on/unstack", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](^_cloopSecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayReadV3_5/_31)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "obj_det.py", line 144, in <module>
output_dict = run_inference_for_single_image(image_np_expanded, detection_graph)
File "obj_det.py", line 121, in run_inference_for_single_image
output_dict = sess.run(tensor_dict,feed_dict={image_tensor: image})
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 929, in run
run_metadata_ptr)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run
run_metadata)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[500,17,17,4032] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node CropAndResize/CropAndResize (defined at obj_det.py:70) = CropAndResize[T=DT_FLOAT, extrapolation_value=0, method="bilinear", _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/concat-1-0-TransposeNCHWToNHWC-LayoutOptimizer, CropAndResize/Reshape, CropAndResize/Reshape_1/_171, CropAndResize/CropAndResize/crop_size)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[{{node SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/unstack/_249}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4101_...on/unstack", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](^_cloopSecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayReadV3_5/_31)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
Caused by op 'CropAndResize/CropAndResize', defined at:
File "obj_det.py", line 70, in <module>
tf.import_graph_def(od_graph_def, name='')
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\util\deprecation.py", line 488, in new_func
return func(*args, **kwargs)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\framework\importer.py", line 442, in import_graph_def
_ProcessNewOps(graph)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\framework\importer.py", line 234, in _ProcessNewOps
for new_op in graph._add_new_tf_operations(compute_devices=False): # pylint: disable=protected-access
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\framework\ops.py", line 3440, in _add_new_tf_operations
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\framework\ops.py", line 3440, in <listcomp>
for c_op in c_api_util.new_tf_operations(self)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\framework\ops.py", line 3299, in _create_op_from_tf_operation
ret = Operation(c_op, self)
File "C:\Users\Alvin\Anaconda3\envs\tf12\lib\site-packages\tensorflow\python\framework\ops.py", line 1770, in __init__
self._traceback = tf_stack.extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[500,17,17,4032] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[node CropAndResize/CropAndResize (defined at obj_det.py:70) = CropAndResize[T=DT_FLOAT, extrapolation_value=0, method="bilinear", _device="/job:localhost/replica:0/task:0/device:GPU:0"](FirstStageFeatureExtractor/concat-1-0-TransposeNCHWToNHWC-LayoutOptimizer, CropAndResize/Reshape, CropAndResize/Reshape_1/_171, CropAndResize/CropAndResize/crop_size)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.
[[{{node SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/unstack/_249}} = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4101_...on/unstack", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"](^_cloopSecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/TensorArrayReadV3_5/_31)]]
Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.