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私はtensorflowオブジェクト検出apisを試しています。fast_rcnn_resnet101の微調整に成功し、推論ファイルをエクスポートできました。Tensorflowオブジェクト検出リソースの枯渇OOM例外
私のモデルを使用して画像を検出しようとすると、以下のエラーが表示されます。
---------------------------------------------------------------------------
ResourceExhaustedError Traceback (most recent call last)
<ipython-input-19-ec0c1510b78c> in <module>()
30 (boxes, scores, classes, num_detections) = sess.run(
31 [boxes, scores, classes, num_detections],
---> 32 feed_dict={image_tensor: image_np_expanded})
33 print(" classes = "+str(classes)+" scores = "+str(scores)+" num# = "+str(num_detections))
34 print ("vizualizing boxes")
/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in run(self, fetches, feed_dict, options, run_metadata)
787 try:
788 result = self._run(None, fetches, feed_dict, options_ptr,
--> 789 run_metadata_ptr)
790 if run_metadata:
791 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _run(self, handle, fetches, feed_dict, options, run_metadata)
995 if final_fetches or final_targets:
996 results = self._do_run(handle, final_targets, final_fetches,
--> 997 feed_dict_string, options, run_metadata)
998 else:
999 results = []
/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1130 if handle is None:
1131 return self._do_call(_run_fn, self._session, feed_dict, fetch_list,
-> 1132 target_list, options, run_metadata)
1133 else:
1134 return self._do_call(_prun_fn, self._session, handle, feed_dict,
/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/client/session.pyc in _do_call(self, fn, *args)
1150 except KeyError:
1151 pass
-> 1152 raise type(e)(node_def, op, message)
1153
1154 def _extend_graph(self):
ResourceExhaustedError: OOM when allocating tensor with shape[300,14,14,1024]
[[Node: CropAndResize = CropAndResize[T=DT_FLOAT, extrapolation_value=0, method="bilinear", _device="/job:localhost/replica:0/task:0/gpu:0"](FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_23/bottleneck_v1/Relu, Reshape_7, Reshape_8/_95, CropAndResize/crop_size)]]
[[Node: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Concatenate/concat_3/_151 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_2971_SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Concatenate/concat_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](^_cloopSecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/strided_slice/stack_2/_6)]]
Caused by op u'CropAndResize', defined at:
File "/home/ubuntu/anaconda2/lib/python2.7/runpy.py", line 174, in _run_module_as_main
"__main__", fname, loader, pkg_name)
File "/home/ubuntu/anaconda2/lib/python2.7/runpy.py", line 72, in _run_code
exec code in run_globals
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/__main__.py", line 3, in <module>
app.launch_new_instance()
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/traitlets/config/application.py", line 653, in launch_instance
app.start()
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/kernelapp.py", line 474, in start
ioloop.IOLoop.instance().start()
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/zmq/eventloop/ioloop.py", line 162, in start
super(ZMQIOLoop, self).start()
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tornado/ioloop.py", line 887, in start
handler_func(fd_obj, events)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tornado/stack_context.py", line 275, in null_wrapper
return fn(*args, **kwargs)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 276, in dispatcher
return self.dispatch_shell(stream, msg)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 228, in dispatch_shell
handler(stream, idents, msg)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/kernelbase.py", line 390, in execute_request
user_expressions, allow_stdin)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/ipykernel/zmqshell.py", line 501, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2717, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2821, in run_ast_nodes
if self.run_code(code, result):
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/IPython/core/interactiveshell.py", line 2881, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-5-57652895f483>", line 7, in <module>
tf.import_graph_def(od_graph_def, name='')
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/importer.py", line 311, in import_graph_def
op_def=op_def)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2506, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1269, in __init__
self._traceback = _extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[300,14,14,1024]
[[Node: CropAndResize = CropAndResize[T=DT_FLOAT, extrapolation_value=0, method="bilinear", _device="/job:localhost/replica:0/task:0/gpu:0"](FirstStageFeatureExtractor/resnet_v1_101/resnet_v1_101/block3/unit_23/bottleneck_v1/Relu, Reshape_7, Reshape_8/_95, CropAndResize/crop_size)]]
[[Node: SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Concatenate/concat_3/_151 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/cpu:0", send_device="/job:localhost/replica:0/task:0/gpu:0", send_device_incarnation=1, tensor_name="edge_2971_SecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/MultiClassNonMaxSuppression/Concatenate/concat_3", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/cpu:0"](^_cloopSecondStagePostprocessor/BatchMultiClassNonMaxSuppression/map/while/strided_slice/stack_2/_6)]]
以下は、test_imagesから画像を収集し、それを実行しようとする私のtfコードです。 私は高性能なaws gpuサーバを使用しているので、そのメモリの問題は考えません。 また、私のテスト画像のサイズは400x400です。
tfconfig = tf.ConfigProto()
tfconfig.gpu_options.allocator_type = 'BFC'
tfconfig.gpu_options.per_process_gpu_memory_fraction = 0.40
tfconfig.gpu_options.allow_growth=True
with detection_graph.as_default():
with tf.Session(graph=detection_graph,config=tfconfig) as sess:
for image_path in TEST_IMAGE_PATHS:
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.
print("about to convert image ="+image_path+" into np")
image_np = load_image_into_numpy_array(image)
# Expand dimensions since the model expects images to have shape: [1, None, None, 3]
print ("expanding np ...")
image_np_expanded = np.expand_dims(image_np, axis=0)
print(image_np.shape)
print ("getting image tensor ...")
image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')
# Each box represents a part of the image where a particular object was detected.
print ("detecting boxes ...")
boxes = detection_graph.get_tensor_by_name('detection_boxes:0')
# Each score represent how level of confidence for each of the objects.
# Score is shown on the result image, together with the class label.
print ("getting scores, classes and num of detections ...")
scores = detection_graph.get_tensor_by_name('detection_scores:0')
classes = detection_graph.get_tensor_by_name('detection_classes:0')
num_detections = detection_graph.get_tensor_by_name('num_detections:0')
# Actual detection.
print ("building boxes")
(boxes, scores, classes, num_detections) = sess.run(
[boxes, scores, classes, num_detections],
feed_dict={image_tensor: image_np_expanded})