我想在Object Detection API同时在我自己的数据集上训练/评估ssd_mobile_v1_coco。
然而，当我只是尝试这样做时，我面临GPU内存几乎已满，因此评估脚本无法启动。
以下是我用于培训和评估的命令：
训练脚本在一个终端窗格中调用，如下所示：
python3 train.py \
--logtostderr \
--train_dir=training_ssd_mobile_caltech \
--pipeline_config_path=ssd_mobilenet_v1_coco_2017_11_17/ssd_mobilenet_v1_focal_loss_coco.config
运行正常，培训工作......然后我尝试在第二个终端窗格中运行评估脚本：
python3 eval.py \
--logtostderr \
--checkpoint_dir=training_ssd_mobile_caltech \
--eval_dir=eval_caltech \
--pipeline_config_path=ssd_mobilenet_v1_coco_2017_11_17/ssd_mobilenet_v1_focal_loss_coco.config
失败并出现以下错误：
python3 eval.py \
--logtostderr \
--checkpoint_dir=training_ssd_mobile_caltech \
--eval_dir=eval_caltech \
--pipeline_config_path=ssd_mobilenet_v1_coco_2017_11_17/ssd_mobilenet_v1_focal_loss_coco.config
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
INFO:tensorflow:depth of additional conv before box predictor: 0
2018-02-28 18:40:00.302271: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA
2018-02-28 18:40:00.412808: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:895] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-02-28 18:40:00.413217: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1105] Found device 0 with properties:
name: GeForce GTX 1080 major: 6 minor: 1 memoryClockRate(GHz): 1.835
pciBusID: 0000:01:00.0
totalMemory: 7.92GiB freeMemory: 93.00MiB
2018-02-28 18:40:00.413424: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 1080, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-02-28 18:40:00.957090: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 43.00M (45088768 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-02-28 18:40:00.957919: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 38.70M (40580096 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
INFO:tensorflow:Restoring parameters from training_ssd_mobile_caltech/model.ckpt-4775
INFO:tensorflow:Restoring parameters from training_ssd_mobile_caltech/model.ckpt-4775
2018-02-28 18:40:02.274830: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.17M (8566528 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-02-28 18:40:02.278599: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.17M (8566528 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-02-28 18:40:12.280515: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.17M (8566528 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-02-28 18:40:12.281958: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 8.17M (8566528 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-02-28 18:40:12.282082: W tensorflow/core/common_runtime/bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.75MiB. Current allocation summary follows.
2018-02-28 18:40:12.282160: I tensorflow/core/common_runtime/bfc_allocator.cc:628] Bin (256): Total Chunks: 190, Chunks in use: 190. 47.5KiB allocated for chunks. 47.5KiB in use in bin. 11.8KiB client-requested in use in bin.
2018-02-28 18:40:12.282251: I tensorflow/core/common_runtime/bfc_allocator.cc:628] Bin (512): Total Chunks: 70, Chunks in use: 70. 35.0KiB allocated for chunks. 35.0KiB in use in bin. 35.0KiB client-requested in use in bin.
[.......................................]2018-02-28 18:40:12.290959: I tensorflow/core/common_runtime/bfc_allocator.cc:684] Sum Total of in-use chunks: 29.83MiB
2018-02-28 18:40:12.290971: I tensorflow/core/common_runtime/bfc_allocator.cc:686] Stats:
Limit: 45088768
InUse: 31284736
MaxInUse: 32368384
NumAllocs: 808
MaxAllocSize: 5796864
2018-02-28 18:40:12.291022: W tensorflow/core/common_runtime/bfc_allocator.cc:277] **********************xx*********xx**_*__****______***********************************************xx
2018-02-28 18:40:12.291044: W tensorflow/core/framework/op_kernel.cc:1198] Resource exhausted: OOM when allocating tensor with shape[1,32,150,150] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
WARNING:root:The following classes have no ground truth examples: 1
/home/mm/models/research/object_detection/utils/metrics.py:144: RuntimeWarning: invalid value encountered in true_divide
num_images_correctly_detected_per_class / num_gt_imgs_per_class)
/home/mm/models/research/object_detection/utils/object_detection_evaluation.py:710: RuntimeWarning: Mean of empty slice
mean_ap = np.nanmean(self.average_precision_per_class)
/home/mm/models/research/object_detection/utils/object_detection_evaluation.py:711: RuntimeWarning: Mean of empty slice
mean_corloc = np.nanmean(self.corloc_per_class)
Traceback (most recent call last):
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1350, in _do_call
return fn(*args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1329, in _run_fn
status, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/errors_impl.py", line 473, in __exit__
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,32,150,150] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/sub, FeatureExtractor/MobilenetV1/Conv2d_0/weights/read)]]
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: Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ClipToWindow/Gather/Gather_1/_469 = _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_1068_Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ClipToWindow/Gather/Gather_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
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 "eval.py", line 146, in <module>
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 124, in run
_sys.exit(main(argv))
File "eval.py", line 142, in main
FLAGS.checkpoint_dir, FLAGS.eval_dir)
File "/home/mm/models/research/object_detection/evaluator.py", line 240, in evaluate
save_graph_dir=(eval_dir if eval_config.save_graph else ''))
File "/home/mm/models/research/object_detection/eval_util.py", line 407, in repeated_checkpoint_run
save_graph_dir)
File "/home/mm/models/research/object_detection/eval_util.py", line 286, in _run_checkpoint_once
result_dict = batch_processor(tensor_dict, sess, batch, counters)
File "/home/mm/models/research/object_detection/evaluator.py", line 183, in _process_batch
result_dict = sess.run(tensor_dict)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 895, in run
run_metadata_ptr)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1128, in _run
feed_dict_tensor, options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1344, in _do_run
options, run_metadata)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/client/session.py", line 1363, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[1,32,150,150] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/sub, FeatureExtractor/MobilenetV1/Conv2d_0/weights/read)]]
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: Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ClipToWindow/Gather/Gather_1/_469 = _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_1068_Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ClipToWindow/Gather/Gather_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
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 'FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D', defined at:
File "eval.py", line 146, in <module>
tf.app.run()
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/platform/app.py", line 124, in run
_sys.exit(main(argv))
File "eval.py", line 142, in main
FLAGS.checkpoint_dir, FLAGS.eval_dir)
File "/home/mm/models/research/object_detection/evaluator.py", line 161, in evaluate
ignore_groundtruth=eval_config.ignore_groundtruth)
File "/home/mm/models/research/object_detection/evaluator.py", line 72, in _extract_prediction_tensors
prediction_dict = model.predict(preprocessed_image, true_image_shapes)
File "/home/mm/models/research/object_detection/meta_architectures/ssd_meta_arch.py", line 334, in predict
preprocessed_inputs)
File "/home/mm/models/research/object_detection/models/ssd_mobilenet_v1_feature_extractor.py", line 112, in extract_features
scope=scope)
File "/home/mm/models/research/slim/nets/mobilenet_v1.py", line 232, in mobilenet_v1_base
scope=end_point)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/framework/python/ops/arg_scope.py", line 182, in func_with_args
return func(*args, **current_args)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/contrib/layers/python/layers/layers.py", line 1057, in convolution
outputs = layer.apply(inputs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 762, in apply
return self.__call__(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/base.py", line 652, in __call__
outputs = self.call(inputs, *args, **kwargs)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/layers/convolutional.py", line 167, in call
outputs = self._convolution_op(inputs, self.kernel)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 838, in __call__
return self.conv_op(inp, filter)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 502, in __call__
return self.call(inp, filter)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/nn_ops.py", line 190, in __call__
name=self.name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/ops/gen_nn_ops.py", line 639, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 3160, in create_op
op_def=op_def)
File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/framework/ops.py", line 1625, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[1,32,150,150] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
[[Node: FeatureExtractor/MobilenetV1/MobilenetV1/Conv2d_0/Conv2D = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 2, 2, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](Preprocessor/sub, FeatureExtractor/MobilenetV1/Conv2d_0/weights/read)]]
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: Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ClipToWindow/Gather/Gather_1/_469 = _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_1068_Postprocessor/BatchMultiClassNonMaxSuppression/MultiClassNonMaxSuppression/ClipToWindow/Gather/Gather_1", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
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.
在启动eval.py TF培训之前，所有GPU内存都已提前分配，因此我无法弄清楚如何让它们同时运行，或者至少有ODA，运行评估具体间隔。
因此，首先是否可以与培训同时进行评估？如果是这样怎么做？
系统信息
您正在使用的模型的顶级目录是什么： object_detection
我是否编写过自定义代码：尚未...
OS平台和发行版： Linux Ubuntu 16.04 LTS
从（源代码或二进制代码）安装的TensorFlow： pip3 tensorflow-gpu
TensorFlow版本（使用下面的命令）： 1.5.0
CUDA / cuDNN版本： 9.0 / 7.0
GPU型号和内存： GTX 1080,8Gb
答案 0 :(得分：6)
执行此操作的一种简单方法是在命令之前添加CUDA_VISIBILE_DEVICES
CUDA_VISIBLE_DEVICES="" python eval.py --logtostderr --pipeline_config_path=multires.config --checkpoint_dir=/train_dir/ --eval_dir=eval_dir/
这将阻止您的评估脚本看到任何GPU，它应该自动回退到CPU。
答案 1 :(得分：3)
要强制eval作业在你的CPU上运行（并防止它占用宝贵的GPU内存），创建一个virtualenv，你可以在其中安装用于训练的tensorflow-gpu（名为virtual_tf_gpu），另一个你在哪里安装tensorflow没有gpu支持（例如virtual_tf）。在两个独立的终端窗口中激活您的两个虚拟环境，并在支持CPU的环境中开始培训，并在CPU支持的环境中进行评估。
祝你好运！