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How to train and evaluate simultaneously in Object Detection API ?
Asked Answered
Solved tensorflowmachine-learningdeep-learningobject-detectionobject-detection-api
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I want to have train/evaluate the ssd_mobile_v1_coco on my own dataset at the same time in Object Detection API.

However, when I simply try to do so, I am faced with GPU memory being nearly full and thus the evaluation script fails to start. Here are the commands I use for training and then evaluation:
Training script is called in one terminal pane like this : 

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

That runs fine, training works... then I try to run the evaluation script in the second terminal pane : 

 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

It fails with the following error : 

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.

Prior to initiating the eval.py TF training has all the GPU memory allocated in advance and therefore I cant figure out how to have both of them running at the same time, or at least have the ODA, run evaluation in specific intervals.

Therefore Is it possible in first place to have evaluation run simultaneously with training? if so How is it done ?

System information

What is the top-level directory of the model you are using: object_detection

Have I written custom code: not yet...

OS Platform and Distribution : Linux Ubuntu 16.04 LTS

TensorFlow installed from (source or binary): pip3 tensorflow-gpu

TensorFlow version (use command below): 1.5.0

CUDA/cuDNN version: 9.0/7.0

GPU model and memory: GTX 1080, 8Gb

Heartstricken answered 28/2, 2018 at 15:24 Comment(0)
C
6

To force the eval job to run on your CPU (and prevent it from taking precious GPU-memory), create one virtualenv where you install tensorflow-gpu which you use for training (named e.g. virtual_tf_gpu), and another one where you install tensorflow WITHOUT gpu support (e.g. virtual_tf). Activate your two virtualenvs in two separate terminal windows and start training in your GPU-supported environment and evaluation in your CPU-supported environment.

Good luck!!!

Chud answered 20/3, 2018 at 9:46 Comment(3)
Thanks . yes this is one of way for this but I think this way not efficient,right? the second way that must be have allocate percent of usage for training process and the rest for validation. – Heartstricken
In my experience 'efficiency' is not a problem when running eval on CPU, it's naturally slower than GPU, but I only run eval every now and then (every 10-15 min) so it's not a problem that the model runs slower, it's much more important that the training is run on the GPU! – Chud
yes , exactly you are right , but when the GPU utility Full not filled. in my opinion that is better to run over empty section of GPU for eval. – Heartstricken
A
19

One simple way to do this is to add CUDA_VISIBILE_DEVICES before your command

CUDA_VISIBLE_DEVICES="" python eval.py --logtostderr --pipeline_config_path=multires.config --checkpoint_dir=/train_dir/ --eval_dir=eval_dir/

which will prevent your evaluation script from seeing any GPU, and it should fall back to CPU automatically.

Anetteaneurin answered 26/4, 2018 at 23:36 Comment(2)
are you sure that is work ? in the CUDA_VISIBLE_DEVICES="" in mu opinion should be set -1 for cpu , right? – Heartstricken
I think both of them should work (i.e. CUDA_VISIBLE_DEVICES="" and CUDA_VISIBLE_DEVICES=-1). At least when I was trying CUDA_VISIBLE_DEVICES="" it worked. – Anetteaneurin
C
6

To force the eval job to run on your CPU (and prevent it from taking precious GPU-memory), create one virtualenv where you install tensorflow-gpu which you use for training (named e.g. virtual_tf_gpu), and another one where you install tensorflow WITHOUT gpu support (e.g. virtual_tf). Activate your two virtualenvs in two separate terminal windows and start training in your GPU-supported environment and evaluation in your CPU-supported environment.

Good luck!!!

Chud answered 20/3, 2018 at 9:46 Comment(3)
Thanks . yes this is one of way for this but I think this way not efficient,right? the second way that must be have allocate percent of usage for training process and the rest for validation. – Heartstricken
In my experience 'efficiency' is not a problem when running eval on CPU, it's naturally slower than GPU, but I only run eval every now and then (every 10-15 min) so it's not a problem that the model runs slower, it's much more important that the training is run on the GPU! – Chud
yes , exactly you are right , but when the GPU utility Full not filled. in my opinion that is better to run over empty section of GPU for eval. – Heartstricken

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