Ошибка подтверждения утверждения при использовании API обнаружения объектов tenorflow для точной настройки модели mask_rcnn_inception_resnet_v2_atrous_coco - PullRequest
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Ошибка подтверждения утверждения при использовании API обнаружения объектов tenorflow для точной настройки модели mask_rcnn_inception_resnet_v2_atrous_coco

0 голосов
/ 24 апреля 2018

Я пытался использовать API обнаружения объектов tenorflow для точной настройки модели mask_rcnn_inception_resnet_v2_atrous_coco и использовать ее для обучения на наборе данных MIO-TCD. Я преобразовал набор данных MIO-TCD в TFRecord.

Однако я застрял со следующей ошибкой InvalidArgumentError: 

INFO:tensorflow:Error reported to Coordinator: assertion failed: [] [Condition x == y did not hold element-wise:] [x (Loss/BoxClassifierLoss/assert_equal_2/x:0) = ] [0] [y (Loss/BoxClassifierLoss/assert_equal_2/y:0) = ] [5]
         [[Node: Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert = Assert[T=[DT_STRING, DT_STRING, DT_STRING, DT_INT32, DT_STRING, DT_INT32], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Loss/BoxClassifierLoss/assert_equal_2/All/_155, Loss/RPNLoss/assert_equal/Assert/Assert/data_0, Loss/RPNLoss/assert_equal/Assert/Assert/data_1, Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert/data_2, Loss/BoxClassifierLoss/assert_equal_2/x/_157, Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert/data_4, Loss/RPNLoss/ones_1/shape/_147)]]
         [[Node: FirstStageFeatureExtractor/InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean/read/_225 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_2248_FirstStageFeatureExtractor/InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]

Caused by op 'Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert', defined at:
  File "train.py", line 167, in <module>
    tf.app.run()
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\platform\app.py", line 124, in run
    _sys.exit(main(argv))
  File "train.py", line 163, in main
    worker_job_name, is_chief, FLAGS.train_dir)
  File "C:\Users\hedey\models\research\object_detection\trainer.py", line 246, in train
    clones = model_deploy.create_clones(deploy_config, model_fn, [input_queue])
  File "C:\Users\hedey\models\research\deployment\model_deploy.py", line 193, in create_clones
    outputs = model_fn(*args, **kwargs)
  File "C:\Users\hedey\models\research\object_detection\trainer.py", line 181, in _create_losses
    losses_dict = detection_model.loss(prediction_dict, true_image_shapes)
  File "C:\Users\hedey\models\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 1580, in loss
    groundtruth_masks_list,
  File "C:\Users\hedey\models\research\object_detection\meta_architectures\faster_rcnn_meta_arch.py", line 1813, in _loss_box_classifier
    groundtruth_boxlists, groundtruth_masks_list)
  File "C:\Users\hedey\models\research\object_detection\core\target_assigner.py", line 447, in batch_assign_targets
    anchors, gt_boxes, gt_class_targets, gt_weights)
  File "C:\Users\hedey\models\research\object_detection\core\target_assigner.py", line 151, in assign
    groundtruth_boxes.get())[:1])
  File "C:\Users\hedey\models\research\object_detection\utils\shape_utils.py", line 279, in assert_shape_equal
    return tf.assert_equal(shape_a, shape_b)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\check_ops.py", line 392, in assert_equal
    return control_flow_ops.Assert(condition, data, summarize=summarize)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\util\tf_should_use.py", line 118, in wrapped
    return _add_should_use_warning(fn(*args, **kwargs))
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\control_flow_ops.py", line 169, in Assert
    condition, data, summarize, name="Assert")
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\ops\gen_logging_ops.py", line 48, in _assert
    name=name)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 3160, in create_op
    op_def=op_def)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): assertion failed: [] [Condition x == y did not hold element-wise:] [x (Loss/BoxClassifierLoss/assert_equal_2/x:0) = ] [0] [y (Loss/BoxClassifierLoss/assert_equal_2/y:0) = ] [5]
         [[Node: Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert = Assert[T=[DT_STRING, DT_STRING, DT_STRING, DT_INT32, DT_STRING, DT_INT32], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Loss/BoxClassifierLoss/assert_equal_2/All/_155, Loss/RPNLoss/assert_equal/Assert/Assert/data_0, Loss/RPNLoss/assert_equal/Assert/Assert/data_1, Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert/data_2, Loss/BoxClassifierLoss/assert_equal_2/x/_157, Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert/data_4, Loss/RPNLoss/ones_1/shape/_147)]]
         [[Node: FirstStageFeatureExtractor/InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean/read/_225 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_2248_FirstStageFeatureExtractor/InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]
Traceback (most recent call last):
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1350, in _do_call
    return fn(*args)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\client\session.py", line 1329, in _run_fn
    status, run_metadata)
  File "C:\Users\hedey\AppData\Local\Programs\Python\Python36\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [] [Condition x == y did not hold element-wise:] [x (Loss/BoxClassifierLoss/assert_equal_2/x:0) = ] [0] [y (Loss/BoxClassifierLoss/assert_equal_2/y:0) = ] [5]
         [[Node: Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert = Assert[T=[DT_STRING, DT_STRING, DT_STRING, DT_INT32, DT_STRING, DT_INT32], summarize=3, _device="/job:localhost/replica:0/task:0/device:CPU:0"](Loss/BoxClassifierLoss/assert_equal_2/All/_155, Loss/RPNLoss/assert_equal/Assert/Assert/data_0, Loss/RPNLoss/assert_equal/Assert/Assert/data_1, Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert/data_2, Loss/BoxClassifierLoss/assert_equal_2/x/_157, Loss/BoxClassifierLoss/assert_equal_2/Assert/Assert/data_4, Loss/RPNLoss/ones_1/shape/_147)]]
         [[Node: FirstStageFeatureExtractor/InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean/read/_225 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device_incarnation=1, tensor_name="edge_2248_FirstStageFeatureExtractor/InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean/read", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:GPU:0"]()]]

Я обнаружил, что другие люди писали об одной и той же проблеме более чем в одной из проблем github. Ниже приведен пример, и я уже прокомментировал его там, и мне посоветовали опубликовать информацию об этом в stackoverflow: https://github.com/tensorflow/models/issues/3972#issuecomment-381535604 https://github.com/tensorflow/models/issues/3972#issuecomment-381535604

Ответы [ 2 ]

0 голосов
/ 07 октября 2018

Проблема в вашем файле tfrecord, созданном с помощью программы create_pet_tf_record.py, вам нужно создать его, используя следующий аргумент arg --faces_only, установленный в false, потому что если вы оставите значение True (значение по умолчанию), то сегментация не будет в то время как это то, что вы пытаетесь тренировать.

Проверьте это: https://github.com/tensorflow/models/issues/3972

0 голосов
/ 16 мая 2018

Когда вы конвертируете набор данных MIO-TCD в TFRecord, вы должны установить параметр include_masks следующим образом. 

--include_masks=True

Вы можете попробовать.

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