Colab教程使用object_detection进行学习转移，为什么要使用lookup_ops.py中的TypeError？

时间：2019-01-03 15:10:07

标签： python tensorflow google-colaboratory object-detection-api

我正在关注RomRoc上的本教程https://hackernoon.com/object-detection-in-google-colab-with-custom-dataset-5a7bb2b0e97e，并且在lookup_ops.py中遇到了问题（针对TensorFlow 1.12的Python 2.7）。有谁知道TensorFlow的这一部分是如何工作的，并且可以建议出什么问题吗？这是引发错误的对model_main.py的调用，

!python ~/models/research/object_detection/model_main.py \
    --pipeline_config_path=/root/models/research/object_detection/samples/configs/faster_rcnn_inception_v2_pets.config \
    --model_dir=/root/datalab/trained \
    --alsologtostderr \
    --num_train_steps=3000 \
    --num_eval_steps=500

还有堆栈跟踪，

/root/datalab
/root/models/research/object_detection/utils/visualization_utils.py:26: UserWarning: 
This call to matplotlib.use() has no effect because the backend has already
been chosen; matplotlib.use() must be called *before* pylab, matplotlib.pyplot,
or matplotlib.backends is imported for the first time.

The backend was *originally* set to 'module://ipykernel.pylab.backend_inline' by the following code:
  File "/root/models/research/object_detection/model_main.py", line 26, in <module>
    from object_detection import model_lib
  File "/root/models/research/object_detection/model_lib.py", line 27, in <module>
    from object_detection import eval_util
  File "/root/models/research/object_detection/eval_util.py", line 27, in <module>
    from object_detection.metrics import coco_evaluation
  File "/root/models/research/object_detection/metrics/coco_evaluation.py", line 20, in <module>
    from object_detection.metrics import coco_tools
  File "/root/models/research/object_detection/metrics/coco_tools.py", line 47, in <module>
    from pycocotools import coco
  File "/usr/local/lib/python2.7/dist-packages/pycocotools/coco.py", line 49, in <module>
    import matplotlib.pyplot as plt
  File "/usr/local/lib/python2.7/dist-packages/matplotlib/pyplot.py", line 72, in <module>
    from matplotlib.backends import pylab_setup
  File "/usr/local/lib/python2.7/dist-packages/matplotlib/backends/__init__.py", line 14, in <module>
    line for line in traceback.format_stack()


  import matplotlib; matplotlib.use('Agg')  # pylint: disable=multiple-statements
WARNING:tensorflow:Forced number of epochs for all eval validations to be 1.
WARNING:tensorflow:Expected number of evaluation epochs is 1, but instead encountered `eval_on_train_input_config.num_epochs` = 0. Overwriting `num_epochs` to 1.
WARNING:tensorflow:Estimator's model_fn (<function model_fn at 0x7f0dbdee1230>) includes params argument, but params are not passed to Estimator.
/root/models/research/object_detection/utils/label_map_util.py:138: RuntimeWarning: Unexpected end-group tag: Not all data was converted
  label_map.ParseFromString(label_map_string)
WARNING:tensorflow:num_readers has been reduced to 1 to match input file shards.
WARNING:tensorflow:From /root/models/research/object_detection/builders/dataset_builder.py:80: parallel_interleave (from tensorflow.contrib.data.python.ops.interleave_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.data.experimental.parallel_interleave(...)`.
WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/sparse_ops.py:1165: sparse_to_dense (from tensorflow.python.ops.sparse_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Create a `tf.sparse.SparseTensor` and use `tf.sparse.to_dense` instead.
Traceback (most recent call last):
  File "/root/models/research/object_detection/model_main.py", line 109, in <module>
    tf.app.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/platform/app.py", line 125, in run
    _sys.exit(main(argv))
  File "/root/models/research/object_detection/model_main.py", line 105, in main
    tf.estimator.train_and_evaluate(estimator, train_spec, eval_specs[0])
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 471, in train_and_evaluate
    return executor.run()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 610, in run
    return self.run_local()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/training.py", line 711, in run_local
    saving_listeners=saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 354, in train
    loss = self._train_model(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1207, in _train_model
    return self._train_model_default(input_fn, hooks, saving_listeners)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1234, in _train_model_default
    input_fn, model_fn_lib.ModeKeys.TRAIN))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1075, in _get_features_and_labels_from_input_fn
    self._call_input_fn(input_fn, mode))
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/estimator/estimator.py", line 1162, in _call_input_fn
    return input_fn(**kwargs)
  File "/root/models/research/object_detection/inputs.py", line 488, in _train_input_fn
    batch_size=params['batch_size'] if params else train_config.batch_size)
  File "/root/models/research/object_detection/builders/dataset_builder.py", line 145, in build
    num_parallel_calls=num_parallel_calls)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 1040, in map
    return ParallelMapDataset(self, map_func, num_parallel_calls)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 2649, in __init__
    use_inter_op_parallelism)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 2611, in __init__
    map_func, "Dataset.map()", input_dataset)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 1860, in __init__
    self._function.add_to_graph(ops.get_default_graph())
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/function.py", line 479, in add_to_graph
    self._create_definition_if_needed()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/function.py", line 335, in _create_definition_if_needed
    self._create_definition_if_needed_impl()
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/function.py", line 344, in _create_definition_if_needed_impl
    self._capture_by_value, self._caller_device)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/framework/function.py", line 864, in func_graph_from_py_func
    outputs = func(*func_graph.inputs)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/data/ops/dataset_ops.py", line 1794, in tf_data_structured_function_wrapper
    ret = func(*nested_args)
  File "/root/models/research/object_detection/builders/dataset_builder.py", line 127, in process_fn
    processed_tensors = decoder.decode(value)
  File "/root/models/research/object_detection/data_decoders/tf_example_decoder.py", line 363, in decode
    tensors = decoder.decode(serialized_example, items=keys)
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/contrib/slim/python/slim/data/tfexample_decoder.py", line 525, in decode
    outputs.append(handler.tensors_to_item(keys_to_tensors))
  File "/root/models/research/object_detection/data_decoders/tf_example_decoder.py", line 117, in tensors_to_item
    item = self._handler.tensors_to_item(keys_to_tensors)
  File "/root/models/research/object_detection/data_decoders/tf_example_decoder.py", line 86, in tensors_to_item
    return tf.maximum(self._name_to_id_table.lookup(unmapped_tensor),
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/ops/lookup_ops.py", line 224, in lookup
    (self._key_dtype, keys.dtype))
TypeError: Signature mismatch. Keys must be dtype <dtype: 'float32'>, got <dtype: 'string'>.

这是教程中的代码： https://github.com/RomRoc/objdet_train_tensorflow_colab/blob/master/objdet_custom_tf_colab.ipynb

我完全遵循了教程，但是使用了自己的训练数据。为了使该部分正常工作，我必须对create_pet_tf_record.py进行一些更改。您能帮我了解如何调试吗？谢谢！

这是keys的值传递给lookup()中引起问题的函数lookup_ops.py的原因：

Tensor("SparseToDense:0", shape=(?,), dtype=string, device=/device:CPU:0)

编辑：可能是因为它应该使用GPU吗？检测到GPU ... 

from tensorflow.python.client import device_lib
device_lib.list_local_devices()

[name: "/device:CPU:0"
 device_type: "CPU"
 memory_limit: 268435456
 locality {
 }
 incarnation: 6030853355760210263, name: "/device:XLA_CPU:0"
 device_type: "XLA_CPU"
 memory_limit: 17179869184
 locality {
 }
 incarnation: 8061591699314310064
 physical_device_desc: "device: XLA_CPU device", name: "/device:XLA_GPU:0"
 device_type: "XLA_GPU"
 memory_limit: 17179869184
 locality {
 }
 incarnation: 1730080795414704681
 physical_device_desc: "device: XLA_GPU device", name: "/device:GPU:0"
 device_type: "GPU"
 memory_limit: 11281553818
 locality {
   bus_id: 1
   links {
   }
 }
 incarnation: 5476104669001907939
 physical_device_desc: "device: 0, name: Tesla K80, pci bus id: 0000:00:04.0, compute capability: 3.7"]

1 个答案:

答案 0 :(得分：0)

知道了！

在该教程中，应该使用此行创建label_map.pbtxt，

echo "item {\n id: 1\n name: 'dog'\n}" > label_map.pbtxt

但是这把\ n放在了文字上。当我在Vim中创建自己的label_map.pbtxt时，它可以工作（训练）。

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