Python Tensorflow数据集映射：输入似乎是占位符，导致tf.read\u文件出错

我正在尝试使用Tensorflow数据集API读入数据。我已将文件名和标签文件名加载到数组中，并将其加载到数据集中。然后，我尝试将这些文件名映射到实际的图像文件，但得到一个错误，该错误似乎表明映射函数的输入接收的是占位符，而不是实际的张量

class DatasetReader:

def __init__(self, records_list, batch_size=1):
    self.batch_size = batch_size
    self.records = {}
    self.records["image"] =  tf.convert_to_tensor([record['image'] for record in records_list])
    self.records["filename"] =  tf.convert_to_tensor([record['filename'] for record in records_list])
    self.records["annotation"] = tf.convert_to_tensor([record['annotation'] for record in records_list])
    self.dataset = Dataset.from_tensor_slices(self.records)
    self.dataset = self.dataset.map(self._input_parser)
    self.dataset = self.dataset.batch(batch_size)
    self.dataset = self.dataset.repeat()

def _input_parser(self, record):
    filename = record['filename']
    image_name = record['image']
    annotation_file = record['annotation']
    image = tf.image.decode_image(tf.read_file(filename))
    annotation = tf.image.decode_image(tf.read_file(annotation_file))
    return self._augment_image(image, annotation)

我得到的错误在image=tf.image.decode\u imagetf.read\u filefilefilename行中。堆栈跟踪在下面

  File "FCN.py", line 269, in <module>
    tf.app.run()
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/platform/app.py", line 48, in run
    _sys.exit(main(_sys.argv[:1] + flags_passthrough))
  File "FCN.py", line 179, in main
    train_records, valid_records, image_options_train, image_options_val, FLAGS.batch_size, FLAGS.batch_size)
  File "/home/ubuntu/FCN.tensorflow/TFReader.py", line 89, in from_records
    train_reader = DatasetReader(train_records, train_image_options, train_batch_size)
  File "/home/ubuntu/FCN.tensorflow/TFReader.py", line 34, in __init__
    self.dataset = self.dataset.map(self._input_parser)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/data/python/ops/dataset_ops.py", line 964, in map
    return MapDataset(self, map_func, num_threads, output_buffer_size)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/data/python/ops/dataset_ops.py", line 1735, in __init__
    self._map_func.add_to_graph(ops.get_default_graph())
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/function.py", line 449, in add_to_graph
    self._create_definition_if_needed()
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/data/python/framework/function.py", line 168, in _create_definition_if_needed
    outputs = self._func(*inputs)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/data/python/ops/dataset_ops.py", line 1723, in tf_map_func
    ret = map_func(nested_args)
  File "/home/ubuntu/FCN.tensorflow/TFReader.py", line 42, in _input_parser
    image = tf.image.decode_image(tf.read_file(filename))
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/ops/gen_io_ops.py", line 223, in read_file
    result = _op_def_lib.apply_op("ReadFile", filename=filename, name=name)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/op_def_library.py", line 767, in apply_op
    op_def=op_def)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/contrib/data/python/framework/function.py", line 80, in create_op
    data_types, **kwargs)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/function.py", line 665, in create_op
    **kwargs)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 2632, in create_op
    set_shapes_for_outputs(ret)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1911, in set_shapes_for_outputs
    shapes = shape_func(op)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/ops.py", line 1861, in call_with_requiring
    return call_cpp_shape_fn(op, require_shape_fn=True)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 595, in call_cpp_shape_fn
    require_shape_fn)
  File "/home/ubuntu/anaconda2/lib/python2.7/site-packages/tensorflow/python/framework/common_shapes.py", line 659, in _call_cpp_shape_fn_impl
    raise ValueError(err.message)
ValueError: Shape must be rank 0 but is rank 1 for 'ReadFile' (op: 'ReadFile') with input shapes: [?].

---

不能将秩1张量传递给tf.read_文件。以下是一些例子：

import tensorflow as tf

# Correct: input can be a string.
tf.image.decode_image(tf.read_file("filename"))
# Correct: input can be a rank-0 tensor.
tf.image.decode_image(tf.read_file(tf.convert_to_tensor("filename")))

# Wrong: input cannot be a list.
tf.image.decode_image(tf.read_file(["filename"]))
# Wrong: input cannot be a rank-1 tensor
tf.image.decode_image(tf.read_file(tf.convert_to_tensor(["filename"])))

---

在代码中，self.records[filename]似乎是一个秩1张量；您可能会错误地将其作为参数传递给_input_parser中的tf.read_文件

谢谢。那我是不是误解了地图？据我所知，map的工作原理与Python中的map函数类似，将张量的一个元素/切片作为输入，并返回替换元素/切片。由于我对数据集的输入是{'image'：[图像名称列表]，'filename'：[文件名称列表]，'annotations'：[标签列表]}形式的字典，其中字典中的每个元素都是转换为秩-1张量的1d数组，map的字典输入中的每个元素都应该是秩-0张量。我误解了什么？