python - 值错误: Cannot take the length of Shape with unknown rank

Question

我正在尝试将我们的输入管道移动到tensorflow数据集API。为此，我们将图像和标签转换为 tfrecords。然后我们通过数据集API读取tfrecords并比较初始数据和读取的数据是否相同。到目前为止，一切都很好。下面是将 tfrecords 读入数据集的代码

def _parse_function2(proto):

    # define your tfrecord again. Remember that you saved your image as a string.

    keys_to_features = {"im_path": tf.FixedLenSequenceFeature([], tf.string, allow_missing=True),
                        "im_shape": tf.FixedLenSequenceFeature([], tf.int64, allow_missing=True),
                        "score_shape": tf.FixedLenSequenceFeature([], tf.int64, allow_missing=True),
                        "geo_shape": tf.FixedLenSequenceFeature([], tf.int64, allow_missing=True),
                        "im_patches": tf.FixedLenSequenceFeature([], tf.string, allow_missing=True),
                        "score_patches": tf.FixedLenSequenceFeature([], tf.string, allow_missing=True),
                        "geo_patches": tf.FixedLenSequenceFeature([], tf.string, allow_missing=True),
                        }

    # Load one example
    parsed_features = tf.parse_single_example(serialized=proto, features=keys_to_features)

    parsed_features['im_patches'] = parsed_features['im_patches'][0]
    parsed_features['score_patches'] = parsed_features['score_patches'][0]
    parsed_features['geo_patches'] = parsed_features['geo_patches'][0]

    parsed_features['im_patches'] = tf.decode_raw(parsed_features['im_patches'], tf.uint8)
    parsed_features['im_patches'] = tf.reshape(parsed_features['im_patches'], parsed_features['im_shape'])

    parsed_features['score_patches'] = tf.decode_raw(parsed_features['score_patches'], tf.uint8)
    parsed_features['score_patches'] = tf.reshape(parsed_features['score_patches'], parsed_features['score_shape'])

    parsed_features['geo_patches'] = tf.decode_raw(parsed_features['geo_patches'], tf.int16)
    parsed_features['geo_patches'] = tf.reshape(parsed_features['geo_patches'], parsed_features['geo_shape'])

    return parsed_features['im_patches'], tf.cast(parsed_features["score_patches"],tf.int16), parsed_features["geo_patches"]



def create_dataset2(tfrecord_path):
    # This works with arrays as well
    dataset = tf.data.TFRecordDataset([tfrecord_path], compression_type="ZLIB")

    # Maps the parser on every filepath in the array. You can set the number of parallel loaders here
    dataset = dataset.map(_parse_function2, num_parallel_calls=8)

    # This dataset will go on forever
    dataset = dataset.repeat()

    # Set the batchsize
    dataset = dataset.batch(1)

    return dataset

现在，上述函数创建的数据集将传递给 model.fit 方法，如下所示。我正在关注github gist其中给出了如何将数据集传递到 model.fit 的示例

train_tfrecord = 'data/tfrecords/train/train.tfrecords'
test_tfrecord = 'data/tfrecords/test/test.tfrecords'

train_dataset  = create_dataset2(train_tfrecord)
test_dataset  = create_dataset2(test_tfrecord)


model.fit(
    train_dataset.make_one_shot_iterator(),
    steps_per_epoch=5,
    epochs=10,
    shuffle=True,
    validation_data=test_dataset.make_one_shot_iterator(),
    callbacks=[function1, function2, function3],
    verbose=1)

但我收到错误 ValueError: Cannot take the length of Shape with unknown rank.在上面的 model.fit 函数调用中。

编辑 1: 我使用下面的代码来迭代数据集并提取张量的等级、形状和类型。

train_tfrecord = 'data/tfrecords/train/train.tfrecords'

with tf.Graph().as_default():

    # Deserialize and report on the fake data
    sess = tf.Session()
    sess.run(tf.global_variables_initializer())

    dataset = tf.data.TFRecordDataset([train_tfrecord], compression_type="ZLIB")
    dataset = dataset.map(_parse_function2)

    iterator = dataset.make_one_shot_iterator()


    while True:
        try:
            next_element = iterator.get_next()
            im_patches, score_patches, geo_patches = next_element

            rank_im_shape = tf.rank(im_patches)
            rank_score_shape = tf.rank(score_patches)
            rank_geo_shape = tf.rank(geo_patches)


            shape_im_shape = tf.shape(im_patches)
            shape_score_shape = tf.shape(score_patches)
            shape_geo_shape = tf.shape(geo_patches)

            [ some_imshape, some_scoreshape, some_geoshape,\
             some_rank_im_shape, some_rank_score_shape, some_rank_geo_shape,
             some_shape_im_shape, some_shape_score_shape, some_shape_geo_shape] = \
                sess.run([ im_patches, score_patches, geo_patches,
                          rank_im_shape, rank_score_shape, rank_geo_shape,
                          shape_im_shape, shape_score_shape, shape_geo_shape])



            print("Rank of the 3 patches ")
            print(some_rank_im_shape)
            print(some_rank_score_shape)
            print(some_rank_geo_shape)

            print("Shapes of the 3 patches ")
            print(some_shape_im_shape)
            print(some_shape_score_shape)
            print(some_shape_geo_shape)

            print("Types of the 3 patches ")
            print(type(im_patches))
            print(type(score_patches))
            print(type(geo_patches))

        except tf.errors.OutOfRangeError:
            break

下面是这 2 个 tfrecord 的输出。

Rank of the 3 patches 
4
4
4
Shapes of the 3 patches 
[   1 3553 2529    3]
[   1 3553 2529    2]
[   1 3553 2529    5]
Types of the 3 patches 
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>
Rank of the 3 patches 
4
4
4
Shapes of the 3 patches 
[   1 3553 5025    3]
[   1 3553 5025    2]
[   1 3553 5025    5]
Types of the 3 patches 
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>
<class 'tensorflow.python.framework.ops.Tensor'>

我确实意识到的一件事是，如果我尝试将多个标签作为列表返回并比较上述迭代器脚本的返回值，我会收到错误

def _parse_function2(proto):

    ---- everything same as above ----
    ---- just returning the labels as list---


    return parsed_features['im_patches'], [tf.cast(parsed_features["score_patches"],tf.int16), parsed_features["geo_patches"]]

捕获上述返回值:

    while True:
        try:
            next_element = iterator.get_next()
            im_patches, [score_patches, geo_patches] = next_element

错误如下:TypeError: Tensor objects are only iterable when eager execution is enabled. To iterate over this tensor use tf.map_fn.

编辑2:这是拟合函数的定义。看来可以拿tensorflow tensors以及 steps_per_epoch

def fit(self,
      x=None,
      y=None,
      batch_size=None,
      epochs=1,
      verbose=1,
      callbacks=None,
      validation_split=0.,
      validation_data=None,
      shuffle=True,
      class_weight=None,
      sample_weight=None,
      initial_epoch=0,
      steps_per_epoch=None,
      validation_steps=None,
      max_queue_size=10,
      workers=1,
      use_multiprocessing=False,
      **kwargs):
"""Trains the model for a fixed number of epochs (iterations on a dataset).

Arguments:
    x: Input data. It could be:
      - A Numpy array (or array-like), or a list of arrays
        (in case the model has multiple inputs).
      - A TensorFlow tensor, or a list of tensors
        (in case the model has multiple inputs).
      - A dict mapping input names to the corresponding array/tensors,
        if the model has named inputs.
      - A `tf.data` dataset or a dataset iterator. Should return a tuple
        of either `(inputs, targets)` or
        `(inputs, targets, sample_weights)`.
      - A generator or `keras.utils.Sequence` returning `(inputs, targets)`
        or `(inputs, targets, sample weights)`.
    y: Target data. Like the input data `x`,
      it could be either Numpy array(s) or TensorFlow tensor(s).
      It should be consistent with `x` (you cannot have Numpy inputs and
      tensor targets, or inversely). If `x` is a dataset, dataset
      iterator, generator, or `keras.utils.Sequence` instance, `y` should
      not be specified (since targets will be obtained from `x`).

Answer 1

这似乎是tensorflow.keras模块中的一个错误。下面的 github 问题中建议了有效的修复。

https://github.com/tensorflow/tensorflow/issues/24520