小编Ale*_*NON的帖子

我尝试优化我的数据输入管道。该数据集是一组 450 个 TFRecord 文件，每个文件大小约为 70MB，托管在 GCS 上。该作业使用 GCP ML Engine 执行。没有 GPU。

这是管道：

def build_dataset(file_pattern):
    return tf.data.Dataset.list_files(
        file_pattern
    ).interleave(
        tf.data.TFRecordDataset,
        num_parallel_calls=tf.data.experimental.AUTOTUNE
    ).shuffle(
        buffer_size=2048
    ).batch(
        batch_size=2048,
        drop_remainder=True,
    ).cache(
    ).repeat(
    ).map(
        map_func=_parse_example_batch,
        num_parallel_calls=tf.data.experimental.AUTOTUNE
    ).prefetch(
        buffer_size=1
    )

使用映射函数：

def _bit_to_float(string_batch: tf.Tensor):
    return tf.reshape(tf.math.floormod(tf.dtypes.cast(tf.bitwise.right_shift(
        tf.expand_dims(tf.io.decode_raw(string_batch, tf.uint8), 2),
        tf.reshape(tf.dtypes.cast(tf.range(7, -1, -1), tf.uint8), (1, 1, 8))
    ), tf.float32), 2), (tf.shape(string_batch)[0], -1))


def _parse_example_batch(example_batch):
    preprocessed_sample_columns = {
        "features": tf.io.VarLenFeature(tf.float32),
        "booleanFeatures": tf.io.FixedLenFeature((), tf.string, ""),
        "label": tf.io.FixedLenFeature((), tf.float32, -1)
    }
    samples = tf.io.parse_example(example_batch, preprocessed_sample_columns)
    dense_float = tf.sparse.to_dense(samples["features"])
    bits_to_float …

目前我遇到了这个错误，有人可以帮忙解决吗？

---------------------------------------------------------------------------
OperatorNotAllowedInGraphError            Traceback (most recent call last)
<ipython-input-24-0211c82920d0> in <module>
      7 warnings.filterwarnings("ignore")
      8 model.train(dataset_train,dataset_val, learning_rate=config.LEARNING_RATE,epochs=5,
----> 9             layers='heads')
/kaggle/working/maskrcnn/Mask_RCNN-master/mrcnn/model.py in train(self, train_dataset, val_dataset, learning_rate, epochs, layers, augmentation, custom_callbacks, no_augmentation_sources)
   2355         log("Checkpoint Path: {}".format(self.checkpoint_path))
   2356         self.set_trainable(layers)
-> 2357         self.compile(learning_rate, self.config.LEARNING_MOMENTUM)
   2358
   2359         # Work-around for Windows: Keras fails on Windows when using
/kaggle/working/maskrcnn/Mask_RCNN-master/mrcnn/model.py in compile(self, learning_rate, momentum)
   2168         for name in loss_names:
   2169             layer = self.keras_model.get_layer(name)
-> 2170             if layer.output in self.keras_model.losses:
   2171                 continue
   2172             loss = (
/opt/conda/lib/python3.6/site-packages/tensorflow_core/python/framework/ops.py in …

我有一个自定义tf.keras.layers.Layer，它只使用 TF 运算符进行某种位解包（将整数转换为布尔值（0 或 1 浮点数））。

class CharUnpack(keras.layers.Layer):

    def __init__(self, name="CharUnpack", *args, **kwargs):
        super(CharUnpack, self).__init__(trainable=False, name=name, *args, **kwargs)
        # Range [7, 6, ..., 0] to bit-shift integers
        self._shifting_range = tf.reshape(
            tf.dtypes.cast(
                tf.range(7, -1, -1, name='shifter_range'),
                tf.uint8,
                name='shifter_cast'),
            (1, 1, 8),
            name='shifter_reshape')
        # Constant value 0b00000001 to use as bitwise and operator
        self._selection_bit = tf.constant(0x01, dtype=tf.uint8, name='and_selection_bit')

    def call(self, inputs):
        return tf.dtypes.cast(
            tf.reshape(
                tf.bitwise.bitwise_and(
                    tf.bitwise.right_shift(
                        tf.expand_dims(inputs, 2),
                        self._shifting_range,
                    ),
                    self._selection_bit,
                ),
                [x if x else -1 for x in …