python - 文件系统方案 '[local]' 未在 Google Colab TPU 中实现

Question

我在 Google Colab 中使用 TPU 运行时，但在读取文件时遇到问题(不确定)。我使用以下方法初始化了 TPU:

import tensorflow as tf
import os
import tensorflow_datasets as tfds

resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
tf.config.experimental_connect_to_cluster(resolver)
# This is the TPU initialization code that has to be at the beginning.
tf.tpu.experimental.initialize_tpu_system(resolver)
print("All devices: ", tf.config.list_logical_devices('TPU'))

我在 Google Colab 存储的文件夹中有很多图像(例如 '/content/train2017/000000000009.jpg')。我运行以下代码:

import tensorflow as tf
def load_image(image_path):
    img = tf.io.read_file(image_path)
    img = tf.image.decode_jpeg(img, channels=3)
    img = tf.image.resize(img, (299, 299))
    img = tf.keras.applications.inception_v3.preprocess_input(img)
    return img, image_path
load_image('/content/train2017/000000000009.jpg')

但是，我收到以下错误:

---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-33-a7fbb45f3b76> in <module>()
----> 1 load_image('/content/train2017/000000000009.jpg')

5 frames
<ipython-input-7-862c73d29b96> in load_image(image_path)
      2     img = tf.io.read_file(image_path)
      3     img = tf.image.decode_jpeg(img, channels=3)
----> 4     img = tf.image.resize(img, (299, 299))
      5     img = tf.keras.applications.inception_v3.preprocess_input(img)
      6     return img, image_path

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/image_ops_impl.py in resize_images_v2(images, size, method, preserve_aspect_ratio, antialias, name)
   1515       preserve_aspect_ratio=preserve_aspect_ratio,
   1516       name=name,
-> 1517       skip_resize_if_same=False)
   1518 
   1519 

/usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/image_ops_impl.py in _resize_images_common(images, resizer_fn, size, preserve_aspect_ratio, name, skip_resize_if_same)
   1183   with ops.name_scope(name, 'resize', [images, size]):
   1184     images = ops.convert_to_tensor(images, name='images')
-> 1185     if images.get_shape().ndims is None:
   1186       raise ValueError('\'images\' contains no shape.')
   1187     # TODO(shlens): Migrate this functionality to the underlying Op's.

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in get_shape(self)
   1071   def get_shape(self):
   1072     """Alias of Tensor.shape."""
-> 1073     return self.shape
   1074 
   1075   def _shape_as_list(self):

/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py in shape(self)
   1065         self._tensor_shape = tensor_shape.TensorShape(self._shape_tuple())
   1066       except core._NotOkStatusException as e:
-> 1067         six.raise_from(core._status_to_exception(e.code, e.message), None)
   1068 
   1069     return self._tensor_shape

/usr/local/lib/python3.6/dist-packages/six.py in raise_from(value, from_value)

UnimplementedError: File system scheme '[local]' not implemented (file: '/content/train2017/000000000009.jpg')

我该如何解决？我找到了类似 gs 桶的东西，但它是付费的。有没有其他方法可以解决这个问题？

Answer 1

用于在使用 TPU 时从本地文件加载文件 - 将它们作为正常的 python file.read()(而不是 tf.io)读取。在你的情况下:

def load_image(image_path):
    with open(image_path, "rb") as local_file: # <= change here
      img = local_file.read()
    img = tf.image.decode_jpeg(img, channels=3)
    img = tf.image.resize(img, (299, 299))
    img = tf.keras.applications.inception_v3.preprocess_input(img)
    return img, image_path
load_image('/content/train2017/000000000009.jpg')