Question

我正在尝试使用两个数据集“数据”和“标签”创建一个HDF5文件。但是，当我尝试访问该文件时，出现如下错误：

Traceback (most recent call last):
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\pydevd.py", line 1664, in <module>
    main()
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\pydevd.py", line 1658, in main
    globals = debugger.run(setup['file'], None, None, is_module)
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\pydevd.py", line 1068, in run
    pydev_imports.execfile(file, globals, locals)  # execute the script
  File "C:\Program Files\JetBrains\PyCharm Community Edition 2018.1.4\helpers\pydev\_pydev_imps\_pydev_execfile.py", line 18, in execfile
    exec(compile(contents+"\n", file, 'exec'), glob, loc)
  File "C:/pycharm/Input_Pipeline.py", line 140, in <module>
    data_h5 = f['data'][:]
  File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "C:\Users\u20x47\PycharmProjects\PCL\venv\lib\site-packages\h5py\_hl\group.py", line 177, in __getitem__
    oid = h5o.open(self.id, self._e(name), lapl=self._lapl)
  File "h5py\_objects.pyx", line 54, in h5py._objects.with_phil.wrapper
  File "h5py\_objects.pyx", line 55, in h5py._objects.with_phil.wrapper
  File "h5py\h5o.pyx", line 190, in h5py.h5o.open
ValueError: Not a location (invalid object ID)

用于创建数据集的代码：

h5_file.create_dataset('data', data=data_x, compression='gzip', compression_opts=4, dtype='float32')
h5_file.create_dataset('label', data=label, compression='gzip', compression_opts=1, dtype='uint8')

data_x an array of arrays. Each element in data_x is a 3D array of 1024 elements. 
label is an array of arrays as well. Each element is a 1D array of 1 element.

访问该文件的代码：

f = h5_file
data_h5 = f['data'][:]
label_h5 = f['label'][:]
print (data_h5, label_h5)

我该如何解决？这是语法错误还是逻辑错误？

Answer 1

我无法重现该错误。也许您忘记了关闭文件，或者在执行过程中更改了h5的内容。

您还可以使用print h5_file.items()检查h5文件的内容

经过测试的代码：

import h5py
import numpy as np

h5_file = h5py.File('test.h5', 'w')
# bogus data with the correct size
data_x = np.random.rand(16,8,8)
label = np.random.randint(100, size=(1,1),dtype='uint8')
#
h5_file.create_dataset('data', data=data_x, compression='gzip', compression_opts=4, dtype='float32')
h5_file.create_dataset('label', data=label, compression='gzip', compression_opts=1, dtype='uint8')
h5_file.close()

h5_file = h5py.File('test.h5', 'r')
f = h5_file
print f.items()
data_h5 = f['data'][...]
label_h5 = f['label'][...]
print (data_h5, label_h5)
h5_file.close()

生产

[(u'data', <HDF5 dataset "data": shape (16, 8, 8), type "<f4">), (u'label', <HDF5 dataset "label": shape (1, 1), type "|u1">)]
(array([[[4.36837107e-01, 8.05664659e-01, 3.34415197e-01, ...,
     8.89135897e-01, 1.84097692e-01, 3.60782951e-01],
      [8.86442482e-01, 6.07181549e-01, 2.42844030e-01, ...,
      [4.24369454e-01, 6.04596496e-01, 5.56676507e-01, ...,
     7.22884715e-01, 2.45932683e-01, 9.18777227e-01]]], dtype=float32), array([[25]], dtype=uint8))

在HDF5文件中创建和访问数据集

1 个答案: