我有一个大的HDF5文件(~30GB),我需要在每个数据集中随机输入(沿0轴).通过h5py文档查看我无法找到randomAccess或shuffle功能,但我希望我错过了一些东西.
是否有人熟悉HDF5,想到一种快速随机播放数据的方法?
这是我用我有限的知识实现的伪代码:
for dataset in datasets:
unshuffled = range(dataset.dims[0])
while unshuffled.length != 0:
if unshuffled.length <= 100:
dataset[:unshuffled.length/2], dataset[unshuffled.length/2:] = dataset[unshuffled.length/2:], dataset[:unshuffled.length/2]
break
else:
randomIndex1 = rand(unshuffled.length - 100)
randomIndex2 = rand(unshuffled.length - 100)
unshuffled.removeRange(randomIndex1..<randomIndex1+100)
unshuffled.removeRange(randomIndex2..<randomIndex2+100)
dataset[randomIndex1:randomIndex1 + 100], dataset[randomIndex2:randomIndex2 + 100] = dataset[randomIndex2:randomIndex2 + 100], dataset[randomIndex1:randomIndex1 + 100]
最佳答案 您可以使用random.shuffle(数据集).对于配备Core i5处理器,8 GB RAM和256 GB SSD的笔记本电脑上的30 GB数据集,这需要11分钟多一点.请参阅以下内容:
>>> import os
>>> import random
>>> import time
>>> import h5py
>>> import numpy as np
>>>
>>> h5f = h5py.File('example.h5', 'w')
>>> h5f.create_dataset('example', (40000, 256, 256, 3), dtype='float32')
>>> # set all values of each instance equal to its index
... for i, instance in enumerate(h5f['example']):
... h5f['example'][i, ...] = \
... np.ones(instance.shape, dtype='float32') * i
...
>>> # get file size in bytes
... file_size = os.path.getsize('example.h5')
>>> print('Size of example.h5: {:.3f} GB'.format(file_size/2.0**30))
Size of example.h5: 29.297 GB
>>> def shuffle_time():
... t1 = time.time()
... random.shuffle(h5f['example'])
... t2 = time.time()
... print('Time to shuffle: {:.3f} seconds'.format(str(t2 - t1)))
...
>>> print('Value of first 5 instances:\n{}'
... ''.format(str(h5f['example'][:10, 0, 0, 0])))
Value of first 5 instances:
[ 0. 1. 2. 3. 4.]
>>> shuffle_time()
Time to shuffle: 673.848 seconds
>>> print('Value of first 5 instances after '
... 'shuffling:\n{}'.format(str(h5f['example'][:10, 0, 0, 0])))
Value of first 5 instances after shuffling:
[ 15733. 28530. 4234. 14869. 10267.]
>>> h5f.close()
改组几个较小数据集的性能不应该比这更差.