Question

我有一个巨大的训练数据集，无法容纳在RAM中。我尝试将随机批次的图像加载到堆栈中，而不加载整个.h5。我的方法是创建索引列表并对其进行混排，而不是对整个.h5文件进行混排。假设：

a = np.arange(2000*2000*2000).reshape(2000, 2000, 2000)
idx = np.random.randint(2000, size = 800) #so that I only need to shuffle this idx at the end of epoch

# create this huge data 32GBs > my RAM
with h5py.File('./tmp.h5', 'w') as f:
     tmp = f.create_dataset('a', (2000, 2000, 2000))
     tmp[:] = a

# read it
with h5py.File('./tmp.h5', 'r') as f:
     tensor = f['a'][:][idx] #if I don't do [:] there will be error if I do so it will load whole file which I don't want

有人可以解决吗？

Answer 1

感谢@ max9111，这是我建议解决的方法：

batch_size = 100 
idx = np.arange(2000)
# shuffle
idx = np.random.shuffle(idx)

由于constraint of h5py：

选择坐标必须按升序给出

阅读前应该先排序

for step in range(epoch_len // batch_size):
     try:
          with h5py.File(path, 'r') as f:
               return f['img'][np.sort(idx[step * batch_size])], f['label'][np.sort(idx[step * batch_size])]
     except:
          raise('epoch finished and drop the remainder')

随机读取.h5文件中的元素，而无需加载整个矩阵

1 个答案: