关于如何将数据集封装为 Bunch 可参考 关于 『AI 专属数据库的定制』的改进。
PyTables 是 Python 与 HDF5 数据库/文件标准的结合。它专门为优化 I/O 操作的性能、最大限度地利用可用硬件而设计，并且它还支持压缩功能。
下面的代码均是在 Jupyter NoteBook 下完成的：
import sys
sys.path.append('E:/xinlib')from base.filez import DataBunchimport tables as tbimport numpy as npdef bunch2hdf5(root):
''' 这里我仅仅封装了 Cifar10、Cifar100、MNIST、Fashion MNIST 数据集， 使用者还可以自己追加数据集。 '''
db = DataBunch(root)
filters = tb.Filters(complevel=7, shuffle=False) # 这里我采用了压缩表，因而保存为 `.h5c` 但也可以保存为 `.h5`
with tb.open_file(f'{root}X.h5c', 'w', filters=filters, title='Xinet\'s dataset') as h5: for name in db.keys():
h5.create_group('/', name, title=f'{db[name].url}') if name != 'cifar100':
h5.create_array(h5.root[name], 'trainX', db[name].trainX, title='训练数据')
h5.create_array(h5.root[name], 'trainY', db[name].trainY, title='训练标签')
h5.create_array(h5.root[name], 'testX', db[name].testX, title='测试数据')
h5.create_array(h5.root[name], 'testY', db[name].testY, title='测试标签') else:
h5.create_array(h5.root[name], 'trainX', db[name].trainX, title='训练数据')
h5.create_array(h5.root[name], 'testX', db[name].testX, title='测试数据')
h5.create_array(h5.root[name], 'train_coarse_labels', db[name].train_coarse_labels, title='超类训练标签')
h5.create_array(h5.root[name], 'test_coarse_labels', db[name].test_coarse_labels, title='超类测试标签')
h5.create_array(h5.root[name], 'train_fine_labels', db[name].train_fine_labels, title='子类训练标签')
h5.create_array(h5.root[name], 'test_fine_labels', db[name].test_fine_labels, title='子类测试标签') for k in ['cifar10', 'cifar100']: for name in db[k].meta.keys():
name = name.decode() if name.endswith('names'):
label_names = np.asanyarray([label_name.decode() for label_name in db[k].meta[name.encode()]])
h5.create_array(h5.root[k], name, label_names, title='标签名称')完成 Bunch到 HDF5 的转换
root = 'E:/Data/Zip/'bunch2hdf5(root)
h5c = tb.open_file('E:/Data/Zip/X.h5c')
h5cFile(filename=E:/Data/Zip/X.h5c, title="Xinet's dataset", mode='r', root_uep='/', filters=Filters(complevel=7, complib='zlib', shuffle=False, bitshuffle=False, fletcher32=False, least_significant_digit=None)) / (RootGroup) "Xinet's dataset"/cifar10 (Group) 'https://www.cs.toronto.edu/~kriz/cifar.html'/cifar10/label_names (Array(10,)) '标签名称' atom := StringAtom(itemsize=10, shape=(), dflt=b'') maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar10/testX (Array(10000, 32, 32, 3)) '测试数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar10/testY (Array(10000,)) '测试标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/cifar10/trainX (Array(50000, 32, 32, 3)) '训练数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar10/trainY (Array(50000,)) '训练标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/cifar100 (Group) 'https://www.cs.toronto.edu/~kriz/cifar.html'/cifar100/coarse_label_names (Array(20,)) '标签名称' atom := StringAtom(itemsize=30, shape=(), dflt=b'') maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar100/fine_label_names (Array(100,)) '标签名称' atom := StringAtom(itemsize=13, shape=(), dflt=b'') maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar100/testX (Array(10000, 32, 32, 3)) '测试数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar100/test_coarse_labels (Array(10000,)) '超类测试标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/cifar100/test_fine_labels (Array(10000,)) '子类测试标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/cifar100/trainX (Array(50000, 32, 32, 3)) '训练数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/cifar100/train_coarse_labels (Array(50000,)) '超类训练标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/cifar100/train_fine_labels (Array(50000,)) '子类训练标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/fashion_mnist (Group) 'https://github.com/zalandoresearch/fashion-mnist'/fashion_mnist/testX (Array(10000, 28, 28, 1)) '测试数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/fashion_mnist/testY (Array(10000,)) '测试标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/fashion_mnist/trainX (Array(60000, 28, 28, 1)) '训练数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/fashion_mnist/trainY (Array(60000,)) '训练标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/mnist (Group) 'http://yann.lecun.com/exdb/mnist'/mnist/testX (Array(10000, 28, 28, 1)) '测试数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/mnist/testY (Array(10000,)) '测试标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None/mnist/trainX (Array(60000, 28, 28, 1)) '训练数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None/mnist/trainY (Array(60000,)) '训练标签' atom := Int32Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'little' chunkshape := None
从上面的结构可看出我将 Cifar10、Cifar100、MNIST、Fashion MNIST 进行了封装，并且还附带了它们各种的数据集信息。比如标签名，数字特征（以数组的形式进行封装）等。
%%time arr = h5c.root.cifar100.trainX.read() # 读取数据十分快速
Wall time: 125 ms
arr.shape
(50000, 32, 32, 3)
h5c.root
/ (RootGroup) "Xinet's dataset" children := ['cifar10' (Group), 'cifar100' (Group), 'fashion_mnist' (Group), 'mnist' (Group)]
X.h5c 使用说明
下面我们以 Cifar100 为例来展示我们自创的数据集 X.h5c（我将其上传到了百度云盘「链接：https://pan.baidu.com/s/1nzaicwHmFZH9Xgf2foSw6Q 密码：bl2e」可以下载直接使用；亦可你自己生成，不过我推荐自己生成，可以对数据集加深理解）
cifar100 = h5c.root.cifar100 cifar100
/cifar100 (Group) 'https://www.cs.toronto.edu/~kriz/cifar.html' children := ['coarse_label_names' (Array), 'fine_label_names' (Array), 'testX' (Array), 'test_coarse_labels' (Array), 'test_fine_labels' (Array), 'trainX' (Array), 'train_coarse_labels' (Array), 'train_fine_labels' (Array)]
'coarse_label_names' 指的是粗粒度或超类标签名，'fine_label_names' 则是细粒度标签名。
可以使用 read() 方法直接获取信息，也可以使用索引的方式获取。
coarse_label_names = cifar100.coarse_label_names[:]# 或者coarse_label_names = cifar100.coarse_label_names.read()
coarse_label_names.astype('str')array(['aquatic_mammals', 'fish', 'flowers', 'food_containers', 'fruit_and_vegetables', 'household_electrical_devices', 'household_furniture', 'insects', 'large_carnivores', 'large_man-made_outdoor_things', 'large_natural_outdoor_scenes', 'large_omnivores_and_herbivores', 'medium_mammals', 'non-insect_invertebrates', 'people', 'reptiles', 'small_mammals', 'trees', 'vehicles_1', 'vehicles_2'], dtype='<U30')
fine_label_names = cifar100.fine_label_names[:].astype('str')
fine_label_namesarray(['apple', 'aquarium_fish', 'baby', 'bear', 'beaver', 'bed', 'bee', 'beetle', 'bicycle', 'bottle', 'bowl', 'boy', 'bridge', 'bus', 'butterfly', 'camel', 'can', 'castle', 'caterpillar', 'cattle', 'chair', 'chimpanzee', 'clock', 'cloud', 'cockroach', 'couch', 'crab', 'crocodile', 'cup', 'dinosaur', 'dolphin', 'elephant', 'flatfish', 'forest', 'fox', 'girl', 'hamster', 'house', 'kangaroo', 'keyboard', 'lamp', 'lawn_mower', 'leopard', 'lion', 'lizard', 'lobster', 'man', 'maple_tree', 'motorcycle', 'mountain', 'mouse', 'mushroom', 'oak_tree', 'orange', 'orchid', 'otter', 'palm_tree', 'pear', 'pickup_truck', 'pine_tree', 'plain', 'plate', 'poppy', 'porcupine', 'possum', 'rabbit', 'raccoon', 'ray', 'road', 'rocket', 'rose', 'sea', 'seal', 'shark', 'shrew', 'skunk', 'skyscraper', 'snail', 'snake', 'spider', 'squirrel', 'streetcar', 'sunflower', 'sweet_pepper', 'table', 'tank', 'telephone', 'television', 'tiger', 'tractor', 'train', 'trout', 'tulip', 'turtle', 'wardrobe', 'whale', 'willow_tree', 'wolf', 'woman', 'worm'], dtype='<U13')
'testX' 与 'trainX' 分别代表数据的测试数据和训练数据，而其他的节点所代表的含义也是类似的。
例如，我们可以看看训练集的数据和标签：
trainX = cifar100.trainX train_coarse_labels = cifar100.train_coarse_labels
array([11, 15, 4, ..., 8, 7, 1])
shape 为 (50000, 32, 32, 3)，数据的获取，我们一样可以采用索引的形式或者使用 read()：
train_data = trainX[:]print(train_data[0].shape)print(train_data.dtype)
(32, 32, 3)uint8
当然，我们也可以直接使用 trainX 做运算。
for x in cifar100.trainX: y = x * 2 breakprint(y.shape)
(32, 32, 3)
h5c.get_node(h5c.root.cifar100, 'trainX')
/cifar100/trainX (Array(50000, 32, 32, 3)) '训练数据' atom := UInt8Atom(shape=(), dflt=0) maindim := 0 flavor := 'numpy' byteorder := 'irrelevant' chunkshape := None
更甚者，我们可以直接定义迭代器来获取数据：
trainX = cifar100.trainX train_coarse_labels = cifar100.train_coarse_labels
def data_iter(X, Y, batch_size):
n = X.nrows
idx = np.arange(n) if X.name.startswith('train'):
np.random.shuffle(idx) for i in range(0, n ,batch_size):
k = idx[i: min(n, i + batch_size)].tolist() yield np.take(X, k, 0), np.take(Y, k, 0)for x, y in data_iter(trainX, train_coarse_labels, 8): print(x.shape, y) break
(8, 32, 32, 3) [ 7 7 0 15 4 8 8 3]
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