『MXNet』第六弹_数据处理API（待续）

一、Gluon数据加载

图片数据(含标签)加载函数：gluon.data.vision.ImageFolderDataset

给出ImageFolderDataset类的描述，

Init signature: mxnet.gluon.data.vision.datasets.ImageFolderDataset(root, flag=1, transform=None)
Source:        
class ImageFolderDataset(dataset.Dataset):
    """A dataset for loading image files stored in a folder structure like::

        root/car/0001.jpg
        root/car/xxxa.jpg
        root/car/yyyb.jpg
        root/bus/123.jpg
        root/bus/023.jpg
        root/bus/wwww.jpg

    Parameters
    ----------
    root : str
        Path to root directory.
    flag : {0, 1}, default 1  # 控制彩色or灰度
        If 0, always convert loaded images to greyscale (1 channel).
        If 1, always convert loaded images to colored (3 channels).
    transform : callable, default None
        A function that takes data and label and transforms them:
    ::

        transform = lambda data, label: (data.astype(np.float32)/255, label)

    Attributes
    ----------
    synsets : list  # 查看类别，实际就是文件名
        List of class names. `synsets[i]` is the name for the integer label `i`
    items : list of tuples  # 生成的数据
        List of all images in (filename, label) pairs.
    """

实例：

train_imgs = gluon.data.vision.ImageFolderDataset(
    data_dir+'/hotdog/train',
    transform=lambda X, y: transform(X, y, train_augs))
test_imgs = gluon.data.vision.ImageFolderDataset(
    data_dir+'/hotdog/test',
    transform=lambda X, y: transform(X, y, test_augs))

print(train_imgs)
print(train_imgs.synsets)
data = gluon.data.DataLoader(train_imgs, 32, shuffle=True)

<mxnet.gluon.data.vision.datasets.ImageFolderDataset object at 0x7fbed5641c18>
['hotdog', 'not-hotdog']

batch迭代器：gluon.data.DataLoader

具有特殊方法，def __iter__(self)，其实例可以被迭代，也就是每次返回一个batch的数据，在第一维度上切割。

首个定位参数文档如下：

dataset : Dataset
        Source dataset. Note that numpy and mxnet arrays can be directly used
        as a Dataset.

最后生成的X_batch送入net(X_batch)向前传播，y_batch送入loss(output,y_batch)计算loss后反向传播。

二、MXNet数据加载

 https://blog.csdn.net/qq_36165459/article/details/78394322

https://blog.csdn.net/u012759136/article/details/50208733#%E4%BA%8C%E4%BB%8E%E5%8E%9F%E7%94%9F%E6%95%B0%E6%8D%AE%E7%94%9F%E6%88%90lst%E6%96%87%E4%BB%B6