全稱應該叫Data Descriptor 數據描述子
一個namedtuple的子類
本質也是一個tuple，只不過對不同位置的元素命名了而已，可以通過命名訪問到元素，和ordinaried字典類似
mxnet.DataDesc 使用了兩個信息描述數據
- name 是名字 字符串 str
- shape 是數據形狀 元組 裏面存int類型的數
class DataDesc(namedtuple('DataDesc', ['name', 'shape'])):
"""DataDesc is used to store name, shape, type and layout
information of the data or the label.
The `layout` describes how the axes in `shape` should be interpreted,
for example for image data setting `layout=NCHW` indicates
that the first axis is number of examples in the batch(N),
C is number of channels, H is the height and W is the width of the image.
For sequential data, by default `layout` is set to ``NTC``, where
N is number of examples in the batch, T the temporal axis representing time
and C is the number of channels.
Parameters
----------
cls : DataDesc
The class.
name : str
Data name.
shape : tuple of int
Data shape.
dtype : np.dtype, optional
Data type.
layout : str, optional
Data layout.
"""
def __new__(cls, name, shape, dtype=mx_real_t, layout='NCHW'): # pylint: disable=super-on-old-class
ret = super(cls, DataDesc).__new__(cls, name, shape)
ret.dtype = dtype
ret.layout = layout
return ret
def __repr__(self):
return "DataDesc[%s,%s,%s,%s]" % (self.name, self.shape, self.dtype,
self.layout)
@staticmethod
def get_batch_axis(layout):
"""Get the dimension that corresponds to the batch size.
When data parallelism is used, the data will be automatically split and
concatenated along the batch-size dimension. Axis can be -1, which means
the whole array will be copied for each data-parallelism device.
Parameters
----------
layout : str
layout string. For example, "NCHW".
Returns
-------
int
An axis indicating the batch_size dimension.
"""
if layout is None:
return 0
return layout.find('N')
@staticmethod
def get_list(shapes, types):
"""Get DataDesc list from attribute lists.
Parameters
----------
shapes : a tuple of (name, shape)
types : a tuple of (name, type)
"""
if types is not None:
type_dict = dict(types)
return [DataDesc(x[0], x[1], type_dict[x[0]]) for x in shapes]
else:
return [DataDesc(x[0], x[1]) for x in shapes]