本文整理汇总了Python中mxnet.io.DataDesc.get_batch_axis方法的典型用法代码示例。如果您正苦于以下问题：Python DataDesc.get_batch_axis方法的具体用法？Python DataDesc.get_batch_axis怎么用？Python DataDesc.get_batch_axis使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mxnet.io.DataDesc的用法示例。
在下文中一共展示了DataDesc.get_batch_axis方法的2个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: decide_slices
# 需要导入模块: from mxnet.io import DataDesc [as 别名]
# 或者: from mxnet.io.DataDesc import get_batch_axis [as 别名]
def decide_slices(self, data_shapes):
"""Decide the slices for each context according to the workload.
Parameters
----------
data_shapes : list
list of (name, shape) specifying the shapes for the input data or label.
"""
assert len(data_shapes) > 0
major_axis = [DataDesc.get_batch_axis(x.layout) for x in data_shapes]
for (name, shape), axis in zip(data_shapes, major_axis):
if axis == -1:
continue
batch_size = shape[axis]
if self.batch_size is not None:
assert batch_size == self.batch_size, ("all data must have the same batch size: "
+ ("batch_size = %d, but " % self.batch_size)
+ ("%s has shape %s" % (name, shape)))
else:
self.batch_size = batch_size
self.slices = _split_input_slice(self.batch_size, self.workload)
return major_axis 示例2: decide_slices
# 需要导入模块: from mxnet.io import DataDesc [as 别名]
# 或者: from mxnet.io.DataDesc import get_batch_axis [as 别名]
def decide_slices(self, data_shapes):
"""Decide the slices for each context according to the workload.
Parameters
----------
data_shapes : list
list of (name, shape) specifying the shapes for the input data or label.
"""
assert len(data_shapes) > 0
major_axis = [DataDesc.get_batch_axis(x.layout) for x in data_shapes]
for (name, shape), axis in zip(data_shapes, major_axis):
if axis == -1:
continue
batch_size = shape[axis]
if self.batch_size is not None:
assert batch_size == self.batch_size, (
"all data must have the same batch size: "
+ ("batch_size = %d, but " % self.batch_size)
+ ("%s has shape %s" % (name, shape))
)
else:
self.batch_size = batch_size
self.slices = _split_input_slice(self.batch_size, self.workload)
return major_axis