torch.masked_select 用于带mask的图像分割测试集DRIVE

类似与DRIVE这样的数据集，输入图像有一个非矩形的边界，并非我们图像分割感兴趣的部分，数据集给出了二值mask，用于屏蔽

网络结果向前传播后得到output，在training中与grand truth计算loss，在test中计算准确度指数，显然都需要忽略在mask黑色区域，test中相对容易解决mask的使用问题，那么training中怎么忽略mask黑色区域的损失呢？

torch.masked_select(很遗憾pytorch的官网手册被墙了)，一下先搬运原文

torch.masked_select(input, mask, out=None) → Tensor

Returns a new 1-D tensor which indexes the input tensor according to the binary mask mask which is a ByteTensor.

The shapes of the mask tensor and the input tensor don’t need to match, but they mmaskust be broadcastable. s

mask表示一个dtype=ByteTensor的tensor，用mask=mask.byte()即可转化，但mask只能取0，1值

mask的shape与input一致这种情况很好理解，返回一个一维tensor，表示input中对应位置上mask值为1的tensor

mask的shape与input不一致时，比如dim数量不相等，维度长度不相等。显然需要在某种条件下，将mask唯一扩充为input的shape的方法，这就是文中的broadcasting字眼，下面是原文描述

Two tensors are “broadcastable” if the following rules hold:

• Each tensor has at least one dimension.
• When iterating over the dimension sizes, starting at the trailing dimension, the dimension sizes must either be equal, one of them is 1, or one of them does not exist.

>>> x=torch.empty(5,7,3)
>>> y=torch.empty(5,7,3)
# same shapes are always broadcastable (i.e. the above rules always hold)>>> x=torch.empty((0,))
>>> y=torch.empty(2,2)
# x and y are not broadcastable, because x does not have at least 1 dimension# can line up trailing dimensions
>>> x=torch.empty(5,3,4,1)
>>> y=torch.empty(  3,1,1)
# x and y are broadcastable.
# 1st trailing dimension: both have size 1
# 2nd trailing dimension: y has size 1
# 3rd trailing dimension: x size == y size
# 4th trailing dimension: y dimension doesn't exist# but:
>>> x=torch.empty(5,2,4,1)
>>> y=torch.empty(  3,1,1)
# x and y are not broadcastable, because in the 3rd trailing dimension 2 != 3

1. 维数短的一方在torch前部，注意是前面插入size为1的维至维数一致
2. 逐维比较长度，如果不相等，则必需有一方在该维度长为1，然后在这个维度上复制叠加，至长度相等，否则失败

input 与mask满足broadcasting时就不会报错，一个小细节：

当mask是那个维度长的一方时，多出来的维度会自动忽略