output = torch.masked_fill(input, mask, value) output = input.masked_fill(mask, value) imgs_masked= torch.masked_fill(input=imgs, mask=~mask, value=0)#这里mask取反:true表示被“遮住的”"""tensor([[[182., 0., 0.], [ 0., 92., 0.], [ 0., 0., 86.]], [[157., 0., ...
一、函数 1.1 masked_fill pytorch masked_fill 输入数据的维度为【batch_size,seq_len,embedding_size】mask和输入数据是相同的数据维度,但mask的整型数据,并且要不是0,要不是1,masked_fill会对数据数据对应的mask,如果是1则替换成设定值,0则不变 # -*-coding:utf-8-*-importtorchorigin=torch.nn.init.xavi...
tensor([0.0000, 0.0000, 0.2689, 0.7311])容易报错：Expected object of scalar type Byte but got scalar type Long for argument #2 'mask'原因，mask = torch.LongTensor()解决⽅法：mask = torch.ByteTensor()在mask值为1的位置处⽤value填充。mask的元素个数需和本tensor相同，但尺⼨可以不同...
pytorch masked_fill方法理解 mask必须是一个 ByteTensor 而且shape的最大维度必须和 a一样 并且元素只能是 0或者1 , 是将mask中为1的 元素所在的索引,在a中相同的的索引处替换为 value importtorcha=torch.tensor([[[5,5,5,5],[6,6,6,6],[7,7,7,7]],[[1,1,1,1],[2,2,2,2],[3,3,3,...
mask = dist.ge(0) grad_input1.masked_fill_(mask,1) grad_input1 = grad_input1.mul_(-1) * y grad_input2.masked_fill_(mask,1) * y grad_input2 = grad_input2 * yifctx.size_average: grad_input1.div_(y.size(0)) grad_input2.div_(y.size(0))returngrad_input1 * grad_output...
1. 2. 3. 4. 其中mask必须是一个 ByteTensor ,shape必须和 a一样,且元素只能是 0或者1 ,是将 mask中为1的 元素所在的索引,在a中相同的的索引处替换为 value ,mask value必须同为tensor 。 在图像、视频相关的任务中,还可实现空间上的mask。
Tensor. masked_ fill_ And Tensor.masked_fill is two different methods import torch torch.manual_seed(2022) tensor = torch.randn(4, 4) tensor2 = torch.randn(4, 4) mask = torch.zeros(4,4) # build mask for index, data in enumerate(range(3, ...
1 torch.Tensor.masked_fill_(mask, value) Fills elements of self tensor with value where mask is True.The shape of mask must be broadcastable with the shape of the underlying tensor. Parameters mask (BoolTensor)– the boolean mask value (float)– the value to fill in with...
a = a.masked_fill(mask = torch.ByteTensor([1,1,0,0]), value=-np.inf)print(a) b = F.softmax(a)print(b) tensor([-inf, -inf, 3., 4.]) d:/pycharmdaima/star-transformer/ceshi.py:8: UserWarning: Implicit dimension choice for softmax has been deprecated. Change ...
masked_fill_ returns an incorrect result on MPS where as masked_fill does not. See discussion here pytorch/pytorch#131285