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torch. masked_select (input, mask, *, out=None) → Tensor. Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which ...

torch.masked_select. Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor. The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable. input ( Tensor) – the input tensor. out ( Tensor, optional) – the output tensor.

pytorch - 如何用2D mask mask 3D 张量并保持原始向量的尺寸？ 标签 pytorch tensor. 假设，我有一个3D 张量A A = torch.arange(24).view(4, 3, 2) print(A)

torch.masked_select. torch.masked_select(input, mask, *, out=None) → Tensor. Returns a new 1-D tensor which indexes the input tensor according to the boolean mask mask which is a BoolTensor. The shapes of the mask tensor and the input tensor don’t need to match, but they must be broadcastable. Note.

For example I have 3d tensor like this: a = torch.ones((3, 3, 3)) a[:, 1, 1] = 2 a[:, 2, 2] = 5 And I have a 2d “mask” like this: b ...

Using masked_select functionality from PyTorch leads to the following error. torch.masked_select(X, (mask == 1)) ----------------- ...

May 22, 2020 · Essentially, we need to match the dimension of the tensor mask with the tensor being masked. There are two ways to do it. Approach 1: Does not preserve original tensor dimensions.

16.12.2021 · The masked_select and masked_scatter etc. are intended to mask on values and return a 1D tensor while I need to retain the 2D format, so this will not work. test = torch.Tensor (parameter_space) print (test.shape) mask = torch.ones (len (test), dtype=torch.bool) mask [1] = False print (mask) print (test [mask, :].shape) This yields the ...

Suppose i have a 3D tensor A of size num_class * batch_size * Dim, and a mask B of size num_class * batch_size. Then , i want to mask A ...

Hi I have a 3D tensor like (batch_size, seq_len, dim), and some of them in the 2nd dimension are zero-padded. And I have a mask generated by ...

14.07.2018 · For example I have 3d tensor like this: a = torch.ones((3, 3, 3)) a[:, 1, 1] = 2 a[:, 2, 2] = 5 And I have a 2d “mask” like this: b = torch.zeros((3, 3)) b[1, 1] = 1 b[2, 2] = 1 And I want to get a list of 3d vectors from ‘a’ by mask ‘b’: the output should contain two vectors: [[2, 2, 2], [5, 5, 5]] I tried to use some constructions like torch.masked_select, but it always return 1d ...

Oct 13, 2020 · Hi I have a 3D tensor like (batch_size, seq_len, dim), and some of them in the 2nd dimension are zero-padded. And I have a mask generated by the lengths. I want to select the tensor by the mask. The behavior is like masked_select but returns a 2D tensor. a = torch.rand((3, 3, 3)) a[1, 2] = 0 a[2, 2] = 0 a[2, 1] = 0 print(a) tensor([[[0.7910, 0.4829, 0.7381], [0.9005, 0.2266, 0.5940], [0.8811 ...

I'm trying to create a mask based on an index tensor. ... How do I convert a 2d mask to index while retaining the dimensions.

A 2D mask will be broadcasted across the batch while a 3D mask allows for a different mask for each entry in the batch. Binary, byte, and float masks are supported. For a binary mask, a True value indicates that the corresponding position is not allowed to attend. For a byte mask, a non-zero value indicates that the corresponding position is ...

21.05.2020 · A = torch.arange (24).view (4, 3, 2) print (A) and require masking it using 2D tensor mask = torch.zeros ( (4, 3), dtype=torch.int64) # or dtype=torch.ByteTensor mask [0, 0] = 1 mask [1, 1] = 1 mask [3, 0] = 1 print ('Mask: ', mask) Using masked_select functionality from PyTorch leads to the following error.

13.10.2020 · Hi I have a 3D tensor like (batch_size, seq_len, dim), and some of them in the 2nd dimension are zero-padded. And I have a mask generated by the lengths. I want to select the tensor by the mask. The behavior is like masked_select but returns a 2D tensor. a = torch.rand((3, 3, 3)) a[1, 2] = 0 a[2, 2] = 0 a[2, 1] = 0 print(a) tensor([[[0.7910, 0.4829, 0.7381], [0.9005, …

where ⋆ \star ⋆ is the valid 2D cross-correlation operator, N N N is a batch size, C C C denotes a number of channels, H H H is a height of input planes in pixels, and W W W is width in pixels. This module supports TensorFloat32 .

Supose i have a tensor A of size batch_size x num_class x Dim, and a batch of labels L of size batch_size, where each element specifies ...

Jul 14, 2018 · For example I have 3d tensor like this: a = torch.ones((3, 3, 3)) a[:, 1, 1] = 2 a[:, 2, 2] = 5 And I have a 2d “mask” like this: b = torch.zeros((3, 3)) b[1, 1] = 1 b[2, 2] = 1 And I want to get a list of 3d vectors from ‘a’ by mask ‘b’: the output should contain two vectors: [[2, 2, 2], [5, 5, 5]] I tried to use some constructions like torch.masked_select, but it always return 1d ...

Jun 24, 2018 · Suppose i have a 3D tensor A of size num_class * batch_size * Dim, and a mask B of size num_class * batch_size. Then , i want to mask A using the B tensor. However, I really dont know how to do it, can anyone help me ? Thanks.

Hi, I have two sets of labels, for example labels_a = torch.Tensor([0,1,2,3]) with a shape of torch.size([4]) labels_b = torch.

Hello, Question 1) If I have a Tensor of size (A, B, C) and a mask of size (A, B). The mask only has values of 1 or 0 which indicates whether we should use ...

24.06.2018 · Suppose i have a 3D tensor A of size num_class * batch_size * Dim, and a mask B of size num_class * batch_size. Then , i want to mask A using the B tensor. However, I really dont know how to do it, can anyone help me ? Thanks.