Row-wise Element Indexing in PyTorch for C++
I am using the C++ frontend for PyTorch and am struggling with a relatively basic indexing problem.
I have an 8 by 6 Tensor such as the one below:
[ Variable[CUDAFloatType]{8,6} ]
0 1 2 3 4 5
0 1.7107e-14 4.0448e-17 4.9708e-06 1.1664e-08 9.9999e-01 2.1857e-20
1 1.8288e-14 5.9356e-17 5.3042e-06 1.2369e-08 9.9999e-01 2.4799e-20
2 2.6828e-04 9.0390e-18 1.7517e-02 1.0529e-03 9.8116e-01 6.7854e-26
3 5.7521e-10 3.1037e-11 1.5021e-03 1.2304e-06 9.9850e-01 1.4888e-17
4 1.7811e-13 1.8383e-15 1.6733e-05 3.8466e-08 9.9998e-01 5.2815e-20
5 9.6191e-06 2.6217e-23 3.1345e-02 2.3024e-04 9.6842e-01 2.9435e-34
6 2.2653e-04 8.4642e-18 1.6085e-02 9.7405e-04 9.8271e-01 6.3059e-26
7 3.8951e-14 2.9903e-16 8.3518e-06 1.7974e-08 9.9999e-01 3.6993e-20
I have another Tensor with just 8 elements in it such as:
[ Variable[CUDALongType]{8} ]
0
3
4
4
4
4
4
4
I would like to index the rows of my first tensor using the second to produce:
0
0 1.7107e-14
1 1.2369e-08
2 9.8116e-01
3 9.9850e-01
4 9.9998e-01
5 9.6842e-01
6 9.8271e-01
7 9.9999e-01
I have tried a few different approaches including index_select but it seems to produce an output that has the same dimensions as the input (8x6).
In Python I think I could index with Python's built-in indexing as discussed here: https://github.com/pytorch/pytorch/issues/1080
Unfortunately, in C++ I can only index a Tensor with a scalar (zero-dimensional Tensor) so I don't think that approach works for me here.
How can I achieve my desired result without resorting to loops?
c++ pytorch
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
add a comment |
I am using the C++ frontend for PyTorch and am struggling with a relatively basic indexing problem.
I have an 8 by 6 Tensor such as the one below:
[ Variable[CUDAFloatType]{8,6} ]
0 1 2 3 4 5
0 1.7107e-14 4.0448e-17 4.9708e-06 1.1664e-08 9.9999e-01 2.1857e-20
1 1.8288e-14 5.9356e-17 5.3042e-06 1.2369e-08 9.9999e-01 2.4799e-20
2 2.6828e-04 9.0390e-18 1.7517e-02 1.0529e-03 9.8116e-01 6.7854e-26
3 5.7521e-10 3.1037e-11 1.5021e-03 1.2304e-06 9.9850e-01 1.4888e-17
4 1.7811e-13 1.8383e-15 1.6733e-05 3.8466e-08 9.9998e-01 5.2815e-20
5 9.6191e-06 2.6217e-23 3.1345e-02 2.3024e-04 9.6842e-01 2.9435e-34
6 2.2653e-04 8.4642e-18 1.6085e-02 9.7405e-04 9.8271e-01 6.3059e-26
7 3.8951e-14 2.9903e-16 8.3518e-06 1.7974e-08 9.9999e-01 3.6993e-20
I have another Tensor with just 8 elements in it such as:
[ Variable[CUDALongType]{8} ]
0
3
4
4
4
4
4
4
I would like to index the rows of my first tensor using the second to produce:
0
0 1.7107e-14
1 1.2369e-08
2 9.8116e-01
3 9.9850e-01
4 9.9998e-01
5 9.6842e-01
6 9.8271e-01
7 9.9999e-01
I have tried a few different approaches including index_select but it seems to produce an output that has the same dimensions as the input (8x6).
In Python I think I could index with Python's built-in indexing as discussed here: https://github.com/pytorch/pytorch/issues/1080
Unfortunately, in C++ I can only index a Tensor with a scalar (zero-dimensional Tensor) so I don't think that approach works for me here.
How can I achieve my desired result without resorting to loops?
c++ pytorch
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
2
you want to look at pytorch.org/docs/stable/torch.html#torch.gather instead of index_select
– Shai
Nov 27 '18 at 22:02
add a comment |
I am using the C++ frontend for PyTorch and am struggling with a relatively basic indexing problem.
I have an 8 by 6 Tensor such as the one below:
[ Variable[CUDAFloatType]{8,6} ]
0 1 2 3 4 5
0 1.7107e-14 4.0448e-17 4.9708e-06 1.1664e-08 9.9999e-01 2.1857e-20
1 1.8288e-14 5.9356e-17 5.3042e-06 1.2369e-08 9.9999e-01 2.4799e-20
2 2.6828e-04 9.0390e-18 1.7517e-02 1.0529e-03 9.8116e-01 6.7854e-26
3 5.7521e-10 3.1037e-11 1.5021e-03 1.2304e-06 9.9850e-01 1.4888e-17
4 1.7811e-13 1.8383e-15 1.6733e-05 3.8466e-08 9.9998e-01 5.2815e-20
5 9.6191e-06 2.6217e-23 3.1345e-02 2.3024e-04 9.6842e-01 2.9435e-34
6 2.2653e-04 8.4642e-18 1.6085e-02 9.7405e-04 9.8271e-01 6.3059e-26
7 3.8951e-14 2.9903e-16 8.3518e-06 1.7974e-08 9.9999e-01 3.6993e-20
I have another Tensor with just 8 elements in it such as:
[ Variable[CUDALongType]{8} ]
0
3
4
4
4
4
4
4
I would like to index the rows of my first tensor using the second to produce:
0
0 1.7107e-14
1 1.2369e-08
2 9.8116e-01
3 9.9850e-01
4 9.9998e-01
5 9.6842e-01
6 9.8271e-01
7 9.9999e-01
I have tried a few different approaches including index_select but it seems to produce an output that has the same dimensions as the input (8x6).
In Python I think I could index with Python's built-in indexing as discussed here: https://github.com/pytorch/pytorch/issues/1080
Unfortunately, in C++ I can only index a Tensor with a scalar (zero-dimensional Tensor) so I don't think that approach works for me here.
How can I achieve my desired result without resorting to loops?
c++ pytorch
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
I am using the C++ frontend for PyTorch and am struggling with a relatively basic indexing problem.
I have an 8 by 6 Tensor such as the one below:
[ Variable[CUDAFloatType]{8,6} ]
0 1 2 3 4 5
0 1.7107e-14 4.0448e-17 4.9708e-06 1.1664e-08 9.9999e-01 2.1857e-20
1 1.8288e-14 5.9356e-17 5.3042e-06 1.2369e-08 9.9999e-01 2.4799e-20
2 2.6828e-04 9.0390e-18 1.7517e-02 1.0529e-03 9.8116e-01 6.7854e-26
3 5.7521e-10 3.1037e-11 1.5021e-03 1.2304e-06 9.9850e-01 1.4888e-17
4 1.7811e-13 1.8383e-15 1.6733e-05 3.8466e-08 9.9998e-01 5.2815e-20
5 9.6191e-06 2.6217e-23 3.1345e-02 2.3024e-04 9.6842e-01 2.9435e-34
6 2.2653e-04 8.4642e-18 1.6085e-02 9.7405e-04 9.8271e-01 6.3059e-26
7 3.8951e-14 2.9903e-16 8.3518e-06 1.7974e-08 9.9999e-01 3.6993e-20
I have another Tensor with just 8 elements in it such as:
[ Variable[CUDALongType]{8} ]
0
3
4
4
4
4
4
4
I would like to index the rows of my first tensor using the second to produce:
0
0 1.7107e-14
1 1.2369e-08
2 9.8116e-01
3 9.9850e-01
4 9.9998e-01
5 9.6842e-01
6 9.8271e-01
7 9.9999e-01
I have tried a few different approaches including index_select but it seems to produce an output that has the same dimensions as the input (8x6).
In Python I think I could index with Python's built-in indexing as discussed here: https://github.com/pytorch/pytorch/issues/1080
Unfortunately, in C++ I can only index a Tensor with a scalar (zero-dimensional Tensor) so I don't think that approach works for me here.
How can I achieve my desired result without resorting to loops?
c++ pytorch
c++ pytorch
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
edited Nov 27 '18 at 19:53
JoshVarty
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
asked Nov 27 '18 at 19:45
JoshVartyJoshVarty
5,59633357
5,59633357
2
you want to look at pytorch.org/docs/stable/torch.html#torch.gather instead of index_select
– Shai
Nov 27 '18 at 22:02
add a comment |
2
you want to look at pytorch.org/docs/stable/torch.html#torch.gather instead of index_select
– Shai
Nov 27 '18 at 22:02
2
2
you want to look at pytorch.org/docs/stable/torch.html#torch.gather instead of index_select
– Shai
Nov 27 '18 at 22:02
you want to look at pytorch.org/docs/stable/torch.html#torch.gather instead of index_select
– Shai
Nov 27 '18 at 22:02
add a comment |
1 Answer
1
active
oldest
votes
It turns out you can do this in a couple different ways. One with gather and one with index. From the PyTorch discussions where I asked the same question:
Using torch::gather
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto result = x.gather(1, idx.unsqueeze(1));
Using the C++ specific torch::index
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto rows = torch::arange(0, x.size(0), torch::kLong);
auto result = x.index({rows, idx});
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
add a comment |
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1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
It turns out you can do this in a couple different ways. One with gather and one with index. From the PyTorch discussions where I asked the same question:
Using torch::gather
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto result = x.gather(1, idx.unsqueeze(1));
Using the C++ specific torch::index
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto rows = torch::arange(0, x.size(0), torch::kLong);
auto result = x.index({rows, idx});
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
add a comment |
It turns out you can do this in a couple different ways. One with gather and one with index. From the PyTorch discussions where I asked the same question:
Using torch::gather
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto result = x.gather(1, idx.unsqueeze(1));
Using the C++ specific torch::index
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto rows = torch::arange(0, x.size(0), torch::kLong);
auto result = x.index({rows, idx});
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
add a comment |
It turns out you can do this in a couple different ways. One with gather and one with index. From the PyTorch discussions where I asked the same question:
Using torch::gather
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto result = x.gather(1, idx.unsqueeze(1));
Using the C++ specific torch::index
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto rows = torch::arange(0, x.size(0), torch::kLong);
auto result = x.index({rows, idx});
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
It turns out you can do this in a couple different ways. One with gather and one with index. From the PyTorch discussions where I asked the same question:
Using torch::gather
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto result = x.gather(1, idx.unsqueeze(1));
Using the C++ specific torch::index
auto x = torch::randn({8, 6});
int64_t idx_data[8] = { 0, 3, 4, 4, 4, 4, 4, 4 };
auto idx = x.type().toScalarType(torch::kLong).tensorFromBlob(idx_data, 8);
auto rows = torch::arange(0, x.size(0), torch::kLong);
auto result = x.index({rows, idx});
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
answered Nov 29 '18 at 5:16
JoshVartyJoshVarty
5,59633357
5,59633357
add a comment |
add a comment |
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2
you want to look at pytorch.org/docs/stable/torch.html#torch.gather instead of index_select
– Shai
Nov 27 '18 at 22:02