Initial PyTorch support

[NikEfth]

Changes in this pull request

• Link and compile with PyTorch

Testing performed

• make tests

Related issues

Checklist before requesting a review

• [] I have performed a self-review of my code
• [] I have added docstrings/doxygen in line with the guidance in the developer guide
• [] I have implemented unit tests that cover any new or modified functionality (if applicable)
• [] The code builds and runs on my machine
• [] documentation/release_XXX.md has been updated with any functionality change (if applicable)

[NikEfth]

This is where we are now:

Running tests...
Test project /local_mount/space/celer/2/users/pytorch_playground/STIR.b
    Start 1: test_Array
1/2 Test #1: test_Array .......................   Passed    2.58 sec
    Start 2: test_VectorWithOffset
2/2 Test #2: test_VectorWithOffset ............   Passed    0.50 sec

100% tests passed, 0 tests failed out of 2

• I know, it says absolutely nothing :)

[NikEfth]

Made quite a bit of progress, a few more boring parts left:

Running tests...
Test project /local_mount/space/celer/2/users/pytorch_playground/STIR.b
    Start 1: test_Array
1/3 Test #1: test_Array .......................   Passed    8.19 sec
    Start 2: test_VectorWithOffset
2/3 Test #2: test_VectorWithOffset ............   Passed    0.49 sec
    Start 3: test_Tensor
3/3 Test #3: test_Tensor ......................   Passed    0.50 sec

100% tests passed, 0 tests failed out of 3

Total Test time (real) =   9.18 sec

[KrisThielemans]

See #1589. Possibly move some stuff from here to there if I didn't cover everything yet. Let's merge #1589 quickly though (before 6.3).

[NikEfth]

@KrisThielemans Hi, I wrote the 2D check next() test for the TensorWrapper. Can you please confirm that this is correct (BasicCoordinate always gives me doubts):

std::cerr << "\tTest on next() with regular array\n";
          BasicCoordinate<2, int> index;
          index[1] = test.get_min_index(0);
          index[2] = test.get_min_index(1);
          cerr << ">>>>" << index[1] << "  " << index[2] << std::endl;
          TensorWrapper<2, int>::const_full_iterator iter = test.begin_all_const();
          do
            {
              cerr << ">>" << index[1] << "  " << index[2] << std::endl;
              check(*iter == test.at(index), "test on next(): element out of sequence?");
              ++iter;
              index[2]+=1;
              if (index[2] > test.get_max_index(1))
                {
                  index[2] = test.get_min_index(1);
                  index[1]+=1;
                }
          } while (iter != test.end_all());
          check(iter == test.end_all(), "test on next() : did we cover all elements?");
        }
      }

[KrisThielemans]

Looks ok, but I guess I'd write it with a loop in terms of the index (which is closer to how STIR will be using it)

auto iter = test.begin_all_const();
for (index[1] = test.get_min_index(0); index[1] <= test.get_max_index(0); ++index[1])
  for (index[2] = test.get_min_index(1); index[2] <= test.get_max_index(1); ++index[2])
    {
       check(*iter == test.at(index), "test on next(): element out of sequence?");
       ++iter;
    }

The choice to start from 1 was a very bad one... At some point, we'll have to introduce a new index-type that starts from 0 (could probably just use std::array), maybe auto-converting the BasicCoordinate to the new type.

[NikEfth]

Looks ok, but I guess I'd write it with a loop in terms of the index (which is closer to how STIR will be using it)

I know. I wanted to replicate closely the spelling of that test. it is based on a similar do .. while

[NikEfth]

@KrisThielemans in NumericVector operators you have this line

  this->grow(std::min(this->get_min_index(), v.get_min_index()), std::max(this->get_max_index(), v.get_max_index()));

This automatically resizes the vector if it is smaller. Do you want this behavior? Shouldn't the user handle this?

I did it like this

if(!tensor.sizes().equals(v.tensor.sizes())) {
        if(!is_on_gpu())
          {
            this->grow(v.get_index_range());
          }
        else
          throw std::invalid_argument("Tensors must have the same shape for element-wise addition.");
      }