Non square image rasterization for meshes

Summary:
There are a couple of options for supporting non square images:
1) NDC stays at [-1, 1] in both directions with the distance calculations all modified by (W/H). There are a lot of distance based calculations (e.g. triangle areas for barycentric coordinates etc) so this requires changes in many places.
2) NDC is scaled by (W/H) so the smallest side has [-1, 1]. In this case none of the distance calculations need to be updated and only the pixel to NDC calculation needs to be modified.

I decided to go with option 2 after trying option 1!

API Changes:
- Image size can now be specified optionally as a tuple

TODO:
- add a benchmark test for the non square case.

Reviewed By: jcjohnson

Differential Revision: D24404975

fbshipit-source-id: 545efb67c822d748ec35999b35762bce58db2cf4

tests/test_rasterize_meshes.py

@@ -304,7 +304,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
     def test_compare_coarse_cpu_vs_cuda(self):
         torch.manual_seed(231)
         N = 1
-        image_size = 512
+        image_size = (512, 512)
         blur_radius = 0.0
         bin_size = 32
         max_faces_per_bin = 20
@@ -1077,7 +1077,7 @@ class TestRasterizeMeshes(TestCaseMixin, unittest.TestCase):
             self.assertClose(dists, expected_dists)
 
     def _test_coarse_rasterize(self, device):
-        image_size = 16
+        image_size = (16, 16)
         # No blurring. This test checks that the XY directions are
         # correctly oriented.
         blur_radius = 0.0