PyTorch 1.7 compatibility

Summary: Small changes discovered based on circleCI failures.

Reviewed By: patricklabatut

Differential Revision: D34426807

fbshipit-source-id: 819860f34b2f367dd24057ca7490284204180a13

tests/test_point_mesh_distance.py

@@ -11,11 +11,7 @@ import torch
 from common_testing import TestCaseMixin, get_random_cuda_device
 from pytorch3d import _C
 from pytorch3d.loss import point_mesh_edge_distance, point_mesh_face_distance
-from pytorch3d.structures import (
-    Meshes,
-    Pointclouds,
-    packed_to_list,
-)
+from pytorch3d.structures import Meshes, Pointclouds, packed_to_list
 
 
 class TestPointMeshDistance(TestCaseMixin, unittest.TestCase):

tests/test_pointclouds.py

@@ -1033,7 +1033,7 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         for i, cloud in enumerate(clouds.points_list()):
             within_box_naive.append(inside_box_naive(cloud, box[i, 0], box[i, 1]))
         within_box_naive = torch.cat(within_box_naive, 0)
-        self.assertClose(within_box, within_box_naive)
+        self.assertTrue(torch.equal(within_box, within_box_naive))
 
         # box of shape 2x3
         box2 = box[0, :]
@@ -1044,13 +1044,12 @@ class TestPointclouds(TestCaseMixin, unittest.TestCase):
         for cloud in clouds.points_list():
             within_box_naive2.append(inside_box_naive(cloud, box2[0], box2[1]))
         within_box_naive2 = torch.cat(within_box_naive2, 0)
-        self.assertClose(within_box2, within_box_naive2)
-
+        self.assertTrue(torch.equal(within_box2, within_box_naive2))
         # box of shape 1x2x3
         box3 = box2.expand(1, 2, 3)
 
         within_box3 = clouds.inside_box(box3)
-        self.assertClose(within_box2, within_box3)
+        self.assertTrue(torch.equal(within_box2, within_box3))
 
         # invalid box
         invalid_box = torch.cat(

tests/test_raysampling.py

@@ -9,6 +9,7 @@ from typing import Callable
 
 import torch
 from common_testing import TestCaseMixin
+from pytorch3d.common.compat import meshgrid_ij
 from pytorch3d.ops import eyes
 from pytorch3d.renderer import (
     MonteCarloRaysampler,
@@ -86,7 +87,7 @@ class TestNDCRaysamplerConvention(TestCaseMixin, unittest.TestCase):
         min_y = range_y - half_pix_height
         max_y = -range_y + half_pix_height
 
-        y_grid, x_grid = torch.meshgrid(
+        y_grid, x_grid = meshgrid_ij(
             torch.linspace(min_y, max_y, h, dtype=torch.float32),
             torch.linspace(min_x, max_x, w, dtype=torch.float32),
         )
@@ -540,7 +541,7 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
         self.assertTupleEqual(out.shape, data.shape)
 
         # Check `out` is in ascending order
-        self.assertGreater(torch.diff(out, dim=-1).min(), 0)
+        self.assertGreater((out[..., 1:] - out[..., :-1]).min(), 0)
 
         self.assertConstant(out[..., :-1] < data[..., 1:], True)
         self.assertConstant(data[..., :-1] < out[..., 1:], True)

tests/test_rendering_utils.py

@@ -10,6 +10,7 @@ import unittest
 import numpy as np
 import torch
 from common_testing import TestCaseMixin
+from pytorch3d.common.compat import meshgrid_ij
 from pytorch3d.ops import eyes
 from pytorch3d.renderer import (
     AlphaCompositor,
@@ -129,8 +130,8 @@ class TestTensorProperties(TestCaseMixin, unittest.TestCase):
         point_radius = 0.015
         n_pts = n_grid_pts * n_grid_pts
         pts = torch.stack(
-            torch.meshgrid(
-                [torch.linspace(-grid_scale, grid_scale, n_grid_pts)] * 2, indexing="ij"
+            meshgrid_ij(
+                [torch.linspace(-grid_scale, grid_scale, n_grid_pts)] * 2,
             ),
             dim=-1,
         )