Split Volumes class to data and location part

Summary: Split Volumes class to data and location part so that location part can be reused in planned VoxelGrid classes.

Reviewed By: bottler

Differential Revision: D38782015

fbshipit-source-id: 489da09c5c236f3b81961ce9b09edbd97afaa7c8

tests/test_volumes.py

@@ -11,7 +11,7 @@ import unittest
 
 import numpy as np
 import torch
-from pytorch3d.structures.volumes import Volumes
+from pytorch3d.structures.volumes import VolumeLocator, Volumes
 from pytorch3d.transforms import Scale
 
 from .common_testing import TestCaseMixin
@@ -53,8 +53,8 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
         for selectedIdx, index in indices:
             self.assertClose(selected.densities()[selectedIdx], v.densities()[index])
             self.assertClose(
-                v._local_to_world_transform.get_matrix()[index],
-                selected._local_to_world_transform.get_matrix()[selectedIdx],
+                v.locator._local_to_world_transform.get_matrix()[index],
+                selected.locator._local_to_world_transform.get_matrix()[selectedIdx],
             )
             if selected.features() is not None:
                 self.assertClose(selected.features()[selectedIdx], v.features()[index])
@@ -149,10 +149,55 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
             with self.assertRaises(IndexError):
                 v_selected = v[index]
 
+    def test_locator_init(self, batch_size=9, resolution=(3, 5, 7)):
+        with self.subTest("VolumeLocator init with all sizes equal"):
+            grid_sizes = [resolution for _ in range(batch_size)]
+            locator_tuple = VolumeLocator(
+                batch_size=batch_size, grid_sizes=resolution, device=torch.device("cpu")
+            )
+            locator_list = VolumeLocator(
+                batch_size=batch_size, grid_sizes=grid_sizes, device=torch.device("cpu")
+            )
+            locator_tensor = VolumeLocator(
+                batch_size=batch_size,
+                grid_sizes=torch.tensor(grid_sizes),
+                device=torch.device("cpu"),
+            )
+            expected_grid_sizes = torch.tensor(grid_sizes)
+            expected_resolution = resolution
+            assert torch.allclose(expected_grid_sizes, locator_tuple._grid_sizes)
+            assert torch.allclose(expected_grid_sizes, locator_list._grid_sizes)
+            assert torch.allclose(expected_grid_sizes, locator_tensor._grid_sizes)
+            self.assertEqual(expected_resolution, locator_tuple._resolution)
+            self.assertEqual(expected_resolution, locator_list._resolution)
+            self.assertEqual(expected_resolution, locator_tensor._resolution)
+
+        with self.subTest("VolumeLocator with different sizes in different grids"):
+            grid_sizes_list = [
+                torch.randint(low=1, high=42, size=(3,)) for _ in range(batch_size)
+            ]
+            grid_sizes_tensor = torch.cat([el[None] for el in grid_sizes_list])
+            locator_list = VolumeLocator(
+                batch_size=batch_size,
+                grid_sizes=grid_sizes_list,
+                device=torch.device("cpu"),
+            )
+            locator_tensor = VolumeLocator(
+                batch_size=batch_size,
+                grid_sizes=grid_sizes_tensor,
+                device=torch.device("cpu"),
+            )
+            expected_grid_sizes = grid_sizes_tensor
+            expected_resolution = tuple(torch.max(expected_grid_sizes, dim=0).values)
+            assert torch.allclose(expected_grid_sizes, locator_list._grid_sizes)
+            assert torch.allclose(expected_grid_sizes, locator_tensor._grid_sizes)
+            self.assertEqual(expected_resolution, locator_list._resolution)
+            self.assertEqual(expected_resolution, locator_tensor._resolution)
+
     def test_coord_transforms(self, num_volumes=3, num_channels=4, dtype=torch.float32):
         """
         Test the correctness of the conversion between the internal
-        Transform3D Volumes._local_to_world_transform and the initialization
+        Transform3D Volumes.VolumeLocator._local_to_world_transform and the initialization
         from the translation and voxel_size.
         """
 
@@ -440,7 +485,10 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
         for var_name, var in vars(v).items():
             if var_name != "device":
                 if var is not None:
-                    self.assertTrue(var.device.type == desired_device.type)
+                    self.assertTrue(
+                        var.device.type == desired_device.type,
+                        (var_name, var.device, desired_device),
+                    )
             else:
                 self.assertTrue(var.type == desired_device.type)
 
@@ -456,33 +504,38 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
         )
         densities = torch.rand(size=[num_volumes, 1, *size], dtype=dtype)
         volumes = Volumes(densities=densities, features=features)
+        locator = VolumeLocator(
+            batch_size=5, grid_sizes=(3, 5, 7), device=volumes.device
+        )
 
+        for name, obj in (("VolumeLocator", locator), ("Volumes", volumes)):
+            with self.subTest(f"Moving {name} from/to gpu and cpu"):
                 # Test support for str and torch.device
                 cpu_device = torch.device("cpu")
 
-        converted_volumes = volumes.to("cpu")
-        self.assertEqual(cpu_device, converted_volumes.device)
-        self.assertEqual(cpu_device, volumes.device)
-        self.assertIs(volumes, converted_volumes)
+                converted_obj = obj.to("cpu")
+                self.assertEqual(cpu_device, converted_obj.device)
+                self.assertEqual(cpu_device, obj.device)
+                self.assertIs(obj, converted_obj)
 
-        converted_volumes = volumes.to(cpu_device)
-        self.assertEqual(cpu_device, converted_volumes.device)
-        self.assertEqual(cpu_device, volumes.device)
-        self.assertIs(volumes, converted_volumes)
+                converted_obj = obj.to(cpu_device)
+                self.assertEqual(cpu_device, converted_obj.device)
+                self.assertEqual(cpu_device, obj.device)
+                self.assertIs(obj, converted_obj)
 
                 cuda_device = torch.device("cuda:0")
 
-        converted_volumes = volumes.to("cuda:0")
-        self.assertEqual(cuda_device, converted_volumes.device)
-        self.assertEqual(cpu_device, volumes.device)
-        self.assertIsNot(volumes, converted_volumes)
+                converted_obj = obj.to("cuda:0")
+                self.assertEqual(cuda_device, converted_obj.device)
+                self.assertEqual(cpu_device, obj.device)
+                self.assertIsNot(obj, converted_obj)
 
-        converted_volumes = volumes.to(cuda_device)
-        self.assertEqual(cuda_device, converted_volumes.device)
-        self.assertEqual(cpu_device, volumes.device)
-        self.assertIsNot(volumes, converted_volumes)
+                converted_obj = obj.to(cuda_device)
+                self.assertEqual(cuda_device, converted_obj.device)
+                self.assertEqual(cpu_device, obj.device)
+                self.assertIsNot(obj, converted_obj)
 
-        # Test device placement of internal tensors
+        with self.subTest("Test device placement of internal tensors of Volumes"):
             features = features.to(cuda_device)
             densities = features.to(cuda_device)
 
@@ -511,6 +564,15 @@ class TestVolumes(TestCaseMixin, unittest.TestCase):
                         else:
                             self._check_vars_on_device(volumes_, cuda_device)
 
+        with self.subTest("Test device placement of internal tensors of VolumeLocator"):
+            for device1, device2 in itertools.combinations(
+                (torch.device("cpu"), torch.device("cuda:0")), 2
+            ):
+                locator = locator.to(device1)
+                locator = locator.to(device2)
+                self.assertEqual(locator._grid_sizes.device, device2)
+                self.assertEqual(locator._local_to_world_transform.device, device2)
+
     def _check_padded(self, x_pad, x_list, grid_sizes):
         """
         Check that padded tensors x_pad are the same as x_list tensors.