Amend D38407094: Fisheye Camera for PyTorch3D

Summary:
Amend FisheyeCamera by adding tests for all
combination of params and for different batch_sizes.

Reviewed By: kjchalup

Differential Revision: D39176747

fbshipit-source-id: 830d30da24beeb2f0df52db0b17a4303ed53b59c

Author: davidsonic

pytorch3d/renderer/fisheyecameras.py

@@ -425,7 +425,7 @@ def _compute_xr_yr_from_uv_distorted(
         # do Newton iterations to find xr_yr
         for _ in range(self.num_distortion_iters):
             # compute the estimated uvDistorted
-            uv_distorted_est = xr_yr
+            uv_distorted_est = xr_yr.clone()
             xr_yr_squared_norm = torch.pow(xr_yr, 2).sum(dim=-1, keepdim=True)
 
             if self.use_tangential:
@@ -479,7 +479,6 @@ def _get_theta_from_norm_xr_yr(
             th: angle theta (in radians) of shape (...), E.g., (P), (1, P), (M, P)
         """
         sh = list(th_radial_desired.shape)
-        # th = th_radial_desired.clone()
         th = th_radial_desired
         c = torch.tensor(
             [2.0 * i + 3 for i in range(self.num_radial)], device=self.device
@@ -552,7 +551,7 @@ def _compute_duv_distorted_dxryr(
                 + 2.0 * xr_yr[..., 0] * tangential_params[..., 0]
             )
         else:
-            duv_distorted_dxryr = torch.eye(2).view(*sh, 2, 2).expand(*sh, 1, 1)
+            duv_distorted_dxryr = torch.eye(2).repeat(*sh, 1, 1)
 
         if self.use_thin_prism:
             temp1 = 2.0 * (

tests/test_cameras.py

@@ -33,6 +33,7 @@
 import math
 import typing
 import unittest
+from itertools import product
 
 import numpy as np
 import torch
@@ -1389,7 +1390,7 @@ def setUpAriaCase(self) -> None:
             thin_prism_params,
         )
 
-    def setUpBatchCameras(self) -> None:
+    def setUpBatchCameras(self, combination: None) -> None:
         super().setUp()
         focal, principal_point, p_3d = self.setUpSimpleCase()
         radial_params = torch.tensor(
@@ -1412,8 +1413,12 @@ def setUpBatchCameras(self) -> None:
         radial_params = torch.cat([radial_params, radial_params1], dim=0)
         tangential_params = torch.cat([tangential_params, tangential_params1], dim=0)
         thin_prism_params = torch.cat([thin_prism_params, thin_prism_params1], dim=0)
-
+        if combination is None:
+            combination = [True, True, True]
         cameras = FishEyeCameras(
+            use_radial=combination[0],
+            use_tangential=combination[1],
+            use_thin_prism=combination[2],
             focal_length=focal,
             principal_point=principal_point,
             radial_params=radial_params,
@@ -1474,21 +1479,31 @@ def test_fisheye_against_perspective_cameras(self):
 
     def test_project_shape_broadcasts(self):
         focal, principal_point, p_3d = self.setUpSimpleCase()
-        # test case 1:
-        # 1 transform with points of shape (P, 3) -> (P, 3)
-        # 1 transform with points of shape (1, P, 3) -> (1, P, 3)
-        # 1 transform with points of shape (M, P, 3) -> (M, P, 3)
-        points = p_3d.repeat(1, 1, 1)
-        cameras = FishEyeCameras(
-            focal_length=focal,
-            principal_point=principal_point,
-            use_radial=False,
-            use_tangential=False,
-            use_thin_prism=False,
-        )
-        uv = cameras.transform_points(p_3d)
-        uv_point_batch = cameras.transform_points(points)
-        self.assertClose(uv_point_batch, uv.repeat(1, 1, 1))
+        torch.set_printoptions(precision=6)
+        combinations = product([0, 1], repeat=3)
+        for combination in combinations:
+            cameras = FishEyeCameras(
+                use_radial=combination[0],
+                use_tangential=combination[1],
+                use_thin_prism=combination[2],
+                focal_length=focal,
+                principal_point=principal_point,
+            )
+            # test case 1:
+            # 1 transform with points of shape (P, 3) -> (P, 3)
+            # 1 transform with points of shape (1, P, 3) -> (1, P, 3)
+            # 1 transform with points of shape (M, P, 3) -> (M, P, 3)
+            points = p_3d.repeat(1, 1, 1)
+            cameras = FishEyeCameras(
+                focal_length=focal,
+                principal_point=principal_point,
+                use_radial=False,
+                use_tangential=False,
+                use_thin_prism=False,
+            )
+            uv = cameras.transform_points(p_3d)
+            uv_point_batch = cameras.transform_points(points)
+            self.assertClose(uv_point_batch, uv.repeat(1, 1, 1))
 
         points = p_3d.repeat(3, 1, 1)
         uv_point_batch = cameras.transform_points(points)
@@ -1497,35 +1512,105 @@ def test_project_shape_broadcasts(self):
         # test case 2
         # test with N transforms and points of shape (P, 3) -> (N, P, 3)
         # test with N transforms and points of shape (1, P, 3) -> (N, P, 3)
-
-        # first camera transform params
-        cameras = self.setUpBatchCameras()
+        torch.set_printoptions(sci_mode=False)
         p_3d = torch.tensor(
             [
-                [2.0, 3.0, 1.0],
                 [2.0, 3.0, 1.0],
                 [3.0, 2.0, 1.0],
             ]
         )
         expected_res = torch.tensor(
             [
                 [
-                    [493.0993, 499.6489, 1.0],
-                    [493.0993, 499.6489, 1.0],
-                    [579.6489, 413.0993, 1.0],
+                    [
+                        [800.000000, 960.000000, 1.000000],
+                        [1040.000000, 720.000000, 1.000000],
+                    ],
+                    [
+                        [1929.862549, 2533.643311, 1.000000],
+                        [2538.788086, 1924.717773, 1.000000],
+                    ],
+                ],
+                [
+                    [
+                        [800.000000, 960.000000, 1.000000],
+                        [1040.000000, 720.000000, 1.000000],
+                    ],
+                    [
+                        [1927.272095, 2524.220459, 1.000000],
+                        [2536.197754, 1915.295166, 1.000000],
+                    ],
+                ],
+                [
+                    [
+                        [800.000000, 960.000000, 1.000000],
+                        [1040.000000, 720.000000, 1.000000],
+                    ],
+                    [
+                        [1930.050293, 2538.434814, 1.000000],
+                        [2537.956543, 1927.569092, 1.000000],
+                    ],
+                ],
+                [
+                    [
+                        [800.000000, 960.000000, 1.000000],
+                        [1040.000000, 720.000000, 1.000000],
+                    ],
+                    [
+                        [1927.459839, 2529.011963, 1.000000],
+                        [2535.366211, 1918.146484, 1.000000],
+                    ],
+                ],
+                [
+                    [
+                        [493.099304, 499.648926, 1.000000],
+                        [579.648926, 413.099304, 1.000000],
+                    ],
+                    [
+                        [1662.673950, 2132.860352, 1.000000],
+                        [2138.005127, 1657.529053, 1.000000],
+                    ],
+                ],
+                [
+                    [
+                        [493.099304, 499.648926, 1.000000],
+                        [579.648926, 413.099304, 1.000000],
+                    ],
+                    [
+                        [1660.083496, 2123.437744, 1.000000],
+                        [2135.414795, 1648.106445, 1.000000],
+                    ],
+                ],
+                [
+                    [
+                        [493.099304, 499.648926, 1.000000],
+                        [579.648926, 413.099304, 1.000000],
+                    ],
+                    [
+                        [1662.861816, 2137.651855, 1.000000],
+                        [2137.173828, 1660.380371, 1.000000],
+                    ],
                 ],
                 [
-                    [1660.2700, 2128.2273, 1.0],
-                    [1660.2700, 2128.2273, 1.0],
-                    [2134.5815, 1650.9565, 1.0],
+                    [
+                        [493.099304, 499.648926, 1.000000],
+                        [579.648926, 413.099304, 1.000000],
+                    ],
+                    [
+                        [1660.271240, 2128.229248, 1.000000],
+                        [2134.583496, 1650.957764, 1.000000],
+                    ],
                 ],
             ]
         )
-        uv_point_batch = cameras.transform_points(p_3d)
-        self.assertClose(uv_point_batch, expected_res)
+        combinations = product([0, 1], repeat=3)
+        for i, combination in enumerate(combinations):
+            cameras = self.setUpBatchCameras(combination)
+            uv_point_batch = cameras.transform_points(p_3d)
+            self.assertClose(uv_point_batch, expected_res[i])
 
-        uv_point_batch = cameras.transform_points(p_3d.repeat(1, 1, 1))
-        self.assertClose(uv_point_batch, expected_res)
+            uv_point_batch = cameras.transform_points(p_3d.repeat(1, 1, 1))
+            self.assertClose(uv_point_batch, expected_res[i].repeat(1, 1, 1))
 
     def test_cuda(self):
         """
@@ -1573,42 +1658,64 @@ def test_unproject_shape_broadcasts(self):
         rep_3d = cameras.unproject_points(xy_depth)
         expected_res = torch.tensor(
             [
-                [3.0000, 2.0000, 1.0000],
-                [0.666667, 0.833333, 1.0000],
-            ],
-        )
-        self.assertClose(rep_3d, expected_res)
-
-        rep_3d = cameras.unproject_points(xy_depth.repeat(3, 1, 1))
-        self.assertClose(rep_3d, expected_res.repeat(3, 1, 1))
-
-        # test case 2:
-        # N transforms with points of (P, 3) -> (N, P, 3)
-        # N transforms with points of (1, P, 3) -> (N, P, 3)
-        cameras = FishEyeCameras(
-            focal_length=focal.repeat(2, 1),
-            principal_point=principal_point.repeat(2, 1),
-            radial_params=radial_params.repeat(2, 1),
-            tangential_params=tangential_params.repeat(2, 1),
-            thin_prism_params=thin_prism_params.repeat(2, 1),
+                [[2.999442, 1.990583, 1.000000], [0.666728, 0.833142, 1.000000]],
+                [[2.997338, 2.005411, 1.000000], [0.666859, 0.834456, 1.000000]],
+                [[3.002090, 1.985229, 1.000000], [0.666537, 0.832025, 1.000000]],
+                [[2.999999, 2.000000, 1.000000], [0.666667, 0.833333, 1.000000]],
+                [[2.999442, 1.990583, 1.000000], [0.666728, 0.833142, 1.000000]],
+                [[2.997338, 2.005411, 1.000000], [0.666859, 0.834456, 1.000000]],
+                [[3.002090, 1.985229, 1.000000], [0.666537, 0.832025, 1.000000]],
+                [[2.999999, 2.000000, 1.000000], [0.666667, 0.833333, 1.000000]],
+            ]
         )
-        rep_3d = cameras.unproject_points(xy_depth)
-        self.assertClose(rep_3d, expected_res.repeat(2, 1, 1))
+        torch.set_printoptions(precision=6)
+        combinations = product([0, 1], repeat=3)
+        for i, combination in enumerate(combinations):
+            cameras = FishEyeCameras(
+                use_radial=combination[0],
+                use_tangential=combination[1],
+                use_thin_prism=combination[2],
+                focal_length=focal,
+                principal_point=principal_point,
+                radial_params=radial_params,
+                tangential_params=tangential_params,
+                thin_prism_params=thin_prism_params,
+            )
+            rep_3d = cameras.unproject_points(xy_depth)
+            self.assertClose(rep_3d, expected_res[i])
+            rep_3d = cameras.unproject_points(xy_depth.repeat(3, 1, 1))
+            self.assertClose(rep_3d, expected_res[i].repeat(3, 1, 1))
+
+            # test case 2:
+            # N transforms with points of (P, 3) -> (N, P, 3)
+            # N transforms with points of (1, P, 3) -> (N, P, 3)
+            cameras = FishEyeCameras(
+                use_radial=combination[0],
+                use_tangential=combination[1],
+                use_thin_prism=combination[2],
+                focal_length=focal.repeat(2, 1),
+                principal_point=principal_point.repeat(2, 1),
+                radial_params=radial_params.repeat(2, 1),
+                tangential_params=tangential_params.repeat(2, 1),
+                thin_prism_params=thin_prism_params.repeat(2, 1),
+            )
+            rep_3d = cameras.unproject_points(xy_depth)
+            self.assertClose(rep_3d, expected_res[i].repeat(2, 1, 1))
 
     def test_unhandled_shape(self):
         """
         Test error handling when shape of transforms
         and points are not expected.
         """
-        cameras = self.setUpBatchCameras()
+        cameras = self.setUpBatchCameras(None)
         points = torch.rand(3, 3, 1)
         with self.assertRaises(ValueError):
             cameras.transform_points(points)
 
     def test_getitem(self):
         # Check get item returns an instance of the same class
         # with all the same keys
-        cam = self.setUpBatchCameras()
+        cam = self.setUpBatchCameras(None)
         c0 = cam[0]
         self.assertTrue(isinstance(c0, FishEyeCameras))
         self.assertEqual(cam.__dict__.keys(), c0.__dict__.keys())