Benchmark Cameras

Summary: Address comments to add benchmarkings for cameras and the new fisheye cameras. The dependency functions in test_cameras have been updated in Diff 1. The following two snapshots show benchmarking results.

Reviewed By: kjchalup

Differential Revision: D38991914

fbshipit-source-id: 51fe9bb7237543e4ee112c9f5068a4cf12a9d482

Author: davidsonic

tests/benchmarks/bm_cameras.py

@@ -0,0 +1,59 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import itertools
+
+from fvcore.common.benchmark import benchmark
+from tests.test_cameras import TestCamerasCommon
+
+
+def _setUp():
+    case_grid = {
+        "cam_type": [
+            "OpenGLOrthographicCameras",
+            "OpenGLPerspectiveCameras",
+            "SfMOrthographicCameras",
+            "SfMPerspectiveCameras",
+            "FoVOrthographicCameras",
+            "FoVPerspectiveCameras",
+            "OrthographicCameras",
+            "PerspectiveCameras",
+            "FishEyeCameras",
+        ],
+        "batch_size": [1, 10],
+        "num_points": [10, 100],
+        "device": ["cpu", "cuda:0"],
+    }
+    test_cases = itertools.product(*case_grid.values())
+    kwargs_list = [dict(zip(case_grid.keys(), case)) for case in test_cases]
+    return kwargs_list
+
+
+def _bm_cameras_project() -> None:
+    kwargs_list = _setUp()
+    benchmark(
+        TestCamerasCommon.transform_points,
+        "TEST_TRANSFORM_POINTS",
+        kwargs_list,
+    )
+
+
+def _bm_cameras_unproject() -> None:
+    kwargs_list = _setUp()
+    benchmark(
+        TestCamerasCommon.unproject_points,
+        "TEST_UNPROJECT_POINTS",
+        kwargs_list,
+    )
+
+
+def bm_cameras() -> None:
+    _bm_cameras_project()
+    _bm_cameras_unproject()
+
+
+if __name__ == "__main__":
+    bm_cameras()

tests/test_cameras.py

@@ -36,6 +36,7 @@
 
 import numpy as np
 import torch
+from pytorch3d.common.datatypes import Device
 from pytorch3d.renderer.camera_utils import join_cameras_as_batch
 from pytorch3d.renderer.cameras import (
     camera_position_from_spherical_angles,
@@ -149,14 +150,17 @@ def ndc_to_screen_points_naive(points, imsize):
 
 
 def init_random_cameras(
-    cam_type: typing.Type[CamerasBase], batch_size: int, random_z: bool = False
+    cam_type: typing.Type[CamerasBase],
+    batch_size: int,
+    random_z: bool = False,
+    device: Device = "cpu",
 ):
     cam_params = {}
     T = torch.randn(batch_size, 3) * 0.03
     if not random_z:
         T[:, 2] = 4
     R = so3_exp_map(torch.randn(batch_size, 3) * 3.0)
-    cam_params = {"R": R, "T": T}
+    cam_params = {"R": R, "T": T, "device": device}
     if cam_type in (OpenGLPerspectiveCameras, OpenGLOrthographicCameras):
         cam_params["znear"] = torch.rand(batch_size) * 10 + 0.1
         cam_params["zfar"] = torch.rand(batch_size) * 4 + 1 + cam_params["znear"]
@@ -613,15 +617,33 @@ def test_unproject_points(self, batch_size=50, num_points=100):
                     self.assertTrue(torch.allclose(xyz_unproj, matching_xyz, atol=1e-4))
 
     @staticmethod
-    def unproject_points(cam_type, batch_size=50, num_points=100):
+    def unproject_points(
+        cam_type, batch_size=50, num_points=100, device: Device = "cpu"
+    ):
         """
         Checks that an unprojection of a randomly projected point cloud
         stays the same.
         """
+        if device == "cuda":
+            device = torch.device("cuda:0")
+        else:
+            device = torch.device("cpu")
+
+        str2cls = {  # noqa
+            "OpenGLOrthographicCameras": OpenGLOrthographicCameras,
+            "OpenGLPerspectiveCameras": OpenGLPerspectiveCameras,
+            "SfMOrthographicCameras": SfMOrthographicCameras,
+            "SfMPerspectiveCameras": SfMPerspectiveCameras,
+            "FoVOrthographicCameras": FoVOrthographicCameras,
+            "FoVPerspectiveCameras": FoVPerspectiveCameras,
+            "OrthographicCameras": OrthographicCameras,
+            "PerspectiveCameras": PerspectiveCameras,
+            "FishEyeCameras": FishEyeCameras,
+        }
 
         def run_cameras():
             # init the cameras
-            cameras = init_random_cameras(cam_type, batch_size)
+            cameras = init_random_cameras(str2cls[cam_type], batch_size, device=device)
             # xyz - the ground truth point cloud
             xyz = torch.randn(num_points, 3) * 0.3
             xyz = cameras.unproject_points(xyz, scaled_depth_input=True)
@@ -666,15 +688,33 @@ def test_project_points_screen(self, batch_size=50, num_points=100):
             self.assertClose(xyz_project_screen, xyz_project_screen_naive, atol=1e-4)
 
     @staticmethod
-    def transform_points(cam_type, batch_size=50, num_points=100):
+    def transform_points(
+        cam_type, batch_size=50, num_points=100, device: Device = "cpu"
+    ):
         """
         Checks that an unprojection of a randomly projected point cloud
         stays the same.
         """
 
+        if device == "cuda":
+            device = torch.device("cuda:0")
+        else:
+            device = torch.device("cpu")
+        str2cls = {  # noqa
+            "OpenGLOrthographicCameras": OpenGLOrthographicCameras,
+            "OpenGLPerspectiveCameras": OpenGLPerspectiveCameras,
+            "SfMOrthographicCameras": SfMOrthographicCameras,
+            "SfMPerspectiveCameras": SfMPerspectiveCameras,
+            "FoVOrthographicCameras": FoVOrthographicCameras,
+            "FoVPerspectiveCameras": FoVPerspectiveCameras,
+            "OrthographicCameras": OrthographicCameras,
+            "PerspectiveCameras": PerspectiveCameras,
+            "FishEyeCameras": FishEyeCameras,
+        }
+
         def run_cameras():
             # init the cameras
-            cameras = init_random_cameras(cam_type, batch_size)
+            cameras = init_random_cameras(str2cls[cam_type], batch_size, device=device)
             # xyz - the ground truth point cloud
             xy = torch.randn(num_points, 2) * 2.0 - 1.0
             z = torch.randn(num_points, 1) * 3.0 + 1.0