move benchmarks to separate directory

Summary: Move benchmarks to a separate directory as tests/ is getting big.

Reviewed By: nikhilaravi

Differential Revision: D32885462

fbshipit-source-id: a832662a494ee341ab77d95493c95b0af0a83f43

tests/benchmarks/bm_points_alignment.py

@@ -0,0 +1,80 @@
+# Copyright (c) Facebook, Inc. and its 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.
+
+from copy import deepcopy
+from itertools import product
+
+from fvcore.common.benchmark import benchmark
+from test_points_alignment import TestCorrespondingPointsAlignment, TestICP
+
+
+def bm_iterative_closest_point() -> None:
+
+    case_grid = {
+        "batch_size": [1, 10],
+        "dim": [3, 20],
+        "n_points_X": [100, 1000],
+        "n_points_Y": [100, 1000],
+        "use_pointclouds": [False],
+    }
+
+    test_args = sorted(case_grid.keys())
+    test_cases = product(*case_grid.values())
+    kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
+
+    # add the use_pointclouds=True test cases whenever we have dim==3
+    kwargs_to_add = []
+    for entry in kwargs_list:
+        if entry["dim"] == 3:
+            entry_add = deepcopy(entry)
+            entry_add["use_pointclouds"] = True
+            kwargs_to_add.append(entry_add)
+    kwargs_list.extend(kwargs_to_add)
+
+    benchmark(
+        TestICP.iterative_closest_point,
+        "IterativeClosestPoint",
+        kwargs_list,
+        warmup_iters=1,
+    )
+
+
+def bm_corresponding_points_alignment() -> None:
+
+    case_grid = {
+        "allow_reflection": [True, False],
+        "batch_size": [1, 10, 100],
+        "dim": [3, 20],
+        "estimate_scale": [True, False],
+        "n_points": [100, 10000],
+        "random_weights": [False, True],
+        "use_pointclouds": [False],
+    }
+
+    test_args = sorted(case_grid.keys())
+    test_cases = product(*case_grid.values())
+    kwargs_list = [dict(zip(test_args, case)) for case in test_cases]
+
+    # add the use_pointclouds=True test cases whenever we have dim==3
+    kwargs_to_add = []
+    for entry in kwargs_list:
+        if entry["dim"] == 3:
+            entry_add = deepcopy(entry)
+            entry_add["use_pointclouds"] = True
+            kwargs_to_add.append(entry_add)
+    kwargs_list.extend(kwargs_to_add)
+
+    benchmark(
+        TestCorrespondingPointsAlignment.corresponding_points_alignment,
+        "CorrespodingPointsAlignment",
+        kwargs_list,
+        warmup_iters=1,
+    )
+
+
+if __name__ == "__main__":
+    bm_corresponding_points_alignment()
+    bm_iterative_closest_point()