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# 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.
from itertools import product
import torch
from fvcore.common.benchmark import benchmark
from tests.test_chamfer import TestChamfer
def bm_chamfer() -> None:
# Currently disabled.
return
devices = ["cpu"]
if torch.cuda.is_available():
devices.append("cuda:0")
kwargs_list_naive = []
batch_size = [1, 32]
return_normals = [True, False]
test_cases = product(batch_size, return_normals, devices)
for case in test_cases:
b, n, d = case
kwargs_list_naive.append(
{"batch_size": b, "P1": 32, "P2": 64, "return_normals": n, "device": d}
)
benchmark(
TestChamfer.chamfer_naive_with_init,
"CHAMFER_NAIVE",
kwargs_list_naive,
warmup_iters=1,
)
if torch.cuda.is_available():
device = "cuda:0"
kwargs_list = []
batch_size = [1, 32]
P1 = [32, 1000, 10000]
P2 = [64, 3000, 30000]
return_normals = [True, False]
homogeneous = [True, False]
test_cases = product(batch_size, P1, P2, return_normals, homogeneous)
for case in test_cases:
b, p1, p2, n, h = case
kwargs_list.append(
{
"batch_size": b,
"P1": p1,
"P2": p2,
"return_normals": n,
"homogeneous": h,
"device": device,
}
)
benchmark(TestChamfer.chamfer_with_init, "CHAMFER", kwargs_list, warmup_iters=1)
if __name__ == "__main__":
bm_chamfer()
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