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https://github.com/facebookresearch/pytorch3d.git
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9eeb456e82
Summary: Update all FB license strings to the new format. Reviewed By: patricklabatut Differential Revision: D33403538 fbshipit-source-id: 97a4596c5c888f3c54f44456dc07e718a387a02c
66 lines
1.8 KiB
Python
66 lines
1.8 KiB
Python
# Copyright (c) Meta Platforms, Inc. and affiliates.
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# All rights reserved.
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#
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# This source code is licensed under the BSD-style license found in the
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# LICENSE file in the root directory of this source tree.
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from itertools import product
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import torch
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from fvcore.common.benchmark import benchmark
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from test_chamfer import TestChamfer
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def bm_chamfer() -> None:
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# Currently disabled.
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return
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devices = ["cpu"]
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if torch.cuda.is_available():
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devices.append("cuda:0")
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kwargs_list_naive = []
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batch_size = [1, 32]
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return_normals = [True, False]
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test_cases = product(batch_size, return_normals, devices)
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for case in test_cases:
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b, n, d = case
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kwargs_list_naive.append(
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{"batch_size": b, "P1": 32, "P2": 64, "return_normals": n, "device": d}
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)
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benchmark(
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TestChamfer.chamfer_naive_with_init,
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"CHAMFER_NAIVE",
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kwargs_list_naive,
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warmup_iters=1,
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)
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if torch.cuda.is_available():
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device = "cuda:0"
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kwargs_list = []
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batch_size = [1, 32]
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P1 = [32, 1000, 10000]
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P2 = [64, 3000, 30000]
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return_normals = [True, False]
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homogeneous = [True, False]
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test_cases = product(batch_size, P1, P2, return_normals, homogeneous)
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for case in test_cases:
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b, p1, p2, n, h = case
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kwargs_list.append(
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{
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"batch_size": b,
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"P1": p1,
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"P2": p2,
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"return_normals": n,
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"homogeneous": h,
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"device": device,
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}
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)
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benchmark(TestChamfer.chamfer_with_init, "CHAMFER", kwargs_list, warmup_iters=1)
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if __name__ == "__main__":
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bm_chamfer()
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