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790eb8c402
Summary: Allow Pointclouds objects and heterogenous data to be provided for Chamfer loss. Remove "none" as an option for point_reduction because it doesn't make sense and in the current implementation is effectively the same as "sum". Possible improvement: create specialised operations for sum and cosine_similarity of padded tensors, to avoid having to create masks. sum would be useful elsewhere. Reviewed By: gkioxari Differential Revision: D20816301 fbshipit-source-id: 0f32073210225d157c029d80de450eecdb64f4d2
44 lines
1.3 KiB
Python
44 lines
1.3 KiB
Python
# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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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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kwargs_list_naive = [
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{"batch_size": 1, "P1": 32, "P2": 64, "return_normals": False},
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{"batch_size": 1, "P1": 32, "P2": 64, "return_normals": True},
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{"batch_size": 32, "P1": 32, "P2": 64, "return_normals": False},
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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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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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}
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)
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benchmark(TestChamfer.chamfer_with_init, "CHAMFER", kwargs_list, warmup_iters=1)
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