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https://github.com/facebookresearch/pytorch3d.git
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a0e2d2e3c3
Summary: Move benchmarks to a separate directory as tests/ is getting big. Reviewed By: nikhilaravi Differential Revision: D32885462 fbshipit-source-id: a832662a494ee341ab77d95493c95b0af0a83f43
56 lines
1.7 KiB
Python
56 lines
1.7 KiB
Python
# Copyright (c) Facebook, Inc. and its 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 pytorch3d.renderer.lighting import diffuse, specular
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def _bm_diffuse_cuda_with_init(N, S, K):
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device = torch.device("cuda")
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normals = torch.randn(N, S, S, K, 3, device=device)
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color = torch.randn(1, 3, device=device)
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direction = torch.randn(N, S, S, K, 3, device=device)
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args = (normals, color, direction)
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torch.cuda.synchronize()
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return lambda: diffuse(*args)
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def _bm_specular_cuda_with_init(N, S, K):
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device = torch.device("cuda")
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points = torch.randn(N, S, S, K, 3, device=device)
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normals = torch.randn(N, S, S, K, 3, device=device)
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direction = torch.randn(N, S, S, K, 3, device=device)
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color = torch.randn(1, 3, device=device)
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camera_position = torch.randn(N, 3, device=device)
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shininess = torch.randn(N, device=device)
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args = (points, normals, direction, color, camera_position, shininess)
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torch.cuda.synchronize()
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return lambda: specular(*args)
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def bm_lighting() -> None:
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# For now only benchmark lighting on GPU
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if not torch.cuda.is_available():
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return
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kwargs_list = []
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Ns = [1, 8]
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Ss = [128, 256]
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Ks = [1, 10, 80]
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test_cases = product(Ns, Ss, Ks)
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for case in test_cases:
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N, S, K = case
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kwargs_list.append({"N": N, "S": S, "K": K})
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benchmark(_bm_diffuse_cuda_with_init, "DIFFUSE", kwargs_list, warmup_iters=3)
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benchmark(_bm_specular_cuda_with_init, "SPECULAR", kwargs_list, warmup_iters=3)
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if __name__ == "__main__":
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bm_lighting()
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