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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_graph_conv import TestGraphConv
def bm_graph_conv() -> None:
backends = ["cpu"]
if torch.cuda.is_available():
backends.append("cuda")
kwargs_list = []
gconv_dim = [128, 256]
num_meshes = [32, 64]
num_verts = [100]
num_faces = [1000]
directed = [False, True]
test_cases = product(
gconv_dim, num_meshes, num_verts, num_faces, directed, backends
)
for case in test_cases:
g, n, v, f, d, b = case
kwargs_list.append(
{
"gconv_dim": g,
"num_meshes": n,
"num_verts": v,
"num_faces": f,
"directed": d,
"backend": b,
}
)
benchmark(
TestGraphConv.graph_conv_forward_backward,
"GRAPH CONV",
kwargs_list,
warmup_iters=1,
)
if __name__ == "__main__":
bm_graph_conv()
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