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import operator_benchmark as op_bench
import torch
import torch.nn.functional as F
"""Microbenchmarks for instancenorm operator."""
instancenorm_configs_short = op_bench.cross_product_configs(
dims=(
(32, 8, 16),
(32, 8, 56, 56),
),
tags=["short"],
)
class InstanceNormBenchmark(op_bench.TorchBenchmarkBase):
def init(self, dims):
num_channels = dims[1]
self.inputs = {
"input": (torch.rand(*dims) - 0.5) * 256,
"weight": torch.rand(num_channels, dtype=torch.float),
"bias": torch.rand(num_channels, dtype=torch.float),
"eps": 1e-5
}
def forward(self, input, weight, bias, eps: float):
return F.instance_norm(
input, weight=weight, bias=bias, eps=eps)
op_bench.generate_pt_test(instancenorm_configs_short, InstanceNormBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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