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import operator_benchmark as op_bench
import torch
"""Microbenchmarks for interpolate operator."""
class InterpolateBenchmark(op_bench.TorchBenchmarkBase):
def init(self, input_size, output_size, channels_last=False, mode='linear', dtype=torch.float):
input_image = torch.randint(0, 256, size=input_size, dtype=dtype, device='cpu',
requires_grad=self.auto_set())
if channels_last:
if input_image.ndim == 4:
input_image = input_image.contiguous(memory_format=torch.channels_last)
elif input_image.ndim == 5:
input_image = input_image.contiguous(memory_format=torch.channels_last_3d)
else:
raise ValueError(
f"Can not set channels_last to the input of {input_image.ndim} dims"
)
align_corners = None if mode == "nearest" else False
if mode == "linear":
mode = {
3: 'linear',
4: 'bilinear',
5: 'trilinear',
}[input_image.ndim]
self.inputs = {
"input_image": input_image,
"output_size": output_size,
"mode": mode,
"align_corners": align_corners,
}
self.set_module_name("interpolate")
def forward(self, input_image, output_size, mode, align_corners):
return torch.nn.functional.interpolate(input_image, size=output_size, mode=mode,
align_corners=align_corners)
config_short = op_bench.config_list(
attr_names=["input_size", "output_size"],
attrs=[
[(1, 3, 60, 40), (24, 24)],
[(1, 3, 600, 400), (240, 240)],
[(1, 3, 320, 320), (256, 256)],
],
cross_product_configs={
'channels_last': [True, False],
'mode': ["nearest", "linear", "bicubic"],
},
tags=["short"],
)
config_short += op_bench.config_list(
attr_names=["input_size", "output_size"],
attrs=[
[(1, 3, 60, 40), (24, 24)],
[(1, 3, 600, 400), (240, 240)],
[(1, 3, 320, 320), (256, 256)],
],
cross_product_configs={
'channels_last': [True, False],
'mode': ["nearest", ],
'dtype': [torch.uint8, ],
},
tags=["short"],
)
config_long = op_bench.config_list(
attr_names=["input_size", "output_size"],
attrs=[
[(1, 3, 320, 320), (512, 512)],
[(1, 3, 500, 500), (256, 256)],
[(1, 3, 500, 500), (800, 800)],
# vectorization test-case
[(2, 128, 64, 46), (128, 128)],
[(2, 128, 64, 46), (32, 24)],
],
cross_product_configs={
'channels_last': [True, False],
'mode': ["nearest", "linear", "bicubic"],
},
tags=["long"],
)
config_3d = op_bench.config_list(
# no channels_last for 3D tensors
attr_names=["input_size", "output_size"],
attrs=[
[(4, 512, 320), (256,)],
[(4, 512, 320), (512,)],
],
cross_product_configs={
'mode': ["nearest", "linear"],
},
tags=["long"],
)
config_5d = op_bench.config_list(
attr_names=["input_size", "output_size"],
attrs=[
[(1, 3, 16, 320, 320), (8, 256, 256)],
[(1, 3, 16, 320, 320), (32, 512, 512)],
# vectorization test-case
[(1, 16, 32, 64, 64), (16, 32, 32)],
[(1, 16, 32, 64, 64), (64, 128, 128)],
],
cross_product_configs={
'channels_last': [True, False],
'mode': ["nearest", "linear"],
},
tags=["long"],
)
for config in (config_short, config_long, config_3d, config_5d):
op_bench.generate_pt_test(config, InterpolateBenchmark)
if __name__ == "__main__":
op_bench.benchmark_runner.main()
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