1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
|
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)],
[(1, 1, 60, 40), (24, 24)],
[(1, 1, 600, 400), (240, 240)],
[(1, 1, 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)],
[(1, 1, 60, 40), (24, 24)],
[(1, 1, 600, 400), (240, 240)],
[(1, 1, 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)],
[(1, 1, 320, 320), (512, 512)],
[(1, 1, 500, 500), (256, 256)],
[(1, 1, 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()
|
