-
Notifications
You must be signed in to change notification settings - Fork 1
/
Copy pathpybench.py
216 lines (161 loc) · 6.12 KB
/
pybench.py
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
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
from time import time_ns
import rlbot_flatbuffers as flat
def test_gtp():
print("Testing GamePacket")
pack_times = []
unpack_times = []
gtp = flat.GamePacket(
balls=[flat.BallInfo(shape=flat.SphereShape()) for _ in range(128)],
players=[flat.PlayerInfo() for _ in range(128)],
boost_pads=[flat.BoostPadState() for _ in range(128)],
teams=[flat.TeamInfo() for _ in range(2)],
)
for _ in range(20_000):
start = time_ns()
packed = gtp.pack()
pack_times.append(time_ns() - start)
start = time_ns()
flat.GamePacket.unpack(packed)
unpack_times.append(time_ns() - start)
avg_time_ns = sum(pack_times) / len(pack_times)
print(f"Average pack time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum pack time per: {min(pack_times) / 1000:.1f}us")
avg_time_ns = sum(unpack_times) / len(unpack_times)
print(f"Average unpack time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum unpack time per: {min(unpack_times) / 1000:.1f}us")
def test_ballpred():
print("Testing 10s BallPrediction")
times = []
ballPred = flat.BallPrediction([flat.PredictionSlice(1) for _ in range(120 * 10)])
print(len(ballPred.pack()))
for _ in range(40_000):
start = time_ns()
packed = ballPred.pack()
flat.BallPrediction.unpack(packed)
times.append(time_ns() - start)
print(f"Total time: {sum(times) / 1_000_000_000:.3f}s")
avg_time_ns = sum(times) / len(times)
print(f"Average time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum time per: {min(times) / 1000:.1f}us")
print()
print("Testing 6s BallPrediction")
times = []
ballPred = flat.BallPrediction([flat.PredictionSlice(1) for _ in range(120 * 6)])
print(len(ballPred.pack()))
for _ in range(40_000):
start = time_ns()
packed = ballPred.pack()
flat.BallPrediction.unpack(packed)
times.append(time_ns() - start)
print(f"Total time: {sum(times) / 1_000_000_000:.3f}s")
avg_time_ns = sum(times) / len(times)
print(f"Average time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum time per: {min(times) / 1000:.1f}us")
def find_slice_at_time(ball_prediction: flat.BallPrediction, game_time: float):
"""
This will find the future position of the ball at the specified time. The returned
Slice object will also include the ball's velocity, etc.
"""
start_time = ball_prediction.slices[0].game_seconds
approx_index = int(
(game_time - start_time) * 120
) # We know that there are 120 slices per second.
if 0 <= approx_index < len(ball_prediction.slices):
return ball_prediction.slices[approx_index]
return None
def test_loop():
print("Testing access times")
ballPred = flat.BallPrediction([flat.PredictionSlice(1) for _ in range(120 * 6)])
start = time_ns()
for _ in range(100):
li = []
for t in range(1, 301):
ball_in_future = find_slice_at_time(ballPred, t / 60)
li.append(ball_in_future)
print(f"Total time: {(time_ns() - start) / 1_000_000:.3f}ms")
times = []
for _ in range(50_000):
start = time_ns()
li = []
for t in range(1, 301):
ball_in_future = find_slice_at_time(ballPred, t / 60)
li.append(ball_in_future)
times.append(time_ns() - start)
print()
print(f"Total time: {sum(times) / 1_000_000_000:.3f}s")
avg_time_ns = sum(times) / len(times)
print(f"Average time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum time per: {min(times) / 1000:.1f}us")
times = []
for _ in range(50_000):
start = time_ns()
li = [find_slice_at_time(ballPred, t / 60) for t in range(1, 301)]
times.append(time_ns() - start)
print()
print(f"Total time: {sum(times) / 1_000_000_000:.3f}s")
avg_time_ns = sum(times) / len(times)
print(f"Average time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum time per: {min(times) / 1000:.1f}us")
times = []
for _ in range(1_000_000):
start = time_ns()
li = list(ballPred.slices[1:602:2])
times.append(time_ns() - start)
print()
print(f"Total time: {sum(times) / 1_000_000_000:.3f}s")
avg_time_ns = sum(times) / len(times)
print(f"Average time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum time per: {min(times) / 1000:.1f}us")
def test_renders():
print("Testing rendering")
times = []
for _ in range(20_000):
start = time_ns()
renders = [
flat.RenderMessage(
flat.Line3D(
flat.RenderAnchor(world=flat.Vector3(0, 0, 0)),
flat.RenderAnchor(
relative=flat.CarAnchor(0, flat.Vector3(1, 1, 1))
),
flat.Color(255, a=150),
)
),
flat.RenderMessage(
flat.Line3D(
flat.RenderAnchor(world=flat.Vector3(0, 0, 0)),
flat.RenderAnchor(
relative=flat.CarAnchor(0, flat.Vector3(1, 1, 1))
),
flat.Color(255, a=150),
)
),
flat.RenderMessage(
flat.Line3D(
flat.RenderAnchor(world=flat.Vector3(0, 0, 0)),
flat.RenderAnchor(
relative=flat.CarAnchor(0, flat.Vector3(1, 1, 1))
),
flat.Color(255, a=150),
)
),
flat.RenderMessage(
flat.String3D(
"Hello, world!", flat.RenderAnchor(world=flat.Vector3(0, 0, 0)), 1
)
),
]
render_group = flat.RenderGroup(renders)
render_group.pack()
times.append(time_ns() - start)
avg_time_ns = sum(times) / len(times)
print(f"Average time per: {avg_time_ns / 1000:.1f}us")
print(f"Minimum time per: {min(times) / 1000:.1f}us")
if __name__ == "__main__":
test_gtp()
print()
test_ballpred()
print()
test_loop()
print()
test_renders()