-
Notifications
You must be signed in to change notification settings - Fork 2
/
JetsonInferenceSpeedROCKET.py
48 lines (39 loc) · 1.67 KB
/
JetsonInferenceSpeedROCKET.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
import gc
import multiprocessing
import warnings
import numpy as np
from numba import set_num_threads
warnings.filterwarnings("ignore")
import glob
from timeit import default_timer as timer
from sktime.datatypes._panel._convert import from_3d_numpy_to_nested
from sktime.transformations.panel.rocket import Rocket
def measure_transform_times():
"""
Loads the datasets, trains a ROCKET model with seed 0 on each dataset and measures the time required for transforming a random test sample. The measured times are saved to a csv file.
"""
global_times = []
for filename in sorted(glob.glob(F"UEA/*.npz")):
local_times = []
dataset = filename.split("/")[-1].split(".")[0]
data = np.load(filename)
train_x, test_x = data['train_x'].astype(np.float64), data['test_x'].astype(np.float64)
train_x = from_3d_numpy_to_nested(train_x)
rocket = Rocket(random_state=0, n_jobs=-1)
set_num_threads(multiprocessing.cpu_count())
rocket.fit(train_x)
with open("sum_channels.txt", "a") as of:
of.write(f"{dataset},{rocket.kernels[-2].sum()}\n")
for i in range(100):
print(f'{dataset} - Rep: {i}')
ind = np.random.choice(test_x.shape[0])
tr_test = from_3d_numpy_to_nested(test_x[ind:ind + 1])
gc.collect()
start = timer()
X_test_transform = rocket.transform(tr_test)
end = timer()
local_times.append(end - start)
global_times.append(local_times)
np.savetxt(F"jetson_rocket_transform_times.csv", np.array(global_times), delimiter=',')
if __name__ == "__main__":
measure_transform_times()