cvsa/pred/inference.py

32 lines
1.2 KiB
Python

import datetime
import numpy as np
from model import CompactPredictor
import torch
def main():
model = CompactPredictor(12).to('cpu', dtype=torch.float32)
model.load_state_dict(torch.load('./pred/checkpoints/model_20250315_0226.pt'))
model.eval()
# inference
initial = 999469
last = initial
start_time = '2025-03-11 15:03:31'
for i in range(1, 32):
hour = i / 4.2342
sec = hour * 3600
time_d = np.log2(sec)
data = [time_d, np.log2(initial+1), # time_delta, current_views
6.319254, 9.0611, 9.401403, 10.653134, 12.008604, 13.230796, 16.3302, # grows_feat
0.627442, 0.232492, 24.778674 # time_feat
]
np_arr = np.array([data])
tensor = torch.from_numpy(np_arr).to('cpu', dtype=torch.float32)
output = model(tensor)
num = output.detach().numpy()[0][0]
views_pred = int(np.exp2(num)) + initial
current_time = datetime.datetime.strptime(start_time, '%Y-%m-%d %H:%M:%S') + datetime.timedelta(hours=hour)
print(current_time.strftime('%m-%d %H:%M'), views_pred, views_pred - last)
last = views_pred
if __name__ == '__main__':
main()