main.train(params, buffer2, device, frame_idx2, exp_source2,
reward_tracker2, optimizer2, net2, tgt_net2, writer2)
while True:
if frame // args.units % 2 == 0:
frame_idx1 += 1
if main.train(params, buffer1, device, frame_idx1, exp_source1,
reward_tracker1, optimizer1, net1, tgt_net1,
writer1):
break
else:
frame_idx2 += 1
if main.train(params, buffer2, device, frame_idx2, exp_source2,
reward_tracker2, optimizer2, net2, tgt_net2,
writer2):
break
frame += 1
if args.maxFrames > 0 and frame_idx1 > args.maxFrames:
break
if __name__ == "__main__":
params = common.HYPERPARAMS['bfw']
args = utils.parse_args()
device = torch.device("cuda" if args.cuda else "cpu")
while True:
execute(args, params, device)
import pandas as pd
from lib.utils import parse_args, seconds_to_time
TOTAL_TIME = 6000
def gen_prediction_every_n_const(files, n, event):
y_pred = []
for file in files:
for time in range(n // 2, TOTAL_TIME, n):
y = {
'file_name': file,
'event_type': event,
'event_time': seconds_to_time(time)
}
y_pred.append(y)
return pd.DataFrame(y_pred)
if __name__ == '__main__':
args = parse_args()
output = args['output']
predict_table_path = args['predict_table']
predict_table = pd.read_csv(predict_table_path)
files = predict_table['file_name'].unique()
e_1 = gen_prediction_every_n_const(files, 120, 'удар по воротам')
e_2 = gen_prediction_every_n_const(files, 120, 'угловой')
concat = pd.concat([predict_table, e_1, e_2], ignore_index=True)
concat = concat[['file_name', 'event_type', 'event_time']]
concat.to_csv(output)