('throughput', '{:.2f}'.format(fps)),
])
if statistics:
statistics.dump()
print('Count: {} iterations'.format(iteration))
print('Duration: {:.2f} ms'.format(get_duration_in_milliseconds(total_duration_sec)))
if MULTI_DEVICE_NAME not in device_name:
print('Latency: {:.2f} ms'.format(latency_ms))
print('Throughput: {:.2f} FPS'.format(fps))
del exe_network
next_step.step_id = 0
except Exception as e:
logger.exception(e)
if statistics:
statistics.add_parameters(StatisticsReport.Category.EXECUTION_RESULTS,
[
('error', str(e)),
])
statistics.dump()
if __name__ == "__main__":
# ------------------------------ 1. Parsing and validating input arguments -------------------------------------
next_step()
main(parse_args())
if args.enable_gpu:
log_vecs = log_vecs.cuda(device=device, non_blocking=True)
log_mask = log_mask.cuda(device=device, non_blocking=True)
user_vecs = model.user_encoder(
log_vecs, log_mask,
user_log_mask=True).to(torch.device("cpu")).detach().numpy()
for id, user_vec, news_vec in zip(impids, user_vecs,
candidate_vec):
score = np.dot(news_vec, user_vec)
pred_rank = (np.argsort(np.argsort(score)[::-1]) + 1).tolist()
f.write(
str(id) + ' ' + '[' +
','.join([str(x) for x in pred_rank]) + ']' + '\n')
f.close()
zip_file = zipfile.ZipFile('prediction.zip', 'w')
zip_file.write('prediction.txt')
zip_file.close()
os.remove('prediction.txt')
if __name__ == "__main__":
from parameters import parse_args
setuplogger()
args = parse_args()
generate_submission(args)