def compute_time_full()

in pycls/core/benchmark.py [0:0]

• 24 lines of code
• 1 McCabe index (conditional complexity)

def compute_time_full(model, loss_fun, train_loader, test_loader):
    """Times model and data loader."""
    logger.info("Computing model and loader timings...")
    # Compute timings
    test_fw_time = compute_time_eval(model)
    train_fw_time, train_bw_time = compute_time_train(model, loss_fun)
    train_fw_bw_time = train_fw_time + train_bw_time
    train_loader_time = compute_time_loader(train_loader)
    # Output iter timing
    iter_times = {
        "test_fw_time": test_fw_time,
        "train_fw_time": train_fw_time,
        "train_bw_time": train_bw_time,
        "train_fw_bw_time": train_fw_bw_time,
        "train_loader_time": train_loader_time,
    }
    logger.info(logging.dump_log_data(iter_times, "iter_times"))
    # Output epoch timing
    epoch_times = {
        "test_fw_time": test_fw_time * len(test_loader),
        "train_fw_time": train_fw_time * len(train_loader),
        "train_bw_time": train_bw_time * len(train_loader),
        "train_fw_bw_time": train_fw_bw_time * len(train_loader),
        "train_loader_time": train_loader_time * len(train_loader),
    }
    logger.info(logging.dump_log_data(epoch_times, "epoch_times"))
    # Compute data loader overhead (assuming DATA_LOADER.NUM_WORKERS>1)
    overhead = max(0, train_loader_time - train_fw_bw_time) / train_fw_bw_time
    logger.info("Overhead of data loader is {:.2f}%".format(overhead * 100))