def train_one_epoch()

in train.py [0:0]

• 29 lines of code
• 14 McCabe index (conditional complexity)

def train_one_epoch():
    stat_dict = {} # collect statistics
    adjust_learning_rate(optimizer, EPOCH_CNT)
    bnm_scheduler.step() # decay BN momentum
    net.train() # set model to training mode
    for batch_idx, batch_data_label in enumerate(TRAIN_DATALOADER):
        for key in batch_data_label:
            batch_data_label[key] = batch_data_label[key].to(device)

        # Forward pass
        optimizer.zero_grad()
        inputs = {'point_clouds': batch_data_label['point_clouds']}
        end_points = net(inputs)
        
        # Compute loss and gradients, update parameters.
        for key in batch_data_label:
            assert(key not in end_points)
            end_points[key] = batch_data_label[key]
        loss, end_points = criterion(end_points, DATASET_CONFIG)
        loss.backward()
        optimizer.step()

        # Accumulate statistics and print out
        for key in end_points:
            if 'loss' in key or 'acc' in key or 'ratio' in key:
                if key not in stat_dict: stat_dict[key] = 0
                stat_dict[key] += end_points[key].item()

        batch_interval = 10
        if (batch_idx+1) % batch_interval == 0:
            log_string(' ---- batch: %03d ----' % (batch_idx+1))
            TRAIN_VISUALIZER.log_scalars({key:stat_dict[key]/batch_interval for key in stat_dict},
                (EPOCH_CNT*len(TRAIN_DATALOADER)+batch_idx)*BATCH_SIZE)
            for key in sorted(stat_dict.keys()):
                log_string('mean %s: %f'%(key, stat_dict[key]/batch_interval))
                stat_dict[key] = 0