使用monitor监测firing rate时，平均发放率一直都很小

[mengqiShen]

首先感谢您优秀的工作。

我在跑spikformer时，使用monitor.get_avg_firing_rate(all=True)记录平均脉冲发放率，但我发现不管跑几个epoch，返回的发放率总是很小（但网络的训练、测试结果正确），大约在0.04左右，我的代码如下，请问我是否正确使用了monitor?(除了有关monitor的代码是我自己加的，其他均来自spikformer的开源代码)

`def validate(model, loader, loss_fn, args, amp_autocast=suppress, log_suffix=''):
batch_time_m = AverageMeter()
losses_m = AverageMeter()
top1_m = AverageMeter()
top5_m = AverageMeter()

model.eval()
mon = Monitor(model, device='cuda:0', backend='torch')
mon.enable()

end = time.time()
last_idx = len(loader) - 1
with torch.no_grad():
    for batch_idx, (input, target) in enumerate(loader):
        last_batch = batch_idx == last_idx
        if not args.prefetcher:
            input = input.cuda()
            target = target.cuda()
        if args.channels_last:
            input = input.contiguous(memory_format=torch.channels_last)

        with amp_autocast():
            output = model(input)
        if isinstance(output, (tuple, list)):
            output = output[0]

        # augmentation reduction
        reduce_factor = args.tta
        if reduce_factor > 1:
            output = output.unfold(0, reduce_factor, reduce_factor).mean(dim=2)
            target = target[0:target.size(0):reduce_factor]

        loss = loss_fn(output, target)
        functional.reset_net(model)

        acc1, acc5 = accuracy(output, target, topk=(1, 5))

        if args.distributed:
            reduced_loss = reduce_tensor(loss.data, args.world_size)
            acc1 = reduce_tensor(acc1, args.world_size)
            acc5 = reduce_tensor(acc5, args.world_size)
        else:
            reduced_loss = loss.data

        torch.cuda.synchronize()

        losses_m.update(reduced_loss.item(), input.size(0))
        top1_m.update(acc1.item(), output.size(0))
        top5_m.update(acc5.item(), output.size(0))

        batch_time_m.update(time.time() - end)
        end = time.time()
        if args.local_rank == 0 and (last_batch or batch_idx % args.log_interval == 0):
            log_name = 'Test' + log_suffix
            _logger.info(
                '{0}: [{1:>4d}/{2}]  '
                'Time: {batch_time.val:.3f} ({batch_time.avg:.3f})  '
                'Loss: {loss.val:>7.4f} ({loss.avg:>6.4f})  '
                'Acc@1: {top1.val:>7.4f} ({top1.avg:>7.4f})  '
                'Acc@5: {top5.val:>7.4f} ({top5.avg:>7.4f})'.format(
                    log_name, batch_idx, last_idx, batch_time=batch_time_m,
                    loss=losses_m, top1=top1_m, top5=top5_m))
    mon_fire_rate = mon.get_avg_firing_rate(all=True)
    mon_nonfire_rate = mon.get_nonfire_ratio(all=True)
    print('-----firing rate by moniter------', mon_fire_rate)
    print('-----firing non-rate by moniter------', mon_nonfire_rate)
    mon.reset()
metrics = OrderedDict([('loss', losses_m.avg), ('top1', top1_m.avg), ('top5', top5_m.avg)])

return metrics

`
运行的结果为：