def loss_meter(ctx: Context, train_ctx: Context) -> float:
"""Loss meter.
"""
if not 'meter' in ctx:
ctx.meter = AverageMeter()
if train_ctx.batch_idx == 0:
ctx.meter.reset()
ctx.meter.update(train_ctx.loss.item(), train_ctx.target.size(0))
return ctx.meter.avg
def topk_meter(ctx: Context, train_ctx: Context, k: int = 1) -> float:
"""Topk meter.
"""
def accuracy(output, target, k=1):
batch_size = target.size(0)
_, pred = output.topk(k, 1, True, True)
pred = pred.t()
correct = pred.eq(target.view(1, -1).expand_as(pred))
correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
return correct_k.mul_(100.0 / batch_size)
if not 'meter' in ctx:
ctx.meter = AverageMeter()
if train_ctx.batch_idx == 0:
ctx.meter.reset()
acc = accuracy(train_ctx.output, train_ctx.target, k)
ctx.meter.update(acc.item())
return ctx.meter.avg