Python utils.accuracy方法代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def base_val(base_train_loader, model_E, criterion, epoch):
    losses = AverageMeter()
    top1 = AverageMeter()

    model_E.eval()

    with torch.no_grad():
        for batch_idx, (input, target) in enumerate(base_train_loader):
            input = input.cuda()
            target = target.cuda(non_blocking=True)

            # compute output
            _, output = model_E(input)
            loss = criterion(output, target)

            # measure accuracy and record loss
            prec1, prec5 = accuracy(output, target, topk=(1, 5))
            losses.update(loss.item(), input.size(0))
            top1.update(prec1.item(), input.size(0))

            model_E.weight_norm()
            if (batch_idx+1)%250==0:
                print('base_test:', batch_idx+1, 'loss:', losses.avg, 'acc:', top1.avg)

    return (losses.avg, top1.avg) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def epoch_test_multiple_crops(model, loader, xp, cuda):
    metrics = xp.get_metric(tag=loader.tag, name='parent')
    xp.Temperature.update()
    for batch_idx, (data, target) in tqdm(enumerate(loader), desc='Test Epoch',
                                          leave=False, total=len(loader)):
        avg = 0
        for img in data:
            img, target = data_to_var(img, target, cuda)
            output = model(img)
            # cross-entropy
            if xp.temperature == -1:
                avg += nn.functional.softmax(output).data
            # smooth-svm
            else:
                avg += output.data
                # avg += torch.exp(output.data / xp.temperature)

        prec1 = accuracy(avg, target.data, topk=1)
        preck = accuracy(avg, target.data, topk=xp.config['topk'])
        metrics.update(acck=float(preck), acc1=float(prec1), n=target.size(0)) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def child_valid(valid_queue, model, arch_pool, criterion):
    valid_acc_list = []
    with torch.no_grad():
        model.eval()
        for i, arch in enumerate(arch_pool):
            # for step, (input, target) in enumerate(valid_queue):
            inputs, targets = next(iter(valid_queue))
            inputs = inputs.cuda()
            targets = targets.cuda()
                
            logits, _ = model(inputs, arch, bn_train=True)
            loss = criterion(logits, targets)
                
            prec1, prec5 = utils.accuracy(logits, targets, topk=(1, 5))
            valid_acc_list.append(prec1.data/100)
            
            if (i+1) % 100 == 0:
                logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', ' '.join(map(str, arch[0] + arch[1])), loss, prec1, prec5)
        
    return valid_acc_list 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def valid(valid_queue, model, criterion):
    objs = utils.AvgrageMeter()
    top1 = utils.AvgrageMeter()
    top5 = utils.AvgrageMeter()
    with torch.no_grad():
        model.eval()
        for step, (input, target) in enumerate(valid_queue):
            input = input.cuda()
            target = target.cuda()
        
            logits, _ = model(input)
            loss = criterion(logits, target)
        
            prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
            n = input.size(0)
            objs.update(loss.data, n)
            top1.update(prec1.data, n)
            top5.update(prec5.data, n)
        
            if (step+1) % 100 == 0:
                logging.info('valid %03d %e %f %f', step+1, objs.avg, top1.avg, top5.avg)

    return top1.avg, top5.avg, objs.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def valid(valid_queue, model, criterion):
    objs = utils.AvgrageMeter()
    top1 = utils.AvgrageMeter()
    top5 = utils.AvgrageMeter()
    with torch.no_grad():
        model.eval()
        for step, (input, target) in enumerate(valid_queue):
            input = input.cuda()
            target = target.cuda()
        
            logits, _ = model(input)
            loss = criterion(logits, target)
        
            prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
            n = input.size(0)
            objs.update(loss.data, n)
            top1.update(prec1.data, n)
            top5.update(prec5.data, n)
        
            if (step+1) % 100 == 0:
                logging.info('valid %03d %e %f %f', step+1, objs.avg, top1.avg, top5.avg)

    return top1.avg, objs.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def child_valid(valid_queue, model, arch_pool, criterion):
    valid_acc_list = []
    with torch.no_grad():
        model.eval()
        for i, arch in enumerate(arch_pool):
            #for step, (inputs, targets) in enumerate(valid_queue):
            inputs, targets = next(iter(valid_queue))
            inputs = inputs.cuda()
            targets = targets.cuda()
                
            logits, _ = model(inputs, arch, bn_train=True)
            loss = criterion(logits, targets)
                
            prec1, prec5 = utils.accuracy(logits, targets, topk=(1, 5))
            valid_acc_list.append(prec1.data/100)
            
            if (i+1) % 100 == 0:
                logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', ' '.join(map(str, arch[0] + arch[1])), loss, prec1, prec5)
        
    return valid_acc_list 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def train(epoch):
    model.train()
    global iteration
    st = time.time()
    for batch_idx, (data, target) in enumerate(train_loader):
        iteration += 1
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        prec1, = accuracy(output, target)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()
        if batch_idx % args.print_freq == 0:
            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}, Accuracy: {:.2f}'.format(
                epoch, batch_idx * len(data), len(train_loader.dataset),
                100. * batch_idx / len(train_loader), loss.item(), prec1.item()))
            writer.add_scalar('Loss/Train', loss.item(), iteration)
            writer.add_scalar('Accuracy/Train', prec1, iteration)
    epoch_time = time.time() - st
    print('Epoch time:{:0.2f}s'.format(epoch_time))
    scheduler.step() 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def _summarize_shared_train(curr_step, total_loss, raw_total_loss, acc=0, acc_5=0, lr=0.0, epoch_steps=1, writer=None):
    """Logs a set of training steps."""
    cur_loss = utils.to_item(total_loss) / epoch_steps
    cur_raw_loss = utils.to_item(raw_total_loss) / epoch_steps

    logging.info(f'| step {curr_step:3d} '
                 f'| lr {lr:4.2f} '
                 f'| raw loss {cur_raw_loss:.2f} '
                 f'| loss {cur_loss:.2f} '
                 f'| acc {acc:8.2f}'
                 f'| acc-5 {acc_5: 8.2f}')

    # Tensorboard
    if writer is not None:
        writer.scalar_summary('shared/loss',
                              cur_loss,
                              epoch_steps)
        writer.scalar_summary('shared/accuracy',
                              acc,
                              epoch_steps) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def nao_model_validation(valid_queue, model, arch_pool, criterion):
    valid_acc_list = []
    with torch.no_grad():
        model.eval()
        for i, arch in enumerate(arch_pool):
            # for step, (inputs, targets) in enumerate(valid_queue):
            inputs, targets = next(iter(valid_queue))
            inputs = inputs.cuda()
            targets = targets.cuda()

            logits, _ = model(inputs, arch, bn_train=True)
            loss = criterion(logits, targets)

            prec1, prec5 = nao_utils.accuracy(logits, targets, topk=(1, 5))
            valid_acc_list.append(prec1.data / 100)

            if (i + 1) % 100 == 0:
                logging.info('Valid arch %s\n loss %.2f top1 %f top5 %f', ' '.join(map(str, arch[0] + arch[1])), loss,
                             prec1, prec5)

    return valid_acc_list 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def infer(valid_queue, model, criterion):
  objs = utils.AvgrageMeter()
  top1 = utils.AvgrageMeter()
  top5 = utils.AvgrageMeter()
  model.eval()

  for step, (input, target) in enumerate(valid_queue):
    input = Variable(input, volatile=True).cuda()
    target = Variable(target, volatile=True).cuda(async=True)

    logits, _ = model(input)
    loss = criterion(logits, target)

    prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
    n = input.size(0)
    objs.update(loss.data[0], n)
    top1.update(prec1.data[0], n)
    top5.update(prec5.data[0], n)

    if step % args.report_freq == 0:
      logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)

  return top1.avg, top5.avg, objs.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def infer(test_queue, model, criterion):
  objs = utils.AvgrageMeter()
  top1 = utils.AvgrageMeter()
  top5 = utils.AvgrageMeter()
  model.eval()

  for step, (input, target) in enumerate(test_queue):
    input = Variable(input, volatile=True).cuda()
    target = Variable(target, volatile=True).cuda(async=True)

    logits, _ = model(input)
    loss = criterion(logits, target)

    prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
    n = input.size(0)
    objs.update(loss.data[0], n)
    top1.update(prec1.data[0], n)
    top5.update(prec5.data[0], n)

    if step % args.report_freq == 0:
      logging.info('test %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)

  return top1.avg, objs.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def base_train(base_train_loader, model_E, criterion, optimizer, epoch):
    losses = AverageMeter()
    top1 = AverageMeter()

    model_E.train()
    
    # for param in model_E.parameters():
    #   param.requires_grad = True

    for batch_idx, (input, target) in enumerate(base_train_loader):
        # print(target)
        input = input.cuda()
        target = target.cuda(non_blocking=True)

        # compute output
        _, output = model_E(input)
        loss = criterion(output, target)

        # measure accuracy and record loss
        prec1, prec5 = accuracy(output, target, topk=(1, 5))
        losses.update(loss.item(), input.size(0))
        top1.update(prec1.item(), input.size(0))

        # compute gradient and do SGD step
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        model_E.weight_norm()
        if (batch_idx+1)%250==0:
            print('base_train:', batch_idx+1, 'loss:', losses.avg, 'acc:', top1.avg)

    return (losses.avg, top1.avg) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def test(model, loader, args, xp):
    model.eval()

    if loader.tag == 'val':
        xp_group = xp.val
    else:
        xp_group = xp.test

    for metric in xp_group.metrics():
        metric.reset()

    for x, y in tqdm(loader, disable=not args.tqdm,
                     desc='{} Epoch'.format(loader.tag.title()),
                     leave=False, total=len(loader)):
        (x, y) = (x.cuda(), y.cuda()) if args.cuda else (x, y)
        scores = model(x)
        xp_group.acc.update(accuracy(scores, y), weighting=x.size(0))

    xp_group.timer.update()

    print('Epoch: [{0}] ({tag})\t'
          '({timer:.2f}s) \t'
          'Obj ----\t'
          'Loss ----\t'
          'Acc {acc:.2f}% \t'
          .format(int(xp.epoch.value),
                  tag=loader.tag.title(),
                  timer=xp_group.timer.value,
                  acc=xp_group.acc.value))

    if loader.tag == 'val':
        xp.max_val.update(xp.val.acc.value).log(time=xp.epoch.value)

    for metric in xp_group.metrics():
        metric.log(time=xp.epoch.value) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def train(model, loss, optimizer, loader, xp, args):

    if not len(loader):
        return 0

    model.train()

    xp.Parent_Train.reset()

    for batch_idx, (data, target) in tqdm(enumerate(loader), desc='Train Epoch',
                                          leave=False, total=len(loader)):
        data, target = data_to_var(data, target, args.cuda)

        output = model(data)
        obj = loss(output, target)

        optimizer.zero_grad()
        obj.backward()
        optimizer.step()

        prec1 = accuracy(output.data, target.data, topk=1)
        preck = accuracy(output.data, target.data, topk=xp.config['topk'])
        xp.Parent_Train.update(loss=float(obj), acck=float(preck), acc1=float(prec1), n=data.size(0))

    # compute objective function (including regularization)
    obj = xp.Loss_Train.get() + regularization(model, xp.mu)
    xp.Obj_Train.update(float(obj))
    # measure elapsed time
    xp.Timer_Train.update()

    xp.log_with_tag('train')

    if args.verbosity:
        print_stats(xp, 'train') 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import accuracy [as 别名]
def epoch_test(model, loader, xp, cuda):
    metrics = xp.get_metric(tag=loader.tag, name='parent')
    for batch_idx, (data, target) in tqdm(enumerate(loader), desc='Test Epoch',
                                          leave=False, total=len(loader)):
        data, target = data_to_var(data, target, cuda)
        output = model(data)

        prec1 = accuracy(output.data, target.data, topk=1)
        preck = accuracy(output.data, target.data, topk=xp.config['topk'])
        metrics.update(acck=float(preck), acc1=float(prec1), n=data.size(0))