Python utils.AvgrageMeter方法代碼示例

本文整理匯總了Python中utils.AvgrageMeter方法的典型用法代碼示例。如果您正苦於以下問題：Python utils.AvgrageMeter方法的具體用法？Python utils.AvgrageMeter怎麽用？Python utils.AvgrageMeter使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類utils的用法示例。

在下文中一共展示了utils.AvgrageMeter方法的15個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。

示例1: valid

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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

開發者ID:renqianluo，項目名稱:NAO_pytorch，代碼行數:25，代碼來源:train_imagenet.py

示例2: nao_valid

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
def nao_valid(queue, model):
    pa = utils.AvgrageMeter()
    hs = utils.AvgrageMeter()
    with torch.no_grad():
        model.eval()
        for step, sample in enumerate(queue):
            encoder_input = sample['encoder_input']
            encoder_target = sample['encoder_target']
            decoder_target = sample['decoder_target']
            
            encoder_input = encoder_input.cuda()
            encoder_target = encoder_target.cuda()
            decoder_target = decoder_target.cuda()
            
            predict_value, logits, arch = model(encoder_input)
            n = encoder_input.size(0)
            pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(),
                                                predict_value.data.squeeze().tolist())
            hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
            pa.update(pairwise_acc, n)
            hs.update(hamming_dis, n)
    return pa.avg, hs.avg

開發者ID:renqianluo，項目名稱:NAO_pytorch，代碼行數:24，代碼來源:test_controller.py

示例3: valid

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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

開發者ID:renqianluo，項目名稱:NAO_pytorch，代碼行數:25，代碼來源:train_cifar.py

示例4: nao_valid

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
def nao_valid(queue, model):
    pa = utils.AvgrageMeter()
    hs = utils.AvgrageMeter()
    mse = utils.AvgrageMeter()
    with torch.no_grad():
        model.eval()
        for step, sample in enumerate(queue):
            encoder_input = sample['encoder_input']
            encoder_target = sample['encoder_target']
            decoder_target = sample['decoder_target']
            
            encoder_input = encoder_input.cuda()
            encoder_target = encoder_target.cuda()
            decoder_target = decoder_target.cuda()
            
            predict_value, logits, arch = model(encoder_input)
            n = encoder_input.size(0)
            pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(),
                                                predict_value.data.squeeze().tolist())
            hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
            mse.update(F.mse_loss(predict_value.data.squeeze(), encoder_target.data.squeeze()), n)
            pa.update(pairwise_acc, n)
            hs.update(hamming_dis, n)
    return mse.avg, pa.avg, hs.avg

開發者ID:renqianluo，項目名稱:NAO_pytorch，代碼行數:26，代碼來源:train_controller.py

示例5: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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

開發者ID:kcyu2014，項目名稱:eval-nas，代碼行數:25，代碼來源:train_imagenet.py

示例6: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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

開發者ID:kcyu2014，項目名稱:eval-nas，代碼行數:25，代碼來源:test.py

示例7: nao_valid

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
def nao_valid(queue, model):
    pa = utils.AvgrageMeter()
    hs = utils.AvgrageMeter()
    with torch.no_grad():
        model.eval()
        for step, sample in enumerate(queue):
            encoder_input = sample['encoder_input']
            encoder_target = sample['encoder_target']
            decoder_target = sample['decoder_target']
            
            encoder_input = encoder_input.cuda()
            encoder_target = encoder_target.cuda()
            decoder_target = decoder_target.cuda()
            
            predict_value, logits, arch = model(encoder_input)
            n = encoder_input.size(0)
            pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(), predict_value.data.squeeze().tolist())
            hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
            pa.update(pairwise_acc, n)
            hs.update(hamming_dis, n)
    return pa.avg, hs.avg

開發者ID:kcyu2014，項目名稱:eval-nas，代碼行數:23，代碼來源:train_search.py

示例8: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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, objs.avg

開發者ID:quark0，項目名稱:darts，代碼行數:25，代碼來源:train_search.py

示例9: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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, objs.avg

開發者ID:quark0，項目名稱:darts，代碼行數:25，代碼來源:train.py

示例10: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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 = input.cuda()
    target = target.cuda(non_blocking=True)
    #input = Variable(input).cuda()
    #target = Variable(target).cuda(async=True)
    with torch.no_grad():
      logits = model(input)
      loss = criterion(logits, target)

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

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

  return top1.avg, objs.avg

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:27，代碼來源:train_search.py

示例11: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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 = input.cuda(non_blocking=True)
    target = target.cuda(non_blocking=True)
    #input = Variable(input).cuda()
    #target = Variable(target).cuda(async=True)
    with torch.no_grad():
      logits, _ = model(input)
      loss = criterion(logits, target)

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

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

  return top1.avg, objs.avg

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:27，代碼來源:train.py

示例12: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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 = input.cuda()
        target = target.cuda(non_blocking=True)
        with torch.no_grad():
            logits = model(input)
            loss = criterion(logits, target)

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

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

    return top1.avg, objs.avg

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:25，代碼來源:train_search.py

示例13: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
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 = input.cuda()
    target = target.cuda()
    with torch.no_grad():
        logits, _ = model(input)
        loss = criterion(logits, target)

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

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:25，代碼來源:test.py

示例14: infer

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
def infer(valid_queue, model, criterion):
    objs = utils.AvgrageMeter()
    top1 = utils.AvgrageMeter()
    model.eval()

    for step, (input, target) in enumerate(valid_queue):
        input = input.cuda(non_blocking=True)
        target = target.cuda(non_blocking=True)
        with torch.no_grad():
            logits, _ = model(input)
            loss = criterion(logits, target)

        prec1, _ = utils.accuracy(logits, target, topk=(1,5))
        n = input.size(0)
        objs.update(loss.data.item(), n)
        top1.update(prec1.data.item(), n)

        if step % args.report_freq == 0:
            logging.info('Valid Step: %03d Objs: %e Acc: %f', step, objs.avg, top1.avg)

    return top1.avg, objs.avg

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:23，代碼來源:train_cifar.py

示例15: nao_valid

# 需要導入模塊: import utils [as 別名]
# 或者: from utils import AvgrageMeter [as 別名]
def nao_valid(queue, model):
    pa = utils.AvgrageMeter()
    hs = utils.AvgrageMeter()
    with torch.no_grad():
        model.eval()
        for step, sample in enumerate(queue):
            encoder_input = sample['encoder_input']
            encoder_target = sample['encoder_target']
            decoder_target = sample['decoder_target']

            encoder_input = encoder_input.cuda()
            encoder_target = encoder_target.cuda()
            decoder_target = decoder_target.cuda()

            predict_value, logits, arch = model(encoder_input)
            n = encoder_input.size(0)
            pairwise_acc = utils.pairwise_accuracy(encoder_target.data.squeeze().tolist(),
                                                   predict_value.data.squeeze().tolist())
            hamming_dis = utils.hamming_distance(decoder_target.data.squeeze().tolist(), arch.data.squeeze().tolist())
            pa.update(pairwise_acc, n)
            hs.update(hamming_dis, n)
    return pa.avg, hs.avg

開發者ID:antoyang，項目名稱:NAS-Benchmark，代碼行數:24，代碼來源:train_search.py

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