Python config.print_freq方法代碼示例

本文整理匯總了Python中config.print_freq方法的典型用法代碼示例。如果您正苦於以下問題：Python config.print_freq方法的具體用法？Python config.print_freq怎麽用？Python config.print_freq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config的用法示例。

在下文中一共展示了config.print_freq方法的7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: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, optimizer, epoch, logger):
    model.train()  # train mode (dropout and batchnorm is used)

    losses = AverageMeter()

    # Batches
    for i, (img, alpha_label) in enumerate(train_loader):
        # Move to GPU, if available
        img = img.type(torch.FloatTensor).to(device)  # [N, 4, 320, 320]
        alpha_label = alpha_label.type(torch.FloatTensor).to(device)  # [N, 2, 320, 320]
        alpha_label = alpha_label.reshape((-1, 2, im_size * im_size))  # [N, 2, 320*320]

        # Forward prop.
        alpha_out = model(img)  # [N, 320, 320]
        alpha_out = alpha_out.reshape((-1, 1, im_size * im_size))  # [N, 320*320]

        # Calculate loss
        # loss = criterion(alpha_out, alpha_label)
        loss = alpha_prediction_loss(alpha_out, alpha_label)

        # Back prop.
        optimizer.zero_grad()
        loss.backward()

        # Clip gradients
        clip_gradient(optimizer, grad_clip)

        # Update weights
        optimizer.step()

        # Keep track of metrics
        losses.update(loss.item())

        # Print status

        if i % print_freq == 0:
            status = 'Epoch: [{0}][{1}/{2}]\t' \
                     'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch, i, len(train_loader), loss=losses)
            logger.info(status)

    return losses.avg

開發者ID:foamliu，項目名稱:Mobile-Image-Matting，代碼行數:43，代碼來源:train.py

示例2: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, optimizer, epoch, logger):
    model.train()  # train mode (dropout and batchnorm is used)

    losses = AverageMeter()

    # Batches
    for i, (img, alpha_label) in enumerate(train_loader):
        # Move to GPU, if available
        img = img.type(torch.FloatTensor).to(device)  # [N, 4, 320, 320]
        alpha_label = alpha_label.type(torch.FloatTensor).to(device)  # [N, 320, 320]
        alpha_label = alpha_label.reshape((-1, 2, im_size * im_size))  # [N, 320*320]

        # Forward prop.
        alpha_out = model(img)  # [N, 3, 320, 320]
        alpha_out = alpha_out.reshape((-1, 1, im_size * im_size))  # [N, 320*320]

        # Calculate loss
        # loss = criterion(alpha_out, alpha_label)
        loss = alpha_prediction_loss(alpha_out, alpha_label)

        # Back prop.
        optimizer.zero_grad()
        loss.backward()

        # Clip gradients
        clip_gradient(optimizer, grad_clip)

        # Update weights
        optimizer.step()

        # Keep track of metrics
        losses.update(loss.item())

        # Print status

        if i % print_freq == 0:
            status = 'Epoch: [{0}][{1}/{2}]\t' \
                     'Loss {loss.val:.4f} ({loss.avg:.4f})\t'.format(epoch, i, len(train_loader), loss=losses)
            logger.info(status)

    return losses.avg

開發者ID:foamliu，項目名稱:Deep-Image-Matting-PyTorch，代碼行數:43，代碼來源:train.py

示例3: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, optimizer, epoch, logger):
    model.train()  # train mode (dropout and batchnorm is used)

    losses = AverageMeter()

    # Batches
    for i, (data) in enumerate(train_loader):
        # Move to GPU, if available
        padded_input, padded_target, input_lengths = data
        padded_input = padded_input.to(device)
        padded_target = padded_target.to(device)
        input_lengths = input_lengths.to(device)

        # Forward prop.
        pred, gold = model(padded_input, input_lengths, padded_target)
        loss, n_correct = cal_performance(pred, gold, smoothing=args.label_smoothing)

        # Back prop.
        optimizer.zero_grad()
        loss.backward()

        # Update weights
        optimizer.step()

        # Keep track of metrics
        losses.update(loss.item())

        # Print status
        if i % print_freq == 0:
            logger.info('Epoch: [{0}][{1}/{2}]\t'
                        'Loss {loss.val:.5f} ({loss.avg:.5f})'.format(epoch, i, len(train_loader), loss=losses))

    return losses.avg

開發者ID:foamliu，項目名稱:Speech-Transformer，代碼行數:35，代碼來源:train.py

示例4: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, criterion, scheduler, optimizer, epoch):
    start = time.time()
    losses = AverageMeter()
    batch_time = AverageMeter()
    data_time = AverageMeter()
    end = time.time()
    model.train()
    
    for i, (img, score_map, geo_map, training_mask) in enumerate(train_loader):
        data_time.update(time.time() - end)

        if cfg.gpu is not None:
            img, score_map, geo_map, training_mask = img.cuda(), score_map.cuda(), geo_map.cuda(), training_mask.cuda()

        f_score, f_geometry = model(img)
        loss1 = criterion(score_map, f_score, geo_map, f_geometry, training_mask)
        losses.update(loss1.item(), img.size(0))

        # backward
        scheduler.step()
        optimizer.zero_grad()
        loss1.backward()
        optimizer.step()

        # measure elapsed time
        batch_time.update(time.time() - end)
        end = time.time()

        if i % cfg.print_freq == 0:
            print('EAST <==> TRAIN <==> Epoch: [{0}][{1}/{2}] Loss {loss.val:.4f} Avg Loss {loss.avg:.4f})\n'.format(epoch, i, len(train_loader), loss=losses))

        save_loss_info(losses, epoch, i, train_loader)

開發者ID:songdejia，項目名稱:EAST，代碼行數:34，代碼來源:main.py

示例5: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, metric_fc, criterion, optimizer, epoch):
    model.train()  # train mode (dropout and batchnorm is used)
    metric_fc.train()

    losses = AverageMeter()
    top1_accs = AverageMeter()

    # Batches
    for i, (img, label) in enumerate(train_loader):
        # Move to GPU, if available
        img = img.to(device)
        label = label.to(device)  # [N, 1]

        # Forward prop.
        feature = model(img)  # embedding => [N, 512]
        output = metric_fc(feature, label)  # class_id_out => [N, 10575]

        # Calculate loss
        loss = criterion(output, label)

        # Back prop.
        optimizer.zero_grad()
        loss.backward()

        # Clip gradients
        clip_gradient(optimizer, grad_clip)

        # Update weights
        optimizer.step()

        # Keep track of metrics
        losses.update(loss.item())
        top1_accuracy = accuracy(output, label, 1)
        top1_accs.update(top1_accuracy)

        # Print status
        if i % print_freq == 0:
            logger.info('Epoch: [{0}][{1}/{2}]\t'
                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                        'Top1 Accuracy {top1_accs.val:.3f} ({top1_accs.avg:.3f})'.format(epoch, i, len(train_loader),
                                                                                         loss=losses,
                                                                                         top1_accs=top1_accs))

    return losses.avg, top1_accs.avg

開發者ID:foamliu，項目名稱:InsightFace-PyTorch，代碼行數:46，代碼來源:train.py

示例6: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, metric_fc, criterion, optimizer, epoch, logger):
    model.train()  # train mode (dropout and batchnorm is used)
    metric_fc.train()

    losses = AverageMeter()
    top1_accs = AverageMeter()

    # Batches
    for i, (img, label) in enumerate(train_loader):
        # Move to GPU, if available
        img = img.to(device)
        label = label.to(device)  # [N, 1]

        # Forward prop.
        feature = model(img)  # embedding => [N, 512]
        output = metric_fc(feature, label)  # class_id_out => [N, 10575]

        # Calculate loss
        loss = criterion(output, label)

        # Back prop.
        optimizer.zero_grad()
        loss.backward()

        # Clip gradients
        optimizer.clip_gradient(grad_clip)

        # Update weights
        optimizer.step()

        # Keep track of metrics
        losses.update(loss.item())
        top1_accuracy = accuracy(output, label, 1)
        top1_accs.update(top1_accuracy)

        # Print status
        if i % print_freq == 0:
            logger.info('Epoch: [{0}][{1}/{2}]\t'
                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                        'Top1 Accuracy {top1_accs.val:.3f} ({top1_accs.avg:.3f})'.format(epoch, i, len(train_loader),
                                                                                         loss=losses,
                                                                                         top1_accs=top1_accs))

    return losses.avg, top1_accs.avg

開發者ID:LcenArthas，項目名稱:CCF-BDCI2019-Multi-person-Face-Recognition-Competition-Baseline，代碼行數:46，代碼來源:train.py

示例7: train

# 需要導入模塊: import config [as 別名]
# 或者: from config import print_freq [as 別名]
def train(train_loader, model, metric_fc, criterion, optimizer, epoch, logger):
    model.train()  # train mode (dropout and batchnorm is used)
    metric_fc.train()

    losses = AverageMeter()
    top5_accs = AverageMeter()

    # Batches
    for i, (img, label) in enumerate(train_loader):
        # Move to GPU, if available
        img = img.to(device)
        label = label.to(device)  # [N, 1]

        # Forward prop.
        feature = model(img)  # embedding => [N, 512]
        output = metric_fc(feature, label)  # class_id_out => [N, 93431]

        # Calculate loss
        loss = criterion(output, label)

        # Back prop.
        optimizer.zero_grad()
        loss.backward()

        # Clip gradients
        optimizer.clip_gradient(grad_clip)

        # Update weights
        optimizer.step()

        # Keep track of metrics
        losses.update(loss.item())
        top5_accuracy = accuracy(output, label, 5)
        top5_accs.update(top5_accuracy)

        # Print status
        if i % print_freq == 0:
            logger.info('Epoch: [{0}][{1}/{2}]\t'
                        'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
                        'Top5 Accuracy {top5_accs.val:.3f} ({top5_accs.avg:.3f})'.format(epoch, i, len(train_loader),
                                                                                         loss=losses,
                                                                                         top5_accs=top5_accs))

    return losses.avg, top5_accs.avg

開發者ID:foamliu，項目名稱:InsightFace-v2，代碼行數:46，代碼來源:train.py

注：本文中的config.print_freq方法示例由純淨天空整理自Github/MSDocs等開源代碼及文檔管理平台，相關代碼片段篩選自各路編程大神貢獻的開源項目，源碼版權歸原作者所有，傳播和使用請參考對應項目的License；未經允許，請勿轉載。