Python utils.AverageMeter方法代码示例

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def test(model, target_test_loader):
    model.eval()
    test_loss = utils.AverageMeter()
    correct = 0
    criterion = torch.nn.CrossEntropyLoss()
    len_target_dataset = len(target_test_loader.dataset)
    with torch.no_grad():
        for data, target in target_test_loader:
            data, target = data.to(DEVICE), target.to(DEVICE)
            s_output = model.predict(data)
            loss = criterion(s_output, target)
            test_loss.update(loss.item())
            pred = torch.max(s_output, 1)[1]
            correct += torch.sum(pred == target)

    print('{} --> {}: max correct: {}, accuracy{: .2f}%\n'.format(
        source_name, target_name, correct, 100. * correct / len_target_dataset)) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [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 AverageMeter [as 别名]
def validate(data_loader, model):
    losses = AverageMeter()
    accuracy = AverageMeter()
    model.eval()
    with torch.no_grad():
        for idx, (input_seq, target) in tqdm(enumerate(data_loader), total=len(data_loader)):
            input_seq = input_seq.to(cuda)
            target = target.to(cuda)
            B = input_seq.size(0)
            output, _ = model(input_seq)

            [_, N, D] = output.size()
            output = output.view(B*N, D)
            target = target.repeat(1, N).view(-1)

            loss = criterion(output, target)
            acc = calc_accuracy(output, target)

            losses.update(loss.item(), B)
            accuracy.update(acc.item(), B)
                
    print('Loss {loss.avg:.4f}\t'
          'Acc: {acc.avg:.4f} \t'.format(loss=losses, acc=accuracy))
    return losses.avg, accuracy.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def train(source_loader, target_train_loader, target_test_loader, model, optimizer, CFG):
    len_source_loader = len(source_loader)
    len_target_loader = len(target_train_loader)
    for e in range(CFG['epoch']):
        train_loss_clf = utils.AverageMeter()
        train_loss_transfer = utils.AverageMeter()
        train_loss_total = utils.AverageMeter()
        model.train()
        iter_source, iter_target = iter(
            source_loader), iter(target_train_loader)
        n_batch = min(len_source_loader, len_target_loader)
        criterion = torch.nn.CrossEntropyLoss()
        for i in range(n_batch):
            data_source, label_source = iter_source.next()
            data_target, _ = iter_target.next()
            data_source, label_source = data_source.to(
                DEVICE), label_source.to(DEVICE)
            data_target = data_target.to(DEVICE)

            optimizer.zero_grad()
            label_source_pred, transfer_loss = model(data_source, data_target)
            clf_loss = criterion(label_source_pred, label_source)
            loss = clf_loss + CFG['lambda'] * transfer_loss
            loss.backward()
            optimizer.step()
            train_loss_clf.update(clf_loss.item())
            train_loss_transfer.update(transfer_loss.item())
            train_loss_total.update(loss.item())
            if i % CFG['log_interval'] == 0:
                print('Train Epoch: [{}/{} ({:02d}%)], cls_Loss: {:.6f}, transfer_loss: {:.6f}, total_Loss: {:.6f}'.format(
                    e + 1,
                    CFG['epoch'],
                    int(100. * i / n_batch), train_loss_clf.avg, train_loss_transfer.avg, train_loss_total.avg))
        log.append([train_loss_clf.avg, train_loss_transfer.avg, train_loss_total.avg])
        np_log = np.array(log, dtype=float)
        np.savetxt('train_log.csv', np_log, delimiter=',', fmt='%.6f')
        # Test
        test(model, target_test_loader) 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [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 AverageMeter [as 别名]
def train(args, data_loader, model, global_stats):
    """Run through one epoch of model training with the provided data loader."""
    # Initialize meters + timers
    train_loss = utils.AverageMeter()
    epoch_time = utils.Timer()

    # Run one epoch
    for idx, ex in enumerate(data_loader):
        train_loss.update(*model.update(ex))

        if idx % args.display_iter == 0:
            logger.info('train: Epoch = %d | iter = %d/%d | ' %
                        (global_stats['epoch'], idx, len(data_loader)) +
                        'loss = %.2f | elapsed time = %.2f (s)' %
                        (train_loss.avg, global_stats['timer'].time()))
            train_loss.reset()

    logger.info('train: Epoch %d done. Time for epoch = %.2f (s)' %
                (global_stats['epoch'], epoch_time.time()))

    # Checkpoint
    if args.checkpoint:
        model.checkpoint(args.model_file + '.checkpoint',
                         global_stats['epoch'] + 1)


# ------------------------------------------------------------------------------
# Validation loops. Includes both "unofficial" and "official" functions that
# use different metrics and implementations.
# ------------------------------------------------------------------------------ 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def validate_unofficial(args, data_loader, model, global_stats, mode):
    """Run one full unofficial validation.
    Unofficial = doesn't use SQuAD script.
    """
    eval_time = utils.Timer()
    start_acc = utils.AverageMeter()
    end_acc = utils.AverageMeter()
    exact_match = utils.AverageMeter()

    # Make predictions
    examples = 0
    for ex in data_loader:
        batch_size = ex[0].size(0)
        pred_s, pred_e, _ = model.predict(ex)
        target_s, target_e = ex[-3:-1]

        # We get metrics for independent start/end and joint start/end
        accuracies = eval_accuracies(pred_s, target_s, pred_e, target_e)
        start_acc.update(accuracies[0], batch_size)
        end_acc.update(accuracies[1], batch_size)
        exact_match.update(accuracies[2], batch_size)

        # If getting train accuracies, sample max 10k
        examples += batch_size
        if mode == 'train' and examples >= 1e4:
            break

    logger.info('%s valid unofficial: Epoch = %d | start = %.2f | ' %
                (mode, global_stats['epoch'], start_acc.avg) +
                'end = %.2f | exact = %.2f | examples = %d | ' %
                (end_acc.avg, exact_match.avg, examples) +
                'valid time = %.2f (s)' % eval_time.time())

    return {'exact_match': exact_match.avg} 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def validate_official(args, data_loader, model, global_stats,
                      offsets, texts, answers):
    """Run one full official validation. Uses exact spans and same
    exact match/F1 score computation as in the SQuAD script.

    Extra arguments:
        offsets: The character start/end indices for the tokens in each context.
        texts: Map of qid --> raw text of examples context (matches offsets).
        answers: Map of qid --> list of accepted answers.
    """
    eval_time = utils.Timer()
    f1 = utils.AverageMeter()
    exact_match = utils.AverageMeter()

    # Run through examples
    examples = 0
    for ex in data_loader:
        ex_id, batch_size = ex[-1], ex[0].size(0)
        pred_s, pred_e, _ = model.predict(ex)

        for i in range(batch_size):
            s_offset = offsets[ex_id[i]][pred_s[i][0]][0]
            e_offset = offsets[ex_id[i]][pred_e[i][0]][1]
            prediction = texts[ex_id[i]][s_offset:e_offset]

            # Compute metrics
            ground_truths = answers[ex_id[i]]
            exact_match.update(utils.metric_max_over_ground_truths(
                utils.exact_match_score, prediction, ground_truths))
            f1.update(utils.metric_max_over_ground_truths(
                utils.f1_score, prediction, ground_truths))

        examples += batch_size

    logger.info('dev valid official: Epoch = %d | EM = %.2f | ' %
                (global_stats['epoch'], exact_match.avg * 100) +
                'F1 = %.2f | examples = %d | valid time = %.2f (s)' %
                (f1.avg * 100, examples, eval_time.time()))

    return {'exact_match': exact_match.avg * 100, 'f1': f1.avg * 100} 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def eval_accuracies(pred_s, target_s, pred_e, target_e):
    """An unofficial evalutation helper.
    Compute exact start/end/complete match accuracies for a batch.
    """
    # Convert 1D tensors to lists of lists (compatibility)
    if torch.is_tensor(target_s):
        target_s = [[e] for e in target_s]
        target_e = [[e] for e in target_e]

    # Compute accuracies from targets
    batch_size = len(pred_s)
    start = utils.AverageMeter()
    end = utils.AverageMeter()
    em = utils.AverageMeter()
    for i in range(batch_size):
        # Start matches
        if pred_s[i] in target_s[i]:
            start.update(1)
        else:
            start.update(0)

        # End matches
        if pred_e[i] in target_e[i]:
            end.update(1)
        else:
            end.update(0)

        # Both start and end match
        if any([1 for _s, _e in zip(target_s[i], target_e[i])
                if _s == torch.from_numpy(pred_s[i]) and _e == torch.from_numpy(pred_e[i])]):
            em.update(1)
        else:
            em.update(0)
    return start.avg * 100, end.avg * 100, em.avg * 100


# ------------------------------------------------------------------------------
# Main.
# ------------------------------------------------------------------------------ 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def validate(data_loader, model, epoch):
    losses = AverageMeter()
    accuracy = AverageMeter()
    accuracy_list = [AverageMeter(), AverageMeter(), AverageMeter()]
    model.eval()

    with torch.no_grad():
        for idx, input_seq in tqdm(enumerate(data_loader), total=len(data_loader)):
            input_seq = input_seq.to(cuda)
            B = input_seq.size(0)
            [score_, mask_] = model(input_seq)
            del input_seq

            if idx == 0: target_, (_, B2, NS, NP, SQ) = process_output(mask_)

            # [B, P, SQ, B, N, SQ]
            score_flattened = score_.view(B*NP*SQ, B2*NS*SQ)
            target_flattened = target_.view(B*NP*SQ, B2*NS*SQ)
            target_flattened = target_flattened.argmax(dim=1)

            loss = criterion(score_flattened, target_flattened)
            top1, top3, top5 = calc_topk_accuracy(score_flattened, target_flattened, (1,3,5))

            losses.update(loss.item(), B)
            accuracy.update(top1.item(), B)

            accuracy_list[0].update(top1.item(),  B)
            accuracy_list[1].update(top3.item(), B)
            accuracy_list[2].update(top5.item(), B)

    print('[{0}/{1}] Loss {loss.local_avg:.4f}\t'
          'Acc: top1 {2:.4f}; top3 {3:.4f}; top5 {4:.4f} \t'.format(
           epoch, args.epochs, *[i.avg for i in accuracy_list], loss=losses))
    return losses.local_avg, accuracy.local_avg, [i.local_avg for i in accuracy_list] 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def test(data_loader, model):
    losses = AverageMeter()
    acc_top1 = AverageMeter()
    acc_top5 = AverageMeter()
    confusion_mat = ConfusionMeter(args.num_class)
    model.eval()
    with torch.no_grad():
        for idx, (input_seq, target) in tqdm(enumerate(data_loader), total=len(data_loader)):
            input_seq = input_seq.to(cuda)
            target = target.to(cuda)
            B = input_seq.size(0)
            input_seq = input_seq.squeeze(0) # squeeze the '1' batch dim
            output, _ = model(input_seq)
            del input_seq
            top1, top5 = calc_topk_accuracy(torch.mean(
                                            torch.mean(
                                                nn.functional.softmax(output,2),
                                                0),0, keepdim=True), 
                                            target, (1,5))
            acc_top1.update(top1.item(), B)
            acc_top5.update(top5.item(), B)
            del top1, top5

            output = torch.mean(torch.mean(output, 0), 0, keepdim=True)
            loss = criterion(output, target.squeeze(-1))

            losses.update(loss.item(), B)
            del loss


            _, pred = torch.max(output, 1)
            confusion_mat.update(pred, target.view(-1).byte())
      
    print('Loss {loss.avg:.4f}\t'
          'Acc top1: {top1.avg:.4f} Acc top5: {top5.avg:.4f} \t'.format(loss=losses, top1=acc_top1, top5=acc_top5))
    confusion_mat.plot_mat(args.test+'.svg')
    write_log(content='Loss {loss.avg:.4f}\t Acc top1: {top1.avg:.4f} Acc top5: {top5.avg:.4f} \t'.format(loss=losses, top1=acc_top1, top5=acc_top5, args=args),
              epoch=num_epoch,
              filename=os.path.join(os.path.dirname(args.test), 'test_log.md'))
    import ipdb; ipdb.set_trace()
    return losses.avg, [acc_top1.avg, acc_top5.avg] 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def test(test_data, model, device, logger):
    loading_time_meter = AverageMeter()
    batch_time_meter = AverageMeter()
    ce_loss_meter = AverageMeter()
    accuracy_meter = AverageMeter()
    entropy_meter = AverageMeter()
    n_entropy_meter = AverageMeter()

    model.eval()
    start = time.time()
    with torch.no_grad():
        for labels, tokens, mask in test_data:
            labels = labels.to(device=device, non_blocking=True)
            tokens = tokens.to(device=device, non_blocking=True)
            mask = mask.to(device=device, non_blocking=True)

            loading_time_meter.update(time.time() - start)

            pred_labels, ce_loss, rewards, actions, actions_log_prob, entropy, normalized_entropy = \
                model(tokens, mask, labels)
            entropy = entropy.mean()
            normalized_entropy = normalized_entropy.mean()

            accuracy = (labels == pred_labels).to(dtype=torch.float32).mean()
            n = mask.shape[0]
            accuracy_meter.update(accuracy.item(), n)
            ce_loss_meter.update(ce_loss.item(), n)
            entropy_meter.update(entropy.item(), n)
            n_entropy_meter.update(normalized_entropy.item(), n)
            batch_time_meter.update(time.time() - start)
            start = time.time()

    logger.info(f"Test: ce_loss: {ce_loss_meter.avg:.4f} accuracy: {accuracy_meter.avg:.4f} "
                f"entropy: {entropy_meter.avg:.4f} n_entropy: {n_entropy_meter.avg:.4f} "
                f"loading_time: {loading_time_meter.avg:.4f} batch_time: {batch_time_meter.avg:.4f}")
    logger.info("done")

    return accuracy_meter.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def validate(valid_data, model, epoch, device, logger, summary_writer):
    loading_time_meter = AverageMeter()
    batch_time_meter = AverageMeter()
    ce_loss_meter = AverageMeter()
    accuracy_meter = AverageMeter()
    entropy_meter = AverageMeter()
    n_entropy_meter = AverageMeter()

    model.eval()
    start = time.time()
    with torch.no_grad():
        for labels, tokens, mask in valid_data:
            labels = labels.to(device=device, non_blocking=True)
            tokens = tokens.to(device=device, non_blocking=True)
            mask = mask.to(device=device, non_blocking=True)
            loading_time_meter.update(time.time() - start)

            pred_labels, ce_loss, rewards, actions, actions_log_prob, entropy, normalized_entropy = \
                model(tokens, mask, labels)
            entropy = entropy.mean()
            normalized_entropy = normalized_entropy.mean()

            accuracy = (labels == pred_labels).to(dtype=torch.float32).mean()
            n = mask.shape[0]
            accuracy_meter.update(accuracy.item(), n)
            ce_loss_meter.update(ce_loss.item(), n)
            entropy_meter.update(entropy.item(), n)
            n_entropy_meter.update(normalized_entropy.item(), n)
            batch_time_meter.update(time.time() - start)
            start = time.time()

    logger.info(f"Valid: epoch: {epoch} ce_loss: {ce_loss_meter.avg:.4f} accuracy: {accuracy_meter.avg:.4f} "
                f"entropy: {entropy_meter.avg:.4f} n_entropy: {n_entropy_meter.avg:.4f} "
                f"loading_time: {loading_time_meter.avg:.4f} batch_time: {batch_time_meter.avg:.4f}")

    summary_writer["valid"].add_scalar(tag="ce", scalar_value=ce_loss_meter.avg, global_step=global_step)
    summary_writer["valid"].add_scalar(tag="accuracy", scalar_value=accuracy_meter.avg, global_step=global_step)
    summary_writer["valid"].add_scalar(tag="n_entropy", scalar_value=n_entropy_meter.avg, global_step=global_step)

    model.train()
    return accuracy_meter.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def test(test_data, model, device, logger):
    loading_time_meter = AverageMeter()
    batch_time_meter = AverageMeter()
    ce_loss_meter = AverageMeter()
    accuracy_meter = AverageMeter()

    model.eval()
    start = time.time()
    with torch.no_grad():
        for labels, tokens, trees, mask in test_data:
            labels = labels.to(device=device, non_blocking=True)
            tokens = tokens.to(device=device, non_blocking=True)
            trees = [e.to(device=device, non_blocking=True) for e in trees]
            mask = mask.to(device=device, non_blocking=True)

            loading_time_meter.update(time.time() - start)

            ce_loss, pred_labels = model(tokens, trees, mask, labels)

            accuracy = (labels == pred_labels).to(dtype=torch.float32).mean()
            n = mask.shape[0]
            accuracy_meter.update(accuracy.item(), n)
            ce_loss_meter.update(ce_loss.item(), n)
            model.reset_memory_managers()
            batch_time_meter.update(time.time() - start)
            start = time.time()

    logger.info(f"Test: ce_loss: {ce_loss_meter.avg:.4f} accuracy: {accuracy_meter.avg:.4f} "
                f"loading_time: {loading_time_meter.avg:.4f} batch_time: {batch_time_meter.avg:.4f}")
    logger.info("done")

    return accuracy_meter.avg 

# 需要导入模块: import utils [as 别名]
# 或者: from utils import AverageMeter [as 别名]
def test(test_data, model, device, logger):
    loading_time_meter = AverageMeter()
    batch_time_meter = AverageMeter()
    ce_loss_meter = AverageMeter()
    accuracy_meter = AverageMeter()
    entropy_meter = AverageMeter()
    n_entropy_meter = AverageMeter()

    model.eval()
    start = time.time()
    with torch.no_grad():
        for batch in test_data:
            tokens, length = batch.text
            labels = batch.label
            mask = length_to_mask(length)
            loading_time_meter.update(time.time() - start)

            pred_labels, ce_loss, rewards, actions, actions_log_prob, entropy, normalized_entropy = \
                model(tokens, mask, labels)
            entropy = entropy.mean()
            normalized_entropy = normalized_entropy.mean()

            accuracy = (labels == pred_labels).to(dtype=torch.float32).mean()
            n = mask.shape[0]
            accuracy_meter.update(accuracy.item(), n)
            ce_loss_meter.update(ce_loss.item(), n)
            entropy_meter.update(entropy.item(), n)
            n_entropy_meter.update(normalized_entropy.item(), n)
            batch_time_meter.update(time.time() - start)
            start = time.time()

    logger.info(f"Test: ce_loss: {ce_loss_meter.avg:.4f} accuracy: {accuracy_meter.avg:.4f} "
                f"entropy: {entropy_meter.avg:.4f} n_entropy: {n_entropy_meter.avg:.4f} "
                f"loading_time: {loading_time_meter.avg:.4f} batch_time: {batch_time_meter.avg:.4f}")
    logger.info("done")

    return accuracy_meter.avg