Question

我正在使用pytorch中的u-net架构解决多类细分问题。根据{{3}}文件中的规定，我正在尝试实现自定义权重图以抵消类的不平衡。

下面是我要应用的操作- U-NET

我还简化了batch_size=1，以便可以在将其传递给precompute_to_masks函数时删除该尺寸。我尝试了以下方法-

def precompute_for_image(masks):
    masks = masks.cpu()
    cls = masks.unique()
    res = torch.stack([torch.where(masks==cls_val, torch.tensor(1), torch.tensor(0)) for cls_val in cls])
    return res

def train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path):

        ###################
        # train the model #
        ###################
        model.train()
        for batch_idx, (data, target) in enumerate(final_train_loader):
            # move to GPU
            if use_cuda:
                data, target = data.cuda(), target.cuda()
            optimizer.zero_grad()
            output = model(data)
            temp_target = precompute_for_image(target)
            w = weight_map(temp_target)
            loss = criterion(output,target)
            loss = w*loss
            loss.backward()
            optimizer.step()

            train_loss = train_loss + ((1 / (batch_idx + 1)) * (loss.data - train_loss))

    return model

其中weight_map是用于计算我从获得的权重蒙版的函数我面临的问题是应用以下方法时遇到memory error的问题。
我正在使用61GB RAM和Tesla V100 GPU。我真的认为我以错误的方式应用它。怎么做？
我从训练循环中忽略了不必要的细节。以下是我的weight_map函数：

from skimage.segmentation import find_boundaries

w0 = 10
sigma = 5

def make_weight_map(masks):
    """
    Generate the weight maps as specified in the UNet paper
    for a set of binary masks.

    Parameters
    ----------
    masks: array-like
        A 3D array of shape (n_masks, image_height, image_width),
        where each slice of the matrix along the 0th axis represents one binary mask.

    Returns
    -------
    array-like
        A 2D array of shape (image_height, image_width)

    """
    nrows, ncols = masks.shape[1:]
    masks = (masks > 0).astype(int)
    distMap = np.zeros((nrows * ncols, masks.shape[0]))
    X1, Y1 = np.meshgrid(np.arange(nrows), np.arange(ncols))
    X1, Y1 = np.c_[X1.ravel(), Y1.ravel()].T
    for i, mask in enumerate(masks):
        # find the boundary of each mask,
        # compute the distance of each pixel from this boundary
        bounds = find_boundaries(mask, mode='inner')
        X2, Y2 = np.nonzero(bounds)
        xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
        ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
        distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)
    ix = np.arange(distMap.shape[0])
    if distMap.shape[1] == 1:
        d1 = distMap.ravel()
        border_loss_map = w0 * np.exp((-1 * (d1) ** 2) / (2 * (sigma ** 2)))
    else:
        if distMap.shape[1] == 2:
            d1_ix, d2_ix = np.argpartition(distMap, 1, axis=1)[:, :2].T
        else:
            d1_ix, d2_ix = np.argpartition(distMap, 2, axis=1)[:, :2].T
        d1 = distMap[ix, d1_ix]
        d2 = distMap[ix, d2_ix]
        border_loss_map = w0 * np.exp((-1 * (d1 + d2) ** 2) / (2 * (sigma ** 2)))
    xBLoss = np.zeros((nrows, ncols))
    xBLoss[X1, Y1] = border_loss_map
    # class weight map
    loss = np.zeros((nrows, ncols))
    w_1 = 1 - masks.sum() / loss.size
    w_0 = 1 - w_1
    loss[masks.sum(0) == 1] = w_1
    loss[masks.sum(0) == 0] = w_0
    ZZ = xBLoss + loss
    return ZZ

错误的跟踪-

MemoryError                               Traceback (most recent call last)
<ipython-input-30-f0a595b8de7e> in <module>
      1 # train the model
      2 model_scratch = train(20, final_train_loader, unet, optimizer, 
----> 3                       criterion, train_on_gpu, 'model_scratch.pt')

<ipython-input-29-b481b4f3120e> in train(n_epochs, loaders, model, optimizer, criterion, use_cuda, save_path)
     24             loss = criterion(output,target)
     25             target.requires_grad = False
---> 26             w = make_weight_map(target)
     27             loss = W*loss
     28             loss.backward()

<ipython-input-5-e75a6281476f> in make_weight_map(masks)
     33         X2, Y2 = np.nonzero(bounds)
     34         xSum = (X2.reshape(-1, 1) - X1.reshape(1, -1)) ** 2
---> 35         ySum = (Y2.reshape(-1, 1) - Y1.reshape(1, -1)) ** 2
     36         distMap[:, i] = np.sqrt(xSum + ySum).min(axis=0)
     37     ix = np.arange(distMap.shape[0])

MemoryError:

Answer 1

您的final_train_loader为您提供了输入图像data和预期的按像素标注target。我假设（按照pytorch的约定）data的形状为B-3-H-W，形状为dtype=torch.float。
更重要的是，target的形状为B-H-W，形状为dtype=torch.long。

另一方面，make_weight_map期望其输入为numpy数组的C-H-W（其中C =类数，而不是批处理大小）。

尝试按预期提供make_weight_map输入掩码，看看是否遇到类似的错误。
我还建议您可视化生成的权重图-确保您的功能符合您的期望。

Pytorch：使用自定义权重图应用交叉熵损失

1 个答案: