keras/tensorflow中多类加权损失的语义图像分割

Question

给定批处理RGB图像作为输入，shape=(batch_size，width，height，3)

和一个表示为one-hot，shape=(batch_size，width，height，n_classes)的多类目标

以及在最后一层具有softmax激活的模型(Unet，DeepLab)。

我在kera/tensorflow中寻找加权分类交叉熵损失函数。

fit_generator中的class_weight参数似乎不起作用，我在这里或在https://github.com/keras-team/keras/issues/2115中都找不到答案。

def weighted_categorical_crossentropy(weights):
    # weights = [0.9,0.05,0.04,0.01]
    def wcce(y_true, y_pred):
        # y_true, y_pred shape is (batch_size, width, height, n_classes)
        loos = ?...
        return loss

    return wcce

Answer 1

我将回答我的问题：

def weighted_categorical_crossentropy(weights):
    # weights = [0.9,0.05,0.04,0.01]
    def wcce(y_true, y_pred):
        Kweights = K.constant(weights)
        if not K.is_tensor(y_pred): y_pred = K.constant(y_pred)
        y_true = K.cast(y_true, y_pred.dtype)
        return K.categorical_crossentropy(y_true, y_pred) * K.sum(y_true * Kweights, axis=-1)
    return wcce

用法：

loss = weighted_categorical_crossentropy(weights)
optimizer = keras.optimizers.Adam(lr=0.01)
model.compile(optimizer=optimizer, loss=loss)

Answer 2

我使用的是广义骰子损失。在我的例子中，它比加权分类Crossentropy更好。我的实现是用PyTorch实现的，但是翻译起来应该相当容易。

class GeneralizedDiceLoss(nn.Module):
    def __init__(self):
        super(GeneralizedDiceLoss, self).__init__()

    def forward(self, inp, targ):
        inp = inp.contiguous().permute(0, 2, 3, 1)
        targ = targ.contiguous().permute(0, 2, 3, 1)

        w = torch.zeros((targ.shape[-1],))
        w = 1. / (torch.sum(targ, (0, 1, 2))**2 + 1e-9)

        numerator = targ * inp
        numerator = w * torch.sum(numerator, (0, 1, 2))
        numerator = torch.sum(numerator)

        denominator = targ + inp
        denominator = w * torch.sum(denominator, (0, 1, 2))
        denominator = torch.sum(denominator)

        dice = 2. * (numerator + 1e-9) / (denominator + 1e-9)

        return 1. - dice

Answer 3

这个问题可能类似于：Unbalanced data and weighted cross entropy，它有一个公认的答案。