我正在尝试分离U-Net论文（here）中显示的关闭对象。为此，产生一个加权图，该加权图可用于逐像素损失。以下代码描述了我在this博客文章中使用的网络。
x_train_val = # list of images (imgs, 256, 256, 3)
y_train_val = # list of masks (imgs, 256, 256, 1)
y_weights = # list of weight maps (imgs, 256, 256, 1) according to the blog post
# visual inspection confirms the correct calculation of these maps
# Blog posts' loss function
def my_loss(target, output):
return - tf.reduce_sum(target * output,
len(output.get_shape()) - 1)
# Standard Unet model from blog post
_epsilon = tf.convert_to_tensor(K.epsilon(), np.float32)
def make_weighted_loss_unet(input_shape, n_classes):
ip = L.Input(shape=input_shape)
weight_ip = L.Input(shape=input_shape[:2] + (n_classes,))
conv1 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(ip)
conv1 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv1)
conv1 = L.Dropout(0.1)(conv1)
mpool1 = L.MaxPool2D()(conv1)
conv2 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool1)
conv2 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv2)
conv2 = L.Dropout(0.2)(conv2)
mpool2 = L.MaxPool2D()(conv2)
conv3 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool2)
conv3 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv3)
conv3 = L.Dropout(0.3)(conv3)
mpool3 = L.MaxPool2D()(conv3)
conv4 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool3)
conv4 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv4)
conv4 = L.Dropout(0.4)(conv4)
mpool4 = L.MaxPool2D()(conv4)
conv5 = L.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(mpool4)
conv5 = L.Conv2D(1024, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv5)
conv5 = L.Dropout(0.5)(conv5)
up6 = L.Conv2DTranspose(512, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv5)
conv6 = L.Concatenate()([up6, conv4])
conv6 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
conv6 = L.Conv2D(512, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv6)
conv6 = L.Dropout(0.4)(conv6)
up7 = L.Conv2DTranspose(256, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv6)
conv7 = L.Concatenate()([up7, conv3])
conv7 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv7 = L.Conv2D(256, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv7)
conv7 = L.Dropout(0.3)(conv7)
up8 = L.Conv2DTranspose(128, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv7)
conv8 = L.Concatenate()([up8, conv2])
conv8 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = L.Conv2D(128, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv8)
conv8 = L.Dropout(0.2)(conv8)
up9 = L.Conv2DTranspose(64, 2, strides=2, kernel_initializer='he_normal', padding='same')(conv8)
conv9 = L.Concatenate()([up9, conv1])
conv9 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(conv9)
conv9 = L.Dropout(0.1)(conv9)
c10 = L.Conv2D(n_classes, 1, activation='softmax', kernel_initializer='he_normal')(conv9)
# Mimic crossentropy loss
c11 = L.Lambda(lambda x: x / tf.reduce_sum(x, len(x.get_shape()) - 1, True))(c10)
c11 = L.Lambda(lambda x: tf.clip_by_value(x, _epsilon, 1. - _epsilon))(c11)
c11 = L.Lambda(lambda x: K.log(x))(c11)
weighted_sm = L.multiply([c11, weight_ip])
model = Model(inputs=[ip, weight_ip], outputs=[weighted_sm])
return model
然后我编译并拟合模型，如下所示：
model = make_weighted_loss_unet((256, 256, 3), 1) # shape of input, number of classes
model.compile(optimizer='adam', loss=my_loss, metrics=['acc'])
model.fit([x_train_val, y_weights], y_train_val, validation_split=0.1, epochs=1)
然后可以像往常一样训练模型。但是，损失似乎并没有太大改善。此外，当我尝试预测新图像时，我显然没有权重图（因为它们是在标记的蒙版上计算的）。我尝试使用形状像权重图的空/零数组，但是只能产生空白/零预测。我还尝试了不同的指标和更多的标准损失，但没有成功。
有人有没有遇到相同的问题，或者在实施此加权损失时有其他选择？提前致谢。 BBQuercus
答案 0 :(得分：3)
在您的代码中，损失分散在my_loss和make_weighted_loss_unet函数之间。您可以添加目标作为输入，并使用model.add_loss更好地构建代码：
def make_weighted_loss_unet(input_shape, n_classes):
ip = L.Input(shape=input_shape)
weight_ip = L.Input(shape=input_shape[:2] + (n_classes,))
targets = L.input(shape=input_shape[:2] + (n_classes,))
# .... rest of your model definition code ...
c10 = L.Conv2D(n_classes, 1, activation='softmax', kernel_initializer='he_normal')(conv9)
model.add_loss(pixel_weighted_cross_entropy(weights_ip, targets, c10))
# .... return Model .... NO NEED to specify loss in model.compile
def pixel_weighted_cross_entropy(weights, targets, predictions)
loss_val = keras.losses.categorical_crossentropy(targets, predictions)
weighted_loss_val = weights * loss_val
return K.mean(weighted_loss_val)
如果您不将代码重构为上述方法，则下一节将说明如何仍然运行推理而不会出现问题
Model对象进行推断您可以创建一个Model用于训练，另一个用于推理。两者基本相同，只是推论Model不占用weights_ip并给出早期输出c10。
下面是一个示例代码，其中添加了一个参数is_training=True来决定要返回哪个Model：
def make_weighted_loss_unet(input_shape, n_classes, is_training=True):
ip = L.Input(shape=input_shape)
conv1 = L.Conv2D(64, 3, activation='relu', padding='same', kernel_initializer='he_normal')(ip)
# .... rest of your model definition code ...
c10 = L.Conv2D(n_classes, 1, activation='softmax', kernel_initializer='he_normal')(conv9)
if is_training:
# Mimic crossentropy loss
c11 = L.Lambda(lambda x: x / tf.reduce_sum(x, len(x.get_shape()) - 1, True))(c10)
c11 = L.Lambda(lambda x: tf.clip_by_value(x, _epsilon, 1. - _epsilon))(c11)
c11 = L.Lambda(lambda x: K.log(x))(c11)
weight_ip = L.Input(shape=input_shape[:2] + (n_classes,))
weighted_sm = L.multiply([c11, weight_ip])
return Model(inputs=[ip, weight_ip], outputs=[weighted_sm])
else:
return Model(inputs=[ip], outputs=[c10])
return model
K.function 如果您不想弄乱模型定义方法（make_weighted_loss_unet）并想在外部获得相同的结果，则可以使用提取与推理相关的子图的函数。
在您的推理功能中：
from keras import backend as K
model = make_weighted_loss_unet(input_shape, n_classes)
inference_function = K.function([model.get_layer("input_layer").input],
[model.get_layer("output_softmax_layer").output])
predicted_heatmap = inference_function(new_image)
请注意，您必须将name=赋予ip层和c10层才能通过model.get_layer(name)检索它们：
ip = L.Input(shape=input_shape, name="input_layer")
和
c10 = L.Conv2D(n_classes, 1, activation='softmax', kernel_initializer='he_normal', name="output_softmax_layer")(conv9)