.. _cn_api_fluid_layers_sigmoid_focal_loss:

sigmoid_focal_loss
-------------------------------

.. py:function:: paddle.fluid.layers.sigmoid_focal_loss(x, label, fg_num, gamma=2.0, alpha=0.25)




`Focal Loss <https://arxiv.org/abs/1708.02002>`_ 被提出用于解决计算机视觉任务中前景-背景不平衡的问题。该OP先计算输入x中每个元素的sigmoid值，然后计算sigmoid值与类别目标值label之间的Focal Loss。

Focal Loss的计算过程如下：

.. math::

  \mathop{loss_{i,\,j}}\limits_{i\in\mathbb{[0,\,N-1]},\,j\in\mathbb{[0,\,C-1]}}=\left\{
  \begin{array}{rcl}
  - \frac{1}{fg\_num} * \alpha * {(1 - \sigma(x_{i,\,j}))}^{\gamma} * \log(\sigma(x_{i,\,j})) & & {(j +1) = label_{i,\,0}}\\
  - \frac{1}{fg\_num} * (1 - \alpha) * {\sigma(x_{i,\,j})}^{ \gamma} * \log(1 - \sigma(x_{i,\,j})) & & {(j +1)!= label_{i,\,0}}
  \end{array} \right.

其中，已知：

.. math::

  \sigma(x_{i,\,j}) = \frac{1}{1 + \exp(-x_{i,\,j})}


参数：
    - **x**  (Variable) – 维度为 :math:`[N, C]` 的2-D Tensor，表示全部样本的分类预测值。其中，第一维N是批量内参与训练的样本数量，例如在目标检测中，样本为框级别，N为批量内所有图像的正负样本的数量总和；在图像分类中，样本为图像级别，N为批量内的图像数量总和。第二维:math:`C` 是类别数量（ **不包括背景类** ）。数据类型为float32或float64。
    - **label**  (Variable) – 维度为 :math:`[N, 1]` 的2-D Tensor，表示全部样本的分类目标值。其中，第一维N是批量内参与训练的样本数量，第二维1表示每个样本只有一个类别目标值。正样本的目标类别值的取值范围是 :math:`[1, C]` , 负样本的目标类别值是0。数据类型为int32。
    - **fg_num**  (Variable) – 维度为 :math:`[1]` 的1-D Tensor，表示批量内正样本的数量，需在进入此OP前获取正样本的数量。数据类型为int32。
    - **gamma**  (int|float) –  用于平衡易分样本和难分样本的超参数， 默认值设置为2.0。
    - **alpha**  (int|float) – 用于平衡正样本和负样本的超参数，默认值设置为0.25。


返回：  输入x中每个元素的Focal loss，即维度为 :math:`[N, C]` 的2-D Tensor。

返回类型： 变量（Variable），数据类型为float32或float64。

**代码示例**

..  code-block:: python


    import numpy as np
    import paddle.fluid as fluid
    
    num_classes = 10  # exclude background
    image_width = 16
    image_height = 16
    batch_size = 32
    max_iter = 20
    
    
    def gen_train_data():
        x_data = np.random.uniform(0, 255, (batch_size, 3, image_height,
                                            image_width)).astype('float64')
        label_data = np.random.randint(0, num_classes,
                                       (batch_size, 1)).astype('int32')
        return {"x": x_data, "label": label_data}
    
    
    def get_focal_loss(pred, label, fg_num, num_classes):
        pred = fluid.layers.reshape(pred, [-1, num_classes])
        label = fluid.layers.reshape(label, [-1, 1])
        label.stop_gradient = True
        loss = fluid.layers.sigmoid_focal_loss(
            pred, label, fg_num, gamma=2.0, alpha=0.25)
        loss = fluid.layers.reduce_sum(loss)
        return loss
    
    
    def build_model(mode='train'):
        x = fluid.data(name="x", shape=[-1, 3, -1, -1], dtype='float64')
        output = fluid.layers.pool2d(input=x, pool_type='avg', global_pooling=True)
        output = fluid.layers.fc(
            input=output,
            size=num_classes,
            # Notice: size is set to be the number of target classes (excluding backgorund)
            # because sigmoid activation will be done in the sigmoid_focal_loss op.
            act=None)
        if mode == 'train':
            label = fluid.data(name="label", shape=[-1, 1], dtype='int32')
            # Obtain the fg_num needed by the sigmoid_focal_loss op:
            # 0 in label represents background, >=1 in label represents foreground,
            # find the elements in label which are greater or equal than 1, then
            # computed the numbers of these elements.
            data = fluid.layers.fill_constant(shape=[1], value=1, dtype='int32')
            fg_label = fluid.layers.greater_equal(label, data)
            fg_label = fluid.layers.cast(fg_label, dtype='int32')
            fg_num = fluid.layers.reduce_sum(fg_label)
            fg_num.stop_gradient = True
            avg_loss = get_focal_loss(output, label, fg_num, num_classes)
            return avg_loss
        else:
            # During evaluating or testing phase,
            # output of the final fc layer should be connected to a sigmoid layer.
            pred = fluid.layers.sigmoid(output)
            return pred
    
    
    loss = build_model('train')
    moment_optimizer = fluid.optimizer.MomentumOptimizer(
        learning_rate=0.001, momentum=0.9)
    moment_optimizer.minimize(loss)
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(fluid.default_startup_program())
    for i in range(max_iter):
        outs = exe.run(feed=gen_train_data(), fetch_list=[loss.name])
        print(outs)
