Expose softmax_with_cross_entropy and smooth_l1 into Python API. (#8375)

* Add softmax_with_cross_entropy and smooth_l1 in Python API.

* Fix doc format.

paddle/fluid/operators/smooth_l1_loss_op.cc

@@ -44,7 +44,6 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
   }
 };
 
-template <typename AttrType>
 class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   SmoothL1LossOpMaker(OpProto* proto, OpAttrChecker* op_checker)
@@ -73,10 +72,10 @@ class SmoothL1LossOpMaker : public framework::OpProtoAndCheckerMaker {
     AddOutput("Out",
               "(Tensor, default Tensor<float>) A tensor with rank be 2. "
               "The output smooth l1 loss with shape [batch_size, 1].");
-    AddAttr<AttrType>("sigma",
-                      "Hyper parameter of smooth l1 loss op."
-                      "A float scalar with default value 3.0.")
-        .SetDefault(3.0);
+    AddAttr<float>("sigma",
+                   "Hyper parameter of smooth l1 loss op."
+                   "A float scalar with default value 3.0.")
+        .SetDefault(1.0);
     AddComment(R"DOC(
 Smooth L1 Loss Operator.
 
@@ -133,9 +132,8 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
 }  // namespace paddle
 
 namespace ops = paddle::operators;
-REGISTER_OP(smooth_l1_loss, ops::SmoothL1LossOp,
-            ops::SmoothL1LossOpMaker<float>, smooth_l1_loss_grad,
-            ops::SmoothL1LossGradOp);
+REGISTER_OP(smooth_l1_loss, ops::SmoothL1LossOp, ops::SmoothL1LossOpMaker,
+            smooth_l1_loss_grad, ops::SmoothL1LossGradOp);
 REGISTER_OP_CPU_KERNEL(
     smooth_l1_loss,
     ops::SmoothL1LossKernel<paddle::platform::CPUDeviceContext, float>);

python/paddle/v2/fluid/layers/nn.py

@@ -66,6 +66,8 @@ __all__ = [
     'row_conv',
     'multiplex',
     'layer_norm',
+    'softmax_with_cross_entropy',
+    'smooth_l1',
 ]
 
 
@@ -3091,3 +3093,122 @@ def multiplex(inputs, index):
                 'Ids': index},
         outputs={'Out': [out]})
     return out
+
+
+def softmax_with_cross_entropy(logits, label, soft_label=False):
+    """
+    **Softmax With Cross Entropy Operator.**
+    
+    Cross entropy loss with softmax is used as the output layer extensively. This
+    operator computes the softmax normalized values for each row of the input
+    tensor, after which cross-entropy loss is computed. This provides a more
+    numerically stable gradient.
+    
+    Because this operator performs a softmax on logits internally, it expects
+    unscaled logits. This operator should not be used with the output of
+    softmax operator since that would produce incorrect results.
+    
+    When the attribute soft_label is set false, this operators expects mutually
+    exclusive hard labels, each sample in a batch is in exactly one class with a
+    probability of 1.0. Each sample in the batch will have a single label.
+    
+    The equation is as follows:
+    
+    1) Hard label (one-hot label, so every sample has exactly one class)
+    
+    .. math::
+
+        loss_j =  -\\text{logit}_{label_j} +
+        \\log\\left(\\sum_{i=0}^{K}\\exp(\\text{logit}_i)\\right), j = 1,..., K
+    
+    2) Soft label (each sample can have a distribution over all classes)
+
+    .. math::
+    
+        loss_j =  -\\sum_{i=0}^{K}\\text{label}_i
+        \\left(\\text{logit}_i - \\log\\left(\\sum_{i=0}^{K}
+        \\exp(\\text{logit}_i)\\right)\\right), j = 1,...,K
+
+    Args:
+        logits (Variable): The unscaled log probabilities, which is a 2-D tensor
+            with shape [N x K]. N is the batch_size, and K is the class number.
+        label (Variable): The ground truth which is a 2-D tensor. If soft_label
+            is set to false, Label is a Tensor<int64> with shape [N x 1]. If
+            soft_label is set to true, Label is a Tensor<float/double> with
+        soft_label (bool): A flag to indicate whether to interpretate the given
+            labels as soft labels. By default, `soft_label` is set to False.
+    Returns:
+        Variable: The cross entropy loss is a 2-D tensor with shape [N x 1].
+
+    Examples:
+        .. code-block:: python
+
+            data = fluid.layers.data(name='data', shape=[128], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[1], dtype='int64')
+            fc = fluid.layers.fc(input=data, size=100)
+            out = fluid.layers.softmax_with_cross_entropy(logits=fc, label=label)
+    """
+    helper = LayerHelper('softmax_with_cross_entropy', **locals())
+    softmax = helper.create_tmp_variable(dtype=logits.dtype)
+    loss = helper.create_tmp_variable(dtype=logits.dtype)
+    helper.append_op(
+        type='softmax_with_cross_entropy',
+        inputs={'Logits': logits,
+                'Label': label},
+        outputs={'Softmax': softmax,
+                 'Loss': loss},
+        attrs={'soft_label': soft_label})
+    return loss
+
+
+def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
+    """
+    **Smooth L1 Loss Operator. **
+
+    This operator computes the smooth l1 loss for X and Y.
+    The operator takes the first dimension of X and Y as batch size.
+    For each instance, it computes the smooth l1 loss element by element first
+    and then sums all the losses. So the shape of Out is [batch_size, 1].
+    
+    Args:
+        x (Variable): A tensor with rank at least 2. The input value of smooth
+            l1 loss op with shape [batch_size, dim1, ..., dimN].
+        y (Variable): A tensor with rank at least 2. The target value of smooth
+            l1 loss op with same shape as x.
+        inside_weight (Variable|None):  A tensor with rank at least 2. This
+            input is optional and should have same shape with x. If provided,
+            the result of (x - y) will be multiplied by this tensor element by
+            element.
+        outside_weight (Variable|None): A tensor with rank at least 2. This
+            input is optional and should have same shape with x. If provided,
+            the out smooth l1 loss will be multiplied by this tensor element
+            by element.
+        sigma (float|None): Hyper parameter of smooth l1 loss op. A float scalar
+            with default value 1.0.
+    Returns:
+        Variable: A tensor with rank be 2. The output smooth l1 loss with
+            shape [batch_size, 1].
+
+    Examples:
+        .. code-block:: python
+
+            data = fluid.layers.data(name='data', shape=[128], dtype='float32')
+            label = fluid.layers.data(name='label', shape=[100], dtype='int64')
+            fc = fluid.layers.fc(input=data, size=100)
+            out = fluid.layers.smooth_l1(logits=fc, label=label)
+    """
+    helper = LayerHelper('smooth_l1_loss', **locals())
+    diff = helper.create_tmp_variable(dtype=x.dtype)
+    loss = helper.create_tmp_variable(dtype=x.dtype)
+    helper.append_op(
+        type='smooth_l1_loss',
+        inputs={
+            'X': x,
+            'Y': y,
+            'InsideWeight': inside_weight,
+            'OutsideWeight': outside_weight
+        },
+        outputs={'Diff': diff,
+                 'Out': loss},
+        attrs={'sigma': sigma})
+    return loss

python/paddle/v2/fluid/tests/test_layers.py

@@ -309,6 +309,24 @@ class TestBook(unittest.TestCase):
             self.assertIsNotNone(out)
         print(str(program))
 
+    def test_softmax_with_cross_entropy(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[16], dtype='float32')
+            y = layers.data(name='label', shape=[1], dtype='int64')
+            loss = layers.softmax_with_cross_entropy(x, y)
+            self.assertIsNotNone(loss)
+        print(str(program))
+
+    def test_smooth_l1(self):
+        program = Program()
+        with program_guard(program):
+            x = layers.data(name='x', shape=[4], dtype='float32')
+            y = layers.data(name='label', shape=[4], dtype='float32')
+            loss = layers.smooth_l1(x, y)
+            self.assertIsNotNone(loss)
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()