Expose sigmoid_cross_entropy_with_logits (#6147)

Also, change the `labels` to `label` for api consistency

paddle/operators/sigmoid_cross_entropy_with_logits_op.cc

@@ -25,20 +25,19 @@ class SigmoidCrossEntropyWithLogitsOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input(Labels) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
     PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should be not null.");
 
     auto x_dims = ctx->GetInputDim("X");
-    auto labels_dims = ctx->GetInputDim("Labels");
+    auto labels_dims = ctx->GetInputDim("Label");
     PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
-                      "Input(Labels)'s rank should be 2.");
+                      "Input(Label)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
-                      "The 1st dimension of Input(X) and Input(Labels) should "
+                      "The 1st dimension of Input(X) and Input(Label) should "
                       "be equal.");
     PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
-                      "The 2nd dimension of Input(X) and Input(Labels) should "
+                      "The 2nd dimension of Input(X) and Input(Label) should "
                       "be equal.");
 
     ctx->SetOutputDim("Out", x_dims);
@@ -53,26 +52,25 @@ class SigmoidCrossEntropyWithLogitsGradOp
 
   void InferShape(framework::InferShapeContext* ctx) const override {
     PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input(Labels) should be not null.");
+    PADDLE_ENFORCE(ctx->HasInput("Label"), "Input(Label) should be not null.");
     PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
                    "Input(Out@GRAD) shoudl be not null.");
     PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
                    "Output(X@GRAD) should be not null.");
 
     auto x_dims = ctx->GetInputDim("X");
-    auto labels_dims = ctx->GetInputDim("Labels");
+    auto labels_dims = ctx->GetInputDim("Label");
     auto dout_dims = ctx->GetInputDim(framework::GradVarName("Out"));
     PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(labels_dims.size(), 2,
-                      "Input(Labels)'s rank should be 2.");
+                      "Input(Label)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(dout_dims.size(), 2,
                       "Input(Out@Grad)'s rank should be 2.");
     PADDLE_ENFORCE_EQ(x_dims[0], labels_dims[0],
-                      "The 1st dimension of Input(X) and Input(Labels) should "
+                      "The 1st dimension of Input(X) and Input(Label) should "
                       "be equal.");
     PADDLE_ENFORCE_EQ(x_dims[1], labels_dims[1],
-                      "The 2nd dimension of Input(X) and Input(Labels) should "
+                      "The 2nd dimension of Input(X) and Input(Label) should "
                       "be equal.");
     PADDLE_ENFORCE_EQ(x_dims[0], dout_dims[0],
                       "The 1st dimension of Input(X) and Input(Out@Grad) "
@@ -97,7 +95,7 @@ class SigmoidCrossEntropyWithLogitsOpMaker
              "This input is a tensor of logits computed by the previous "
              " operator. Logits are unscaled log probabilities given as "
              "log(p/(1-p)).");
-    AddInput("Labels",
+    AddInput("Label",
              "(Tensor, default Tensor<float>), a 2-D tensor of the same type "
              "and shape as X. This input is a tensor of probabalistic labels "
              "for each logit");

paddle/operators/sigmoid_cross_entropy_with_logits_op.h

@@ -25,8 +25,7 @@ class SigmoidCrossEntropyWithLogitsKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &context) const override {
     const framework::Tensor *X = context.Input<framework::Tensor>("X");
-    const framework::Tensor *Labels =
-        context.Input<framework::Tensor>("Labels");
+    const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
     framework::Tensor *Out = context.Output<framework::Tensor>("Out");
     Out->mutable_data<T>(context.GetPlace());
 
@@ -52,8 +51,7 @@ class SigmoidCrossEntropyWithLogitsGradKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &context) const override {
     const framework::Tensor *X = context.Input<framework::Tensor>("X");
-    const framework::Tensor *Labels =
-        context.Input<framework::Tensor>("Labels");
+    const framework::Tensor *Labels = context.Input<framework::Tensor>("Label");
     const framework::Tensor *dOut =
         context.Input<framework::Tensor>(framework::GradVarName("Out"));
     framework::Tensor *dX =

python/paddle/v2/fluid/layers.py

@@ -403,6 +403,7 @@ _create_op_func_('sigmoid')
 _create_op_func_('scale')
 _create_op_func_('reshape')
 _create_op_func_('transpose')
+_create_op_func_('sigmoid_cross_entropy_with_logits')
 
 
 def cast(x, dtype, main_program=None):

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

@@ -137,6 +137,16 @@ class TestBook(unittest.TestCase):
 
         print(str(program))
 
+    def test_sigmoid_cross_entropy(self):
+        program = Program()
+        with program_guard(program):
+            dat = layers.data(name='data', shape=[10], dtype='float32')
+            lbl = layers.data(name='label', shape=[10], dtype='float32')
+            self.assertIsNotNone(
+                layers.sigmoid_cross_entropy_with_logits(
+                    x=dat, label=lbl))
+        print(str(program))
+
 
 if __name__ == '__main__':
     unittest.main()

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

@@ -2,11 +2,12 @@ import numpy as np
 from op_test import OpTest
 from scipy.special import logit
 from scipy.special import expit
+import unittest
 
 
 class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
-    '''Test sigmoid_cross_entropy_with_logit_op with binary labels
-    '''
+    """Test sigmoid_cross_entropy_with_logit_op with binary label
+    """
 
     def setUp(self):
         self.op_type = "sigmoid_cross_entropy_with_logits"
@@ -16,16 +17,16 @@ class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
             'X': logit(
                 np.random.uniform(0, 1, (batch_size, num_classes))
                 .astype("float32")),
-            'Labels': np.random.randint(0, 2, (batch_size, num_classes))
+            'Label': np.random.randint(0, 2, (batch_size, num_classes))
             .astype("float32")
         }
 
         # Fw Pass is implemented as elementwise sigmoid followed by
         # elementwise logistic loss
-        # Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
         sigmoid_X = expit(self.inputs['X'])
-        term1 = self.inputs['Labels'] * np.log(sigmoid_X)
-        term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X)
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
         self.outputs = {'Out': -term1 - term2}
 
     def test_check_output(self):
@@ -36,8 +37,8 @@ class TestSigmoidCrossEntropyWithLogitsOp1(OpTest):
 
 
 class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
-    '''Test sigmoid_cross_entropy_with_logit_op with probabalistic labels
-    '''
+    """Test sigmoid_cross_entropy_with_logit_op with probabalistic label
+    """
 
     def setUp(self):
         self.op_type = "sigmoid_cross_entropy_with_logits"
@@ -47,16 +48,16 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
             'X': logit(
                 np.random.uniform(0, 1, (batch_size, num_classes))
                 .astype("float32")),
-            'Labels': np.random.uniform(0, 1, (batch_size, num_classes))
+            'Label': np.random.uniform(0, 1, (batch_size, num_classes))
             .astype("float32")
         }
 
         # Fw Pass is implemented as elementwise sigmoid followed by
         # elementwise logistic loss
-        # Labels * -log(sigmoid(X)) + (1 - labels) * -log(1 - sigmoid(X))
+        # Label * -log(sigmoid(X)) + (1 - label) * -log(1 - sigmoid(X))
         sigmoid_X = expit(self.inputs['X'])
-        term1 = self.inputs['Labels'] * np.log(sigmoid_X)
-        term2 = (1 - self.inputs['Labels']) * np.log(1 - sigmoid_X)
+        term1 = self.inputs['Label'] * np.log(sigmoid_X)
+        term2 = (1 - self.inputs['Label']) * np.log(1 - sigmoid_X)
         self.outputs = {'Out': -term1 - term2}
 
     def test_check_output(self):
@@ -64,3 +65,7 @@ class TestSigmoidCrossEntropyWithLogitsOp2(OpTest):
 
     def test_check_grad(self):
         self.check_grad(['X'], 'Out')
+
+
+if __name__ == '__main__':
+    unittest.main()