Require x.dims=label.dims in huber_loss (#20017)

* x.dims == y.dims test=develop

* refine comment

paddle/fluid/operators/huber_loss_op.cc

@@ -32,23 +32,16 @@ class HuberLossOp : public framework::OperatorWithKernel {
 
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
-    int rank = x_dims.size();
 
-    if (rank == y_dims.size()) {
-      PADDLE_ENFORCE_EQ(y_dims[rank - 1], 1U,
-                        "The last dimension of Input(Y) should be equal to 1.");
-    } else {
-      PADDLE_ENFORCE_EQ(rank, y_dims.size() + 1,
+    PADDLE_ENFORCE_EQ(x_dims.size(), y_dims.size(),
                       "The rank of Input(X) should be equal to "
-                        "the rank of Input(Y) plus 1.");
-    }
+                      "the rank of Input(Y).");
     bool contain_unknown_dim = framework::contain_unknown_dim(x_dims) ||
                                framework::contain_unknown_dim(y_dims);
     if (ctx->IsRuntime() || !contain_unknown_dim) {
-      PADDLE_ENFORCE_EQ(framework::slice_ddim(x_dims, 0, rank - 1),
-                        framework::slice_ddim(y_dims, 0, rank - 1),
-                        "The Input(X) and Input(Label) should have the same "
-                        "shape except the last dimension.");
+      PADDLE_ENFORCE_EQ(
+          x_dims, y_dims,
+          "The Input(X) and Input(Label) should have the same shape.");
     }
 
     auto out_dims = y_dims;
@@ -64,16 +57,16 @@ class HuberLossOpMaker : public framework::OpProtoAndCheckerMaker {
   void Make() override {
     AddInput("X",
              "The input value of huber loss op."
-             "X is a 2-D tensor with shape [batch_size, 1].");
+             "X is a N-D tensor with shape [N_1, N_2,..., N_n].");
     AddInput("Y",
              "The target value of huber loss op."
-             "Y is a 2-D tensor with shape [batch_size, 1].");
+             "Y is a N-D tensor with shape [N_1, N_2,..., N_n].");
     AddOutput("Residual",
               "Intermediate tensor to cache residual value between Y and X."
               "The shape is same as Input(X) and will be reused in backward.")
         .AsIntermediate();
     AddOutput("Out",
-              "The output tensor with shape [batch_size, 1] "
+              "The output N-D tensor with shape [N_1, N_2,..., N_n] "
               "which represents the huber loss.");
     AddAttr<AttrType>("delta", "Hyper parameter in huber loss.");
     AddComment(R"DOC(
@@ -81,7 +74,7 @@ HuberLoss Operator.
 
 Huber loss is a loss function used in robust regression. We define X as the
 input value and Y as the target value. Huber loss can evaluate the fitness of
-X to Y. Different from MSE loss, Huber loss is more robust for outliers. The
+X to Y. Different from MSE loss, Huber loss is more robust for outliers. If the
 shape of X and Y are [batch_size, 1]. The equation is:
 
 $$

python/paddle/fluid/tests/unittests/test_huber_loss_op.py

@@ -30,27 +30,25 @@ def huber_loss_forward(val, delta):
 class TestHuberLossOp(OpTest):
     def setUp(self):
         self.op_type = 'huber_loss'
-        self.samples_num = 64
         self.delta = 1.0
         self.init_input()
-        residual = self.inputs['Y'].reshape(
-            self.samples_num, 1) - self.inputs['X'].reshape(self.samples_num, 1)
+        shape = self.set_shape()
+        residual = self.inputs['Y'] - self.inputs['X']
         loss = np.vectorize(huber_loss_forward)(residual,
                                                 self.delta).astype('float32')
         self.attrs = {'delta': self.delta}
-        self.outputs = {
-            'Residual': residual,
-            'Out': loss.reshape((self.samples_num, 1))
-        }
+        self.outputs = {'Residual': residual, 'Out': loss.reshape(shape)}
 
     def init_input(self):
+        shape = self.set_shape()
         self.inputs = {
-            'X': np.random.uniform(0, 1.,
-                                   (self.samples_num, 1)).astype('float32'),
-            'Y': np.random.uniform(0, 1.,
-                                   (self.samples_num, 1)).astype('float32'),
+            'X': np.random.uniform(0, 1., shape).astype('float32'),
+            'Y': np.random.uniform(0, 1., shape).astype('float32'),
         }
 
+    def set_shape(self):
+        return (64, 1)
+
     def test_check_output(self):
         self.check_output()
 
@@ -67,12 +65,18 @@ class TestHuberLossOp(OpTest):
 
 
 def TestHuberLossOp1(TestHuberLossOp):
-    def init_input(self):
-        self.inputs = {
-            'X': np.random.uniform(0, 1.,
-                                   (self.samples_num, 1)).astype('float32'),
-            'Y': np.random.uniform(0, 1., (self.samples_num)).astype('float32'),
-        }
+    def set_shape(self):
+        return (64)
+
+
+def TestHuberLossOp2(TestHuberLossOp):
+    def set_shape(self):
+        return (6, 6)
+
+
+def TestHuberLossOp2(TestHuberLossOp):
+    def set_shape(self):
+        return (6, 6, 1)
 
 
 if __name__ == '__main__':