diff --git a/paddle/fluid/operators/pixel_shuffle_op.cc b/paddle/fluid/operators/pixel_shuffle_op.cc
index ee72b3e501fe7ebecf6db8e06efa719067ba00d0..1ed7988dcfcc0831156c09a72e958852f3d45fb5 100644
--- a/paddle/fluid/operators/pixel_shuffle_op.cc
+++ b/paddle/fluid/operators/pixel_shuffle_op.cc
@@ -20,17 +20,27 @@ class PixelShuffleOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of PixelShuffleOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of PixelShuffleOp should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->HasInput("X"), true,
+                      platform::errors::NotFound(
+                          "Input(X) of PixelShuffleOp should not be null."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Out"), true,
+                      platform::errors::NotFound(
+                          "Output(Out) of PixelShuffleOp should not be null."));
 
     auto input_dims = ctx->GetInputDim("X");
-    PADDLE_ENFORCE(input_dims.size() == 4, "The layout of input is NCHW.");
+    PADDLE_ENFORCE_EQ(
+        input_dims.size(), 4,
+        platform::errors::InvalidArgument(
+            "Input should be a 4-D tensor of format [N, C, H, W], but got %u.",
+            input_dims.size()));
+
     auto upscale_factor = ctx->Attrs().Get<int>("upscale_factor");
 
-    PADDLE_ENFORCE(input_dims[1] % (upscale_factor * upscale_factor) == 0,
-                   "Upscale_factor should devide the number of channel");
+    PADDLE_ENFORCE_EQ(input_dims[1] % (upscale_factor * upscale_factor), 0,
+                      platform::errors::InvalidArgument(
+                          "The square of upscale_factor[%u] should divide the "
+                          "number of channel[%u]",
+                          input_dims[1], upscale_factor * upscale_factor));
 
     auto output_dims = input_dims;
     output_dims[0] = input_dims[0];
@@ -57,7 +67,8 @@ class PixelShuffleOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(1)
         .AddCustomChecker([](const int& upscale_factor) {
           PADDLE_ENFORCE_GE(upscale_factor, 1,
-                            "upscale_factor should be larger than 0.");
+                            platform::errors::InvalidArgument(
+                                "upscale_factor should be larger than 0."));
         });
 
     AddComment(R"DOC(
@@ -95,13 +106,19 @@ class PixelShuffleGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
-                   "Input(Out@Grad) should not be null");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "Output(X@Grad) should not be null");
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput(framework::GradVarName("Out")), true,
+        platform::errors::NotFound("Input(Out@Grad) should not be null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasOutput(framework::GradVarName("X")), true,
+        platform::errors::NotFound("Output(X@Grad) should not be null"));
 
     auto do_dims = ctx->GetInputDim(framework::GradVarName("Out"));
-    PADDLE_ENFORCE(do_dims.size() == 4, "The layout of input is NCHW.");
+    PADDLE_ENFORCE_EQ(
+        do_dims.size(), 4,
+        platform::errors::InvalidArgument(
+            "Input should be a 4-D tensor of format [N, C, H, W], but got %u.",
+            do_dims.size()));
 
     auto upscale_factor = ctx->Attrs().Get<int>("upscale_factor");
 
diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py
index 8bd500b9d0fb9a595e6dc0e385d50bcfc7b9669b..90cf1ce11cffce9defc516c87691f4b284a9df5f 100644
--- a/python/paddle/fluid/layers/nn.py
+++ b/python/paddle/fluid/layers/nn.py
@@ -13452,6 +13452,7 @@ def pixel_shuffle(x, upscale_factor):
 
     """
 
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'pixel_shuffle')
     helper = LayerHelper("pixel_shuffle", **locals())
 
     out = helper.create_variable_for_type_inference(dtype=x.dtype)