test=release/1.8 cherry-pick unfold_op (#24505)

paddle/fluid/operators/unfold_op.cc

@@ -61,10 +61,12 @@ class UnfoldOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of UnfoldOp should not be null");
-    PADDLE_ENFORCE(ctx->HasOutput("Y"),
-                   "Output(Y) of UnfoldOp should not be null");
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X"), true,
+        platform::errors::NotFound("Input(X) of UnfoldOp should not be null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasOutput("Y"), true,
+        platform::errors::NotFound("Output(Y) of UnfoldOp should not be null"));
     auto in_dims = ctx->GetInputDim("X");
     std::vector<int> kernel_sizes =
         ctx->Attrs().Get<std::vector<int>>("kernel_sizes");
@@ -74,31 +76,36 @@ class UnfoldOp : public framework::OperatorWithKernel {
         ctx->Attrs().Get<std::vector<int>>("dilations");
 
     // Only [N, C, H, W] input supported now
-    PADDLE_ENFORCE(
-        in_dims.size() == 4,
-        "Input should be 4-D tensor of format [N, C, H, W], but get %u",
-        in_dims.size());
-    PADDLE_ENFORCE(
-        in_dims.size() - kernel_sizes.size() == 2U,
-        "The dims of X should be larger than that of kernel_sizes "
-        "by a number of 2, due to the batch size and input channel dim. "
-        "But recieved dims(X:%u) - dims(kernel_sizes:%u) != 2",
-        in_dims.size(), kernel_sizes.size());
+    PADDLE_ENFORCE_EQ(
+        in_dims.size(), 4,
+        platform::errors::InvalidArgument(
+            "Input should be 4-D tensor of format [N, C, H, W], but get %u",
+            in_dims.size()));
+    PADDLE_ENFORCE_EQ(
+        in_dims.size() - kernel_sizes.size(), 2U,
+        platform::errors::InvalidArgument(
+            "The dims of X should be larger than that of kernel_sizes "
+            "by a number of 2, due to the batch size and input channel dim. "
+            "But recieved dims(X:%u) - dims(kernel_sizes:%u) != 2",
+            in_dims.size(), kernel_sizes.size()));
     PADDLE_ENFORCE_EQ(
         strides.size(), kernel_sizes.size(),
-        "The dims of strides should be the same with that of kernel_sizes. "
-        "But recieved dims(strides: %u) != dims(kernel_sizes: %u).",
-        strides.size(), kernel_sizes.size());
+        platform::errors::InvalidArgument(
+            "The dims of strides should be the same with that of kernel_sizes. "
+            "But recieved dims(strides: %u) != dims(kernel_sizes: %u).",
+            strides.size(), kernel_sizes.size()));
     PADDLE_ENFORCE_EQ(
         paddings.size(), 2 * strides.size(),
-        "The dims of paddings should be 2 times of that of strides. "
-        "But recieved dims(paddings: %u) != 2*dims(strides: %u).",
-        paddings.size(), strides.size());
+        platform::errors::InvalidArgument(
+            "The dims of paddings should be 2 times of that of strides. "
+            "But recieved dims(paddings: %u) != 2*dims(strides: %u).",
+            paddings.size(), strides.size()));
     PADDLE_ENFORCE_EQ(
         strides.size(), dilations.size(),
-        "The dims of strides should be the same with that of dilations. "
-        "But recieved dims(strides: %u) != dims(dilations: %u).",
-        strides.size(), dilations.size());
+        platform::errors::InvalidArgument(
+            "The dims of strides should be the same with that of dilations. "
+            "But recieved dims(strides: %u) != dims(dilations: %u).",
+            strides.size(), dilations.size()));
 
     std::vector<int> out_dims;
     out_dims.push_back(in_dims[0]);
@@ -131,11 +138,15 @@ class UnfoldGradOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
-                   "The gradient of Y should not be null");
-    PADDLE_ENFORCE(ctx->HasInput("X"), "The input X should not be null");
-    PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
-                   "The gradient of X should not be null");
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput(framework::GradVarName("Y")), true,
+        platform::errors::NotFound("The gradient of Y should not be null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X"), true,
+        platform::errors::NotFound("The input X should not be null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasOutput(framework::GradVarName("X")), true,
+        platform::errors::NotFound("The gradient of X should not be null"));
     ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
   }
 

paddle/fluid/operators/unfold_op.h

@@ -29,12 +29,15 @@ inline int CalcOutputSize(int input_size, int filter_size, int dilation,
                           int padding1, int padding2, int stride) {
   const int dkernel = dilation * (filter_size - 1) + 1;
   int output_size = (input_size + padding1 + padding2 - dkernel) / stride + 1;
-  PADDLE_ENFORCE(output_size > 0,
-                 "Due to the settings of padding(%d, %d), filter_size(%d), "
-                 "dilation(%d) and "
-                 "stride(%d), the output size is less than 0, please check "
-                 "again. Input_size:%d",
-                 padding1, padding2, filter_size, dilation, stride, input_size);
+
+  PADDLE_ENFORCE_GT(
+      output_size, 0UL,
+      platform::errors::InvalidArgument(
+          "Due to the settings of padding(%d, %d), filter_size(%d), "
+          "dilation(%d) and "
+          "stride(%d), the output size is less than 0, please check "
+          "again. Input_size:%d",
+          padding1, padding2, filter_size, dilation, stride, input_size));
 
   return output_size;
 }

python/paddle/fluid/layers/nn.py

@@ -15257,6 +15257,8 @@ def unfold(x, kernel_sizes, strides=1, paddings=0, dilations=1, name=None):
 
     helper = LayerHelper("unfold", **locals())
 
+    check_variable_and_dtype(x, 'x', ['float32', 'float64'], 'unfold')
+
     assert len(x.shape) == 4, \
             "input should be the format of [N, C, H, W]"