enhance the error message of box_clip, test=develop (#23638)

paddle/fluid/operators/detection/box_clip_op.cc

@@ -21,22 +21,36 @@ class BoxClipOp : public framework::OperatorWithKernel {
 
  protected:
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Input"),
-                   "Input(Input) of BoxClipOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("ImInfo"),
-                   "Input(ImInfo) of BoxClipOp should not be null.");
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
+                      platform::errors::NotFound("Input(Input) of BoxClipOp "
+                                                 "is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("ImInfo"), true,
+                      platform::errors::NotFound("Input(ImInfo) of BoxClipOp "
+                                                 "is not found."));
 
     auto input_box_dims = ctx->GetInputDim("Input");
     auto im_info_dims = ctx->GetInputDim("ImInfo");
 
     if (ctx->IsRuntime()) {
       auto input_box_size = input_box_dims.size();
-      PADDLE_ENFORCE_EQ(input_box_dims[input_box_size - 1], 4,
-                        "The last dimension of Input must be 4");
+      PADDLE_ENFORCE_EQ(
+          input_box_dims[input_box_size - 1], 4,
+          platform::errors::InvalidArgument(
+              "The last dimension "
+              "of Input must be 4. But received last dimension = %d",
+              input_box_dims[input_box_size - 1]));
       PADDLE_ENFORCE_EQ(im_info_dims.size(), 2,
-                        "The rank of Input(Input) in BoxClipOp must be 2");
-      PADDLE_ENFORCE_EQ(im_info_dims[1], 3,
-                        "The last dimension of ImInfo must be 3");
+                        platform::errors::InvalidArgument(
+                            "The rank of "
+                            "Input(Input) in BoxClipOp must be 2. But received "
+                            "rank = %d",
+                            im_info_dims.size()));
+      PADDLE_ENFORCE_EQ(
+          im_info_dims[1], 3,
+          platform::errors::InvalidArgument(
+              "The last dimension "
+              "of ImInfo must be 3. But received last dimension = %d",
+              im_info_dims[1]));
     }
     ctx->ShareDim("Input", /*->*/ "Output");
     ctx->ShareLoD("Input", /*->*/ "Output");

paddle/fluid/operators/detection/box_clip_op.cu

@@ -46,8 +46,6 @@ template <typename DeviceContext, typename T>
 class GPUBoxClipKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext &context) const override {
-    PADDLE_ENFORCE(platform::is_gpu_place(context.GetPlace()),
-                   "This kernel only runs on GPU device.");
     auto *input = context.Input<LoDTensor>("Input");
     auto *im_info = context.Input<Tensor>("ImInfo");
     auto *output = context.Output<LoDTensor>("Output");

paddle/fluid/operators/detection/box_clip_op.h

@@ -33,7 +33,9 @@ class BoxClipKernel : public framework::OpKernel<T> {
     output_box->mutable_data<T>(context.GetPlace());
     if (input_box->lod().size()) {
       PADDLE_ENFORCE_EQ(input_box->lod().size(), 1UL,
-                        "Only support 1 level of LoD.");
+                        platform::errors::InvalidArgument(
+                            "Input(Input) of "
+                            "BoxClip only supports 1 level of LoD."));
     }
     auto box_lod = input_box->lod().back();
     int64_t n = static_cast<int64_t>(box_lod.size() - 1);

python/paddle/fluid/layers/detection.py

@@ -30,6 +30,7 @@ import math
 import six
 import numpy
 from functools import reduce
+from ..data_feeder import convert_dtype, check_variable_and_dtype, check_type, check_dtype
 
 __all__ = [
     'prior_box',
@@ -2866,6 +2867,10 @@ def box_clip(input, im_info, name=None):
                 input=boxes, im_info=im_info)
     """
 
+    check_variable_and_dtype(input, 'input', ['float32', 'float64'], 'box_clip')
+    check_variable_and_dtype(im_info, 'im_info', ['float32', 'float64'],
+                             'box_clip')
+
     helper = LayerHelper("box_clip", **locals())
     output = helper.create_variable_for_type_inference(dtype=input.dtype)
     inputs = {"Input": input, "ImInfo": im_info}