polish error message (#23696)

* polish error message, test=develop

paddle/fluid/operators/detection/multiclass_nms_op.cc

@@ -26,12 +26,9 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("BBoxes"),
-                   "Input(BBoxes) of MultiClassNMS should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Scores"),
-                   "Input(Scores) of MultiClassNMS should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of MultiClassNMS should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("BBoxes"), "Input", "BBoxes", "MultiClassNMS");
+    OP_INOUT_CHECK(ctx->HasInput("Scores"), "Input", "Scores", "MultiClassNMS");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "MultiClassNMS");
 
     auto box_dims = ctx->GetInputDim("BBoxes");
     auto score_dims = ctx->GetInputDim("Scores");
@@ -41,7 +38,10 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
       PADDLE_ENFORCE(score_size == 2 || score_size == 3,
                      "The rank of Input(Scores) must be 2 or 3");
       PADDLE_ENFORCE_EQ(box_dims.size(), 3,
-                        "The rank of Input(BBoxes) must be 3");
+                        platform::errors::InvalidArgument(
+                            "The rank of Input(BBoxes) must be 3"
+                            "But receive box_dims size(%s)",
+                            box_dims.size()));
       if (score_size == 3) {
         PADDLE_ENFORCE(box_dims[2] == 4 || box_dims[2] == 8 ||
                            box_dims[2] == 16 || box_dims[2] == 24 ||
@@ -55,16 +55,26 @@ class MultiClassNMSOp : public framework::OperatorWithKernel {
                        "16 points: [xi, yi] i= 1,2,...,16");
         PADDLE_ENFORCE_EQ(
             box_dims[1], score_dims[2],
-            "The 2nd dimension of Input(BBoxes) must be equal to "
-            "last dimension of Input(Scores), which represents the "
-            "predicted bboxes.");
+            platform::errors::InvalidArgument(
+                "The 2nd dimension of Input(BBoxes) must be equal to "
+                "last dimension of Input(Scores), which represents the "
+                "predicted bboxes."
+                "But received box_dims[1](%s) != socre_dims[2](%s)",
+                box_dims[1], score_dims[2]));
       } else {
-        PADDLE_ENFORCE(box_dims[2] == 4,
-                       "The last dimension of Input(BBoxes) must be 4");
+        PADDLE_ENFORCE_EQ(box_dims[2], 4,
+                          platform::errors::InvalidArgument(
+                              "The last dimension of Input(BBoxes) must be 4"
+                              "But received box_dims[2](%s).",
+                              box_dims[2]));
         PADDLE_ENFORCE_EQ(box_dims[1], score_dims[1],
-                          "The 2nd dimension of Input(BBoxes)"
-                          "must be equal to the 2nd dimension"
-                          " of Input(Scores)");
+                          platform::errors::InvalidArgument(
+                              "The 2nd dimension of Input(BBoxes)"
+                              "must be equal to the 2nd dimension"
+                              " of Input(Scores)"
+                              "But received box_dims[1](%s) != "
+                              "score_dims[1](%s)",
+                              box_dims[1], score_dims[1]));
       }
     }
     // Here the box_dims[0] is not the real dimension of output.

paddle/fluid/operators/detection/yolo_box_op.cc

@@ -21,14 +21,10 @@ class YoloBoxOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of YoloBoxOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("ImgSize"),
-                   "Input(ImgSize) of YoloBoxOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Boxes"),
-                   "Output(Boxes) of YoloBoxOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Scores"),
-                   "Output(Scores) of YoloBoxOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "YoloBoxOp");
+    OP_INOUT_CHECK(ctx->HasInput("ImgSize"), "Input", "ImgSize", "YoloBoxOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Boxes"), "Output", "Boxes", "YoloBoxOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Scores"), "Output", "Scores", "YoloBoxOp");
 
     auto dim_x = ctx->GetInputDim("X");
     auto dim_imgsize = ctx->GetInputDim("ImgSize");
@@ -36,26 +32,49 @@ class YoloBoxOp : public framework::OperatorWithKernel {
     int anchor_num = anchors.size() / 2;
     auto class_num = ctx->Attrs().Get<int>("class_num");
 
-    PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
+    PADDLE_ENFORCE_EQ(dim_x.size(), 4, platform::errors::InvalidArgument(
+                                           "Input(X) should be a 4-D tensor."
+                                           "But received X dimension(%s)",
+                                           dim_x.size()));
     PADDLE_ENFORCE_EQ(
         dim_x[1], anchor_num * (5 + class_num),
-        "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
-        "+ class_num)).");
+        platform::errors::InvalidArgument(
+            "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
+            "+ class_num))."
+            "But received dim[1](%s) != (anchor_mask_number * "
+            "(5+class_num)(%s).",
+            dim_x[1], anchor_num * (5 + class_num)));
     PADDLE_ENFORCE_EQ(dim_imgsize.size(), 2,
-                      "Input(ImgSize) should be a 2-D tensor.");
+                      platform::errors::InvalidArgument(
+                          "Input(ImgSize) should be a 2-D tensor."
+                          "But received Imgsize size(%s)",
+                          dim_imgsize.size()));
     if ((dim_imgsize[0] > 0 && dim_x[0] > 0) || ctx->IsRuntime()) {
       PADDLE_ENFORCE_EQ(
           dim_imgsize[0], dim_x[0],
           platform::errors::InvalidArgument(
               "Input(ImgSize) dim[0] and Input(X) dim[0] should be same."));
     }
-    PADDLE_ENFORCE_EQ(dim_imgsize[1], 2, "Input(ImgSize) dim[1] should be 2.");
+    PADDLE_ENFORCE_EQ(
+        dim_imgsize[1], 2,
+        platform::errors::InvalidArgument("Input(ImgSize) dim[1] should be 2."
+                                          "But received imgsize dim[1](%s).",
+                                          dim_imgsize[1]));
     PADDLE_ENFORCE_GT(anchors.size(), 0,
-                      "Attr(anchors) length should be greater than 0.");
+                      platform::errors::InvalidArgument(
+                          "Attr(anchors) length should be greater than 0."
+                          "But received anchors length(%s).",
+                          anchors.size()));
     PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
-                      "Attr(anchors) length should be even integer.");
+                      platform::errors::InvalidArgument(
+                          "Attr(anchors) length should be even integer."
+                          "But received anchors length (%s)",
+                          anchors.size()));
     PADDLE_ENFORCE_GT(class_num, 0,
-                      "Attr(class_num) should be an integer greater than 0.");
+                      platform::errors::InvalidArgument(
+                          "Attr(class_num) should be an integer greater than 0."
+                          "But received class_num (%s)",
+                          class_num));
 
     int box_num = dim_x[2] * dim_x[3] * anchor_num;
     std::vector<int64_t> dim_boxes({dim_x[0], box_num, 4});

paddle/fluid/operators/detection/yolov3_loss_op.cc

@@ -23,19 +23,15 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"),
-                   "Input(X) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("GTBox"),
-                   "Input(GTBox) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("GTLabel"),
-                   "Input(GTLabel) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Loss"),
-                   "Output(Loss) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(
-        ctx->HasOutput("ObjectnessMask"),
-        "Output(ObjectnessMask) of Yolov3LossOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("GTMatchMask"),
-                   "Output(GTMatchMask) of Yolov3LossOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "Yolov3LossOp");
+    OP_INOUT_CHECK(ctx->HasInput("GTBox"), "Input", "GTBox", "Yolov3LossOp");
+    OP_INOUT_CHECK(ctx->HasInput("GTLabel"), "Input", "GTLabel",
+                   "Yolov3LossOp");
+    OP_INOUT_CHECK(ctx->HasOutput("Loss"), "Output", "Loss", "Yolov3LossOp");
+    OP_INOUT_CHECK(ctx->HasOutput("ObjectnessMask"), "Output", "ObjectnessMask",
+                   "Yolov3LossOp");
+    OP_INOUT_CHECK(ctx->HasOutput("GTMatchMask"), "Output", "GTMatchMask",
+                   "Yolov3LossOp");
 
     auto dim_x = ctx->GetInputDim("X");
     auto dim_gtbox = ctx->GetInputDim("GTBox");
@@ -46,44 +42,96 @@ class Yolov3LossOp : public framework::OperatorWithKernel {
     int mask_num = anchor_mask.size();
     auto class_num = ctx->Attrs().Get<int>("class_num");
 
-    PADDLE_ENFORCE_EQ(dim_x.size(), 4, "Input(X) should be a 4-D tensor.");
+    PADDLE_ENFORCE_EQ(dim_x.size(), 4,
+                      platform::errors::InvalidArgument(
+                          "Input(X) should be a 4-D tensor. But received "
+                          "X dimension size(%s)",
+                          dim_x.size()));
     PADDLE_ENFORCE_EQ(dim_x[2], dim_x[3],
-                      "Input(X) dim[3] and dim[4] should be euqal.");
+                      platform::errors::InvalidArgument(
+                          "Input(X) dim[3] and dim[4] should be euqal."
+                          "But received dim[3](%s) != dim[4](%s)",
+                          dim_x[2], dim_x[3]));
     PADDLE_ENFORCE_EQ(
         dim_x[1], mask_num * (5 + class_num),
-        "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
-        "+ class_num)).");
+        platform::errors::InvalidArgument(
+            "Input(X) dim[1] should be equal to (anchor_mask_number * (5 "
+            "+ class_num))."
+            "But received dim[1](%s) != (anchor_mask_number * "
+            "(5+class_num)(%s).",
+            dim_x[1], mask_num * (5 + class_num)));
     PADDLE_ENFORCE_EQ(dim_gtbox.size(), 3,
-                      "Input(GTBox) should be a 3-D tensor");
-    PADDLE_ENFORCE_EQ(dim_gtbox[2], 4, "Input(GTBox) dim[2] should be 5");
-    PADDLE_ENFORCE_EQ(dim_gtlabel.size(), 2,
-                      "Input(GTLabel) should be a 2-D tensor");
-    PADDLE_ENFORCE_EQ(dim_gtlabel[0], dim_gtbox[0],
-                      "Input(GTBox) and Input(GTLabel) dim[0] should be same");
-    PADDLE_ENFORCE_EQ(dim_gtlabel[1], dim_gtbox[1],
-                      "Input(GTBox) and Input(GTLabel) dim[1] should be same");
+                      platform::errors::InvalidArgument(
+                          "Input(GTBox) should be a 3-D tensor, but "
+                          "received gtbox dimension size(%s)",
+                          dim_gtbox.size()));
+    PADDLE_ENFORCE_EQ(dim_gtbox[2], 4,
+                      platform::errors::InvalidArgument(
+                          "Input(GTBox) dim[2] should be 4",
+                          "But receive dim[2](%s) != 5. ", dim_gtbox[2]));
+    PADDLE_ENFORCE_EQ(
+        dim_gtlabel.size(), 2,
+        platform::errors::InvalidArgument(
+            "Input(GTLabel) should be a 2-D tensor,"
+            "But received Input(GTLabel) dimension size(%s) != 2.",
+            dim_gtlabel.size()));
+    PADDLE_ENFORCE_EQ(
+        dim_gtlabel[0], dim_gtbox[0],
+        platform::errors::InvalidArgument(
+            "Input(GTBox) dim[0] and Input(GTLabel) dim[0] should be same,"
+            "But received Input(GTLabel) dim[0](%s) != "
+            "Input(GTBox) dim[0](%s)",
+            dim_gtlabel[0], dim_gtbox[0]));
+    PADDLE_ENFORCE_EQ(
+        dim_gtlabel[1], dim_gtbox[1],
+        platform::errors::InvalidArgument(
+            "Input(GTBox) and Input(GTLabel) dim[1] should be same,"
+            "But received Input(GTBox) dim[1](%s) != Input(GTLabel) "
+            "dim[1](%s)",
+            dim_gtbox[1], dim_gtlabel[1]));
     PADDLE_ENFORCE_GT(anchors.size(), 0,
-                      "Attr(anchors) length should be greater then 0.");
+                      platform::errors::InvalidArgument(
+                          "Attr(anchors) length should be greater then 0."
+                          "But received anchors length(%s)",
+                          anchors.size()));
     PADDLE_ENFORCE_EQ(anchors.size() % 2, 0,
-                      "Attr(anchors) length should be even integer.");
+                      platform::errors::InvalidArgument(
+                          "Attr(anchors) length should be even integer."
+                          "But received anchors length(%s)",
+                          anchors.size()));
     for (size_t i = 0; i < anchor_mask.size(); i++) {
       PADDLE_ENFORCE_LT(
           anchor_mask[i], anchor_num,
-          "Attr(anchor_mask) should not crossover Attr(anchors).");
+          platform::errors::InvalidArgument(
+              "Attr(anchor_mask) should not crossover Attr(anchors)."
+              "But received anchor_mask[i](%s) > anchor_num(%s)",
+              anchor_mask[i], anchor_num));
     }
     PADDLE_ENFORCE_GT(class_num, 0,
-                      "Attr(class_num) should be an integer greater then 0.");
+                      platform::errors::InvalidArgument(
+                          "Attr(class_num) should be an integer greater then 0."
+                          "But received class_num(%s) < 0",
+                          class_num));
 
     if (ctx->HasInput("GTScore")) {
       auto dim_gtscore = ctx->GetInputDim("GTScore");
       PADDLE_ENFORCE_EQ(dim_gtscore.size(), 2,
-                        "Input(GTScore) should be a 2-D tensor");
+                        platform::errors::InvalidArgument(
+                            "Input(GTScore) should be a 2-D tensor"
+                            "But received GTScore dimension(%s)",
+                            dim_gtbox.size()));
       PADDLE_ENFORCE_EQ(
           dim_gtscore[0], dim_gtbox[0],
-          "Input(GTBox) and Input(GTScore) dim[0] should be same");
+          platform::errors::InvalidArgument(
+              "Input(GTBox) and Input(GTScore) dim[0] should be same"
+              "But received GTBox dim[0](%s) != GTScore dim[0](%s)",
+              dim_gtbox[0], dim_gtscore[0]));
       PADDLE_ENFORCE_EQ(
           dim_gtscore[1], dim_gtbox[1],
-          "Input(GTBox) and Input(GTScore) dim[1] should be same");
+          platform::errors::InvalidArgument(
+              "Input(GTBox) and Input(GTScore) dim[1] should be same"
+              "But received GTBox dim[1](%s) != GTScore dim[1](%s)",
+              dim_gtscore[1], dim_gtbox[1]));
     }
 
     std::vector<int64_t> dim_out({dim_x[0]});
@@ -245,9 +293,12 @@ class Yolov3LossOpGrad : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null");
-    PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Loss")),
-                   "Input(Loss@GRAD) should not be null");
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("X"), true,
+        platform::errors::NotFound("Input(X) should not be null"));
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput(framework::GradVarName("Loss")), true,
+        platform::errors::NotFound("Input(Loss@GRAD) should not be null"));
     auto dim_x = ctx->GetInputDim("X");
     if (ctx->HasOutput(framework::GradVarName("X"))) {
       ctx->SetOutputDim(framework::GradVarName("X"), dim_x);

python/paddle/fluid/layers/detection.py

@@ -3178,6 +3178,18 @@ def multiclass_nms(bboxes,
                                               keep_top_k=200,
                                               normalized=False)
     """
+    check_variable_and_dtype(bboxes, 'BBoxes', ['float32', 'float64'],
+                             'multiclass_nms')
+    check_variable_and_dtype(scores, 'Scores', ['float32', 'float64'],
+                             'multiclass_nms')
+    check_type(score_threshold, 'score_threshold', float, 'multicalss_nms')
+    check_type(nms_top_k, 'nums_top_k', int, 'multiclass_nms')
+    check_type(keep_top_k, 'keep_top_k', int, 'mutliclass_nms')
+    check_type(nms_threshold, 'nms_threshold', float, 'multiclass_nms')
+    check_type(normalized, 'normalized', bool, 'multiclass_nms')
+    check_type(nms_eta, 'nms_eta', float, 'multiclass_nms')
+    check_type(background_label, 'background_label', int, 'multiclass_nms')
+
     helper = LayerHelper('multiclass_nms', **locals())
 
     output = helper.create_variable_for_type_inference(dtype=bboxes.dtype)
@@ -3192,7 +3204,6 @@ def multiclass_nms(bboxes,
             'nms_threshold': nms_threshold,
             'nms_eta': nms_eta,
             'keep_top_k': keep_top_k,
-            'nms_eta': nms_eta,
             'normalized': normalized
         },
         outputs={'Out': output})

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

@@ -17,6 +17,8 @@ import unittest
 import numpy as np
 import copy
 from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import Program, program_guard
 
 
 def softmax(x):
@@ -487,5 +489,36 @@ class TestMulticlassNMS2LoDNoOutput(TestMulticlassNMS2LoDInput):
         self.score_threshold = 2.0
 
 
+class TestMulticlassNMSError(unittest.TestCase):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            M = 1200
+            N = 7
+            C = 21
+            BOX_SIZE = 4
+
+            boxes_np = np.random.random((M, C, BOX_SIZE)).astype('float32')
+            scores = np.random.random((N * M, C)).astype('float32')
+            scores = np.apply_along_axis(softmax, 1, scores)
+            scores = np.reshape(scores, (N, M, C))
+            scores_np = np.transpose(scores, (0, 2, 1))
+
+            boxes_data = fluid.data(
+                name='bboxes', shape=[M, C, BOX_SIZE], dtype='float32')
+            scores_data = fluid.data(
+                name='scores', shape=[N, C, M], dtype='float32')
+
+            def test_bboxes_Variable():
+                # the bboxes type must be Variable
+                fluid.layers.multiclass_nms(bboxes=boxes_np, scores=scores_data)
+
+            def test_scores_Variable():
+                # the bboxes type must be Variable
+                fluid.layers.multiclass_nms(bboxes=boxes_data, scores=scores_np)
+
+            self.assertRaises(TypeError, test_bboxes_Variable)
+            self.assertRaises(TypeError, test_scores_Variable)
+
+
 if __name__ == '__main__':
     unittest.main()