【cherry-pick】 enhance error message for smooth_l1, mean_iou, accuracy... etc (#24006)

* cherry-pick OP Normal, Uniform, Xavier Initializer, smooth_l1, mean_iou error message enhancement (#23751)  test=release/2.0

paddle/fluid/operators/gaussian_random_op.cc

@@ -50,16 +50,20 @@ class GaussianRandomOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasOutput("Out"),
-                   "Output(Out) of GaussianRandomOp should not be null.");
+    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "GaussianRandom");
+
     auto shape = ctx->Attrs().Get<std::vector<int64_t>>("shape");
     std::vector<int64_t> temp;
     temp.reserve(shape.size());
     for (auto dim : shape) {
       temp.push_back(static_cast<int64_t>(dim));
     }
-    PADDLE_ENFORCE(shape.size() > 0UL,
-                   "shape can be one int or array. shape must be set.");
+    PADDLE_ENFORCE_GT(
+        shape.size(), 0UL,
+        platform::errors::InvalidArgument(
+            "Attribute(shape) of GaussianRandomOp must be set "
+            "and shape.size() > 0, but reveived shape.size() is %d",
+            shape.size()));
     ctx->SetOutputDim("Out", framework::make_ddim(temp));
   }
 

paddle/fluid/operators/mean_iou_op.cc

@@ -22,16 +22,14 @@ class MeanIoUOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext* ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Predictions"),
-                   "Input (Predictions) of MeanIoU op should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Labels"),
-                   "Input (labels) of MeanIoU op should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("OutMeanIou"),
-                   "Output (OutMeanIou) of MeanIoU op should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("OutWrong"),
-                   "Output (OutWrong) of MeanIoU op should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("OutCorrect"),
-                   "Output (OutWrong) of MeanIoU op should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("Predictions"), "Input", "Predictions",
+                   "MeanIoU");
+    OP_INOUT_CHECK(ctx->HasInput("Labels"), "Input", "Labels", "MeanIoU");
+    OP_INOUT_CHECK(ctx->HasOutput("OutMeanIou"), "Output", "OutMeanIou",
+                   "MeanIoU");
+    OP_INOUT_CHECK(ctx->HasOutput("OutWrong"), "Output", "OutWrong", "MeanIoU");
+    OP_INOUT_CHECK(ctx->HasOutput("OutCorrect"), "Output", "OutCorrect",
+                   "MeanIoU");
 
     int64_t num_classes =
         static_cast<int64_t>(ctx->Attrs().Get<int>("num_classes"));

paddle/fluid/operators/metrics/accuracy_op.cc

@@ -22,18 +22,32 @@ class AccuracyOp : public framework::OperatorWithKernel {
   using framework::OperatorWithKernel::OperatorWithKernel;
 
   void InferShape(framework::InferShapeContext *ctx) const override {
-    PADDLE_ENFORCE(ctx->HasInput("Out"),
-                   "Input (Out) of accuracy op should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Indices"),
-                   "Input (Indices) of accuracy op should not be null.");
-    PADDLE_ENFORCE(ctx->HasInput("Label"),
-                   "Input (Label) of accuracy op should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Accuracy"),
-                   "Output (Accuracy) of AccuracyOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Correct"),
-                   "Output (Correct) of AccuracyOp should not be null.");
-    PADDLE_ENFORCE(ctx->HasOutput("Total"),
-                   "Output (Total) of AccuracyOp should not be null.");
+    PADDLE_ENFORCE_EQ(
+        ctx->HasInput("Out"), true,
+        platform::errors::NotFound("Input (Out) of AccuracyOp is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Indices"), true,
+                      platform::errors::NotFound(
+                          "Input (Indices) of AccuracyOp is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasInput("Label"), true,
+                      platform::errors::NotFound(
+                          "Input (Label) of AccuracyOp is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Accuracy"), true,
+                      platform::errors::NotFound(
+                          "Output (Accuracy) of AccuracyOp is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Correct"), true,
+                      platform::errors::NotFound(
+                          "Output (Correct) of AccuracyOp is not found."));
+    PADDLE_ENFORCE_EQ(ctx->HasOutput("Total"), true,
+                      platform::errors::NotFound(
+                          "Output (Total) of AccuracyOp is not found."));
+
+    OP_INOUT_CHECK(ctx->HasInput("Out"), "Input", "Out", "Accuracy");
+    OP_INOUT_CHECK(ctx->HasInput("Indices"), "Input", "Indices", "Accuracy");
+    OP_INOUT_CHECK(ctx->HasInput("Label"), "Input", "Label", "Accuracy");
+    OP_INOUT_CHECK(ctx->HasOutput("Accuracy"), "Output", "Accuracy",
+                   "Accuracy");
+    OP_INOUT_CHECK(ctx->HasOutput("Correct"), "Output", "Correct", "Accuracy");
+    OP_INOUT_CHECK(ctx->HasOutput("Total"), "Output", "Total", "Accuracy");
 
     auto inference_dim = ctx->GetInputDim("Out");
     auto label_dim = ctx->GetInputDim("Label");
@@ -42,22 +56,26 @@ class AccuracyOp : public framework::OperatorWithKernel {
 
     PADDLE_ENFORCE_EQ(
         label_dim.size(), 2,
-        "ShapeError: label's dimensions of AccuracyOp must be 2. "
-        "But received label's dimensions = %d, label's shape = [%s]",
-        label_dim.size(), label_dim);
+        platform::errors::InvalidArgument(
+            "ShapeError: label's dimensions of AccuracyOp must be 2. "
+            "But received label's dimensions = %d, label's shape = [%s]",
+            label_dim.size(), label_dim));
     if (ctx->IsRuntime()) {
       PADDLE_ENFORCE_EQ(label_dim[1], 1,
-                        "ShapeError: label's second dimension of "
-                        "AccuracyOp must be 1. But received label's "
-                        "second dimension is = %d, label's shape = [%s]",
-                        label_dim[1], label_dim);
+                        platform::errors::InvalidArgument(
+                            "ShapeError: label's second dimension of "
+                            "AccuracyOp must be 1. But received label's "
+                            "second dimension is = %d, label's shape = [%s]",
+                            label_dim[1], label_dim));
       PADDLE_ENFORCE_EQ(
           inference_dim[0], label_dim[0],
-          "ShapeError: the output's num_rows of AccuracyOp must be"
-          " the same as label's num_rows. But received output's "
-          "shape = [%s], label's shape = [%s], output's num_rows = %d, label's "
-          "num_rows = %d",
-          inference_dim, label_dim, inference_dim[0], label_dim[0]);
+          platform::errors::InvalidArgument(
+              "ShapeError: the output's num_rows of AccuracyOp must be"
+              " the same as label's num_rows. But received output's "
+              "shape = [%s], label's shape = [%s], output's num_rows = %d, "
+              "label's "
+              "num_rows = %d",
+              inference_dim, label_dim, inference_dim[0], label_dim[0]));
     }
 
     ctx->SetOutputDim("Accuracy", {1});

paddle/fluid/operators/metrics/accuracy_op.cu

@@ -56,8 +56,6 @@ template <typename T>
 class AccuracyOpCUDAKernel : public framework::OpKernel<T> {
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
-    PADDLE_ENFORCE(platform::is_gpu_place(ctx.GetPlace()),
-                   "It must use CUDAPlace.");
     auto* inference = ctx.Input<Tensor>("Out");
     auto* indices = ctx.Input<Tensor>("Indices");
     auto* label = ctx.Input<Tensor>("Label");

paddle/fluid/operators/smooth_l1_loss_op.cc

@@ -23,8 +23,8 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
   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("Y"), "Input(Y) should not be null.");
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SmoothL1Loss");
+    OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "SmoothL1Loss");
 
     auto x_dims = ctx->GetInputDim("X");
     auto y_dims = ctx->GetInputDim("Y");
@@ -34,14 +34,23 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
       check = false;
     }
     if (check) {
-      PADDLE_ENFORCE_EQ(x_dims, y_dims);
+      PADDLE_ENFORCE_EQ(
+          x_dims, y_dims,
+          platform::errors::InvalidArgument(
+              "Input(X) ans Input(Y) of SmoothL1LossOp should "
+              "have the same size, but received X dim is %s, Y dim is %s",
+              x_dims.to_str(), y_dims.to_str()));
     }
     PADDLE_ENFORCE_GE(x_dims.size(), 2,
-                      "The tensor rank of Input(X) should not be less than 2.");
+                      platform::errors::InvalidArgument(
+                          "The tensor rank of Input(X) of SmoothL1LossOp "
+                          "should not be less than 2, but received %d.",
+                          x_dims.size()));
     if (ctx->HasInput("InsideWeight")) {
-      PADDLE_ENFORCE(ctx->HasInput("OutsideWeight"),
-                     "If weights are provided, must specify both "
-                     "inside and outside weights.");
+      PADDLE_ENFORCE_EQ(ctx->HasInput("OutsideWeight"), true,
+                        platform::errors::InvalidArgument(
+                            "If weights are provided, must specify both "
+                            "inside and outside weights."));
       auto dims = ctx->GetInputDim("InsideWeight");
       bool check = true;
       if ((!ctx->IsRuntime()) &&
@@ -49,7 +58,12 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
         check = false;
       }
       if (check) {
-        PADDLE_ENFORCE_EQ(dims, x_dims);
+        PADDLE_ENFORCE_EQ(x_dims, dims,
+                          platform::errors::InvalidArgument(
+                              "Input(X) ans Input(InsideWeight) of "
+                              "SmoothL1LossOp should have the same size, but "
+                              "received X dim is %s, InsideWeight dim is %s",
+                              x_dims.to_str(), dims.to_str()));
       }
 
       dims = ctx->GetInputDim("OutsideWeight");
@@ -59,7 +73,12 @@ class SmoothL1LossOp : public framework::OperatorWithKernel {
         check = false;
       }
       if (check) {
-        PADDLE_ENFORCE_EQ(dims, x_dims);
+        PADDLE_ENFORCE_EQ(x_dims, dims,
+                          platform::errors::InvalidArgument(
+                              "Input(X) ans Input(OutsideWeight) of "
+                              "SmoothL1LossOp should have the same size, but "
+                              "received X dim is %s, OutsideWeight dim is %s",
+                              x_dims.to_str(), dims.to_str()));
       }
     }
 
@@ -133,14 +152,23 @@ class SmoothL1LossGradOp : public framework::OperatorWithKernel {
     auto in_dims = ctx->GetInputDim("Diff");
     auto out_dims = ctx->GetInputDim(framework::GradVarName("Out"));
 
-    PADDLE_ENFORCE_GE(out_dims.size(), 2,
-                      "The tensor rank of Input(Out@Grad) should be 2.");
+    PADDLE_ENFORCE_GE(
+        out_dims.size(), 2,
+        platform::errors::InvalidArgument(
+            "The tensor rank of Input(Out@Grad) should be 2, but received %d.",
+            out_dims.size()));
     if (ctx->IsRuntime()) {
-      PADDLE_ENFORCE_EQ(out_dims[0], in_dims[0],
-                        "The 1st dimension of Input(Out@Grad) must be "
-                        "same as input.");
+      PADDLE_ENFORCE_EQ(
+          out_dims[0], in_dims[0],
+          platform::errors::InvalidArgument(
+              "The 1st dimension of Input(Out@Grad) must be "
+              "same as input in SmoothL1LossGradOp, but received %d and %d.",
+              out_dims[0], in_dims[0]));
       PADDLE_ENFORCE_EQ(out_dims[1], 1,
-                        "The 2nd dimension of Input(Out@Grad) must be 1.");
+                        platform::errors::InvalidArgument(
+                            "The 2nd dimension of Input(Out@Grad) must be 1 in "
+                            "SmoothL1LossGradOp, but received %d.",
+                            out_dims[1]));
     }
 
     auto x_grad_name = framework::GradVarName("X");

paddle/fluid/operators/uniform_random_op.cc

@@ -74,12 +74,13 @@ class CPUUniformRandomKernel : public framework::OpKernel<T> {
         static_cast<unsigned int>(ctx.Attr<int>("diag_step"));
     auto diag_val = static_cast<T>(ctx.Attr<float>("diag_val"));
     if (diag_num > 0) {
-      PADDLE_ENFORCE_GT(size, (diag_num - 1) * (diag_step + 1),
-                        "ShapeError: the diagonal's elements is equal (num-1) "
-                        "* (step-1) with num %d, step %d,"
-                        "It should be smaller than %d, but received %d",
-                        diag_num, diag_step, (diag_num - 1) * (diag_step + 1),
-                        size);
+      PADDLE_ENFORCE_GT(
+          size, (diag_num - 1) * (diag_step + 1),
+          platform::errors::InvalidArgument(
+              "ShapeInvalid: the diagonal's elements is equal (num-1) "
+              "* (step-1) with num %d, step %d,"
+              "It should be smaller than %d, but received %d",
+              diag_num, diag_step, (diag_num - 1) * (diag_step + 1), size));
       for (int64_t i = 0; i < diag_num; ++i) {
         int64_t pos = i * diag_step + i;
         data[pos] = diag_val;
@@ -117,9 +118,10 @@ class UniformRandomOp : public framework::OperatorWithKernel {
       auto inputs_name = ctx->Inputs("ShapeTensorList");
       PADDLE_ENFORCE_GT(
           inputs_name.size(), 0,
-          "Input(ShapeTensorList)'size of Op(uniform_random) can't be zero."
-          "Please check the Attr(shape)'s size of"
-          "Op(fluid.layers.uniform_random).)");
+          platform::errors::InvalidArgument(
+              "Input(ShapeTensorList)'size of Op(uniform_random) can't be zero."
+              "Please check the Attr(shape)'s size of"
+              "Op(fluid.layers.uniform_random).)"));
       auto out_dims = std::vector<int>(inputs_name.size(), -1);
       ctx->SetOutputDim("Out", framework::make_ddim(out_dims));
 
@@ -130,10 +132,11 @@ class UniformRandomOp : public framework::OperatorWithKernel {
       auto shape_dims = ctx->GetInputDim("ShapeTensor");
       PADDLE_ENFORCE_EQ(
           shape_dims.size(), 1,
-          "ShapeError: Input(ShapeTensor)' dimension size of "
-          "Op(uniform_random) must be 1."
-          "But received ShapeTensor's dimensions = %d, shape = [%s]",
-          shape_dims.size(), shape_dims);
+          platform::errors::InvalidArgument(
+              "ShapeError: Input(ShapeTensor)' dimension size of "
+              "Op(uniform_random) must be 1."
+              "But received ShapeTensor's dimensions = %d, shape = [%s]",
+              shape_dims.size(), shape_dims));
       int num_ele = 1;
       for (int i = 0; i < shape_dims.size(); ++i) {
         num_ele *= shape_dims[i];
@@ -144,11 +147,12 @@ class UniformRandomOp : public framework::OperatorWithKernel {
       return;
     }
 
-    PADDLE_ENFORCE_EQ(
-        shape.empty(), false,
-        "if there is no Input(ShapeTensorList) and no Input(ShapeTensor),the "
-        "attr(shape) information must "
-        "be set by Attr(shape).");
+    PADDLE_ENFORCE_EQ(shape.empty(), false,
+                      platform::errors::InvalidArgument(
+                          "if there is no Input(ShapeTensorList) and no "
+                          "Input(ShapeTensor),the "
+                          "attr(shape) information must "
+                          "be set by Attr(shape)."));
     std::vector<int64_t> tensor_shape;
     tensor_shape.reserve(shape.size());
     for (auto dim : shape) {

python/paddle/fluid/initializer.py

@@ -21,6 +21,7 @@ import numpy as np
 from .wrapped_decorator import signature_safe_contextmanager
 from .core import VarDesc
 from . import unique_name
+from .data_feeder import check_variable_and_dtype, check_type, check_dtype
 
 __all__ = [
     'Constant', 'Uniform', 'Normal', 'TruncatedNormal', 'Xavier', 'Bilinear',
@@ -216,8 +217,10 @@ class UniformInitializer(Initializer):
         Returns:
             the initialization op
         """
-        assert isinstance(var, framework.Variable)
         assert isinstance(block, framework.Block)
+        check_variable_and_dtype(var, "Out", ["float16", "float32", "float64"],
+                                 "uniform_random")
+
         # Initialization Ops should be prepended and not appended
         if self._seed == 0:
             self._seed = block.program.random_seed
@@ -303,8 +306,10 @@ class NormalInitializer(Initializer):
         Returns:
             the initialization op
         """
-        assert isinstance(var, framework.Variable)
         assert isinstance(block, framework.Block)
+
+        check_variable_and_dtype(var, "Out", ["float16", "float32", "float64"],
+                                 "guassian_random")
         # Initialization Ops should be prepended and not appended
         if self._seed == 0:
             self._seed = block.program.random_seed
@@ -494,8 +499,10 @@ class XavierInitializer(Initializer):
         Returns:
             the initialization op
         """
-        assert isinstance(var, framework.Variable)
         assert isinstance(block, framework.Block)
+        check_variable_and_dtype(var, "Out", ["float16", "float32", "float64"],
+                                 "xavier_init")
+
         f_in, f_out = self._compute_fans(var)
 
         # If fan_in and fan_out are passed, use them

python/paddle/fluid/layers/nn.py

@@ -5626,8 +5626,11 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
             #      [0.20541131]], dtype=float32)]
 
     """
+    check_variable_and_dtype(x, 'X', ['float32', 'float64'], 'smooth_l1_loss')
+    check_variable_and_dtype(y, 'Y', ['float32', 'float64'], 'smooth_l1_loss')
 
     helper = LayerHelper('smooth_l1_loss', **locals())
+
     diff = helper.create_variable_for_type_inference(dtype=x.dtype)
     loss = helper.create_variable_for_type_inference(dtype=x.dtype)
     helper.append_op(
@@ -8355,6 +8358,9 @@ def mean_iou(input, label, num_classes):
                                                           num_classes)
     """
     helper = LayerHelper('mean_iou', **locals())
+    check_variable_and_dtype(input, 'Predictions', ['int32', 'int64'],
+                             'mean_iou')
+    check_variable_and_dtype(label, 'Labels', ['int32', 'int64'], 'mean_iou')
     dtype = helper.input_dtype()
     out_mean_iou = helper.create_variable_for_type_inference(dtype='float32')
     out_wrong = helper.create_variable_for_type_inference(dtype='int32')

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

@@ -72,5 +72,42 @@ class TestGaussianRandomOp(unittest.TestCase):
         pass
 
 
+class TestGaussianRandomOpError(unittest.TestCase):
+    def setUp(self):
+        self.op_type = "gaussian_random"
+        self.inputs = {}
+        self.use_mkldnn = False
+        self.attrs = {
+            "shape": [1000, 784],
+            "mean": .0,
+            "std": 1.,
+            "seed": 10,
+            "use_mkldnn": self.use_mkldnn
+        }
+
+        self.outputs = ["Out"]
+
+    def test_errors(self):
+        program = fluid.Program()
+        with fluid.program_guard(fluid.Program(), program):
+            input_data = numpy.random.random((2, 4)).astype("float32")
+            block = program.global_block()
+            vout = block.create_var(name="Out", dtype='int32')
+            normal_initializer = fluid.initializer.NormalInitializer(
+                loc=0.0, scale=1.0, seed=0)
+
+            def test_Variable():
+                # the input type must be Variable
+                normal_initializer(input_data)
+
+            self.assertRaises(TypeError, test_Variable)
+
+            def test_type():
+                # dtype must be float32 or float64
+                normal_initializer(vout)
+
+            self.assertRaises(TypeError, test_type)
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -18,6 +18,7 @@ from __future__ import division
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid as fluid
 
 
 def compute_mean_iou(predictions, labels, num_classes, in_wrongs, in_corrects,
@@ -112,5 +113,20 @@ class TestCase1(TestMeanIOUOp):
         self.in_mean_iou_num = 2
 
 
+class TestMeanIOUOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            # The input type of accuracy_op must be Variable.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            y1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, fluid.layers.mean_iou, x1, y1)
+            # The input dtype of accuracy_op must be float32 or float64.
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype="float32")
+            y2 = fluid.layers.data(name='x2', shape=[4], dtype="float32")
+            self.assertRaises(TypeError, fluid.layers.mean_iou, x2, y2)
+
+
 if __name__ == '__main__':
     unittest.main()

python/paddle/fluid/tests/unittests/test_smooth_l1_loss_op.py

@@ -17,6 +17,7 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid as fluid
 
 
 def smooth_l1_loss_forward(val, sigma2):
@@ -105,5 +106,20 @@ class TestSmoothL1LossOp2(OpTest):
             no_grad_set=set(['Y', 'InsideWeight', 'OutsideWeight']))
 
 
+class TestSmoothL1LossOpError(unittest.TestCase):
+    def test_errors(self):
+        with fluid.program_guard(fluid.Program(), fluid.Program()):
+            # The input type of accuracy_op must be Variable.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            y1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, fluid.layers.smooth_l1, x1, y1)
+            # The input dtype of accuracy_op must be float32 or float64.
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
+            y2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
+            self.assertRaises(TypeError, fluid.layers.smooth_l1, x2, y2)
+
+
 if __name__ == '__main__':
     unittest.main()