add input type and dtype check for accuracy_op (#20399)

* add input type and dtype check for accuracy_op

* add input type and dtype check for accuracy_op

* modify python error on accuracy_op,add test=develop

* modify details on accuracy_op, test=develop

* test float16, test=develop

* add warning, test=develop

paddle/fluid/operators/metrics/accuracy_op.cc

@@ -40,12 +40,24 @@ class AccuracyOp : public framework::OperatorWithKernel {
     // Assume indices has same shape as inference, because
     // it's the output of topk.
 
-    PADDLE_ENFORCE_EQ(label_dim.size(), 2, "label's rank must be 2.");
-    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, label_dim[1], 1,
-                                 "label's second dimension must be 1");
-    PADDLE_INFERSHAPE_ENFORCE_EQ(ctx, inference_dim[0], label_dim[0],
-                                 "the inference tensor's num_rows must be"
-                                 " the same as label.");
+    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);
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, 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);
+    PADDLE_INFERSHAPE_ENFORCE_EQ(
+        ctx, 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]);
 
     ctx->SetOutputDim("Accuracy", {1});
     ctx->SetOutputDim("Correct", {1});

python/paddle/fluid/layers/metric_op.py

@@ -23,6 +23,7 @@ from ..initializer import Normal, Constant
 from ..framework import Variable
 from ..param_attr import ParamAttr
 from . import nn
+from ..data_feeder import convert_dtype
 
 __all__ = ['accuracy', 'auc']
 
@@ -71,6 +72,18 @@ def accuracy(input, label, k=1, correct=None, total=None):
             #[array([0.6666667], dtype=float32)]
     """
     helper = LayerHelper("accuracy", **locals())
+    if not isinstance(input, Variable):
+        raise TypeError(
+            "The type of 'input' in accuracy must be Variable, but received %s"
+            % (type(input)))
+    if convert_dtype(input.dtype) in ['float16']:
+        warnings.warn(
+            "The data type of 'input' in accuracy only support float16 in GPU now."
+        )
+    if convert_dtype(input.dtype) not in ['float16', 'float32', 'float64']:
+        raise TypeError(
+            "The data type of 'input' in accuracy must be float16 or float32 or float64, but received %s."
+            % (convert_dtype(input.dtype)))
     topk_out, topk_indices = nn.topk(input, k=k)
     acc_out = helper.create_variable_for_type_inference(dtype="float32")
     if correct is None:

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

@@ -17,6 +17,8 @@ from __future__ import print_function
 import unittest
 import numpy as np
 from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
 
 
 class TestAccuracyOp(OpTest):
@@ -56,5 +58,21 @@ class TestAccuracyOpFp16(TestAccuracyOp):
         self.check_output(atol=1e-3)
 
 
+class TestAccuracyOpError(OpTest):
+    def test_errors(self):
+        with program_guard(Program(), Program()):
+            # The input type of accuracy_op must be Variable.
+            x1 = fluid.create_lod_tensor(
+                np.array([[-1]]), [[1]], fluid.CPUPlace())
+            self.assertRaises(TypeError, fluid.layers.accuracy, x1)
+            # The input dtype of accuracy_op must be float32 or float64.
+            x2 = fluid.layers.data(name='x2', shape=[4], dtype="int32")
+            self.assertRaises(TypeError, fluid.layers.accuracy, x2)
+            x3 = fluid.layers.data(name='input', shape=[-1, 2], dtype="float16")
+            label = fluid.layers.data(
+                name='label', shape=[-1, 1], dtype="int32")
+            fluid.layers.accuracy(input=x3, label=label)
+
+
 if __name__ == '__main__':
     unittest.main()