add unittest for square error cost op (#19746)

* add unit test for square error cost op

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

@@ -1912,6 +1912,14 @@ class TestBook(LayerTest):
             out = layers.pixel_shuffle(x, upscale_factor=3)
             return (out)
 
+    def make_square_error_cost(self):
+        with program_guard(fluid.default_main_program(),
+                           fluid.default_startup_program()):
+            x = self._get_data(name="X", shape=[1], dtype="float32")
+            y = self._get_data(name="Y", shape=[1], dtype="float32")
+            out = layers.square_error_cost(input=x, label=y)
+            return (out)
+
     def test_dynamic_lstmp(self):
         # TODO(minqiyang): dygraph do not support lod now
         with self.static_graph():

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

+#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import sys
+import paddle.fluid.core as core
+import paddle.fluid as fluid
+import paddle.fluid.layers as layers
+from paddle.fluid.executor import Executor
+
+
+class TestSquareErrorCost(unittest.TestCase):
+    def test_square_error_cost(self):
+        input_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
+        label_val = np.random.uniform(0.1, 0.5, (2, 3)).astype("float32")
+
+        sub = input_val - label_val
+        np_result = sub * sub
+
+        input_var = layers.create_tensor(dtype="float32", name="input")
+        label_var = layers.create_tensor(dtype="float32", name="label")
+
+        layers.assign(input=input_val, output=input_var)
+        layers.assign(input=label_val, output=label_var)
+        output = layers.square_error_cost(input=input_var, label=label_var)
+
+        for use_cuda in ([False, True]
+                         if core.is_compiled_with_cuda() else [False]):
+
+            place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()
+            exe = Executor(place)
+            result = exe.run(fluid.default_main_program(),
+                             feed={"input": input_var,
+                                   "label": label_var},
+                             fetch_list=[output])
+
+            self.assertTrue(np.isclose(np_result, result).all())
+
+
+if __name__ == "__main__":
+    unittest.main()