remnove the unsed unittest test=develop (#22999)

* remove the fill_constant_batch_size_like,  gaussian_random_batch_size_like and uniform_random_batch_size_like_cn three Op unittest 

python/paddle/fluid/tests/unittests/CMakeLists.txt

@@ -216,11 +216,9 @@ set(TEST_OPS_WITH_GC
   test_concat_op
   test_elementwise_add_op
   test_elementwise_sub_op
-  test_fill_constant_batch_size_like_op
   test_fill_zeros_like2_op
   test_gather_op
   test_gather_nd_op
-  test_gaussian_random_batch_size_like_op
   test_linear_chain_crf_op
   test_lod_reset_op
   test_lookup_table_op
@@ -238,8 +236,7 @@ set(TEST_OPS_WITH_GC
   test_sequence_slice_op
   test_slice_op
   test_space_to_depth_op
-  test_squared_l2_distance_op
-  test_uniform_random_batch_size_like_op)
+  test_squared_l2_distance_op)
 
 foreach(TEST_OP ${TEST_OPS_WITH_GC})
   list(REMOVE_ITEM TEST_OPS ${TEST_OP})

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

-#   Copyright (c) 2018 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 paddle.fluid as fluid
-from op_test import OpTest
-
-
-class TestFillConstantBatchSizeLikeWhenFirstDimIsBatchSize(OpTest):
-    def setUp(self):
-        self.op_type = "fill_constant_batch_size_like"
-        self.inputs = {'Input': np.random.random((219, 232)).astype("float32")}
-        self.attrs = {'value': 3.5, 'shape': [-1, 132, 7]}
-
-        out = np.random.random((219, 132, 7)).astype("float32")
-        out.fill(3.5)
-        self.outputs = {'Out': out}
-
-    def test_check_output(self):
-        self.check_output()
-
-
-class TestFillConstantBatchSizeLikeWhenSecondDimIsBatchSize(OpTest):
-    def setUp(self):
-        self.op_type = "fill_constant_batch_size_like"
-        self.inputs = {'Input': np.random.random((219, 232)).astype("float32")}
-        self.attrs = {
-            'value': 3.5,
-            'shape': [132, -1, 7],
-            'input_dim_idx': 0,
-            'output_dim_idx': 1
-        }
-
-        out = np.random.random((132, 219, 7)).astype("float32")
-        out.fill(3.5)
-        self.outputs = {'Out': out}
-
-    def test_check_output(self):
-        self.check_output()
-
-
-class TestFillConstantBatchSizeLikeInt64(OpTest):
-    def setUp(self):
-        self.op_type = "fill_constant_batch_size_like"
-        self.inputs = {'Input': np.random.random((219, 232)).astype("int64")}
-        self.attrs = {'value': 5894589485094, 'shape': [-1, 132, 7]}
-
-        out = np.random.random((219, 132, 7)).astype("int64")
-        out.fill(5894589485094)
-        self.outputs = {'Out': out}
-
-    def test_check_output(self):
-        self.check_output()
-
-
-class TestFillConstantBatchSizeLikeWithLoDTensor(OpTest):
-    def setUp(self):
-        self.op_type = "fill_constant_batch_size_like"
-        self.inputs = {
-            'Input': (np.random.random((31, 28)).astype("float32"),
-                      [[9, 14, 8]])
-        }
-        self.attrs = {
-            'value': 3.5,
-            'shape': [-1, 16],
-            'input_dim_idx': 0,
-            'output_dim_idx': 0
-        }
-
-        out = np.random.random((3, 16)).astype("float32")
-        out.fill(3.5)
-        self.outputs = {'Out': out}
-
-    def test_check_output(self):
-        self.check_output()
-
-
-# Test python API
-class TestFillConstantBatchSizeLikeAPI(unittest.TestCase):
-    def test_api(self):
-        like = fluid.layers.fill_constant(
-            shape=[1, 200], value=10, dtype='int64')
-        out = fluid.layers.fill_constant_batch_size_like(
-            input=like, shape=[2, 300], value=1315454564656, dtype='int64')
-        exe = fluid.Executor(place=fluid.CPUPlace())
-        res, = exe.run(fluid.default_main_program(), fetch_list=[out])
-
-        assert np.array_equal(
-            res[0], np.full(
-                [300], 1315454564656, dtype="int64"))
-
-
-if __name__ == "__main__":
-    unittest.main()

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

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# 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
-from op_test import OpTest
-
-
-class TestGaussianRandomBatchSizeLike(OpTest):
-    def setUp(self):
-        self.op_type = "gaussian_random_batch_size_like"
-        self.inputs = {'Input': np.zeros((500, 2000), dtype="float32")}
-        self.attrs = {'mean': 1., 'std': 2., 'shape': [-1, 2000]}
-        self.outputs = {'Out': np.zeros((500, 2000), dtype='float32')}
-
-    def test_check_output(self):
-        self.check_output_customized(self.verify_output)
-
-    def verify_output(self, outs):
-        self.assertEqual(outs[0].shape, (500, 2000))
-        hist, _ = np.histogram(outs[0], range=(-3, 5))
-        hist = hist.astype("float32")
-        hist /= float(outs[0].size)
-        data = np.random.normal(size=(500, 2000), loc=1, scale=2)
-        hist2, _ = np.histogram(data, range=(-3, 5))
-        hist2 = hist2.astype("float32")
-        hist2 /= float(outs[0].size)
-        self.assertTrue(
-            np.allclose(
-                hist, hist2, rtol=0, atol=0.01),
-            "hist: " + str(hist) + " hist2: " + str(hist2))
-
-
-if __name__ == "__main__":
-    unittest.main()

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

-#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
-#
-# 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
-from op_test import OpTest
-
-
-class TestUniformRandomBatchSizeLike(OpTest):
-    def setUp(self):
-        self.op_type = "uniform_random_batch_size_like"
-        self.inputs = {'Input': np.zeros((500, 2000), dtype="float32")}
-        self.attrs = {'min': 1., 'max': 2., 'shape': [-1, 2000]}
-        self.outputs = {'Out': np.zeros((500, 2000), dtype='float32')}
-
-    def test_check_output(self):
-        self.check_output_customized(self.verify_output)
-
-    def verify_output(self, outs):
-        self.assertEqual(outs[0].shape, (500, 2000))
-        hist, _ = np.histogram(outs[0], range=(1, 2))
-        hist = hist.astype("float32")
-        hist /= float(outs[0].size)
-        prob = 0.1 * np.ones((10))
-        self.assertTrue(
-            np.allclose(
-                hist, prob, rtol=0, atol=0.01), "hist: " + str(hist))
-
-
-if __name__ == "__main__":
-    unittest.main()