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remnove the unsed unittest test=develop (#22999)

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* remove the fill_constant_batch_size_like,  gaussian_random_batch_size_like and uniform_random_batch_size_like_cn three Op unittest
revert-22710-feature/integrated_ps_api
wangchaochaohu 5 years ago committed by GitHub
parent db40ee86db
commit 91b0a9ab23
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GPG Key ID: 4AEE18F83AFDEB23
4 changed files with 1 additions and 204 deletions
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5
python/paddle/fluid/tests/unittests/CMakeLists.txt
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@ -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})

108
python/paddle/fluid/tests/unittests/test_fill_constant_batch_size_like_op.py
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@ -1,108 +0,0 @@
# 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()

48
python/paddle/fluid/tests/unittests/test_gaussian_random_batch_size_like_op.py
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@ -1,48 +0,0 @@
# 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()

44
python/paddle/fluid/tests/unittests/test_uniform_random_batch_size_like_op.py
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@ -1,44 +0,0 @@
# 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()
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