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- # Copyright (c) 2022 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.
-
- import unittest
-
- import numpy as np
- from op import Operator
-
- import paddle
- from paddle.base import core
-
-
- class TestSparseSquareOp(unittest.TestCase):
- def check_with_place(self, place):
- scope = core.Scope()
-
- # create and initialize Grad Variable
- height = 10
- rows = [0, 4, 7]
- self.row_numel = 12
-
- x_selected_rows = scope.var('X').get_selected_rows()
- x_selected_rows.set_height(height)
- x_selected_rows.set_rows(rows)
- np_array = np.ones((len(rows), self.row_numel)).astype("float32")
- np_array[0, 0] = 2.0
- np_array[2, 8] = 4.0
-
- x_tensor = x_selected_rows.get_tensor()
- x_tensor.set(np_array, place)
-
- out_selected_rows = scope.var('Out').get_selected_rows()
- # create and run sqrt operator
- square_op = Operator("square", X='X', Out='Out')
- square_op.run(scope, place)
-
- # get and compare result
- result_array = np.array(out_selected_rows.get_tensor())
-
- np.testing.assert_array_equal(result_array, np.square(np_array))
-
- def test_sparse_acti(self):
- places = [core.CPUPlace()]
- if core.is_compiled_with_cuda():
- places.append(core.CUDAPlace(0))
- for place in places:
- self.check_with_place(place)
-
-
- class TestSparseSqrtOp(unittest.TestCase):
- def check_with_place(self, place):
- scope = core.Scope()
-
- # create and initialize Grad Variable
- height = 10
- rows = [0, 4, 7]
- self.row_numel = 12
-
- x_selected_rows = scope.var('X1').get_selected_rows()
- x_selected_rows.set_height(height)
- x_selected_rows.set_rows(rows)
- np_array = np.ones((len(rows), self.row_numel)).astype("float32")
- np_array[0, 0] = 2.0
- np_array[2, 8] = 4.0
-
- x_tensor = x_selected_rows.get_tensor()
- x_tensor.set(np_array, place)
-
- out_selected_rows = scope.var('Out1').get_selected_rows()
- # create and run sqrt operator
- sqrt_op = Operator("sqrt", X='X1', Out='Out1')
- sqrt_op.run(scope, place)
-
- # get and compare result
- result_array = np.array(out_selected_rows.get_tensor())
- np.testing.assert_allclose(result_array, np.sqrt(np_array), rtol=1e-05)
-
- def test_sparse_acti(self):
- places = [core.CPUPlace()]
- if core.is_compiled_with_cuda():
- places.append(core.CUDAPlace(0))
- for place in places:
- self.check_with_place(place)
-
-
- if __name__ == "__main__":
- paddle.enable_static()
- unittest.main()
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