wehub-resource-sync
95 lines
2.9 KiB
Python
95 lines
2.9 KiB
Python
# 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
|
|
from op_test import get_places
|
|
|
|
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):
|
|
for place in get_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):
|
|
for place in get_places():
|
|
self.check_with_place(place)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
paddle.enable_static()
|
|
unittest.main()
|