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91 lines
2.9 KiB
91 lines
2.9 KiB
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import print_function
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import unittest
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import numpy as np
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import six
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from op_test import OpTest
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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class TestSplitIdsOp(OpTest):
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def setUp(self):
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self.op_type = "split_ids"
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ids = np.array([[0], [2], [2], [3], [5], [5], [6]]).astype('int64')
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out0 = np.array([[0], [3], [6]]).astype('int64')
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out1 = np.array([[]]).astype('int64')
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out2 = np.array([[2], [2], [5], [5]]).astype('int64')
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self.inputs = {'Ids': ids}
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self.outputs = {'Out': [('out0', out0), ('out1', out1), ('out2', out2)]}
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def test_check_output(self):
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self.check_output()
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class TestSpliteIds(unittest.TestCase):
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def get_places(self):
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places = [core.CPUPlace()]
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return places
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def test_check_output(self):
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for place in self.get_places():
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self.check_with_place(place)
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def check_with_place(self, place):
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scope = core.Scope()
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rows = [0, 5, 7, 4, 9]
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height = 20
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row_numel = 2
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# initialize input variable X
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x = scope.var('X').get_selected_rows()
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x.set_rows(rows)
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x.set_height(height)
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np_array = np.ones((len(rows), row_numel)).astype("float32")
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for i in range(len(rows)):
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for j in range(row_numel):
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np_array[i, j] = rows[i] + j
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x_tensor = x.get_tensor()
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x_tensor.set(np_array, place)
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outs_name = ["out%d" % i for i in six.moves.xrange(3)]
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outs = [
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scope.var(var_name).get_selected_rows() for var_name in outs_name
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]
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# expected output selected rows
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expected_out_rows = [[0, 9], [7, 4], [5]]
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op = Operator("split_ids", Ids="X", Out=outs_name)
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for _ in range(3):
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op.run(scope, place)
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for i in range(len(outs)):
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expected_rows = expected_out_rows[i]
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self.assertEqual(outs[i].rows(), expected_rows)
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for j in range(len(expected_rows)):
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row = expected_rows[j]
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self.assertAlmostEqual(
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float(row), np.array(outs[i].get_tensor())[j, 0])
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self.assertAlmostEqual(
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float(row + 1), np.array(outs[i].get_tensor())[j, 1])
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if __name__ == '__main__':
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unittest.main()
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