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90 lines
3.0 KiB
90 lines
3.0 KiB
# Copyright (c) 2020 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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from op_test import OpTest
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import paddle.nn.functional as F
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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import paddle.tensor as tensor
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class TestTraceOp(OpTest):
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def setUp(self):
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self.op_type = "trace"
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self.init_config()
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self.outputs = {'Out': self.target}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(['Input'], 'Out')
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def init_config(self):
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self.case = np.random.randn(20, 6).astype('float64')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': 0, 'axis1': 0, 'axis2': 1}
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self.target = np.trace(self.inputs['Input'])
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class TestTraceOpCase1(TestTraceOp):
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def init_config(self):
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self.case = np.random.randn(2, 20, 2, 3).astype('float32')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': 1, 'axis1': 0, 'axis2': 2}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'])
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class TestTraceOpCase2(TestTraceOp):
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def init_config(self):
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self.case = np.random.randn(2, 20, 2, 3).astype('float32')
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self.inputs = {'Input': self.case}
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self.attrs = {'offset': -5, 'axis1': 1, 'axis2': -1}
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self.target = np.trace(
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self.inputs['Input'],
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offset=self.attrs['offset'],
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axis1=self.attrs['axis1'],
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axis2=self.attrs['axis2'])
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class TestTraceAPICase(unittest.TestCase):
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def test_case1(self):
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case = np.random.randn(2, 20, 2, 3).astype('float32')
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data1 = fluid.data(name='data1', shape=[2, 20, 2, 3], dtype='float32')
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out1 = tensor.trace(data1)
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out2 = tensor.trace(data1, offset=-5, axis1=1, axis2=-1)
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place = core.CPUPlace()
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exe = fluid.Executor(place)
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results = exe.run(fluid.default_main_program(),
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feed={"data1": case},
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fetch_list=[out1, out2],
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return_numpy=True)
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target1 = np.trace(case)
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target2 = np.trace(case, offset=-5, axis1=1, axis2=-1)
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self.assertTrue(np.allclose(results[0], target1))
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self.assertTrue(np.allclose(results[1], target2))
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if __name__ == "__main__":
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unittest.main()
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