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84 lines
2.6 KiB
84 lines
2.6 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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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.fluid.core as core
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import paddle.fluid as fluid
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from paddle.fluid.op import Operator
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class TestSamplingIdOp(OpTest):
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def setUp(self):
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self.op_type = "sampling_id"
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self.use_mkldnn = False
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self.init_kernel_type()
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self.X = np.random.random((100, 10)).astype('float32')
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self.inputs = {"X": self.X}
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self.Y = np.random.random(100).astype('int64')
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self.outputs = {'Out': self.Y}
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self.attrs = {'max': 1.0, 'min': 0.0, 'seed': 1}
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def test_check_output(self):
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self.check_output_customized(self.verify_output)
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y1 = self.out
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self.check_output_customized(self.verify_output)
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y2 = self.out
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# check dtype
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assert y1.dtype == np.int64
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assert y2.dtype == np.int64
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# check output is index ids of inputs
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inputs_ids = np.arange(self.X.shape[1])
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assert np.isin(y1, inputs_ids).all()
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assert np.isin(y2, inputs_ids).all()
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self.assertTrue(np.array_equal(y1, y2))
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self.assertEqual(len(y1), len(self.Y))
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def verify_output(self, outs):
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out = np.array(outs[0])
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self.out = out
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def init_kernel_type(self):
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pass
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class TestSamplingIdShape(unittest.TestCase):
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def test_shape(self):
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x = fluid.layers.data(name='x', shape=[3], dtype='float32')
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output = fluid.layers.sampling_id(x)
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place = fluid.CPUPlace()
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exe = fluid.Executor(place=place)
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exe.run(fluid.default_startup_program())
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feed = {
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'x': np.array(
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[[0.2, 0.3, 0.5], [0.2, 0.3, 0.4]], dtype='float32')
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}
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output_np = exe.run(feed=feed, fetch_list=[output])[0]
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self.assertEqual(output.shape[0], -1)
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self.assertEqual(len(output.shape), 1)
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self.assertEqual(output_np.shape[0], 2)
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self.assertEqual(len(output_np.shape), 1)
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if __name__ == "__main__":
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unittest.main()
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