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67 lines
2.6 KiB
67 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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from __future__ import print_function
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import op_test
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import numpy as np
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import unittest
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import paddle.fluid.core as core
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from paddle.fluid.op import Operator
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import paddle.fluid as fluid
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from paddle.fluid import compiler, Program, program_guard
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class TestAssignOp(op_test.OpTest):
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def setUp(self):
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self.op_type = "assign"
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x = np.random.random(size=(100, 10))
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self.inputs = {'X': x}
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self.outputs = {'Out': x}
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def test_forward(self):
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self.check_output()
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def test_backward(self):
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self.check_grad(['X'], 'Out')
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class TestAssignOpError(unittest.TestCase):
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def test_errors(self):
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with program_guard(Program(), Program()):
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# The type of input must be Variable or numpy.ndarray.
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x1 = fluid.create_lod_tensor(
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np.array([[-1]]), [[1]], fluid.CPUPlace())
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self.assertRaises(TypeError, fluid.layers.assign, x1)
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# When the type of input is Variable, the dtype of input must be float32, float64, int32, int64, bool.
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x3 = fluid.layers.data(name='x3', shape=[4], dtype="float16")
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self.assertRaises(TypeError, fluid.layers.assign, x3)
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x4 = fluid.layers.data(name='x4', shape=[4], dtype="uint8")
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self.assertRaises(TypeError, fluid.layers.assign, x4)
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# When the type of input is numpy.ndarray, the dtype of input must be float32, int32.
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x5 = np.array([[2.5, 2.5]], dtype='bool')
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self.assertRaises(TypeError, fluid.layers.assign, x5)
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x6 = np.array([[2.5, 2.5]], dtype='float16')
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self.assertRaises(TypeError, fluid.layers.assign, x6)
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x7 = np.array([[2.5, 2.5]], dtype='float64')
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self.assertRaises(TypeError, fluid.layers.assign, x7)
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x8 = np.array([[2.5, 2.5]], dtype='int64')
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self.assertRaises(TypeError, fluid.layers.assign, x8)
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x9 = np.array([[2.5, 2.5]], dtype='uint8')
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self.assertRaises(TypeError, fluid.layers.assign, x9)
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if __name__ == '__main__':
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unittest.main()
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