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110 lines
4.2 KiB
110 lines
4.2 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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import paddle
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from paddle.fluid import Program, program_guard
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class TestRandnOp(unittest.TestCase):
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def test_api(self):
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x1 = paddle.randn(shape=[1000, 784], dtype='float32')
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x2 = paddle.randn(shape=[1000, 784], dtype='float64')
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x3 = fluid.layers.fill_constant(
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shape=[1000, 784], dtype='float32', value=0)
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paddle.randn(shape=[1000, 784], out=x3, dtype='float32')
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x4 = paddle.randn(shape=[1000, 784], dtype='float32', device='cpu')
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x5 = paddle.randn(shape=[1000, 784], dtype='float32', device='gpu')
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x6 = paddle.randn(
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shape=[1000, 784],
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dtype='float32',
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device='gpu',
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stop_gradient=False)
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place = fluid.CUDAPlace(0) if core.is_compiled_with_cuda(
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) else fluid.CPUPlace()
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exe = fluid.Executor(place)
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res = exe.run(fluid.default_main_program(),
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feed={},
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fetch_list=[x1, x2, x3, x4, x5, x6])
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self.assertAlmostEqual(np.mean(res[0]), .0, delta=0.1)
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self.assertAlmostEqual(np.std(res[0]), 1., delta=0.1)
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self.assertAlmostEqual(np.mean(res[1]), .0, delta=0.1)
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self.assertAlmostEqual(np.std(res[1]), 1., delta=0.1)
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self.assertAlmostEqual(np.mean(res[2]), .0, delta=0.1)
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self.assertAlmostEqual(np.std(res[2]), 1., delta=0.1)
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self.assertAlmostEqual(np.mean(res[3]), .0, delta=0.1)
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self.assertAlmostEqual(np.std(res[3]), 1., delta=0.1)
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self.assertAlmostEqual(np.mean(res[4]), .0, delta=0.1)
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self.assertAlmostEqual(np.std(res[4]), 1., delta=0.1)
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self.assertAlmostEqual(np.mean(res[5]), .0, delta=0.1)
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self.assertAlmostEqual(np.std(res[5]), 1., delta=0.1)
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class TestRandnOpError(unittest.TestCase):
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def test_error(self):
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with program_guard(Program(), Program()):
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# The argument shape's size of randn_op should not be 0.
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def test_shape_size():
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out = paddle.randn(shape=[])
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self.assertRaises(AssertionError, test_shape_size)
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# The argument shape's type of randn_op should be list or tuple.
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def test_shape_type():
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out = paddle.randn(shape=1)
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self.assertRaises(TypeError, test_shape_type)
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# The argument dtype of randn_op should be float32 or float64.
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def test_dtype_float16():
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out = paddle.randn(shape=[1, 2], dtype='float16')
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self.assertRaises(TypeError, test_dtype_float16)
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# The argument dtype of randn_op should be float32 or float64.
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def test_dtype_int32():
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out = paddle.randn(shape=[1, 2], dtype='int32')
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self.assertRaises(TypeError, test_dtype_int32)
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# The argument dtype of randn_op should be float32 or float64.
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def test_dtype_int64():
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out = paddle.randn(shape=[1, 2], dtype='int64')
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self.assertRaises(TypeError, test_dtype_int64)
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# The argument dtype of randn_op should be float32 or float64.
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def test_dtype_uint8():
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out = paddle.randn(shape=[1, 2], dtype='uint8')
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self.assertRaises(TypeError, test_dtype_uint8)
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# The argument dtype of randn_op should be float32 or float64.
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def test_dtype_bool():
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out = paddle.randn(shape=[1, 2], dtype='bool')
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self.assertRaises(TypeError, test_dtype_bool)
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if __name__ == "__main__":
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unittest.main()
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