wehub-resource-sync
118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
# 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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import unittest
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import numpy as np
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from op_test import get_device_place, is_custom_device
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from utils import dygraph_guard
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import paddle
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from paddle.static import Program, program_guard
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class TestRandnOp(unittest.TestCase):
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def test_api(self):
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shape = [1000, 784]
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train_program = Program()
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startup_program = Program()
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with program_guard(train_program, startup_program):
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x1 = paddle.randn(shape, 'float32')
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x2 = paddle.randn(shape, 'float64')
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dim_1 = paddle.tensor.fill_constant([1], "int64", 20)
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dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
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x3 = paddle.randn([dim_1, dim_2, 784])
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var_shape = paddle.static.data('X', [2], 'int32')
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x4 = paddle.randn(var_shape)
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place = get_device_place()
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exe = paddle.static.Executor(place)
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res = exe.run(
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train_program,
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feed={'X': np.array(shape, dtype='int32')},
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fetch_list=[x1, x2, x3, x4],
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)
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for out in res:
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self.assertAlmostEqual(np.mean(out), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(out), 1.0, delta=0.1)
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class TestRandnOpForDygraph(unittest.TestCase):
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def test_api(self):
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shape = [1000, 784]
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place = get_device_place()
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paddle.disable_static(place)
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x1 = paddle.randn(shape, 'float32')
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x2 = paddle.randn(shape, 'float64')
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dim_1 = paddle.tensor.fill_constant([1], "int64", 20)
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dim_2 = paddle.tensor.fill_constant([1], "int32", 50)
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x3 = paddle.randn(shape=[dim_1, dim_2, 784])
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var_shape = paddle.to_tensor(np.array(shape))
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x4 = paddle.randn(var_shape)
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for out in [x1, x2, x3, x4]:
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self.assertAlmostEqual(np.mean(out.numpy()), 0.0, delta=0.1)
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self.assertAlmostEqual(np.std(out.numpy()), 1.0, delta=0.1)
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paddle.enable_static()
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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 dtype of randn_op should be float32 or float64.
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self.assertRaises(TypeError, paddle.randn, [1, 2], 'int32')
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class TestRandnOpCompatibility(unittest.TestCase):
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def setUp(self):
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self.places = [paddle.CPUPlace()]
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if paddle.base.core.is_compiled_with_cuda() or is_custom_device():
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self.places.append(get_device_place())
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self.expected_shape = [2, 3]
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self.dtype = paddle.float32
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def test_gather_with_param_aliases(self):
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with dygraph_guard():
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for place in self.places:
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paddle.device.set_device(place)
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for param_name in ['shape', 'size']:
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tensor = paddle.randn(
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**{param_name: self.expected_shape}, dtype=self.dtype
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)
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self.assertEqual(tensor.shape, self.expected_shape)
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self.assertEqual(tensor.dtype, self.dtype)
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shape_tensor = paddle.to_tensor(
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self.expected_shape, dtype='int32'
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)
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tensor = paddle.randn(
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**{param_name: shape_tensor}, dtype=self.dtype
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)
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self.assertEqual(tensor.shape, self.expected_shape)
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self.assertEqual(tensor.dtype, self.dtype)
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tensor = paddle.randn(*self.expected_shape, dtype=self.dtype)
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self.assertEqual(tensor.shape, self.expected_shape)
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self.assertEqual(tensor.dtype, self.dtype)
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if __name__ == "__main__":
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paddle.enable_static()
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unittest.main()
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