paddlepaddle--paddle
wehub-resource-sync
95 行
3.4 KiB
Python
95 行
3.4 KiB
Python
# Copyright (c) 2023 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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import paddle
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from paddle.base.framework import Variable
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# Parameters
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# data (Tensor) – parameter tensor.
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# requires_grad (bool, optional) – if the parameter requires gradient. Default: True
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class TestPaddleParameter(unittest.TestCase):
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def setUp(self):
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self.data_np = np.array(
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[[1.0, 2.0, 3.0], [2.0, 3.0, 4.0]], dtype='float32'
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)
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def test_case_1(self):
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x = paddle.to_tensor(self.data_np)
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result = paddle.nn.Parameter(x)
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np.testing.assert_array_equal(result.numpy(), x.numpy())
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self.assertEqual(result.trainable, True) # Default requires grad
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def test_case_2(self):
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x = paddle.to_tensor(self.data_np)
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result = paddle.nn.Parameter(x, requires_grad=False)
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np.testing.assert_array_equal(result.numpy(), x.numpy())
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self.assertEqual(result.trainable, False)
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def test_alias_case_1(self):
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x = paddle.to_tensor(self.data_np)
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result = paddle.nn.parameter.Parameter(x)
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np.testing.assert_array_equal(result.numpy(), x.numpy())
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self.assertEqual(result.trainable, True)
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def test_case_3(self):
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x = paddle.to_tensor(self.data_np)
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result = paddle.nn.Parameter(x, False)
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np.testing.assert_array_equal(result.numpy(), x.numpy())
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self.assertEqual(result.trainable, False)
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def test_case_4(self):
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x = paddle.to_tensor(self.data_np)
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result = paddle.nn.Parameter(data=x, requires_grad=False)
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np.testing.assert_array_equal(result.numpy(), x.numpy())
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self.assertEqual(result.trainable, False)
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def test_case_5(self):
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x = paddle.to_tensor(self.data_np)
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result = paddle.nn.Parameter(requires_grad=False, data=x)
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np.testing.assert_array_equal(result.numpy(), x.numpy())
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self.assertEqual(result.trainable, False)
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def test_case_6(self):
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result = paddle.nn.Parameter()
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self.assertEqual(result.shape, [0]) # Empty parameter
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self.assertEqual(result.trainable, True)
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def test_inheritance(self):
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"""Test that Parameter is subclass of both Parameter and Tensor"""
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param = paddle.nn.Parameter()
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self.assertTrue(isinstance(param, paddle.Tensor))
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self.assertTrue(isinstance(param, paddle.nn.Parameter))
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self.assertEqual(type(param), paddle.nn.Parameter)
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self.assertTrue(isinstance(param, Variable))
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def test_repr(self):
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"""Test Parameter.__repr__() output"""
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x = paddle.to_tensor(self.data_np)
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x.stop_gradient = False
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param = paddle.nn.Parameter(x)
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expected_repr = f"Parameter containing:\n{x!s}"
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self.assertEqual(repr(param), expected_repr)
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self.assertEqual(str(param), expected_repr)
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if __name__ == "__main__":
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unittest.main()
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