wehub-resource-sync
130 lines
4.7 KiB
Python
130 lines
4.7 KiB
Python
# Copyright (c) 2021 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 import _legacy_C_ops, base
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class MyLayer(paddle.nn.Layer):
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def __init__(self, num_stacked_param, use_base_api):
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super().__init__()
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# create ParameterList with iterable Parameters
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self.params = self.paddle_imperative_ParameterList(num_stacked_param)
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def paddle_imperative_ParameterList(self, num_stacked_param):
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return paddle.nn.ParameterList(
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[
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paddle.create_parameter(shape=[2, 2], dtype='float32')
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for _ in range(num_stacked_param)
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]
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)
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def forward(self, x):
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for i, p in enumerate(self.params):
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x = _legacy_C_ops.mul(x, p)
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return x
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class TestImperativeContainerParameterList(unittest.TestCase):
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def parameter_list(self, use_base_api):
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data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
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with base.dygraph.guard():
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x = paddle.to_tensor(data_np)
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num_stacked_param = 4
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model = MyLayer(num_stacked_param, use_base_api)
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self.assertEqual(len(model.params), num_stacked_param)
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res = model(x)
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self.assertListEqual(res.shape, [5, 2])
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loss = paddle.mean(res)
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loss.backward()
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model.params[num_stacked_param - 1] = paddle.create_parameter(
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shape=[2, 3], dtype='float32'
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)
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res = model(x)
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self.assertListEqual(res.shape, [5, 3])
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model.params.append(
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paddle.create_parameter(shape=[3, 4], dtype='float32')
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)
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self.assertEqual(len(model.params), num_stacked_param + 1)
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res = model(x)
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self.assertListEqual(res.shape, [5, 4])
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loss = paddle.mean(res)
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loss.backward()
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def test_parameter_list(self):
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self.parameter_list(False)
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class TestParameterListAssignment(unittest.TestCase):
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def test_assign_Tensor(self):
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param_list = paddle.nn.ParameterList(
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[
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paddle.create_parameter(shape=[2, 2], dtype='float32'),
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paddle.create_parameter(shape=[2, 2], dtype='float32'),
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]
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)
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assert isinstance(param_list[0], paddle.base.framework.EagerParamBase)
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assert isinstance(param_list[1], paddle.base.framework.EagerParamBase)
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new_param1 = paddle.randn([2, 3])
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param_list[0] = new_param1
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assert isinstance(param_list[0], paddle.base.framework.EagerParamBase)
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new_param2 = paddle.randn([2, 4])
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param_list[1] = new_param2
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assert isinstance(param_list[1], paddle.base.framework.EagerParamBase)
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np.testing.assert_allclose(new_param1.numpy(), param_list[0].numpy())
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np.testing.assert_allclose(new_param2.numpy(), param_list[1].numpy())
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def test_assign_Parameter(self):
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param_list = paddle.nn.ParameterList(
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[
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paddle.create_parameter(shape=[2, 3], dtype='float32'),
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paddle.create_parameter(shape=[2, 4], dtype='float32'),
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]
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)
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assert isinstance(param_list[0], paddle.base.framework.EagerParamBase)
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assert isinstance(param_list[1], paddle.base.framework.EagerParamBase)
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new_param1 = paddle.create_parameter([2, 5], dtype='float32')
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param_list[0] = new_param1
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assert isinstance(param_list[0], paddle.base.framework.EagerParamBase)
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new_param2 = paddle.create_parameter([2, 6], dtype='float64')
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param_list[1] = new_param2
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assert isinstance(param_list[1], paddle.base.framework.EagerParamBase)
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np.testing.assert_allclose(new_param1.numpy(), param_list[0].numpy())
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np.testing.assert_allclose(new_param2.numpy(), param_list[1].numpy())
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def test_assign_wrong_type(self):
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param_list = paddle.nn.ParameterList(
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[
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paddle.create_parameter(shape=[2, 2], dtype='float32'),
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paddle.create_parameter(shape=[2, 2], dtype='float32'),
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]
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)
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with self.assertRaises(TypeError):
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param_list[0] = 1
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if __name__ == '__main__':
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unittest.main()
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