wehub-resource-sync
129 lines
4.1 KiB
Python
129 lines
4.1 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 optimizer
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class TestOptimizerForVarBase(unittest.TestCase):
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def setUp(self):
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self.lr = 0.01
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def run_optimizer_step_with_varbase_list_input(self, optimizer):
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x = paddle.zeros([2, 3])
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y = paddle.ones([2, 3])
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x.stop_gradient = False
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z = x + y
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opt = optimizer(
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learning_rate=self.lr, parameters=[x], weight_decay=0.01
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)
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z.backward()
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opt.step()
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np.testing.assert_allclose(
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x.numpy(), np.full([2, 3], -self.lr), rtol=1e-05
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)
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def run_optimizer_minimize_with_varbase_list_input(self, optimizer):
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x = paddle.zeros([2, 3])
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y = paddle.ones([2, 3])
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x.stop_gradient = False
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z = x + y
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opt = optimizer(learning_rate=self.lr, parameters=[x])
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z.backward()
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opt.minimize(z)
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np.testing.assert_allclose(
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x.numpy(), np.full([2, 3], -self.lr), rtol=1e-05
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)
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def test_adam_with_varbase_list_input(self):
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self.run_optimizer_step_with_varbase_list_input(optimizer.Adam)
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self.run_optimizer_minimize_with_varbase_list_input(optimizer.Adam)
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def test_sgd_with_varbase_list_input(self):
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self.run_optimizer_step_with_varbase_list_input(optimizer.SGD)
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self.run_optimizer_minimize_with_varbase_list_input(optimizer.SGD)
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def test_adagrad_with_varbase_list_input(self):
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self.run_optimizer_step_with_varbase_list_input(optimizer.Adagrad)
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self.run_optimizer_minimize_with_varbase_list_input(optimizer.Adagrad)
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def test_adamw_with_varbase_list_input(self):
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self.run_optimizer_step_with_varbase_list_input(optimizer.AdamW)
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self.run_optimizer_minimize_with_varbase_list_input(optimizer.AdamW)
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def test_adamax_with_varbase_list_input(self):
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self.run_optimizer_step_with_varbase_list_input(optimizer.Adamax)
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self.run_optimizer_minimize_with_varbase_list_input(optimizer.Adamax)
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def test_momentum_with_varbase_list_input(self):
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self.run_optimizer_step_with_varbase_list_input(optimizer.Momentum)
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self.run_optimizer_minimize_with_varbase_list_input(optimizer.Momentum)
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def test_optimizer_with_varbase_input(self):
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x = paddle.zeros([2, 3])
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with self.assertRaises(TypeError):
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optimizer.Adam(learning_rate=self.lr, parameters=x)
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def test_create_param_lr_with_1_for_coverage(self):
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x = paddle.base.framework.EagerParamBase(
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dtype="float32",
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shape=[5, 10],
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name="x",
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optimize_attr={'learning_rate': 1.0},
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)
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x.value().get_tensor().set(
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np.random.random((5, 10)).astype('float32'),
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paddle.base.framework._current_expected_place(),
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)
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y = paddle.ones([5, 10])
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z = x + y
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opt = optimizer.Adam(learning_rate=self.lr, parameters=[x])
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z.backward()
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opt.step()
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def test_create_param_lr_with_no_1_value_for_coverage(self):
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x = paddle.base.framework.EagerParamBase(
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dtype="float32",
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shape=[5, 10],
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name="x",
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optimize_attr={'learning_rate': 0.12},
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)
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x.value().get_tensor().set(
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np.random.random((5, 10)).astype('float32'),
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paddle.base.framework._current_expected_place(),
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)
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y = paddle.ones([5, 10])
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z = x + y
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opt = optimizer.Adam(learning_rate=self.lr, parameters=[x])
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z.backward()
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opt.step()
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if __name__ == "__main__":
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unittest.main()
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