wehub-resource-sync
83 lines
2.6 KiB
Python
83 lines
2.6 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.jit.sot.symbolic.compile_cache import CompileSIRCache
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class SimpleNet(paddle.nn.Layer):
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def __init__(self):
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super().__init__()
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self.dropout = paddle.nn.Dropout(p=0.5)
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def forward(self, x):
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if self.training:
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out1 = paddle.nn.functional.dropout(x, p=0.5, training=True)
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else:
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out1 = paddle.nn.functional.dropout(x, p=0.5, training=False)
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out1 = self.dropout(out1)
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return out1
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class TestModelSwitchTraining(unittest.TestCase):
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def setUp(self):
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self.seed = 1127
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self.net = SimpleNet()
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# singleton
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self.compile_cache = CompileSIRCache()
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def check_mode(self, is_train):
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self.assertEqual(len(self.compile_cache.cache), 1)
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mode = next(
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iter(self.compile_cache.cache.values())
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).partial_program.training
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self.assertEqual(mode, is_train)
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def get_dygraph_out(self, input):
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paddle.seed(self.seed)
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self.net.eval()
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eval_result = self.net(input)
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self.net.train()
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train_result = self.net(input)
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return eval_result, train_result
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def get_static_out(self, input):
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paddle.seed(self.seed)
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self.compile_cache.clear()
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static_net = paddle.jit.to_static(self.net, full_graph=False)
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static_net.eval()
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eval_result = static_net(input)
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self.check_mode(is_train=False)
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self.compile_cache.clear()
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static_net.train()
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train_result = static_net(input)
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self.check_mode(is_train=True)
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return eval_result, train_result
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def test_model_switch_training(self):
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input = paddle.rand((10, 10))
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dygraph_eval, dygraph_train = self.get_dygraph_out(input)
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static_eval, static_train = self.get_static_out(input)
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np.testing.assert_allclose(dygraph_eval.numpy(), static_eval.numpy())
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np.testing.assert_allclose(dygraph_train.numpy(), static_train.numpy())
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if __name__ == "__main__":
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unittest.main()
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