wehub-resource-sync
101 lines
3.3 KiB
Python
101 lines
3.3 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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from dygraph_to_static_utils import (
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Dy2StTestBase,
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enable_to_static_guard,
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test_default_mode_only,
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)
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from test_resnet import ResNetHelper
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import paddle
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class TestResnetWithPass(Dy2StTestBase):
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def setUp(self):
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self.build_strategy = paddle.static.BuildStrategy()
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self.build_strategy.fuse_elewise_add_act_ops = True
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self.build_strategy.fuse_bn_act_ops = True
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self.build_strategy.fuse_bn_add_act_ops = True
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self.build_strategy.enable_addto = True
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self.resnet_helper = ResNetHelper()
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# NOTE: for enable_addto
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paddle.set_flags({"FLAGS_max_inplace_grad_add": 8})
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def train(self, to_static):
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with enable_to_static_guard(to_static):
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return self.resnet_helper.train(to_static, self.build_strategy)
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def verify_predict(self):
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image = np.random.random([1, 3, 224, 224]).astype('float32')
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dy_pre = self.resnet_helper.predict_dygraph(image)
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st_pre = self.resnet_helper.predict_static(image)
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dy_jit_pre = self.resnet_helper.predict_dygraph_jit(image)
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np.testing.assert_allclose(
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dy_pre,
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st_pre,
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rtol=1e-05,
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err_msg=f'dy_pre:\n {dy_pre}\n, st_pre: \n{st_pre}.',
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)
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np.testing.assert_allclose(
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dy_jit_pre,
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st_pre,
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rtol=1e-05,
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err_msg=f'dy_jit_pre:\n {dy_jit_pre}\n, st_pre: \n{st_pre}.',
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)
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predictor_pre = self.resnet_helper.predict_analysis_inference(image)
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np.testing.assert_allclose(
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predictor_pre,
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st_pre,
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rtol=1e-05,
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err_msg=f'predictor_pre:\n {predictor_pre}\n, st_pre: \n{st_pre}.',
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)
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def test_resnet(self):
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static_loss = self.train(to_static=True)
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dygraph_loss = self.train(to_static=False)
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np.testing.assert_allclose(
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static_loss,
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dygraph_loss,
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rtol=1e-05,
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err_msg=f'static_loss: {static_loss} \n dygraph_loss: {dygraph_loss}',
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)
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self.verify_predict()
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@test_default_mode_only
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def test_in_static_mode_onednn(self):
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paddle.set_flags({'FLAGS_use_onednn': True})
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try:
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if paddle.base.core.is_compiled_with_onednn():
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self.resnet_helper.train(True, self.build_strategy)
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finally:
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paddle.set_flags({'FLAGS_use_onednn': False})
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class TestError(Dy2StTestBase):
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def test_type_error(self):
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def foo(x):
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out = x + 1
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return out
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with self.assertRaises(TypeError):
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static_foo = paddle.jit.to_static(foo, build_strategy="x")
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if __name__ == '__main__':
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unittest.main()
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