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58 lines
2.5 KiB
58 lines
2.5 KiB
# Copyright (c) 2019 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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from __future__ import print_function
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import seresnext_net
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import paddle.fluid.core as core
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from parallel_executor_test_base import TestParallelExecutorBase, DeviceType
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from parallel_executor_test_base import DeviceType
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import numpy as np
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class TestResnetBase(TestParallelExecutorBase):
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def _compare_result_with_origin_model(self,
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check_func,
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use_device,
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delta2=1e-5,
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compare_seperately=True):
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if use_device == DeviceType.CUDA and not core.is_compiled_with_cuda():
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return
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func_1_first_loss, func_1_last_loss = self.check_network_convergence(
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seresnext_net.model,
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feed_dict=seresnext_net.feed_dict(use_device),
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iter=seresnext_net.iter(use_device),
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batch_size=seresnext_net.batch_size(use_device),
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use_device=use_device,
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use_reduce=False,
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optimizer=seresnext_net.optimizer)
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func_2_first_loss, func_2_last_loss = check_func(
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seresnext_net.model,
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feed_dict=seresnext_net.feed_dict(use_device),
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iter=seresnext_net.iter(use_device),
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batch_size=seresnext_net.batch_size(use_device),
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use_device=use_device)
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if compare_seperately:
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for loss in zip(func_1_first_loss, func_2_first_loss):
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self.assertAlmostEquals(loss[0], loss[1], delta=1e-5)
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for loss in zip(func_1_last_loss, func_2_last_loss):
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self.assertAlmostEquals(loss[0], loss[1], delta=delta2)
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else:
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self.assertAlmostEquals(
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np.mean(func_1_first_loss), func_2_first_loss[0], delta=1e-5)
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self.assertAlmostEquals(
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np.mean(func_1_last_loss), func_2_last_loss[0], delta=delta2)
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