forked from mindspore-Ecosystem/mindspore
!47567 Add resnet50+boost st test case in GE process
Merge pull request !47567 from archer2049/master
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# Copyright 2023 Huawei Technologies Co., Ltd
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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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# ============================================================================
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import os
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import pytest
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import numpy as np
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import mindspore as ms
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from tests.st.model_zoo_tests import utils
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ms.set_seed(1)
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np.random.seed(1)
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@pytest.mark.level0
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@pytest.mark.platform_x86_ascend_training
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@pytest.mark.platform_arm_ascend_training
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@pytest.mark.env_single
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def test_ge_resnet50_boost_imagenet2012_ascend():
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"""
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Feature: Resnet50 boost in ge process
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Description: test_ge_resnet50_imagenet2012_ascend
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Expectation: Success
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"""
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os.environ['MS_ENABLE_GE'] = '1'
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os.environ['MS_GE_TRAIN'] = '1'
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current_path = os.path.dirname(os.path.abspath(__file__))
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model_path = "{}/../../../../tests/models/official/cv".format(current_path)
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model = "resnet"
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utils.copy_files(model_path, current_path, model)
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cur_model_path = os.path.join(current_path, "resnet")
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list_old = ["config.epoch_size - config.pretrain_epoch_size", "=dataset.get_dataset_size()", "=dataset_sink_mode",
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"\\\"total_steps\\\""]
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list_new = ["1", "=1", "=True", r"\\\"param_groups\\\"\: 2, \\\"total_steps\\\""]
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utils.exec_sed_command(list_old, list_new, os.path.join(cur_model_path, "train.py"))
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dataset = os.path.join(utils.data_root, "imagenet/imagenet_original/train")
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# Do not execute ckpt graph
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config = os.path.join(cur_model_path, "config", "resnet50_imagenet2012_Boost_config.yaml")
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list_old = ["save_checkpoint: True"]
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list_new = ["save_checkpoint: False"]
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utils.exec_sed_command(list_old, list_new, config)
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exec_network_shell = "cd {}/resnet/scripts; bash run_standalone_train.sh {} {}" \
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.format(current_path, dataset, config)
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os.system(exec_network_shell)
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cmd = "ps -ef | grep python | grep train.py | grep -v grep"
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result = utils.process_check(120, cmd)
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assert result
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log_file = os.path.join(cur_model_path, "scripts/train/log")
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loss_list = utils.get_loss_data_list(log_file)
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assert round(loss_list[-1]) <= 7
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