forked from mindspore-Ecosystem/mindspore
commit network lenet and resnet
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anzhengqi
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# Copyright 2021 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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from tests.st.model_zoo_tests import utils
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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_onecard
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def test_lenet_MNIST():
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cur_path = os.path.dirname(os.path.abspath(__file__))
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model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
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model_name = "lenet"
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utils.copy_files(model_path, cur_path, model_name)
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cur_model_path = os.path.join(cur_path, model_name)
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train_log = os.path.join(cur_model_path, "train_ascend.log")
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ckpt_file = os.path.join(cur_model_path, "ckpt/checkpoint_lenet-10_1875.ckpt")
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infer_log = os.path.join(cur_model_path, "infer_ascend.log")
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dataset_path = os.path.join(utils.data_root, "mnist")
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exec_network_shell = "cd {0}; python train.py --data_path={1} > {2} 2>&1"\
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.format(model_name, dataset_path, train_log)
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ret = os.system(exec_network_shell)
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assert ret == 0
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exec_network_shell = "cd {0}; python eval.py --data_path={1} --ckpt_path={2} > {3} 2>&1"\
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.format(model_name, dataset_path, ckpt_file, infer_log)
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ret = os.system(exec_network_shell)
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assert ret == 0
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per_step_time = utils.get_perf_data(train_log)
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print("per_step_time is", per_step_time)
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assert per_step_time < 1.3
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pattern = r"'Accuracy': ([\d\.]+)}"
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acc = utils.parse_log_file(pattern, infer_log)
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print("acc is", acc)
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assert acc[0] > 0.98
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# Copyright 2021 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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from mindspore import log as logger
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from tests.st.model_zoo_tests import utils
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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_resnet50_cifar10_ascend():
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cur_path = os.path.dirname(os.path.abspath(__file__))
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model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
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model_name = "resnet"
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utils.copy_files(model_path, cur_path, model_name)
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cur_model_path = os.path.join(cur_path, "resnet")
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old_list = ["total_epochs=config.epoch_size", "config.epoch_size - config.pretrain_epoch_size"]
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new_list = ["total_epochs=10", "10"]
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utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
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dataset_path = os.path.join(utils.data_root, "cifar-10-batches-bin")
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exec_network_shell = "cd resnet/scripts; bash run_distribute_train.sh resnet50 cifar10 {} {}"\
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.format(utils.rank_table_path, dataset_path)
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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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ret = utils.process_check(100, cmd)
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assert ret
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log_file = os.path.join(cur_model_path, "scripts/train_parallel{}/log")
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for i in range(8):
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per_step_time = utils.get_perf_data(log_file.format(i))
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assert per_step_time < 20.0
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loss_list = []
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for i in range(8):
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loss = utils.get_loss_data_list(log_file.format(i))
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loss_list.append(loss[-1])
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assert sum(loss_list) / len(loss_list) < 0.70
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_single
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def test_resnet50_cifar10_gpu():
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cur_path = os.getcwd()
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model_path = "{}/../../../../model_zoo/official/cv".format(cur_path)
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model_name = "resnet"
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utils.copy_files(model_path, cur_path, model_name)
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cur_model_path = os.path.join(cur_path, "resnet")
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old_list = ["total_epochs=config.epoch_size", "config.epoch_size - config.pretrain_epoch_size"]
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new_list = ["total_epochs=10", "10"]
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utils.exec_sed_command(old_list, new_list, os.path.join(cur_model_path, "train.py"))
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dataset_path = os.path.join(utils.data_root, "cifar-10-batches-bin")
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exec_network_shell = "cd resnet/scripts; sh run_distribute_train_gpu.sh resnet50 cifar10 {}".format(dataset_path)
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logger.warning("cmd [{}] is running...".format(exec_network_shell))
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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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ret = utils.process_check(100, cmd)
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assert ret
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log_file = os.path.join(cur_model_path, "scripts/train_parallel/log")
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pattern = r"per step time: ([\d\.]+) ms"
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step_time_list = utils.parse_log_file(pattern, log_file)[8:]
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per_step_time = sum(step_time_list) / len(step_time_list)
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print("step time list is", step_time_list)
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assert per_step_time < 115
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loss_list = utils.get_loss_data_list(log_file)[-8:]
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print("loss_list is", loss_list)
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assert sum(loss_list) / len(loss_list) < 0.70
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#!/usr/bin/env python
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# -*- coding: utf-8 -*-
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""" File Description
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Details
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"""
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import os
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import shutil
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import subprocess
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import time
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import re
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from mindspore import log as logger
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rank_table_path = "/home/workspace/mindspore_config/hccl/rank_table_8p.json"
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data_root = "/home/workspace/mindspore_dataset/"
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ckpt_root = "/home/workspace/mindspore_ckpt/"
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cur_path = os.path.split(os.path.realpath(__file__))[0]
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geir_root = os.path.join(cur_path, "mindspore_geir")
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arm_main_path = os.path.join(cur_path, "mindir_310infer_exe")
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model_zoo_path = os.path.join(cur_path, "../../../model_zoo")
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def copy_files(from_, to_, model_name):
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if not os.path.exists(os.path.join(from_, model_name)):
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raise ValueError("There is no file or path", os.path.join(from_, model_name))
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if os.path.exists(os.path.join(to_, model_name)):
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shutil.rmtree(os.path.join(to_, model_name))
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return os.system("cp -r {0} {1}".format(os.path.join(from_, model_name), to_))
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def exec_sed_command(old_list, new_list, file):
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if isinstance(old_list, str):
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old_list = [old_list]
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if isinstance(new_list, str):
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old_list = [new_list]
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if len(old_list) != len(new_list):
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raise ValueError("len(old_list) should be equal to len(new_list)")
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for old, new in zip(old_list, new_list):
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ret = os.system('sed -i "s#{0}#{1}#g" {2}'.format(old, new, file))
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if ret != 0:
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raise ValueError('exec `sed -i "s#{0}#{1}#g" {2}` failed.'.format(old, new, file))
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return ret
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def process_check(cycle_time, cmd, wait_time=5):
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for i in range(cycle_time):
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time.sleep(wait_time)
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sub = subprocess.Popen(args="{}".format(cmd), shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
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stderr=subprocess.PIPE, universal_newlines=True)
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stdout_data, _ = sub.communicate()
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if not stdout_data:
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logger.info("process execute success.")
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return True
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logger.warning("process is running, please wait {}".format(i))
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logger.error("process execute execute timeout.")
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return False
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def get_perf_data(log_path, search_str="per step time", cmd=None):
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if cmd is None:
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get_step_times_cmd = r"""grep -a "{0}" {1}|egrep -v "loss|\]|\["|awk '{{print $(NF-1)}}'""" \
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.format(search_str, log_path)
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else:
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get_step_times_cmd = cmd
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sub = subprocess.Popen(args="{}".format(get_step_times_cmd), shell=True,
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stdin=subprocess.PIPE, stdout=subprocess.PIPE,
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stderr=subprocess.PIPE, universal_newlines=True)
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stdout, _ = sub.communicate()
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if sub.returncode != 0:
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raise RuntimeError("exec {} failed".format(cmd))
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logger.info("execute {} success".format(cmd))
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stdout = stdout.strip().split("\n")
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step_time_list = list(map(float, stdout[1:]))
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if not step_time_list:
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cmd = "cat {}".format(log_path)
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os.system(cmd)
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raise RuntimeError("step_time_list is empty")
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per_step_time = sum(step_time_list) / len(step_time_list)
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return per_step_time
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def get_loss_data_list(log_path, search_str="loss is", cmd=None):
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if cmd is None:
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loss_value_cmd = """ grep -a '{}' {}| awk '{{print $NF}}' """.format(search_str, log_path)
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else:
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loss_value_cmd = cmd
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sub = subprocess.Popen(args="{}".format(loss_value_cmd), shell=True, stdin=subprocess.PIPE, stdout=subprocess.PIPE,
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stderr=subprocess.PIPE, universal_newlines=True)
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stdout, _ = sub.communicate()
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if sub.returncode != 0:
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raise RuntimeError("get loss from {} failed".format(log_path))
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logger.info("execute {} success".format(cmd))
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stdout = stdout.strip().split("\n")
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loss_list = list(map(float, stdout))
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if not loss_list:
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cmd = "cat {}".format(log_path)
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os.system(cmd)
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raise RuntimeError("loss_list is empty")
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return loss_list
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def parse_log_file(pattern, log_path):
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value_list = []
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with open(log_path, "r") as file:
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for line in file.readlines():
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match_result = re.search(pattern, line)
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if match_result is not None:
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value_list.append(float(match_result.group(1)))
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if not value_list:
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print("pattern is", pattern)
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cmd = "cat {}".format(log_path)
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os.system(cmd)
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return value_list
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