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- # Copyright 2020 Huawei Technologies Co., Ltd
- #
- # Licensed under the Apache License, Version 2.0 (the "License");
- # you may not use this file except in compliance with the License.
- # You may obtain a copy of the License at
- #
- # http://www.apache.org/licenses/LICENSE-2.0
- #
- # Unless required by applicable law or agreed to in writing, software
- # distributed under the License is distributed on an "AS IS" BASIS,
- # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
- # See the License for the specific language governing permissions and
- # limitations under the License.
- # ============================================================================
- """train resnet."""
- import os
- import argparse
- import ast
- from mindspore import context
- from mindspore import Tensor
- from mindspore.nn.optim.momentum import Momentum
- from mindspore.nn.optim.sgd import SGD
- from mindspore.train.model import Model
- from mindspore.context import ParallelMode
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
- from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.communication.management import init, get_rank, get_group_size
- from mindspore.common import set_seed
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
- from src.lr_generator import get_lr, warmup_cosine_annealing_lr
- from src.CrossEntropySmooth import CrossEntropySmooth
-
- from param_server import ParamHunter
- from thgy_client import THGYApiClient
- import datetime
-
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--net', type=str, default=None, help='Resnet Model, either resnet50 or resnet101')
- parser.add_argument('--dataset', type=str, default=None, help='Dataset, either cifar10 or imagenet2012')
- parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
- parser.add_argument('--device_num', type=int, default=1, help='Device num.')
-
- parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
- parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
- parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
- parser.add_argument('--parameter_server', type=ast.literal_eval, default=False, help='Run parameter server train')
- parser.add_argument('--initial', type=bool, default=False, help='initial flag')
- parser.add_argument('globalstep', type=int, default=1, help='global step')
- parser.add_argument('uuid', type=str, default='test', help='Whether to fetch average parameters from server.')
- args_opt = parser.parse_args()
-
- set_seed(1)
-
- if args_opt.net == "resnet50":
- from src.resnet import resnet50 as resnet
- if args_opt.dataset == "cifar10":
- from src.config import config1 as config
- from src.dataset import create_dataset1 as create_dataset
- else:
- from src.config import config2 as config
- from src.dataset import create_dataset2 as create_dataset
- elif args_opt.net == "resnet101":
- from src.resnet import resnet101 as resnet
- from src.config import config3 as config
- from src.dataset import create_dataset3 as create_dataset
- else:
- from src.resnet import se_resnet50 as resnet
- from src.config import config4 as config
- from src.dataset import create_dataset4 as create_dataset
-
- step_per_round = int(os.environ["CLIENT_STEP"]
- ) if 'CLIENT_STEP' in os.environ else 1
- if __name__ == '__main__':
- target = args_opt.device_target
- ckpt_save_dir = config.save_checkpoint_path
-
- # init context
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False)
- if args_opt.parameter_server:
- context.set_ps_context(enable_ps=True)
- if args_opt.run_distribute:
- if target == "Ascend":
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
- context.set_auto_parallel_context(device_num=args_opt.device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- if args_opt.net == "resnet50" or args_opt.net == "se-resnet50":
- context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
- else:
- context.set_auto_parallel_context(all_reduce_fusion_config=[180, 313])
- init()
- # GPU target
- else:
- init()
- context.set_auto_parallel_context(device_num=get_group_size(), parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- if args_opt.net == "resnet50":
- context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
- ckpt_save_dir = config.save_checkpoint_path + "ckpt_" + str(get_rank()) + "/"
-
- # create dataset
- dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
- batch_size=config.batch_size, target=target)
- step_size = dataset.get_dataset_size()
- #args_ds = dataset.get_args()
- #print(f"sampler:{args_ds['sampler']}")
-
- print(f"step_size:{step_size}\n")
-
- # define net
- net = resnet(class_num=config.class_num)
- if args_opt.parameter_server:
- net.set_param_ps()
-
- uuid = args_opt.uuid
- if '_' in uuid:
- group_id, task_id = map(int, uuid.split('_'))
- else:
- group_id = 0
- task_id = int(uuid)
- global_step = args_opt.globalStep
- initial = args_opt.initial
- # init weight
- if args_opt.pre_trained:
- param_dict = load_checkpoint(args_opt.pre_trained)
- load_param_into_net(net, param_dict)
- else:
- for _, cell in net.cells_and_names():
- if isinstance(cell, nn.Conv2d):
- cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(),
- cell.weight.shape,
- cell.weight.dtype))
- if isinstance(cell, nn.Dense):
- cell.weight.set_data(weight_init.initializer(weight_init.TruncatedNormal(),
- cell.weight.shape,
- cell.weight.dtype))
-
- # init lr
- if args_opt.net == "resnet50" or args_opt.net == "se-resnet50":
- lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
- warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
- lr_decay_mode=config.lr_decay_mode)
- else:
- lr = warmup_cosine_annealing_lr(config.lr, step_size, config.warmup_epochs, config.epoch_size,
- config.pretrain_epoch_size * step_size)
- lr = Tensor(lr)
-
- # define opt
- decayed_params = []
- no_decayed_params = []
- for param in net.trainable_params():
- if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
- decayed_params.append(param)
- else:
- no_decayed_params.append(param)
-
- group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
- {'params': no_decayed_params},
- {'order_params': net.trainable_params()}]
- opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
- #opt = SGD(group_params, lr, config.momentum, loss_scale=config.loss_scale)
- # define loss, model
- if target == "Ascend":
- if args_opt.dataset == "imagenet2012":
- if not config.use_label_smooth:
- config.label_smooth_factor = 0.0
- loss = CrossEntropySmooth(sparse=True, reduction="mean",
- smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
- else:
- loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
- model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
- amp_level="O2", keep_batchnorm_fp32=False)
- else:
- # GPU target
- if args_opt.dataset == "imagenet2012":
- if not config.use_label_smooth:
- config.label_smooth_factor = 0.0
- loss = CrossEntropySmooth(sparse=True, reduction="mean",
- smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
- else:
- loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction="mean")
-
- if (args_opt.net == "resnet101" or args_opt.net == "resnet50") and not args_opt.parameter_server:
- opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay,
- config.loss_scale)
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
- # Mixed precision
- model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale, metrics={'acc'},
- amp_level="O2", keep_batchnorm_fp32=False)
- else:
- ## fp32 training
- opt = Momentum(filter(lambda x: x.requires_grad, net.get_parameters()), lr, config.momentum, config.weight_decay)
- model = Model(net, loss_fn=loss, optimizer=opt, metrics={'acc'})
-
- # define callbacks
- time_cb = TimeMonitor(data_size=step_size)
- #打印loss信息
- loss_cb = LossMonitor()
- cb = [time_cb, loss_cb]
- if config.save_checkpoint:
- config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
- keep_checkpoint_max=config.keep_checkpoint_max)
- ckpt_cb = ModelCheckpoint(prefix="resnet", directory=ckpt_save_dir, config=config_ck)
- cb += [ckpt_cb]
-
- # train model
- # 参数管理器
- param_hunter = ParamHunter(model, debug=False)
- # api客户端
- api_client = THGYApiClient()
- # 训练之前,需要从JCCE.agent初始化model的参数
- model, init_params_num = param_hunter.init_params(initial)
- # 界面展示用
- api_client.add_training_parameters(0, task_id, init_params_num)
- # 当前轮次开始时间
- start_time = datetime.datetime.now()
- if args_opt.net == "se-resnet50":
- config.epoch_size = config.train_epoch_size
- model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb,
- sink_size=dataset.get_dataset_size(), dataset_sink_mode=(not args_opt.parameter_server))
- # 当前轮次结束时间
- end_time = datetime.datetime.now()
- # 界面展示用
- api_client.add_task_training_data(group_id, task_id, global_step,
- recall=0, precision=0,
- startTime=start_time.strftime(
- "%Y-%m-%d %H:%M:%S.%f"),
- endTime=end_time.strftime("%Y-%m-%d %H:%M:%S.%f"))
- # 训练完成后上传参数到JCCE.agent
- upload_param_nums = param_hunter.upload_params(model, uuid, step_per_round)
- # 界面展示用
- api_client.add_training_parameters(1, task_id, upload_param_nums)
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