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- # Copyright 2021 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 net."""
- import os
- import argparse
- import ast
- from mindspore import context
- from mindspore import Tensor
- from mindspore.nn.optim.momentum import Momentum
- from mindspore.train.model import Model
- from mindspore.context import ParallelMode
- from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
- 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
- from mindspore.common import set_seed
- from mindspore.parallel import set_algo_parameters
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
- from src.lr_generator import get_lr
- from src.CrossEntropySmooth import CrossEntropySmooth
- from src.var_init import KaimingNormal
-
- parser = argparse.ArgumentParser(description='Image classification')
- parser.add_argument('--net', type=str, default="sknet50", help='sknet Model')
- parser.add_argument('--dataset', type=str, default="cifar10", help='Dataset, either cifar10 or imagenet2012')
- parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
- parser.add_argument('--device_id', type=int, default=0, help='Device id.')
- parser.add_argument('--device_num', type=int, default=1, help='Device num.')
-
- parser.add_argument('--dataset_path', type=str, default="/path/to/cifar10", help='Dataset path')
- parser.add_argument('--device_target', type=str, default='Ascend', choices="Ascend",
- help="Device target, support Ascend.")
- 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')
- args_opt = parser.parse_args()
-
- set_seed(1)
- if args_opt.net == "sknet50":
- from src.sknet50 import sknet50 as sknet
- if args_opt.dataset == "cifar10":
- from src.config import config1 as config
- from src.dataset import create_dataset1 as create_dataset
-
- 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:
- 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)
- set_algo_parameters(elementwise_op_strategy_follow=True)
- if args_opt.net == "sknet50":
- context.set_auto_parallel_context(all_reduce_fusion_config=[85, 160])
- init()
- else:
- device_id = args_opt.device_id
- context.set_context(device_id=device_id)
- # create dataset
- dataset = create_dataset(dataset_path=args_opt.dataset_path, do_train=True, repeat_num=1,
- batch_size=config.batch_size, target=target, distribute=args_opt.run_distribute)
- step_size = dataset.get_dataset_size()
- print(step_size)
- # define net
- net = sknet(class_num=config.class_num)
- if args_opt.parameter_server:
- net.set_param_ps()
-
- # 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(KaimingNormal(mode='fan_out'),
- 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
- 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)
- 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()}]
- # define loss, model
- if args_opt.dataset == "imagenet":
- opt = nn.SGD(group_params, lr, config.momentum, loss_scale=config.loss_scale)
- 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:
- opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
- 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)
-
- # define callbacks
- time_cb = TimeMonitor(data_size=step_size)
- 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="sknet", directory=ckpt_save_dir, config=config_ck)
- cb += [ckpt_cb]
-
- # train model
- model.train(config.epoch_size - config.pretrain_epoch_size, dataset, callbacks=cb,
- sink_size=dataset.get_dataset_size(), dataset_sink_mode=True)
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