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151 lines
6.6 KiB
151 lines
6.6 KiB
# 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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"""train resnet."""
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import os
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import argparse
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import ast
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from mindspore import context
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from mindspore import Tensor
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from mindspore.nn.optim.momentum import Momentum
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from mindspore.train.model import Model
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from mindspore.context import ParallelMode
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.communication.management import init, get_rank
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from mindspore.common import set_seed
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import mindspore.nn as nn
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import mindspore.common.initializer as weight_init
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from src.lr_generator import get_lr
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from src.CrossEntropySmooth import CrossEntropySmooth
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from src.resnet import resnet152 as resnet
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from src.config import config5 as config
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from src.dataset import create_dataset2 as create_dataset # imagenet2012
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parser = argparse.ArgumentParser(description='Image classification--resnet152')
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parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
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parser.add_argument('--run_distribute', type=ast.literal_eval, default=False, help='Run distribute')
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parser.add_argument('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
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parser.add_argument('--rank', type=int, default=0, help='local rank of distributed')
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parser.add_argument('--is_save_on_master', type=ast.literal_eval, default=True, help='save ckpt on master or all rank')
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args_opt = parser.parse_args()
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set_seed(1)
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if __name__ == '__main__':
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ckpt_save_dir = config.save_checkpoint_path
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# init context
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print(args_opt.run_distribute)
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context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
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if args_opt.run_distribute:
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device_id = int(os.getenv('DEVICE_ID'))
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rank_size = int(os.environ.get("RANK_SIZE", 1))
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print(rank_size)
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device_num = rank_size
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context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
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context.set_auto_parallel_context(device_num=device_num, parallel_mode=ParallelMode.DATA_PARALLEL,
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gradients_mean=True, all_reduce_fusion_config=[180, 313])
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init()
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args_opt.rank = get_rank()
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print(args_opt.rank)
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# select for master rank save ckpt or all rank save, compatible for model parallel
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args_opt.rank_save_ckpt_flag = 0
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if args_opt.is_save_on_master:
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if args_opt.rank == 0:
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args_opt.rank_save_ckpt_flag = 1
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else:
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args_opt.rank_save_ckpt_flag = 1
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local_data_path = args_opt.data_url
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local_data_path = args_opt.data_url
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print('Download data:')
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# create dataset
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dataset = create_dataset(dataset_path=local_data_path, do_train=True, repeat_num=1,
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batch_size=config.batch_size, target="Ascend", distribute=args_opt.run_distribute)
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step_size = dataset.get_dataset_size()
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print("step"+str(step_size))
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# define net
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net = resnet(class_num=config.class_num)
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# init weight
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if args_opt.pre_trained:
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param_dict = load_checkpoint(args_opt.pre_trained)
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load_param_into_net(net, param_dict)
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else:
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for _, cell in net.cells_and_names():
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if isinstance(cell, nn.Conv2d):
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cell.weight.set_data(weight_init.initializer(weight_init.HeUniform(),
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cell.weight.shape,
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cell.weight.dtype))
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if isinstance(cell, nn.Dense):
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cell.weight.set_data(weight_init.initializer(weight_init.HeNormal(),
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cell.weight.shape,
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cell.weight.dtype))
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# init lr
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lr = get_lr(lr_init=config.lr_init, lr_end=config.lr_end, lr_max=config.lr_max,
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warmup_epochs=config.warmup_epochs, total_epochs=config.epoch_size, steps_per_epoch=step_size,
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lr_decay_mode=config.lr_decay_mode)
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lr = Tensor(lr)
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# define opt
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decayed_params = []
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no_decayed_params = []
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for param in net.trainable_params():
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if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
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decayed_params.append(param)
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else:
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no_decayed_params.append(param)
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group_params = [{'params': decayed_params, 'weight_decay': config.weight_decay},
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{'params': no_decayed_params},
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{'order_params': net.trainable_params()}]
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opt = Momentum(group_params, lr, config.momentum, loss_scale=config.loss_scale)
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# define loss, model
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if not config.use_label_smooth:
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config.label_smooth_factor = 0.0
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loss = CrossEntropySmooth(sparse=True, reduction="mean",
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smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
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loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
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model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale,
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metrics={'top_1_accuracy', 'top_5_accuracy'},
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amp_level="O3", keep_batchnorm_fp32=False)
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# define callbacks
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time_cb = TimeMonitor(data_size=step_size)
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loss_cb = LossMonitor()
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cb = [time_cb, loss_cb]
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if config.save_checkpoint:
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if args_opt.rank_save_ckpt_flag:
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config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
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keep_checkpoint_max=config.keep_checkpoint_max)
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ckpt_cb = ModelCheckpoint(prefix="resnet152", directory=ckpt_save_dir, config=config_ck)
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cb += [ckpt_cb]
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# train model
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dataset_sink_mode = True
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print(dataset.get_dataset_size())
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model.train(config.epoch_size, dataset, callbacks=cb,
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sink_size=dataset.get_dataset_size(), dataset_sink_mode=dataset_sink_mode)
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