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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 efficientnet."""
- import os
- import ast
- import argparse
-
- from mindspore import context
- from mindspore import Tensor
- from mindspore.nn import SGD, RMSProp
- from mindspore.train.model import Model
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.communication.management import init, get_rank, get_group_size
- from mindspore.train.loss_scale_manager import FixedLossScaleManager, DynamicLossScaleManager
- from mindspore.common import dtype as mstype
- from mindspore.common import set_seed
-
- from src.lr_generator import get_lr
- from src.models.effnet import EfficientNet
- from src.config import config
- from src.monitor import Monitor
- from src.dataset import create_dataset
- from src.loss import CrossEntropySmooth
-
- set_seed(1)
-
- if __name__ == '__main__':
- parser = argparse.ArgumentParser(description='image classification training')
- # modelarts parameter
- parser.add_argument('--data_url', type=str, default=None, help='Dataset path')
- parser.add_argument('--train_url', type=str, default=None, help='Train output path')
-
- # Ascend parameter
- parser.add_argument('--device_target', type=str, choices=["Ascend", "GPU"], default="Ascend", help='Device target')
- parser.add_argument('--dataset_path', type=str, default=None, help='Dataset path')
- 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('--run_modelarts', type=ast.literal_eval, default=False, help='Run mode')
- parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
- args_opt = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, save_graphs=False)
-
- # init distributed
- if args_opt.run_modelarts:
- import moxing as mox
-
- device_id = int(os.getenv('DEVICE_ID'))
- rank = int(os.getenv('RANK_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
- context.set_context(device_id=device_id)
- local_data_url = '/cache/data'
- local_train_url = '/cache/ckpt'
- if device_num > 1:
- init()
- context.set_auto_parallel_context(device_num=device_num, parallel_mode='data_parallel', gradients_mean=True)
- local_data_url = os.path.join(local_data_url, str(device_id))
- mox.file.copy_parallel(args_opt.data_url, local_data_url)
- else:
- if args_opt.run_distribute:
- if os.getenv('DEVICE_ID', "not_set").isdigit():
- context.set_context(device_id=int(os.getenv("DEVICE_ID")))
- init()
- rank = get_rank()
- device_num = get_group_size()
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(device_num=device_num,
- parallel_mode=context.ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- else:
- context.set_context(device_id=args_opt.device_id)
- device_num = 1
- rank = 0
-
- # define network
- net = EfficientNet()
- net.to_float(mstype.float16)
-
- # define loss
- if not config.use_label_smooth:
- config.label_smooth_factor = 0.0
- loss = CrossEntropySmooth(smooth_factor=config.label_smooth_factor, num_classes=config.class_num)
-
- # define dataset
- if args_opt.run_modelarts:
- dataset = create_dataset(dataset_path=local_data_url,
- do_train=True,
- batch_size=config.batch_size,
- device_num=device_num, rank=rank)
- else:
- dataset = create_dataset(dataset_path=args_opt.dataset_path,
- do_train=True,
- batch_size=config.batch_size,
- device_num=device_num, rank=rank)
- step_size = dataset.get_dataset_size()
-
- # resume
- if args_opt.resume:
- ckpt = load_checkpoint(args_opt.resume)
- load_param_into_net(net, ckpt)
-
- # get learning rate
- if args_opt.device_target == "Ascend":
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
- else:
- loss_scale = DynamicLossScaleManager(init_loss_scale=2 ** 16, scale_factor=2, scale_window=2000)
- config.loss_scale = 1.0
- lr = Tensor(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))
-
- # define optimization
- if config.opt == 'sgd':
- optimizer = SGD(net.trainable_params(), learning_rate=lr, momentum=config.momentum,
- weight_decay=config.weight_decay, loss_scale=config.loss_scale)
- elif config.opt == 'rmsprop':
- optimizer = RMSProp(net.trainable_params(), learning_rate=lr, decay=0.9, weight_decay=config.weight_decay,
- momentum=config.momentum, epsilon=config.opt_eps, loss_scale=config.loss_scale)
-
- # define model
- model = Model(net, loss_fn=loss, optimizer=optimizer,
- loss_scale_manager=loss_scale, metrics={'acc'}, amp_level='O3')
-
- # define callbacks
- cb = [Monitor(lr_init=lr.asnumpy())]
- if config.save_checkpoint and (device_num == 1 or rank == 0):
- config_ck = CheckpointConfig(save_checkpoint_steps=config.save_checkpoint_epochs * step_size,
- keep_checkpoint_max=config.keep_checkpoint_max)
- if args_opt.run_modelarts:
- ckpt_cb = ModelCheckpoint(f"Efficientnet_b3-rank{rank}", directory=local_train_url, config=config_ck)
- else:
- save_ckpt_path = os.path.join(config.save_checkpoint_path, 'model_' + str(rank) + '/')
- ckpt_cb = ModelCheckpoint(f"Efficientnet_b3-rank{rank}", directory=save_ckpt_path, config=config_ck)
- cb += [ckpt_cb]
-
- # begine train
- model.train(config.epoch_size, dataset, callbacks=cb, dataset_sink_mode=True)
- if args_opt.run_modelarts and config.save_checkpoint and (device_num == 1 or rank == 0):
- mox.file.copy_parallel(local_train_url, args_opt.train_url)
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