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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_imagenet."""
-
- import os
- import ast
- import argparse
- from mindspore import context
- from mindspore import Tensor
-
- from mindspore.nn import RMSProp
- from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
- from mindspore.train.model import Model
- from mindspore.context import ParallelMode
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.serialization import load_checkpoint, load_param_into_net
- from mindspore.common import set_seed
- from mindspore.communication.management import init
-
- from src.dataset import create_dataset
- from src.lr_generator import get_lr
- from src.config import config
- from src.loss import CrossEntropyWithLabelSmooth
- from src.monitor import Monitor
- from src.mobilenetv3 import mobilenet_v3_small
-
- set_seed(1)
-
- parser = argparse.ArgumentParser(description='Image classification')
- # 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('--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('--pre_trained', type=str, default=None, help='Pretrained checkpoint path')
- args_opt = parser.parse_args()
-
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
-
- if __name__ == '__main__':
- # init distributed
- if args_opt.run_modelarts:
- import moxing as mox
- device_id = int(os.getenv('DEVICE_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:
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
- context.set_context(device_id=device_id)
- init()
- context.reset_auto_parallel_context()
- context.set_auto_parallel_context(device_num=device_num,
- parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- else:
- context.set_context(device_id=args_opt.device_id)
- device_num = 1
- device_id = 0
-
- # define net
- net = mobilenet_v3_small(num_classes=config.num_classes, multiplier=1.)
-
- # define loss
- if config.label_smooth > 0:
- loss = CrossEntropyWithLabelSmooth(
- smooth_factor=config.label_smooth, num_classes=config.num_classes)
- else:
- loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
-
- # 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=device_id)
- else:
- dataset = create_dataset(dataset_path=args_opt.dataset_path,
- do_train=True,
- batch_size=config.batch_size,
- device_num=device_num, rank=device_id)
- step_size = dataset.get_dataset_size()
-
- # resume
- if args_opt.pre_trained:
- param_dict = load_checkpoint(args_opt.pre_trained)
- load_param_into_net(net, param_dict)
-
- # define optimizer
- loss_scale = FixedLossScaleManager(
- config.loss_scale, drop_overflow_update=False)
- lr = Tensor(get_lr(global_step=0,
- lr_init=0,
- lr_end=0,
- lr_max=config.lr,
- warmup_epochs=config.warmup_epochs,
- total_epochs=config.epoch_size,
- steps_per_epoch=step_size))
- opt = RMSProp(net.trainable_params(), learning_rate=lr, decay=0.9, weight_decay=config.weight_decay,
- momentum=config.momentum, epsilon=0.001, loss_scale=config.loss_scale)
-
- # define model
- model = Model(net, loss_fn=loss, optimizer=opt,
- loss_scale_manager=loss_scale, amp_level='O3')
-
- # define callbacks
- cb = [Monitor(lr_init=lr.asnumpy())]
- if config.save_checkpoint and (device_num == 1 or device_id == 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(prefix="mobilenetV3", directory=local_train_url, config=config_ck)
- else:
- save_ckpt_path = os.path.join(config.save_checkpoint_path, 'model_' + str(device_id) + '/')
- ckpt_cb = ModelCheckpoint(prefix="mobilenetV3", 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 device_id == 0):
- mox.file.copy_parallel(local_train_url, args_opt.train_url)
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