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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 resnet34 example on imagenet2012########################
- python train.py
- """
- import os
- import ast
- import argparse
- from mindspore import context
- from mindspore.common import set_seed
- from mindspore.nn.optim import Momentum
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.model import Model
- from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
- from mindspore import Tensor
- from mindspore.context import ParallelMode
- from mindspore.communication.management import init
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
-
- from src.resnet import resnet34 as resnet
- from src.config import config
- from src.dataset import create_dataset
- from src.lr_generator import get_linear_lr as get_lr
- from src.cross_entropy_smooth import CrossEntropySmooth
-
- set_seed(1)
-
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
-
- if __name__ == '__main__':
-
- parser = argparse.ArgumentParser(description='Mindspore resnet34 example')
- parser.add_argument('--modelart', required=True, type=ast.literal_eval, default=False,
- help='training on modelart or not, default is False')
- parser.add_argument('--data_url', required=True, default=None, help='Location of data')
- parser.add_argument('--train_url', required=True, default=None, help='Location of training outputs.')
- parser.add_argument('--ckpt_url', required=True, default=None, help='Location of ckpt.')
- args = parser.parse_args()
-
- target = "Ascend"
- context.set_context(mode=context.GRAPH_MODE, device_target=target,
- device_id=int(os.environ["DEVICE_ID"]))
-
- if device_num > 1:
- init()
- context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL, gradients_mean=True)
-
- dataset_sink_mode = True
-
- if args.modelart:
- import moxing as mox
- data_path = '/cache/data_path'
- mox.file.copy_parallel(src_url=args.data_url, dst_url=data_path)
- tar_command = "tar -xvf /cache/data_path/imagenet_original.tar.gz -C /cache/data_path/"
- os.system(tar_command)
- data_path = '/cache/data_path/imagenet_original/'
- else:
- data_path = args.data_url
- data_path_train = os.path.join(data_path, 'train')
-
- # create dataset
- dataset_train = create_dataset(dataset_path=data_path_train, do_train=True,
- batch_size=config.batch_size)
-
- step_size = dataset_train.get_dataset_size()
-
- # define net
- net = resnet(class_num=config.class_num)
-
- 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))
-
- # define loss function
- loss = CrossEntropySmooth(sparse=True, reduction="mean",
- smooth_factor=config.label_smooth_factor,
- num_classes=config.class_num)
- loss_scale = FixedLossScaleManager(loss_scale=config.loss_scale, drop_overflow_update=False)
-
- # 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 = Tensor(lr)
-
- 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)
-
- # define model
- model = Model(net, loss_fn=loss, optimizer=opt, loss_scale_manager=loss_scale,
- metrics={'top_1_accuracy', 'top_5_accuracy'},
- amp_level="O2", keep_batchnorm_fp32=False)
-
- # define callbacks
- time_cb = TimeMonitor(data_size=step_size)
- loss_cb = LossMonitor()
-
- cb = [time_cb, loss_cb]
-
- ckpt_save_dir = config.save_checkpoint_path
-
- 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]
-
- model.train(epoch=config.epoch_size, train_dataset=dataset_train, callbacks=cb,
- dataset_sink_mode=dataset_sink_mode)
-
- if args.modelart:
- import moxing as mox
- mox.file.copy_parallel(src_url=ckpt_save_dir, dst_url=args.ckpt_url)
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