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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 hardnet
- """
- import os
- import math
- import argparse
- import ast
- from mindspore import context
- from mindspore import Tensor
- from mindspore.train.model import Model, ParallelMode
- 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
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
-
- from src.dataset import create_dataset_ImageNet
- from src.lr_scheduler import get_lr
- from src.HarDNet import HarDNet85
- from src.EntropyLoss import CrossEntropySmooth
- from src.config import config
-
- parser = argparse.ArgumentParser(description='Image classification with HarDNet on Imagenet')
-
- parser.add_argument('--dataset_path', type=str, default='/home/hardnet/imagenet_original/train/',
- help='Dataset path')
- parser.add_argument('--device_target', type=str, default='Ascend', help='Device target')
- parser.add_argument('--device_num', type=int, default=8, help='Device num')
- parser.add_argument('--pre_trained', type=str, default=True)
- parser.add_argument('--train_url', type=str)
- parser.add_argument('--data_url', type=str)
- parser.add_argument('--pre_ckpt_path', type=str, default='/home/work/user-job-dir/hardnet/src/HarDNet85.ckpt')
- parser.add_argument('--label_smooth_factor', type=float, default=0.1, help='label_smooth_factor')
- parser.add_argument('--isModelArts', type=ast.literal_eval, default=True)
- parser.add_argument('--distribute', type=ast.literal_eval, default=True)
- parser.add_argument('--device_id', type=int, default=0, help='device_id')
-
- args = parser.parse_args()
-
- if args.isModelArts:
- import moxing as mox
-
- if __name__ == '__main__':
- target = args.device_target
-
- if args.distribute:
- # init context
- device_id = int(os.getenv('DEVICE_ID'))
- context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", save_graphs=False)
- context.set_context(device_id=device_id, enable_auto_mixed_precision=True)
- context.set_auto_parallel_context(parallel_mode=ParallelMode.DATA_PARALLEL,
- gradients_mean=True)
- init()
-
- else:
- device_id = args.device_id
- context.set_context(mode=context.GRAPH_MODE,
- device_target=target,
- save_graphs=False,
- device_id=args.device_id)
-
- if args.isModelArts:
- import moxing as mox
- # download dataset from obs to cache
- mox.file.copy_parallel(src_url=args.data_url, dst_url='/cache/dataset/device_' + os.getenv('DEVICE_ID'))
- train_dataset_path = '/cache/dataset/device_' + os.getenv('DEVICE_ID')
- # create dataset
- train_dataset = create_dataset_ImageNet(dataset_path=train_dataset_path,
- do_train=True,
- repeat_num=1,
- batch_size=config.batch_size,
- target=target)
- else:
- train_dataset = create_dataset_ImageNet(dataset_path=args.dataset_path,
- do_train=True,
- repeat_num=1,
- batch_size=config.batch_size,
- target=target)
-
- step_size = train_dataset.get_dataset_size()
-
- # 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 net
- network = HarDNet85(num_classes=config.class_num)
- print("----network----")
-
- # init weight
- if args.pre_trained:
- param_dict = load_checkpoint(args.pre_ckpt_path)
- load_param_into_net(network, param_dict)
- else:
- for _, cell in network.cells_and_names():
- if isinstance(cell, nn.Conv2d):
- cell.weight.default_input = weight_init.initializer(weight_init.XavierUniform(gain=1 / math.sqrt(3)),
- cell.weight.shape,
- cell.weight.dtype)
- if isinstance(cell, nn.BatchNorm2d):
- cell.gamma.set_data(weight_init.initializer('ones', cell.gamma.shape))
- cell.beta.set_data(weight_init.initializer('zeros', cell.beta.shape))
- if isinstance(cell, nn.Dense):
- cell.bias.default_input = weight_init.initializer('zeros', cell.bias.shape, cell.bias.dtype)
-
- # define opt
- decayed_params = []
- no_decayed_params = []
- for param in network.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': network.trainable_params()}]
-
- net_opt = nn.Momentum(group_params, lr, config.momentum,
- weight_decay=config.weight_decay,
- loss_scale=config.loss_scale)
- # define loss
- loss = CrossEntropySmooth(smooth_factor=args.label_smooth_factor,
- num_classes=config.class_num)
-
- loss_scale = FixedLossScaleManager(config.loss_scale, drop_overflow_update=False)
-
- model = Model(network, loss_fn=loss, optimizer=net_opt,
- loss_scale_manager=loss_scale, metrics={'acc'}, amp_level="O3")
-
- # define callbacks
- time_cb = TimeMonitor(data_size=train_dataset.get_dataset_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)
- if args.isModelArts:
- save_checkpoint_path = '/cache/train_output/device_' + os.getenv('DEVICE_ID') + '/'
- else:
- save_checkpoint_path = config.save_checkpoint_path
-
- ckpt_cb = ModelCheckpoint(prefix="HarDNet85",
- directory=save_checkpoint_path,
- config=config_ck)
- cb += [ckpt_cb]
-
- print("\n\n========================")
- print("Dataset path: {}".format(args.dataset_path))
- print("Total epoch: {}".format(config.epoch_size))
- print("Batch size: {}".format(config.batch_size))
- print("Class num: {}".format(config.class_num))
- print("======= Multiple Training begin========")
- model.train(config.epoch_size, train_dataset,
- callbacks=cb, dataset_sink_mode=True)
- if args.isModelArts:
- mox.file.copy_parallel(src_url='/cache/train_output', dst_url=args.train_url)
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