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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 WideResNet example on cifar10########################
- python train.py
- """
- import ast
- import os
- import argparse
- import numpy as np
- from mindspore.common import set_seed
- from mindspore import context
- from mindspore.communication.management import init
- from mindspore.context import ParallelMode
- from mindspore import Tensor
- from mindspore.nn.optim import Momentum
- from mindspore.train.loss_scale_manager import FixedLossScaleManager
- from mindspore.train.callback import LossMonitor, TimeMonitor
- from mindspore.train.model import Model
- import mindspore.nn as nn
- import mindspore.common.initializer as weight_init
-
- from src.wide_resnet import wideresnet
- from src.dataset import create_dataset
- from src.config import config_WideResnet as cfg
- from src.generator_lr import get_lr
- from src.cross_entropy_smooth import CrossEntropySmooth
- from src.save_callback import SaveCallback
-
- set_seed(1)
-
- if __name__ == '__main__':
-
- device_id = int(os.getenv('DEVICE_ID'))
- device_num = int(os.getenv('RANK_SIZE'))
- parser = argparse.ArgumentParser(description='Ascend WideResnet+CIFAR10 Training')
- parser.add_argument('--data_url', required=True, default=None, help='Location of data')
- parser.add_argument('--ckpt_url', required=True, default=None, help='Location of ckpt.')
- parser.add_argument('--modelart', required=True, type=ast.literal_eval, default=False,
- help='training on modelart or not, default is False')
- args = parser.parse_args()
-
- target = "Ascend"
- context.set_context(mode=context.GRAPH_MODE, device_target=target, save_graphs=False,
- device_id=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)
- else:
- data_path = args.data_url
-
- ds_train = create_dataset(dataset_path=data_path,
- do_train=True,
- batch_size=cfg.batch_size)
- ds_eval = create_dataset(dataset_path=data_path,
- do_train=False,
- batch_size=cfg.batch_size)
- step_size = ds_train.get_dataset_size()
-
- net = wideresnet()
-
- for _, cell in net.cells_and_names():
- if isinstance(cell, nn.Conv2d):
- cell.weight.set_data(weight_init.initializer(weight_init.XavierUniform(gain=np.sqrt(2)),
- cell.weight.shape,
- cell.weight.dtype))
-
- loss = CrossEntropySmooth(sparse=True, reduction="mean",
- smooth_factor=cfg.label_smooth_factor,
- num_classes=cfg.num_classes)
- loss_scale = FixedLossScaleManager(loss_scale=cfg.loss_scale, drop_overflow_update=False)
-
- lr = get_lr(total_epochs=cfg.epoch_size, steps_per_epoch=step_size, lr_init=cfg.lr_init)
- 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': cfg.weight_decay},
- {'params': no_decayed_params},
- {'order_params': net.trainable_params()}]
- opt = Momentum(group_params,
- learning_rate=lr,
- momentum=cfg.momentum,
- loss_scale=cfg.loss_scale,
- use_nesterov=True,
- weight_decay=cfg.weight_decay)
-
- model = Model(net,
- amp_level="O2",
- loss_fn=loss,
- optimizer=opt,
- loss_scale_manager=loss_scale,
- metrics={'accuracy'},
- keep_batchnorm_fp32=False
- )
-
- loss_cb = LossMonitor()
- time_cb = TimeMonitor()
- cb = [loss_cb, time_cb]
- ckpt_path = args.ckpt_url
- cb += [SaveCallback(model, ds_eval, ckpt_path, args.modelart)]
-
- model.train(epoch=cfg.epoch_size, train_dataset=ds_train, callbacks=cb,
- dataset_sink_mode=dataset_sink_mode)
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