mindarmour/examples/privacy/membership_inference_attack/train.py

# Copyright 2020 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 vgg16 example on cifar10########################
python train.py --data_path=$DATA_HOME --device_id=$DEVICE_ID
"""
import argparse
import datetime
import os
import random

import numpy as np

import mindspore.nn as nn
from mindspore import Tensor
from mindspore import context
from mindspore.nn.optim.momentum import Momentum
from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor
from mindspore.train.model import Model
from mindspore.train.serialization import load_param_into_net, load_checkpoint
from mindarmour.utils import LogUtil

from examples.common.dataset.data_processing import vgg_create_dataset100
from examples.common.networks.vgg.warmup_step_lr import warmup_step_lr
from examples.common.networks.vgg.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
from examples.common.networks.vgg.warmup_step_lr import lr_steps
from examples.common.networks.vgg.utils.util import get_param_groups
from examples.common.networks.vgg.vgg import vgg16
from examples.common.networks.vgg.config import cifar_cfg as cfg

TAG = "train"

random.seed(1)
np.random.seed(1)


def parse_args(cloud_args=None):
    """parameters"""
    parser = argparse.ArgumentParser('mindspore classification training')
    parser.add_argument('--device_target', type=str, default='Ascend', choices=['Ascend', 'GPU'],
                        help='device where the code will be implemented. (Default: Ascend)')
    parser.add_argument('--device_id', type=int, default=1, help='device id of GPU or Ascend. (Default: None)')

    # dataset related
    parser.add_argument('--data_path', type=str, default='', help='train data dir')

    # network related
    parser.add_argument('--pre_trained', default='', type=str, help='model_path, local pretrained model to load')
    parser.add_argument('--lr_gamma', type=float, default=0.1,
                        help='decrease lr by a factor of exponential lr_scheduler')
    parser.add_argument('--eta_min', type=float, default=0., help='eta_min in cosine_annealing scheduler')
    parser.add_argument('--T_max', type=int, default=150, help='T-max in cosine_annealing scheduler')

    # logging and checkpoint related
    parser.add_argument('--log_interval', type=int, default=100, help='logging interval')
    parser.add_argument('--ckpt_path', type=str, default='outputs/', help='checkpoint save location')
    parser.add_argument('--ckpt_interval', type=int, default=2, help='ckpt_interval')
    parser.add_argument('--is_save_on_master', type=int, default=1, help='save ckpt on master or all rank')

    args_opt = parser.parse_args()
    args_opt = merge_args(args_opt, cloud_args)

    args_opt.rank = 0
    args_opt.group_size = 1
    args_opt.label_smooth = cfg.label_smooth
    args_opt.label_smooth_factor = cfg.label_smooth_factor
    args_opt.lr_scheduler = cfg.lr_scheduler
    args_opt.loss_scale = cfg.loss_scale
    args_opt.max_epoch = cfg.max_epoch
    args_opt.warmup_epochs = cfg.warmup_epochs
    args_opt.lr = cfg.lr
    args_opt.lr_init = cfg.lr_init
    args_opt.lr_max = cfg.lr_max
    args_opt.momentum = cfg.momentum
    args_opt.weight_decay = cfg.weight_decay
    args_opt.per_batch_size = cfg.batch_size
    args_opt.num_classes = cfg.num_classes
    args_opt.buffer_size = cfg.buffer_size
    args_opt.ckpt_save_max = cfg.keep_checkpoint_max
    args_opt.pad_mode = cfg.pad_mode
    args_opt.padding = cfg.padding
    args_opt.has_bias = cfg.has_bias
    args_opt.batch_norm = cfg.batch_norm
    args_opt.initialize_mode = cfg.initialize_mode
    args_opt.has_dropout = cfg.has_dropout

    args_opt.lr_epochs = list(map(int, cfg.lr_epochs.split(',')))
    args_opt.image_size = list(map(int, cfg.image_size.split(',')))

    return args_opt


def merge_args(args_opt, cloud_args):
    """dictionary"""
    args_dict = vars(args_opt)
    if isinstance(cloud_args, dict):
        for key_arg in cloud_args.keys():
            val = cloud_args[key_arg]
            if key_arg in args_dict and val:
                arg_type = type(args_dict[key_arg])
                if arg_type is not None:
                    val = arg_type(val)
                args_dict[key_arg] = val
    return args_opt


if __name__ == '__main__':
    args = parse_args()

    device_num = int(os.environ.get("DEVICE_NUM", 1))

    context.set_context(device_id=args.device_id)
    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)

    # select for master rank save ckpt or all rank save, compatiable for model parallel
    args.rank_save_ckpt_flag = 0
    if args.is_save_on_master:
        if args.rank == 0:
            args.rank_save_ckpt_flag = 1
    else:
        args.rank_save_ckpt_flag = 1

    # logger
    args.outputs_dir = os.path.join(args.ckpt_path,
                                    datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
    args.logger = LogUtil.get_instance()
    args.logger.set_level(20)

    # load train data set
    dataset = vgg_create_dataset100(args.data_path, args.image_size, args.per_batch_size, args.rank, args.group_size)
    batch_num = dataset.get_dataset_size()
    args.steps_per_epoch = dataset.get_dataset_size()

    # network
    args.logger.info(TAG, 'start create network')

    # get network and init
    network = vgg16(args.num_classes, args)

    # pre_trained
    if args.pre_trained:
        load_param_into_net(network, load_checkpoint(args.pre_trained))

    # lr scheduler
    if args.lr_scheduler == 'exponential':
        lr = warmup_step_lr(args.lr,
                            args.lr_epochs,
                            args.steps_per_epoch,
                            args.warmup_epochs,
                            args.max_epoch,
                            gamma=args.lr_gamma,
                            )
    elif args.lr_scheduler == 'cosine_annealing':
        lr = warmup_cosine_annealing_lr(args.lr,
                                        args.steps_per_epoch,
                                        args.warmup_epochs,
                                        args.max_epoch,
                                        args.T_max,
                                        args.eta_min)
    elif args.lr_scheduler == 'step':
        lr = lr_steps(0, lr_init=args.lr_init, lr_max=args.lr_max, warmup_epochs=args.warmup_epochs,
                      total_epochs=args.max_epoch, steps_per_epoch=batch_num)
    else:
        raise NotImplementedError(args.lr_scheduler)

    # optimizer
    opt = Momentum(params=get_param_groups(network),
                   learning_rate=Tensor(lr),
                   momentum=args.momentum,
                   weight_decay=args.weight_decay,
                   loss_scale=args.loss_scale)

    loss = nn.SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
    model = Model(network, loss_fn=loss, optimizer=opt, metrics={'acc'},
                  amp_level="O2", keep_batchnorm_fp32=False, loss_scale_manager=None)

    # checkpoint save
    callbacks = [LossMonitor()]
    if args.rank_save_ckpt_flag:
        ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval*args.steps_per_epoch,
                                       keep_checkpoint_max=args.ckpt_save_max)
        ckpt_cb = ModelCheckpoint(config=ckpt_config,
                                  directory=args.outputs_dir,
                                  prefix='{}'.format(args.rank))
        callbacks.append(ckpt_cb)

    model.train(args.max_epoch, dataset, callbacks=callbacks)