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118 lines
5.2 KiB
118 lines
5.2 KiB
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""train imagenet."""
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import argparse
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import os
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import random
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import numpy as np
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from mindspore import Tensor
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from mindspore import context
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from mindspore import ParallelMode
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.nn.optim.rmsprop import RMSProp
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train.model import Model
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore import dataset as de
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from src.config import nasnet_a_mobile_config_gpu as cfg
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from src.dataset import create_dataset
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from src.nasnet_a_mobile import NASNetAMobileWithLoss, NASNetAMobileTrainOneStepWithClipGradient
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from src.lr_generator import get_lr
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random.seed(cfg.random_seed)
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np.random.seed(cfg.random_seed)
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de.config.set_seed(cfg.random_seed)
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if __name__ == '__main__':
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parser = argparse.ArgumentParser(description='image classification training')
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parser.add_argument('--dataset_path', type=str, default='', help='Dataset path')
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parser.add_argument('--resume', type=str, default='', help='resume training with existed checkpoint')
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parser.add_argument('--is_distributed', action='store_true', default=False,
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help='distributed training')
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parser.add_argument('--platform', type=str, default='GPU', choices=('Ascend', 'GPU'), help='run platform')
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.platform, save_graphs=False)
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if os.getenv('DEVICE_ID', "not_set").isdigit():
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context.set_context(device_id=int(os.getenv('DEVICE_ID')))
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# init distributed
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if args_opt.is_distributed:
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if args_opt.platform == "Ascend":
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init()
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else:
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init("nccl")
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cfg.rank = get_rank()
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cfg.group_size = get_group_size()
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parallel_mode = ParallelMode.DATA_PARALLEL
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=cfg.group_size,
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parameter_broadcast=True, mirror_mean=True)
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else:
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cfg.rank = 0
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cfg.group_size = 1
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# dataloader
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dataset = create_dataset(args_opt.dataset_path, cfg, True)
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batches_per_epoch = dataset.get_dataset_size()
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# network
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net_with_loss = NASNetAMobileWithLoss(cfg)
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if args_opt.resume:
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ckpt = load_checkpoint(args_opt.resume)
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load_param_into_net(net_with_loss, ckpt)
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# learning rate schedule
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lr = get_lr(lr_init=cfg.lr_init, lr_decay_rate=cfg.lr_decay_rate,
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num_epoch_per_decay=cfg.num_epoch_per_decay, total_epochs=cfg.epoch_size,
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steps_per_epoch=batches_per_epoch, is_stair=True)
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lr = Tensor(lr)
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# optimizer
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decayed_params = []
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no_decayed_params = []
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for param in net_with_loss.trainable_params():
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if 'beta' not in param.name and 'gamma' not in param.name and 'bias' not in param.name:
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decayed_params.append(param)
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else:
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no_decayed_params.append(param)
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group_params = [{'params': decayed_params, 'weight_decay': cfg.weight_decay},
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{'params': no_decayed_params},
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{'order_params': net_with_loss.trainable_params()}]
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optimizer = RMSProp(group_params, lr, decay=cfg.rmsprop_decay, weight_decay=cfg.weight_decay,
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momentum=cfg.momentum, epsilon=cfg.opt_eps, loss_scale=cfg.loss_scale)
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net_with_grads = NASNetAMobileTrainOneStepWithClipGradient(net_with_loss, optimizer)
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net_with_grads.set_train()
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model = Model(net_with_grads)
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print("============== Starting Training ==============")
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loss_cb = LossMonitor(per_print_times=batches_per_epoch)
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time_cb = TimeMonitor(data_size=batches_per_epoch)
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callbacks = [loss_cb, time_cb]
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config_ck = CheckpointConfig(save_checkpoint_steps=batches_per_epoch, keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix=f"nasnet-a-mobile-rank{cfg.rank}", directory=cfg.ckpt_path, config=config_ck)
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if args_opt.is_distributed & cfg.is_save_on_master:
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if cfg.rank == 0:
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callbacks.append(ckpoint_cb)
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model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
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else:
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callbacks.append(ckpoint_cb)
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model.train(cfg.epoch_size, dataset, callbacks=callbacks, dataset_sink_mode=True)
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print("train success")
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