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290 lines
11 KiB
290 lines
11 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 os
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import time
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import argparse
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import datetime
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import mindspore.nn as nn
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from mindspore import Tensor, context
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from mindspore import ParallelMode
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from mindspore.nn.optim import Momentum
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from mindspore.communication.management import init, get_rank, get_group_size
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from mindspore.train.callback import ModelCheckpoint
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from mindspore.train.callback import CheckpointConfig, Callback
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from mindspore.train.serialization import load_checkpoint, load_param_into_net
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from mindspore.train.model import Model
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from mindspore.train.loss_scale_manager import DynamicLossScaleManager, FixedLossScaleManager
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from src.dataset import classification_dataset
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from src.crossentropy import CrossEntropy
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from src.warmup_step_lr import warmup_step_lr
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from src.warmup_cosine_annealing_lr import warmup_cosine_annealing_lr
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from src.utils.logging import get_logger
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from src.utils.optimizers__init__ import get_param_groups
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from src.image_classification import get_network
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from src.config import config
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devid = int(os.getenv('DEVICE_ID'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target="Ascend", save_graphs=False, device_id=devid)
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class BuildTrainNetwork(nn.Cell):
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"""build training network"""
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def __init__(self, network, criterion):
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super(BuildTrainNetwork, self).__init__()
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self.network = network
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self.criterion = criterion
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def construct(self, input_data, label):
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output = self.network(input_data)
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loss = self.criterion(output, label)
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return loss
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class ProgressMonitor(Callback):
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"""monitor loss and time"""
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def __init__(self, args):
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super(ProgressMonitor, self).__init__()
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self.me_epoch_start_time = 0
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self.me_epoch_start_step_num = 0
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self.args = args
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self.ckpt_history = []
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def begin(self, run_context):
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self.args.logger.info('start network train...')
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def epoch_begin(self, run_context):
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pass
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def epoch_end(self, run_context, *me_args):
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cb_params = run_context.original_args()
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me_step = cb_params.cur_step_num - 1
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real_epoch = me_step // self.args.steps_per_epoch
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time_used = time.time() - self.me_epoch_start_time
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fps_mean = self.args.per_batch_size * (me_step-self.me_epoch_start_step_num) * self.args.group_size / time_used
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self.args.logger.info('epoch[{}], iter[{}], loss:{}, mean_fps:{:.2f}'
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'imgs/sec'.format(real_epoch, me_step, cb_params.net_outputs, fps_mean))
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if self.args.rank_save_ckpt_flag:
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import glob
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ckpts = glob.glob(os.path.join(self.args.outputs_dir, '*.ckpt'))
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for ckpt in ckpts:
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ckpt_fn = os.path.basename(ckpt)
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if not ckpt_fn.startswith('{}-'.format(self.args.rank)):
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continue
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if ckpt in self.ckpt_history:
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continue
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self.ckpt_history.append(ckpt)
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self.args.logger.info('epoch[{}], iter[{}], loss:{}, ckpt:{},'
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'ckpt_fn:{}'.format(real_epoch, me_step, cb_params.net_outputs, ckpt, ckpt_fn))
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self.me_epoch_start_step_num = me_step
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self.me_epoch_start_time = time.time()
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def step_begin(self, run_context):
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pass
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def step_end(self, run_context, *me_args):
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pass
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def end(self, run_context):
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self.args.logger.info('end network train...')
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def parse_args(cloud_args=None):
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"""parameters"""
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parser = argparse.ArgumentParser('mindspore classification training')
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# dataset related
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parser.add_argument('--data_dir', type=str, default='', help='train data dir')
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parser.add_argument('--per_batch_size', default=128, type=int, help='batch size for per gpu')
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# network related
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parser.add_argument('--pretrained', default='', type=str, help='model_path, local pretrained model to load')
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# distributed related
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parser.add_argument('--is_distributed', type=int, default=1, help='if multi device')
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# roma obs
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parser.add_argument('--train_url', type=str, default="", help='train url')
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args, _ = parser.parse_known_args()
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args = merge_args(args, cloud_args)
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args.image_size = config.image_size
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args.num_classes = config.num_classes
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args.lr = config.lr
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args.lr_scheduler = config.lr_scheduler
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args.lr_epochs = config.lr_epochs
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args.lr_gamma = config.lr_gamma
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args.eta_min = config.eta_min
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args.T_max = config.T_max
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args.max_epoch = config.max_epoch
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args.backbone = config.backbone
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args.warmup_epochs = config.warmup_epochs
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args.weight_decay = config.weight_decay
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args.momentum = config.momentum
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args.is_dynamic_loss_scale = config.is_dynamic_loss_scale
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args.loss_scale = config.loss_scale
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args.label_smooth = config.label_smooth
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args.label_smooth_factor = config.label_smooth_factor
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args.ckpt_interval = config.ckpt_interval
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args.ckpt_path = config.ckpt_path
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args.is_save_on_master = config.is_save_on_master
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args.rank = config.rank
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args.group_size = config.group_size
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args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
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args.image_size = list(map(int, args.image_size.split(',')))
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return args
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def merge_args(args, cloud_args):
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"""dictionary"""
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args_dict = vars(args)
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if isinstance(cloud_args, dict):
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for key in cloud_args.keys():
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val = cloud_args[key]
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if key in args_dict and val:
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arg_type = type(args_dict[key])
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if arg_type is not type(None):
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val = arg_type(val)
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args_dict[key] = val
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return args
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def train(cloud_args=None):
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"""training process"""
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args = parse_args(cloud_args)
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# init distributed
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if args.is_distributed:
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init()
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args.rank = get_rank()
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args.group_size = get_group_size()
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if args.is_dynamic_loss_scale == 1:
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args.loss_scale = 1 # for dynamic loss scale can not set loss scale in momentum opt
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# select for master rank save ckpt or all rank save, compatiable for model parallel
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args.rank_save_ckpt_flag = 0
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if args.is_save_on_master:
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if args.rank == 0:
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args.rank_save_ckpt_flag = 1
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else:
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args.rank_save_ckpt_flag = 1
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# logger
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args.outputs_dir = os.path.join(args.ckpt_path,
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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args.logger = get_logger(args.outputs_dir, args.rank)
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# dataloader
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de_dataset = classification_dataset(args.data_dir, args.image_size,
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args.per_batch_size, 1,
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args.rank, args.group_size)
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de_dataset.map_model = 4 # !!!important
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args.steps_per_epoch = de_dataset.get_dataset_size()
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args.logger.save_args(args)
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# network
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args.logger.important_info('start create network')
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# get network and init
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network = get_network(args.backbone, args.num_classes)
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if network is None:
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raise NotImplementedError('not implement {}'.format(args.backbone))
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network.add_flags_recursive(fp16=True)
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# loss
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if not args.label_smooth:
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args.label_smooth_factor = 0.0
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criterion = CrossEntropy(smooth_factor=args.label_smooth_factor,
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num_classes=args.num_classes)
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# load pretrain model
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if os.path.isfile(args.pretrained):
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param_dict = load_checkpoint(args.pretrained)
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param_dict_new = {}
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for key, values in param_dict.items():
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if key.startswith('moments.'):
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continue
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elif key.startswith('network.'):
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param_dict_new[key[8:]] = values
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else:
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param_dict_new[key] = values
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load_param_into_net(network, param_dict_new)
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args.logger.info('load model {} success'.format(args.pretrained))
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# lr scheduler
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if args.lr_scheduler == 'exponential':
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lr = warmup_step_lr(args.lr,
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args.lr_epochs,
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args.steps_per_epoch,
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args.warmup_epochs,
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args.max_epoch,
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gamma=args.lr_gamma,
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)
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elif args.lr_scheduler == 'cosine_annealing':
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lr = warmup_cosine_annealing_lr(args.lr,
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args.steps_per_epoch,
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args.warmup_epochs,
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args.max_epoch,
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args.T_max,
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args.eta_min)
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else:
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raise NotImplementedError(args.lr_scheduler)
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# optimizer
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opt = Momentum(params=get_param_groups(network),
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learning_rate=Tensor(lr),
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momentum=args.momentum,
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weight_decay=args.weight_decay,
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loss_scale=args.loss_scale)
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criterion.add_flags_recursive(fp32=True)
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# package training process, adjust lr + forward + backward + optimizer
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train_net = BuildTrainNetwork(network, criterion)
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if args.is_distributed:
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parallel_mode = ParallelMode.DATA_PARALLEL
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else:
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parallel_mode = ParallelMode.STAND_ALONE
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if args.is_dynamic_loss_scale == 1:
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loss_scale_manager = DynamicLossScaleManager(init_loss_scale=65536, scale_factor=2, scale_window=2000)
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else:
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loss_scale_manager = FixedLossScaleManager(args.loss_scale, drop_overflow_update=False)
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# Model api changed since TR5_branch 2020/03/09
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context.set_auto_parallel_context(parallel_mode=parallel_mode, device_num=args.group_size,
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parameter_broadcast=True, mirror_mean=True)
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model = Model(train_net, optimizer=opt, metrics=None, loss_scale_manager=loss_scale_manager)
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# checkpoint save
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progress_cb = ProgressMonitor(args)
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callbacks = [progress_cb,]
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if args.rank_save_ckpt_flag:
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ckpt_max_num = args.max_epoch * args.steps_per_epoch // args.ckpt_interval
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ckpt_config = CheckpointConfig(save_checkpoint_steps=args.ckpt_interval,
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keep_checkpoint_max=ckpt_max_num)
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ckpt_cb = ModelCheckpoint(config=ckpt_config,
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directory=args.outputs_dir,
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prefix='{}'.format(args.rank))
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callbacks.append(ckpt_cb)
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model.train(args.max_epoch, de_dataset, callbacks=callbacks, dataset_sink_mode=True)
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if __name__ == "__main__":
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train()
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