forked from huawei/mindspore2022
260 lines
11 KiB
Python
260 lines
11 KiB
Python
# Copyright 2021 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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"""YoloV5 train."""
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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 as ms
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from mindspore.context import ParallelMode
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from mindspore.nn import Momentum
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from mindspore import Tensor
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from mindspore import context
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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, RunContext
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from mindspore.train.callback import _InternalCallbackParam, CheckpointConfig
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from src.yolo import YOLOV5, YoloWithLossCell, TrainingWrapper
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from src.logger import get_logger
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from src.util import AverageMeter, get_param_groups
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from src.lr_scheduler import get_lr
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from src.yolo_dataset import create_yolo_dataset
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from src.initializer import default_recurisive_init, load_yolov5_params
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from src.config import ConfigYOLOV5
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ms.set_seed(1)
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parser = argparse.ArgumentParser('mindspore coco training')
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# device related
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parser.add_argument('--device_target', type=str, default='Ascend', help='device where the code will be implemented.')
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# dataset related
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parser.add_argument('--data_dir', default='/data/coco', type=str, help='Train dataset directory.')
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parser.add_argument('--per_batch_size', default=32, type=int, help='Batch size for Training. Default: 8')
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# network related
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parser.add_argument('--yolov5_version', default='yolov5s', type=str,
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help='The version of YOLOv5, options: yolov5s, yolov5m, yolov5l, yolov5x')
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parser.add_argument('--pretrained_backbone', default='', type=str, help='The pretrained file of yolov5. Default: "".')
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parser.add_argument('--resume_yolov5', default='', type=str,
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help='The ckpt file of YOLOv5, which used to fine tune. Default: ""')
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# optimizer and lr related
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parser.add_argument('--lr_scheduler', default='cosine_annealing', type=str,
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help='Learning rate scheduler, options: exponential, cosine_annealing. Default: exponential')
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parser.add_argument('--lr', default=0.013, type=float, help='Learning rate. Default: 0.01')
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parser.add_argument('--lr_epochs', type=str, default='220,250',
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help='Epoch of changing of lr changing, split with ",". Default: 220,250')
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parser.add_argument('--lr_gamma', type=float, default=0.1,
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help='Decrease lr by a factor of exponential lr_scheduler. Default: 0.1')
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parser.add_argument('--eta_min', type=float, default=0., help='Eta_min in cosine_annealing scheduler. Default: 0')
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parser.add_argument('--T_max', type=int, default=300, help='T-max in cosine_annealing scheduler. Default: 320')
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parser.add_argument('--max_epoch', type=int, default=300, help='Max epoch num to train the model. Default: 320')
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parser.add_argument('--warmup_epochs', default=20, type=float, help='Warmup epochs. Default: 0')
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parser.add_argument('--weight_decay', type=float, default=0.0005, help='Weight decay factor. Default: 0.0005')
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parser.add_argument('--momentum', type=float, default=0.9, help='Momentum. Default: 0.9')
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# loss related
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parser.add_argument('--loss_scale', type=int, default=1024, help='Static loss scale. Default: 1024')
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parser.add_argument('--label_smooth', type=int, default=0, help='Whether to use label smooth in CE. Default:0')
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parser.add_argument('--label_smooth_factor', type=float, default=0.1,
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help='Smooth strength of original one-hot. Default: 0.1')
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# logging related
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parser.add_argument('--log_interval', type=int, default=100, help='Logging interval steps. Default: 100')
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parser.add_argument('--ckpt_path', type=str, default='outputs/', help='Checkpoint save location. Default: outputs/')
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parser.add_argument('--ckpt_interval', type=int, default=None, help='Save checkpoint interval. Default: None')
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parser.add_argument('--is_save_on_master', type=int, default=1,
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help='Save ckpt on master or all rank, 1 for master, 0 for all ranks. Default: 1')
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# distributed related
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parser.add_argument('--is_distributed', type=int, default=0,
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help='Distribute train or not, 1 for yes, 0 for no. Default: 1')
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parser.add_argument('--rank', type=int, default=0, help='Local rank of distributed. Default: 0')
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parser.add_argument('--group_size', type=int, default=1, help='World size of device. Default: 1')
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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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# profiler init
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parser.add_argument('--need_profiler', type=int, default=0,
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help='Whether use profiler. 0 for no, 1 for yes. Default: 0')
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# reset default config
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parser.add_argument('--training_shape', type=str, default="", help='Fix training shape. Default: ""')
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parser.add_argument('--resize_rate', type=int, default=10, help='Resize rate for multi-scale training. Default: None')
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parser.add_argument('--is_modelArts', type=int, default=0,
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help='Trainning in modelArts or not, 1 for yes, 0 for no. Default: 0')
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args, _ = parser.parse_known_args()
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if args.lr_scheduler == 'cosine_annealing' and args.max_epoch > args.T_max:
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args.T_max = args.max_epoch
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args.lr_epochs = list(map(int, args.lr_epochs.split(',')))
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if args.is_modelArts:
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args.data_root = os.path.join(args.data_dir, 'train2017')
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args.annFile = os.path.join(args.data_dir, 'annotations')
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outputs_dir = os.path.join('/cache', args.ckpt_path)
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else:
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args.data_root = os.path.join(args.data_dir, 'train2017')
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args.annFile = os.path.join(
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args.data_dir, 'annotations/instances_train2017.json')
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outputs_dir = args.ckpt_path
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deviced = int(os.getenv('DEVICE_ID', '0'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True, device_target=args.device_target,
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save_graphs=False, device_id=deviced)
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# init distributed
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if args.is_distributed:
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if args.device_target == "Ascend":
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init()
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else:
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init("nccl")
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args.rank = get_rank()
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args.group_size = get_group_size()
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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(outputs_dir, 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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args.logger.save_args(args)
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def convert_training_shape(args_training_shape):
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training_shape = [int(args_training_shape), int(args_training_shape)]
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return training_shape
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loss_meter = AverageMeter('loss')
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if args.is_modelArts:
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import moxing as mox
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local_data_url = os.path.join('/cache/data', str(args.rank))
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local_annFile = os.path.join('/cache/data', str(args.rank))
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mox.file.copy_parallel(args.data_root, local_data_url)
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args.data_root = local_data_url
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mox.file.copy_parallel(args.annFile, local_annFile)
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args.annFile = os.path.join(local_data_url, 'instances_train2017.json')
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context.reset_auto_parallel_context()
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parallel_mode = ParallelMode.STAND_ALONE
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degree = 1
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if args.is_distributed:
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parallel_mode = ParallelMode.DATA_PARALLEL
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degree = get_group_size()
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context.set_auto_parallel_context(parallel_mode=parallel_mode, gradients_mean=True, device_num=degree)
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dict_version = {'yolov5s': 0, 'yolov5m': 1, 'yolov5l': 2, 'yolov5x': 3}
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network = YOLOV5(is_training=True, version=dict_version[args.yolov5_version])
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# default is kaiming-normal
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default_recurisive_init(network)
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load_yolov5_params(args, network)
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network = YoloWithLossCell(network)
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config = ConfigYOLOV5()
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config.label_smooth = args.label_smooth
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config.label_smooth_factor = args.label_smooth_factor
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if args.training_shape:
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config.multi_scale = [convert_training_shape(args.training_shape)]
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if args.resize_rate:
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config.resize_rate = args.resize_rate
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ds, data_size = create_yolo_dataset(image_dir=args.data_root, anno_path=args.annFile, is_training=True,
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batch_size=args.per_batch_size, max_epoch=args.max_epoch,
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device_num=args.group_size, rank=args.rank, config=config)
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args.logger.info('Finish loading dataset')
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args.steps_per_epoch = int(data_size / args.per_batch_size / args.group_size)
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if not args.ckpt_interval:
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args.ckpt_interval = args.steps_per_epoch
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lr = get_lr(args)
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opt = Momentum(params=get_param_groups(network), momentum=args.momentum, learning_rate=Tensor(lr),
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weight_decay=args.weight_decay, loss_scale=args.loss_scale)
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network = TrainingWrapper(network, opt, args.loss_scale // 2)
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network.set_train()
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if args.rank_save_ckpt_flag:
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# checkpoint save
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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, keep_checkpoint_max=1)
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save_ckpt_path = os.path.join(args.outputs_dir, 'ckpt_' + str(args.rank) + '/')
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ckpt_cb = ModelCheckpoint(config=ckpt_config, directory=save_ckpt_path, prefix='{}'.format(args.rank))
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cb_params = _InternalCallbackParam()
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cb_params.train_network = network
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cb_params.epoch_num = ckpt_max_num
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cb_params.cur_epoch_num = 1
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run_context = RunContext(cb_params)
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ckpt_cb.begin(run_context)
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old_progress = -1
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t_end = time.time()
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data_loader = ds.create_dict_iterator(output_numpy=True, num_epochs=1)
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for i, data in enumerate(data_loader):
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images = data["image"]
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input_shape = images.shape[2:4]
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images = Tensor.from_numpy(images)
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batch_y_true_0 = Tensor.from_numpy(data['bbox1'])
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batch_y_true_1 = Tensor.from_numpy(data['bbox2'])
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batch_y_true_2 = Tensor.from_numpy(data['bbox3'])
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batch_gt_box0 = Tensor.from_numpy(data['gt_box1'])
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batch_gt_box1 = Tensor.from_numpy(data['gt_box2'])
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batch_gt_box2 = Tensor.from_numpy(data['gt_box3'])
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input_shape = Tensor(tuple(input_shape[::-1]), ms.float32)
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loss = network(images, batch_y_true_0, batch_y_true_1, batch_y_true_2, batch_gt_box0, batch_gt_box1,
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batch_gt_box2, input_shape)
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loss_meter.update(loss.asnumpy())
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if args.rank_save_ckpt_flag:
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# ckpt progress
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cb_params.cur_step_num = i + 1 # current step number
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cb_params.batch_num = i + 2
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ckpt_cb.step_end(run_context)
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if i % args.log_interval == 0:
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time_used = time.time() - t_end
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epoch = int(i / args.steps_per_epoch)
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fps = args.per_batch_size * (i - old_progress) * args.group_size / time_used
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if args.rank == 0:
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args.logger.info('epoch[{}], iter[{}], {}, fps:{:.2f} imgs/sec, '
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'lr:{}'.format(epoch, i, loss_meter, fps, lr[i]))
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t_end = time.time()
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loss_meter.reset()
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old_progress = i
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if (i + 1) % args.steps_per_epoch == 0 and args.rank_save_ckpt_flag:
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cb_params.cur_epoch_num += 1
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if args.is_modelArts:
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mox.file.copy_parallel(src_url='/cache/outputs/', dst_url='obs://hit-cyf/yolov5_npu/outputs/')
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args.logger.info('==========end training===============')
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