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297 lines
13 KiB
297 lines
13 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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"""YoloV3 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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from mindspore.context import ParallelMode
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from mindspore.nn.optim.momentum import Momentum
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from mindspore import Tensor
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import mindspore.nn as nn
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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 mindspore import amp
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from mindspore.train.loss_scale_manager import FixedLossScaleManager
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from mindspore.common import set_seed
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from src.yolo import YOLOV3DarkNet53, 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_yolov3_params
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from src.config import ConfigYOLOV3DarkNet53
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from src.util import keep_loss_fp32
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set_seed(1)
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class BuildTrainNetwork(nn.Cell):
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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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def parse_args():
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"""Parse train arguments."""
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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', choices=['Ascend', 'GPU'],
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help='device where the code will be implemented. (Default: Ascend)')
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# dataset related
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parser.add_argument('--data_dir', 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: 32.')
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# network related
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parser.add_argument('--pretrained_backbone', default='', type=str,
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help='The ckpt file of DarkNet53. Default: "".')
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parser.add_argument('--resume_yolov3', default='', type=str,
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help='The ckpt file of YOLOv3, which used to fine tune. Default: ""')
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# optimizer and lr related
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parser.add_argument('--lr_scheduler', default='exponential', 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=0.001, type=float, help='Learning rate. Default: 0.001')
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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=320, help='T-max in cosine_annealing scheduler. Default: 320')
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parser.add_argument('--max_epoch', type=int, default=320, help='Max epoch num to train the model. Default: 320')
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parser.add_argument('--warmup_epochs', default=0, 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=1,
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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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# 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=None,
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help='Resize rate for multi-scale training. Default: None')
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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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args.data_root = os.path.join(args.data_dir, 'train2014')
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args.annFile = os.path.join(args.data_dir, 'annotations/instances_train2014.json')
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return args
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def conver_training_shape(args):
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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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def network_init(args):
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devid = int(os.getenv('DEVICE_ID', '0'))
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context.set_context(mode=context.GRAPH_MODE, enable_auto_mixed_precision=True,
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device_target=args.device_target, save_graphs=False, device_id=devid)
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profiler = None
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if args.need_profiler:
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from mindspore.profiler import Profiler
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profiling_dir = os.path.join("profiling",
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datetime.datetime.now().strftime('%Y-%m-%d_time_%H_%M_%S'))
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profiler = Profiler(output_path=profiling_dir, is_detail=True, is_show_op_path=True)
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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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# select for master rank save ckpt or all rank save, compatible 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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args.logger.save_args(args)
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return profiler
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def parallel_init(args):
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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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def train():
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"""Train function."""
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args = parse_args()
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profiler = network_init(args)
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loss_meter = AverageMeter('loss')
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parallel_init(args)
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network = YOLOV3DarkNet53(is_training=True)
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# default is kaiming-normal
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default_recurisive_init(network)
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load_yolov3_params(args, network)
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network = YoloWithLossCell(network)
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args.logger.info('finish get network')
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config = ConfigYOLOV3DarkNet53()
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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 = [conver_training_shape(args)]
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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),
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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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is_gpu = context.get_context("device_target") == "GPU"
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if is_gpu:
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loss_scale_value = 1.0
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loss_scale = FixedLossScaleManager(loss_scale_value, drop_overflow_update=False)
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network = amp.build_train_network(network, optimizer=opt, loss_scale_manager=loss_scale,
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level="O2", keep_batchnorm_fp32=False)
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keep_loss_fp32(network)
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else:
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network = TrainingWrapper(network, opt, sens=args.loss_scale)
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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,
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keep_checkpoint_max=ckpt_max_num)
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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,
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directory=save_ckpt_path,
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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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args.logger.info('iter[{}], shape{}'.format(i, input_shape[0]))
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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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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)
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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(
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'epoch[{}], iter[{}], {}, {:.2f} imgs/sec, 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.need_profiler:
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if i == 10:
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profiler.analyse()
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break
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args.logger.info('==========end training===============')
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if __name__ == "__main__":
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train()
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