from tqdm import tqdm from torch import nn from apex import amp from torch.utils.data.distributed import DistributedSampler from datasets.coco import COCODataSets from nets.retinanet import RetinaNet from losses.retina_anchor_free import RetinaAnchorFreeLoss from torch.utils.data.dataloader import DataLoader from utils.retinanet import non_max_suppression from commons.model_utils import rand_seed, is_parallel, ModelEMA, freeze_bn from metrics.map import coco_map from torch.nn.functional import interpolate from commons.optims_utils import WarmUpCosineDecayMultiStepLRAdjust, split_optimizer rand_seed(1024) class DDPApexProcessor(object): def __init__(self, cfg_path): with open(cfg_path, 'r') as rf: self.cfg = yaml.safe_load(rf) self.data_cfg = self.cfg['data'] self.model_cfg = self.cfg['model'] self.optim_cfg = self.cfg['optim'] self.hyper_params = self.cfg['hyper_params'] self.val_cfg = self.cfg['val'] print(self.data_cfg) print(self.model_cfg) print(self.optim_cfg) print(self.hyper_params)
import yaml import torch import torch.distributed as dist from tqdm import tqdm from torch import nn from torch.utils.data.dataloader import DataLoader from torch.utils.data.distributed import DistributedSampler from nets import pose_resnet_duc from nets import pose_resnet_dconv from torch.cuda import amp from metrics.pose_metrics import HeatMapAcc, kps_to_dict_, BasicKeyPointDecoder, evaluate_map from datasets.coco import MSCOCO from commons.model_utils import rand_seed, ModelEMA, reduce_sum, AverageLogger from commons.optims_utils import EpochWarmUpCosineDecayLRAdjust rand_seed(512) class DDPProcessor(object): def __init__(self, cfg_path): with open(cfg_path, 'r') as rf: self.cfg = yaml.safe_load(rf) self.data_cfg = self.cfg['data'] self.model_cfg = self.cfg['model'] self.optim_cfg = self.cfg['optim'] self.val_cfg = self.cfg['val'] print(self.data_cfg) print(self.model_cfg) print(self.optim_cfg) print(self.val_cfg) os.environ['CUDA_VISIBLE_DEVICES'] = self.cfg['gpus']