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']
