assert torch!=1.12.0 for DDP training (#8621)

* assert torch!=1.12.0 for DDP training

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

train.py

@@ -27,7 +27,6 @@ import torch
 import torch.distributed as dist
 import torch.nn as nn
 import yaml
-from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import lr_scheduler
 from tqdm import tqdm
 
@@ -46,15 +45,15 @@ from utils.callbacks import Callbacks
 from utils.dataloaders import create_dataloader
 from utils.downloads import attempt_download
 from utils.general import (LOGGER, check_amp, check_dataset, check_file, check_git_status, check_img_size,
-                           check_requirements, check_suffix, check_version, check_yaml, colorstr, get_latest_run,
-                           increment_path, init_seeds, intersect_dicts, labels_to_class_weights,
-                           labels_to_image_weights, methods, one_cycle, print_args, print_mutation, strip_optimizer)
+                           check_requirements, check_suffix, check_yaml, colorstr, get_latest_run, increment_path,
+                           init_seeds, intersect_dicts, labels_to_class_weights, labels_to_image_weights, methods,
+                           one_cycle, print_args, print_mutation, strip_optimizer)
 from utils.loggers import Loggers
 from utils.loggers.wandb.wandb_utils import check_wandb_resume
 from utils.loss import ComputeLoss
 from utils.metrics import fitness
 from utils.plots import plot_evolve, plot_labels
-from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_optimizer,
+from utils.torch_utils import (EarlyStopping, ModelEMA, de_parallel, select_device, smart_DDP, smart_optimizer,
                                torch_distributed_zero_first)
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html
@@ -248,10 +247,7 @@ def train(hyp, opt, device, callbacks):  # hyp is path/to/hyp.yaml or hyp dictio
 
     # DDP mode
     if cuda and RANK != -1:
-        if check_version(torch.__version__, '1.11.0'):
-            model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
-        else:
-            model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
+        model = smart_DDP(model)
 
     # Model attributes
     nl = de_parallel(model).model[-1].nl  # number of detection layers (to scale hyps)