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Implement DDP `static_graph=True` (#6940) 

* Implement DDP `static_graph=True`

Experimental implementation of new PyTorch 1.11.0 DDP feature.

* Add 1.11.0 check

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

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2022-05-13 12:32:47 +02:00 committed by GitHub
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1 changed files with 7 additions and 4 deletions
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train.py
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@ -47,9 +47,9 @@ from utils.callbacks import Callbacks
from utils.datasets import create_dataloader from utils.datasets import create_dataloader
from utils.downloads import attempt_download from utils.downloads import attempt_download
from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements, from utils.general import (LOGGER, check_dataset, check_file, check_git_status, check_img_size, check_requirements,
check_suffix, check_yaml, colorstr, get_latest_run, increment_path, init_seeds, check_suffix, check_version, check_yaml, colorstr, get_latest_run, increment_path,
intersect_dicts, is_ascii, labels_to_class_weights, labels_to_image_weights, methods, init_seeds, intersect_dicts, is_ascii, labels_to_class_weights, labels_to_image_weights,
one_cycle, print_args, print_mutation, strip_optimizer) methods, one_cycle, print_args, print_mutation, strip_optimizer)
from utils.loggers import Loggers from utils.loggers import Loggers
from utils.loggers.wandb.wandb_utils import check_wandb_resume from utils.loggers.wandb.wandb_utils import check_wandb_resume
from utils.loss import ComputeLoss from utils.loss import ComputeLoss
@ -269,7 +269,10 @@ def train(hyp, opt, device, callbacks): # hyp is path/to/hyp.yaml or hyp dictio
# DDP mode # DDP mode
if cuda and RANK != -1: if cuda and RANK != -1:
model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) 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 attributes # Model attributes
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps) nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)