本文整理汇总了Python中mmcv.parallel.MMDistributedDataParallel方法的典型用法代码示例。如果您正苦于以下问题：Python parallel.MMDistributedDataParallel方法的具体用法？Python parallel.MMDistributedDataParallel怎么用？Python parallel.MMDistributedDataParallel使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类mmcv.parallel的用法示例。
在下文中一共展示了parallel.MMDistributedDataParallel方法的9个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, data_loaders, batch_processor, cfg):
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# register hooks
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例2: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
data_loaders = [
build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True)
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# register hooks
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset_cfg = cfg.data.val
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(CocoDistEvalRecallHook(val_dataset_cfg))
else:
dataset_type = getattr(datasets, val_dataset_cfg.type)
if issubclass(dataset_type, datasets.CocoDataset):
runner.register_hook(CocoDistEvalmAPHook(val_dataset_cfg))
else:
runner.register_hook(DistEvalmAPHook(val_dataset_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例3: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
data_loaders = [
build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True)
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
# register hooks
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(CocoDistEvalRecallHook(cfg.data.val))
else:
if cfg.data.val.type == 'CocoDataset':
runner.register_hook(CocoDistEvalmAPHook(cfg.data.val))
else:
runner.register_hook(DistEvalmAPHook(cfg.data.val))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例4: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, datasets, cfg, validate=False, logger=None):
# prepare data loaders
data_loaders = [
build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True) for dataset in datasets
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
runner = NASRunner(model, batch_processor, None, cfg.work_dir, cfg.log_level, cfg=cfg, logger=logger)
# register hooks
weight_optim_config = DistOptimizerHook(**cfg.optimizer.weight_optim.optimizer_config)
arch_optim_config = ArchDistOptimizerHook(**cfg.optimizer.arch_optim.optimizer_config)
runner.register_training_hooks(cfg.lr_config, weight_optim_config, arch_optim_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(CocoDistEvalRecallHook(cfg.data.val))
else:
if cfg.dataset_type == 'CocoDataset':
# runner.register_hook(CocoDistEvalmAPHook_(datasets[1]))
runner.register_hook(CocoDistEvalmAPHook(cfg.data.val_))
else:
runner.register_hook(DistEvalmAPHook(cfg.data.val))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs, cfg.arch_update_epoch) 示例5: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
data_loaders = [
build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True)
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg)
else:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset_cfg = cfg.data.val
eval_cfg = cfg.get('evaluation', {})
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(
CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
else:
dataset_type = getattr(datasets, val_dataset_cfg.type)
if issubclass(dataset_type, datasets.CocoDataset):
runner.register_hook(
CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
else:
runner.register_hook(
DistEvalmAPHook(val_dataset_cfg, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例6: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
for ds in dataset
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg)
else:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset_cfg = cfg.data.val
eval_cfg = cfg.get('evaluation', {})
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(
CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
else:
dataset_type = DATASETS.get(val_dataset_cfg.type)
if issubclass(dataset_type, datasets.CocoDataset):
runner.register_hook(
CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
else:
runner.register_hook(
DistEvalmAPHook(val_dataset_cfg, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例7: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
for ds in dataset
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg)
else:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset_cfg = cfg.data.val
eval_cfg = cfg.get('evaluation', {})
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(
CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
else:
dataset_type = DATASETS.get(val_dataset_cfg.type)
if issubclass(dataset_type, datasets.CocoDataset):
runner.register_hook(
CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
else:
runner.register_hook(
DistEvalF1Hook(val_dataset_cfg, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例8: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
data_loaders = [
build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True)
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# with torch.no_grad():
# for j in range(2):
# print(j)
# for i, data_batch in enumerate(data_loaders[0]):
# _ = model(**data_batch)
# # break
# build runner
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
# register hooks
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
if isinstance(model.module, RPN):
# TODO: implement recall hooks for other datasets
runner.register_hook(CocoDistEvalRecallHook(cfg.data.val))
else:
if cfg.data.val.type == 'CocoDataset':
runner.register_hook(CocoDistEvalmAPHook(cfg.data.val))
else:
runner.register_hook(DistEvalmAPHook(cfg.data.val))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) 示例9: _dist_train
# 需要导入模块: from mmcv import parallel [as 别名]
# 或者: from mmcv.parallel import MMDistributedDataParallel [as 别名]
def _dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
data_loaders = [
build_dataloader(
ds, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
for ds in dataset
]
# put model on gpus
model = MMDistributedDataParallel(model.cuda())
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg)
else:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset_cfg = cfg.data.val
eval_cfg = cfg.get('evaluation', {})
if isinstance(model.module, (RPN, CascadeRPN)):
# TODO: implement recall hooks for other datasets
runner.register_hook(
CocoDistEvalRecallHook(val_dataset_cfg, **eval_cfg))
else:
dataset_type = DATASETS.get(val_dataset_cfg.type)
if issubclass(dataset_type, datasets.CocoDataset):
runner.register_hook(
CocoDistEvalmAPHook(val_dataset_cfg, **eval_cfg))
else:
runner.register_hook(
DistEvalmAPHook(val_dataset_cfg, **eval_cfg))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)