def _non_dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
timestamp=None,
meta=None):
# 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,
cfg.gpus,
dist=False,
shuffle=True,
replace=getattr(cfg.data, 'sampling_replace', False),
seed=cfg.seed,
drop_last=getattr(cfg.data, 'drop_last', False))
for ds in dataset
]
if 'use_fp16' in cfg and cfg.use_fp16 == True:
raise NotImplementedError('apex do not support non_dist_train!')
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model,
batch_processor,
optimizer,
cfg.work_dir,
logger=logger,
meta=meta)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# register custom hooks
for hook in cfg.get('custom_hooks', ()):
priority = hook.pop('priority', None)
if hook.type == 'DeepClusterHook':
common_params = dict(dist_mode=False, data_loaders=data_loaders)
else:
common_params = dict(dist_mode=False)
if priority is None:
runner.register_hook(build_hook(hook, common_params))
else:
runner.register_hook(build_hook(hook, common_params), priority)
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)
def _dist_train(model, dataset, cfg, logger=None, timestamp=None, meta=None):
# 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,
shuffle=True,
replace=getattr(cfg.data, 'sampling_replace', False),
seed=cfg.seed,
drop_last=getattr(cfg.data, 'drop_last', False),
prefetch=cfg.prefetch,
img_norm_cfg=cfg.img_norm_cfg) for ds in dataset
]
optimizer = build_optimizer(model, cfg.optimizer)
if 'use_fp16' in cfg and cfg.use_fp16:
model, optimizer = apex.amp.initialize(model.cuda(),
optimizer,
opt_level="O1")
print_log('**** Initializing mixed precision done. ****')
# put model on gpus
model = MMDistributedDataParallel(
model if next(model.parameters()).is_cuda else model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False)
# build runner
runner = Runner(model,
batch_processor,
optimizer,
cfg.work_dir,
logger=logger,
meta=meta)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
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 custom hooks
for hook in cfg.get('custom_hooks', ()):
if hook.type == 'DeepClusterHook':
common_params = dict(dist_mode=True, data_loaders=data_loaders)
else:
common_params = dict(dist_mode=True)
runner.register_hook(build_hook(hook, common_params))
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)
def _non_dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
timestamp=None,
meta=None):
# use batch size instead of per-gpu batch size
if getattr(cfg.data, 'batch_size', False):
cfg.data.imgs_per_gpu = int(cfg.data.batch_size)
print_log(
f"Using {cfg.data.imgs_per_gpu} per gpu for batch size {cfg.data.batch_size}")
# 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,
cfg.gpus,
dist=False,
shuffle=True,
replace=getattr(cfg.data, 'sampling_replace', False),
seed=cfg.seed,
drop_last=getattr(cfg.data, 'drop_last', False),
prefetch=cfg.prefetch,
img_norm_cfg=cfg.img_norm_cfg) for ds in dataset
]
if 'use_fp16' in cfg and cfg.use_fp16 == True:
raise NotImplementedError('apex do not support non_dist_train!')
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
optimizer_config = NonDistOptimizerHook(**cfg.optimizer_config)
if not cfg.get('by_iter', False):
runner = Runner(
model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
else:
runner = IterBasedRunner(
model,
optimizer=optimizer,
work_dir=cfg.work_dir,
logger=logger,
meta=meta)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# register custom hooks
for hook in cfg.get('custom_hooks', ()):
if hook.type == 'DeepClusterHook':
common_params = dict(dist_mode=False, data_loaders=data_loaders)
else:
common_params = dict(dist_mode=False)
runner.register_hook(build_hook(hook, common_params))
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
if not cfg.get('by_iter', False):
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
else:
runner.run(data_loaders, cfg.workflow, cfg.total_iters)
