def _non_dist_train(model,
train_dataset,
cfg,
eval_dataset=None,
vis_dataset=None,
validate=False,
logger=None):
# prepare data loaders
data_loaders = [
build_data_loader(train_dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False)
]
# 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,
cfg.log_level, logger)
logger.info("Register Optimizer Hook...")
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
logger.info("Register EmptyCache Hook...")
runner.register_hook(EmptyCacheHook(before_epoch=True,
after_iter=False,
after_epoch=True),
priority='VERY_LOW')
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,
train_dataset,
cfg,
eval_dataset=None,
vis_dataset=None,
validate=False,
logger=None):
# prepare data loaders
data_loaders = [
build_data_loader(train_dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True)
]
if cfg.apex.synced_bn:
# using apex synced BN
model = apex.parallel.convert_syncbn_model(model)
model = model.cuda()
# build optimizer
optimizer = build_optimizer(model, cfg.optimizer)
# Initialize mixed-precision training
if cfg.apex.use_mixed_precision:
amp_opt_level = 'O1' if cfg.apex.type == "float16" else 'O0'
model, optimizer = amp.initialize(model,
optimizer,
opt_level=amp_opt_level,
loss_scale=cfg.apex.loss_scale)
# put model on gpus
model = MMDistributedDataParallel(model)
# build runner
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level, logger)
# register optimizer hooks
if cfg.apex.use_mixed_precision:
optimizer_config = DistApexOptimizerHook(**cfg.optimizer_config)
else:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
logger.info("Register Optimizer Hook...")
runner.register_training_hooks(cfg.lr_config,
optimizer_config,
cfg.checkpoint_config,
log_config=None)
# register self-defined logging hooks
for info in cfg.log_config['hooks']:
assert isinstance(info, dict) and 'type' in info
if info['type'] in ['TensorboardLoggerHook']:
logger.info("Register Tensorboard Logger Hook...")
runner.register_hook(TensorboardLoggerHook(
interval=cfg.log_config.interval,
register_logWithIter_keyword=['loss']),
priority='VERY_LOW')
if info['type'] in ['TextLoggerHook']:
logger.info("Register Text Logger Hook...")
runner.register_hook(TextLoggerHook(
interval=cfg.log_config.interval, ),
priority='VERY_LOW')
logger.info("Register SamplerSeed Hook...")
runner.register_hook(DistSamplerSeedHook())
logger.info("Register EmptyCache Hook...")
runner.register_hook(EmptyCacheHook(before_epoch=True,
after_iter=False,
after_epoch=True),
priority='VERY_LOW')
# register eval hooks
if validate:
interval = cfg.get('validate_interval', 1)
if eval_dataset is not None:
logger.info("Register Evaluation Hook...")
runner.register_hook(
DistStereoEvalHook(cfg, eval_dataset, interval))
if vis_dataset is not None:
logger.info("Register Visualization hook...")
runner.register_hook(DistStereoVisHook(vis_dataset, cfg, interval))
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)
