def _non_dist_train(model, dataset_names, cfg, validate=False, **kwargs):
# prepare data loaders
data_loaders = [
build_dataloader(dataset,
cfg.data.tasks_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False,
customized_sampler=not dataset.test_mode)
for dataset in dataset_names
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
runner = Runner(model, batch_processors[cfg.batch_processor_type],
cfg.optimizer, cfg.work_dir, cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# resume
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, **kwargs)
def _non_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,
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)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
# ipdb.set_trace()
# prepare data loaders
# 返回dataloader的迭代器，采用pytorch的DataLoader方法封装数据集
data_loaders = [
build_dataloader(dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False)
]
# put model on gpus # 这里多GPU输入没用list而是迭代器，注意单GPU是range(0,1),遍历的时候只有0
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner # 这里只是将相关参数传递，没有任何的构建和运行。就像model的build一样只放了模块没有连接和顺序
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
# 注册钩子
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# 断点加载或文件加载数据
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 train(
work_dir,
model_cfg,
loss_cfg,
dataset_cfg,
optimizer_cfg,
total_epochs,
training_hooks,
batch_size=None,
gpu_batch_size=None,
workflow=[('train', 1)],
gpus=-1,
log_level=0,
workers=4,
resume_from=None,
load_from=None,
):
# calculate batch size
if gpus < 0:
gpus = torch.cuda.device_count()
if (batch_size is None) and (gpu_batch_size is not None):
batch_size = gpu_batch_size * gpus
assert batch_size is not None, 'Please appoint batch_size or gpu_batch_size.'
# prepare data loaders
if isinstance(dataset_cfg, dict):
dataset_cfg = [dataset_cfg]
data_loaders = [
torch.utils.data.DataLoader(dataset=call_obj(**d),
batch_size=batch_size,
shuffle=True,
num_workers=workers,
drop_last=True) for d in dataset_cfg
]
# put model on gpus
if isinstance(model_cfg, list):
model = [call_obj(**c) for c in model_cfg]
model = torch.nn.Sequential(*model)
else:
model = call_obj(**model_cfg)
model.apply(weights_init)
model = MMDataParallel(model, device_ids=range(gpus)).cuda()
loss = call_obj(**loss_cfg)
# build runner
optimizer = call_obj(params=model.parameters(), **optimizer_cfg)
runner = Runner(model, batch_processor, optimizer, work_dir, log_level)
runner.register_training_hooks(**training_hooks)
if resume_from:
runner.resume(resume_from)
elif load_from:
runner.load_checkpoint(load_from)
# run
workflow = [tuple(w) for w in workflow]
runner.run(data_loaders, workflow, total_epochs, loss=loss)
def _non_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,
cfg.gpus,
dist=False)
]
optimizer = torch.optim.SGD(model.parameters(),
lr=0.01,
momentum=0.9,
weight_decay=0.0001)
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
get_root_logger(cfg.log_level))
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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, **kwargs):
# prepare data loaders
data_loaders = [
build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False,
#repeat_samples=cfg.train_cfg.repeat_samples,
**kwargs)
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
logger = _init_logger(log_dir=cfg.work_dir, level=getattr(logging, cfg.log_level))
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
logger)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
if validate:
raise NotImplementedError('Built-in validation is not implemented '
'yet in not-distributed training. Use '
'distributed training or test.py and '
'*eval.py scripts instead.')
# 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 = 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, cfg=cfg)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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, train_dataset, cfg, validate=False):
build_fn = lambda dataset, cfg_dict: build_dataloader(
dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False,
balanced=cfg_dict.get('balanced', False))
datasets = [train_dataset]
data_loaders = [build_fn(train_dataset, cfg.data.train)]
if validate:
val_dataset = get_dataset(cfg.data.val)
datasets.append(val_dataset)
data_loaders.append(build_fn(val_dataset, cfg.data.val))
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
# prepare data loaders
data_loaders = [
build_dataloader(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
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# load_checkpoint
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
# training
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
def _non_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,
len(cfg.gpus.train),
dist=False)
]
print('dataloader built')
# put model on gpus
model = MMDataParallel(model, device_ids=cfg.gpus.train).cuda()
print('model paralleled')
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
# prepare data loaders
data_loaders = [
build_dataloader(
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
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
'''
# 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)
def train(
work_dir,
model_cfg,
loss_cfg,
dataset_cfg,
optimizer_cfg,
batch_size,
total_epochs,
training_hooks,
workflow=[('train', 1)],
gpus=1,
log_level=0,
workers=2,
resume_from=None,
load_from=None,
):
# prepare data loaders
if isinstance(dataset_cfg, dict):
dataset_cfg = [dataset_cfg]
data_loaders = [
torch.utils.data.DataLoader(dataset=call_obj(**d),
batch_size=batch_size,
shuffle=True,
num_workers=workers,
drop_last=True) for d in dataset_cfg
]
# put model on gpus
if isinstance(model_cfg, list):
model = [call_obj(**c) for c in model_cfg]
model = torch.nn.Sequential(*model)
else:
model = call_obj(**model_cfg)
model.apply(weights_init)
print("Model size: ",
sum(p.numel() for p in model.parameters() if p.requires_grad))
model = MMDataParallel(model, device_ids=range(gpus)).cuda()
loss = call_obj(**loss_cfg)
# build runner
optimizer = call_obj(params=model.parameters(), **optimizer_cfg)
runner = Runner(model, batch_processor, optimizer, work_dir, log_level)
runner.register_training_hooks(**training_hooks)
if resume_from:
runner.resume(resume_from)
elif load_from:
runner.load_checkpoint(load_from)
# run
workflow = [tuple(w) for w in workflow]
runner.run(data_loaders, workflow, total_epochs, loss=loss)
writer.export_scalars_to_json("./all_scalars.json")
writer.close()
def train(cfg_path, dataset_class):
"""借用mmcv的Runner框架进行训练，包括里边的hooks作为lr更新，loss计算的工具
1. dataset的数据集输出打包了img/gt_bbox/label/，采用DataContainer封装
2. Dataloader的default_collate用定制collate替换，从而支持dataset的多类型数据
3. DataParallel外壳用定制MMDataparallel替换，从而支持DataContainer
"""
# 初始化2个默认选项
distributed = False
parallel = True
# get cfg
cfg = Config.fromfile(cfg_path)
# set backends
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
# get logger
logger = get_root_logger(cfg.log_level)
logger.info('Distributed training: {}'.format(distributed))
logger.info('DataParallel training: {}'.format(parallel))
# build model & detector
# model = M2detDetector(cfg)
model = M2detDetector(cfg)
if not parallel:
model = model.cuda()
else:
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# prepare data & dataloader
# Runner要求dataloader放在list里: 使workflow里每个flow对应一个dataloader
dataset = get_dataset(cfg.data.train, dataset_class)
batch_size = cfg.gpus * cfg.data.imgs_per_gpu
num_workers = cfg.gpus * cfg.data.workers_per_gpu
dataloader = [
DataLoader(dataset,
batch_size=batch_size,
sampler=GroupSampler(dataset, cfg.data.imgs_per_gpu),
num_workers=num_workers,
collate_fn=partial(collate,
samples_per_gpu=cfg.data.imgs_per_gpu),
pin_memory=False)
]
# define runner and running type(1.resume, 2.load, 3.train/test)
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from: # 恢复训练: './work_dirs/ssd300_voc/latest.pth'
runner.resume(cfg.resume_from,
map_location=lambda storage, loc: storage)
elif cfg.load_from: # 加载参数进行测试
runner.load_checkpoint(cfg.load_from)
# 开始训练: 采用workflow来区分train还是test
runner.run(dataloader, cfg.workflow, cfg.total_epochs)
def _non_dist_train(model, dataset, cfg, validate=False):
if validate:
raise NotImplementedError('Built-in validation is not implemented '
'yet in not-distributed training. Use '
'distributed training or test.py and '
'*eval.py scripts instead.')
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer,
cfg.get('optimizer_exclude_arch'))
arch_name = None
optimizer_arch = None
if 'optimizer_arch' in cfg:
raise NotImplementedError
runner = Runner(model,
batch_processor,
optimizer,
optimizer_arch,
cfg.work_dir,
cfg.log_level,
arch_name=arch_name)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=False)
else:
optimizer_config = cfg.optimizer_config
optimizer_arch_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
optimizer_arch_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
if 'optimizer_arch' in cfg:
raise NotImplementedError
else:
data_loaders = [
build_dataloader(dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False)
]
runner.run(data_loaders, None, cfg.workflow, cfg.total_epochs)
def runner(self):
def parse_losses(losses):
log_vars = OrderedDict()
for loss_name, loss_value in losses.items():
if isinstance(loss_value, torch.Tensor):
log_vars[loss_name] = loss_value.mean()
elif isinstance(loss_value, list):
log_vars[loss_name] = sum(_loss.mean()
for _loss in loss_value)
else:
raise TypeError(
'{} is not a tensor or list of tensors'.format(
loss_name))
loss = sum(_value for _key, _value in log_vars.items()
if 'loss' in _key)
log_vars['loss'] = loss
for name in log_vars:
log_vars[name] = log_vars[name].item()
return loss, log_vars
def batch_processor(model, data, train_mode):
losses = model(**data)
# losses = model(data)
loss, log_vars = parse_losses(losses)
outputs = dict(loss=loss,
log_vars=log_vars,
num_samples=len(data['batchdata'].data))
return outputs
self.runner = Runner(self.model, batch_processor, self.optimizer,
self.arg.work_dir)
optimizer_config = DistOptimizerHook(
grad_clip=dict(max_norm=20, norm_type=2))
if not "policy" in self.arg.policy:
lr_config = dict(policy='step', step=self.arg.step)
else:
lr_config = dict(**self.arg.policy)
checkpoint_config = dict(interval=5)
log_config = dict(interval=20,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
])
self.runner.register_training_hooks(lr_config, optimizer_config,
checkpoint_config, log_config)
self.runner.register_hook(DistSamplerSeedHook())
Feeder = import_class(self.arg.feeder)
self.runner.register_hook(
DistEvalTopKAccuracyHook(Feeder(**self.arg.test_feeder_args),
interval=self.arg.test_interval,
k=(1, 5)))
def _non_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,
0, #cfg.data.workers_per_gpu,
cfg.gpus,
dist=False) for ds in dataset
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# model.eval() # not original
# model = model.cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
# for param in model.parameters(): # not original
# param.requires_grad = False
runner = Runner(model, batch_processor, optimizer, cfg.work_dir, # original
cfg.log_level)
# runner = RunnerNoBackward(model, batch_processor, optimizer, cfg.work_dir,
# cfg.log_level) # not original
# Add for LVIS by LiYu
import logging
runner.logger.setLevel(logging.INFO)
# ====================
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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 _single_train(model, data_loaders, cfg):
if cfg.gpus > 1:
raise NotImplemented
# 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)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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,
multitask=False,
vis=False):
# prepare data loaders
data_loaders = [[
build_dataloader(
d, cfg.data.imgs_per_gpu, cfg.data.workers_per_gpu, dist=True)
for d in dataset
]] if multitask else [
build_dataloader(dataset,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
dist=True)
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
bp = batch_processor_with_vis if vis else batch_processor
runner = MTLRunner(model, bp, optimizer, cfg.work_dir,
cfg.log_level) if multitask else \
Runner(model, bp, 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,
distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from:
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
if cfg.get('init_asso_head', False):
ori_key = cfg.init_asso_head[0]
new_key = cfg.init_asso_head[1]
for _key in model.module.state_dict().keys():
if 'asso' in _key:
exist_key = _key.replace(ori_key, new_key)
if exist_key in model.module.state_dict().keys():
print('Init "{}" with "{}"'.format(_key, exist_key))
model.module.state_dict()[_key].copy_(
model.module.state_dict()[exist_key])
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
def _non_dist_train(model,
dataset,
cfg,
validate=False,
logger=None,
ignores=None):
# prepare data loaders
data_loaders = [
build_dataloader(dataset,
cfg.data.videos_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False)
]
num_steps_per_epoch = len(data_loaders[0])
if hasattr(model, 'update_state'):
model.update_state(num_steps_per_epoch)
if cfg.load_from:
load_checkpoint(model,
cfg.load_from,
strict=False,
logger=logger,
show_converted=True,
ignores=ignores)
if hasattr(cfg, 'model_partial_init') and cfg.model_partial_init:
model.reset_weights()
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda()
# build runner
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
# fix warm-up bug
if hasattr(cfg.lr_config, 'warmup_iters'):
if not hasattr(cfg.lr_config, 'by_epoch') or cfg.lr_config.by_epoch:
cfg.lr_config.warmup_iters *= len(data_loaders[0])
# register hooks
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from:
runner.resume(cfg.resume_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
def _non_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,
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)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=False)
else:
optimizer_config = cfg.optimizer_config
# import mmcv.runner.hooks.logger as mmcv_logger
# mmcv_logger.LoggerHook
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# To crete a `latest.pth` file to run recursively
# runner._epoch -= 1
# runner.save_checkpoint(cfg.work_dir, filename_tmpl='dummy_{}.pth')
# runner._epoch += 1
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):
# 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,
) for ds in dataset
]
# 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)
#hard fix for incorrect runner logger level due to environment issues
import logging
runner.logger.setLevel(logging.INFO)
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
raise ValueError("This function has been henceforth disabled.")
# prepare data loaders
data_loaders = [
build_dataloader(dataset,
cfg.data.videos_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
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset] # 确认dataset是一个list或者tuple
# template_dataset = template_dataset if isinstance(template_dataset, (list, tuple)) else [template_dataset] # 确认dataset是一个list或者tuple
# load the data for per batch
data_loaders = [ # 调用该函数可实现每次返回minibatch张图片
build_dataloader(
ds,
cfg.data.imgs_per_gpu,
cfg.data.workers_per_gpu,
cfg.gpus,
dist=False) for ds in dataset
]
# put model on gpus
model = MMDataParallel(model, device_ids=range(cfg.gpus)).cuda() # 将模型送到GPU中，how to set data into gpus
# build runner
optimizer = build_optimizer(model, cfg.optimizer) # 建立优化器
runner = Runner(model, batch_processor, optimizer, cfg.work_dir, # 建立辅助Runner用来跑模型，进入后调用里面的train函数
cfg.log_level)
# fp16 setting # 应当是关于FPN层的设置
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(
**cfg.optimizer_config, **fp16_cfg, distributed=False)
else:
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
if cfg.resume_from: # 选择恢复训练或者重新加载模型
runner.resume(cfg.resume_from)
elif cfg.load_from:
runner.load_checkpoint(cfg.load_from)
runner.run(data_loaders, cfg.workflow, cfg.total_epochs) # 调用runner的run方法进行训练
def _non_dist_train(model, dataset, cfg, validate=False):
# prepare data loaders
#导入数据，并且获得数据相关的配置
data_loaders = [
build_dataloader(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()
# 更新和计算影响模型训练和模型输出的网咯参数，使其逼近或达到最优值，从而最小化损失函数，使用各参数的梯度值来最小化损失函数，最常用的一阶优化算法是梯度下降
# build runner optimizer = dict(type='SGD', lr=0.02, momentum=0.9, weight_decay=0.0001)
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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):
# prepare data loaders
data_loaders = [
build_dataloader(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() #for multiple GPU
# refer : torch.nn.DataParallel(model)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model, batch_processor, optimizer, cfg.work_dir,
cfg.log_level)
# runner : https://github.com/open-mmlab/mmcv/blob/master/mmcv/runner/runner.py
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
# print('fp16_cfg:',fp16_cfg) # None
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=False)
else: #default!
optimizer_config = cfg.optimizer_config
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config)
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 train_flownet(model,
dataset,
cfg,
distributed=False,
validate=False,
logger=None):
if logger is None:
logger = get_root_logger(cfg.log_level)
# start training
# prepare data loaders
data_loaders = [
build_dataloader(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
runner = Runner(model, batch_processor, cfg.optimizer, cfg.work_dir,
cfg.log_level)
runner.register_training_hooks(cfg.lr_config, cfg.optimizer_config,
cfg.checkpoint_config, cfg.log_config)
# if cfg.resume_from:
# runner.resume(cfg.resume_from)
# elif cfg.load_from:
# runner.load_checkpoint(cfg.load_from)
model.eval()
for param in model.parameters():
param.requires_grad = False
# model.load_flow()
model.module.flow_head.train()
for param in model.module.flow_head.parameters():
param.requires_grad = True
# training
runner.run(data_loaders, cfg.workflow, cfg.total_epochs)
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)
def train_detector(model,
dataset,
cfg,
distributed=False,
validate=False,
timestamp=None,
meta=None):
logger = get_root_logger(cfg.log_level)
# prepare data loaders
dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
if 'imgs_per_gpu' in cfg.data:
logger.warning('"imgs_per_gpu" is deprecated in MMDet V2.0. '
'Please use "samples_per_gpu" instead')
if 'samples_per_gpu' in cfg.data:
logger.warning(
f'Got "imgs_per_gpu"={cfg.data.imgs_per_gpu} and '
f'"samples_per_gpu"={cfg.data.samples_per_gpu}, "imgs_per_gpu"'
f'={cfg.data.imgs_per_gpu} is used in this experiments')
else:
logger.warning(
'Automatically set "samples_per_gpu"="imgs_per_gpu"='
f'{cfg.data.imgs_per_gpu} in this experiments')
cfg.data.samples_per_gpu = cfg.data.imgs_per_gpu
data_loaders = [
build_dataloader(
ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
# cfg.gpus will be ignored if distributed
len(cfg.gpu_ids),
dist=distributed,
seed=cfg.seed) for ds in dataset
]
# put model on gpus
if distributed:
find_unused_parameters = cfg.get('find_unused_parameters', False)
# Sets the `find_unused_parameters` parameter in
# torch.nn.parallel.DistributedDataParallel
model = MMDistributedDataParallel(
model.cuda(),
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
else:
model = MMDataParallel(model.cuda(cfg.gpu_ids[0]),
device_ids=cfg.gpu_ids)
# build runner
optimizer = build_optimizer(model, cfg.optimizer)
runner = Runner(model,
batch_processor,
optimizer,
cfg.work_dir,
logger=logger,
meta=meta)
# defrost backbone hook
when_defrost = cfg.get('when_defrost')
if when_defrost is not None:
if when_defrost < 0:
raise RuntimeError('when_defrost < 0')
frozen_stages = cfg.get('frozen_stages', -1)
defrost_backbone = DefrostBackbone(when_defrost, frozen_stages)
runner.register_hook(defrost_backbone)
# log hook
custom_log = CustomLog(cfg.data.samples_per_gpu, when_defrost,
os.path.join(cfg.work_dir, 'log.txt'))
runner.register_hook(custom_log)
# an ugly walkaround to make the .log and .log.json filenames the same
runner.timestamp = timestamp
# fp16 setting
fp16_cfg = cfg.get('fp16', None)
if fp16_cfg is not None:
optimizer_config = Fp16OptimizerHook(**cfg.optimizer_config,
**fp16_cfg,
distributed=distributed)
elif distributed and 'type' not in cfg.optimizer_config:
optimizer_config = DistOptimizerHook(**cfg.optimizer_config)
else:
optimizer_config = cfg.optimizer_config
# register hooks
runner.register_training_hooks(cfg.lr_config, optimizer_config,
cfg.checkpoint_config, cfg.log_config,
cfg.get('momentum_config', None))
if distributed:
runner.register_hook(DistSamplerSeedHook())
# register eval hooks
if validate:
val_dataset = build_dataset(cfg.data.val, dict(test_mode=True))
val_dataloader = build_dataloader(
val_dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.workers_per_gpu,
dist=distributed,
shuffle=False)
eval_cfg = cfg.get('evaluation', {})
eval_hook = DistEvalHook if distributed else EvalHook
runner.register_hook(eval_hook(val_dataloader, **eval_cfg))
if cfg.resume_from:
runner.resume(
cfg.resume_from,
map_location=lambda storage, loc: storage.cuda(cfg.gpu_ids[0]))
elif cfg.load_from:
runner.load_checkpoint(
cfg.load_from,
map_location=lambda storage, loc: storage.cuda(cfg.gpu_ids[0]))
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)
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)
