def build_dataloader(cfg, default_args: dict = None):
dataloaders = {}
if DATALOADERS.get(cfg['type']):
packages = DATALOADERS
src = 'registry'
else:
packages = tud
src = 'module'
if isinstance(default_args.get('dataset'), list):
for idx, ds in enumerate(default_args['dataset']):
assert isinstance(ds, tud.Dataset)
dataloader = build_from_cfg(cfg,
packages,
dict(dataset=ds,
sampler=default_args.get(
'sampler', None)),
src=src)
if hasattr(ds, 'root'):
name = getattr(ds, 'root')
else:
name = str(idx)
dataloaders[name] = dataloader
else:
dataloaders = build_from_cfg(cfg, packages, default_args, src=src)
return dataloaders
def build_dataloader(cfg, default_args=None):
try:
dataloader = build_from_cfg(cfg, tud, default_args, src='module')
except:
dataloader = build_from_cfg(cfg, DATALOADERS, default_args)
return dataloader
def build_datasets(cfg, default_args=None):
if isinstance(cfg, list):
datasets = []
for icfg in cfg:
ds = build_from_cfg(icfg, DATASETS, default_args)
datasets.append(ds)
else:
datasets = build_from_cfg(cfg, DATASETS, default_args)
return datasets
def build_transform(cfgs):
tfs = []
for cfg in cfgs:
if TRANSFORMS.get(cfg['type']):
tf = build_from_cfg(cfg, TRANSFORMS)
else:
tf = build_from_cfg(cfg, albu, src='module')
tfs.append(tf)
aug = albu.Compose(tfs)
return aug
def build_datasets(cfg, default_args=None):
datasets = []
for icfg in cfg:
ds = build_from_cfg(icfg, DATASETS, default_args)
datasets.append(ds)
return datasets
def build_datasets(cfg, default_args=None):
datasets = []
for icfg in cfg:
ds = build_from_cfg(icfg, DATASETS, default_args)
logger.info(f'current dataset length {len(ds)}')
datasets.append(ds)
return datasets
def build_transform(cfg):
tfs = []
for icfg in cfg:
tf = build_from_cfg(icfg, TRANSFORMS)
tfs.append(tf)
aug = Compose(tfs)
return aug
def get_model(file_config, weights):
cfg = Config.fromfile(file_config)
deploy_cfg = cfg['deploy']
model = build_from_cfg(deploy_cfg['model'], MODELS)
checkpoint = torch.load(weights, map_location='cpu')
model.load_state_dict(checkpoint['state_dict'])
return model
def build_torch_nn(cfg, default_args=None):
module = build_from_cfg(cfg, nn, default_args, 'module')
return module
def build_decoder(cfg, default_args=None):
decoder = build_from_cfg(cfg, DECODERS, default_args)
return decoder
def build_runner(cfg, default_args=None):
runner = build_from_cfg(cfg, RUNNERS, default_args)
return runner
def build_optimizer(cfg, default_args=None):
optim = build_from_cfg(cfg, torch_optim, default_args, 'module')
return optim
def build_criterion(cfg):
#criterion = CriterionWrapper(cfg)
criterion = build_from_cfg(cfg, CRITERIA, src='registry')
return criterion
def build_attention(cfg, default_args=None):
attention = build_from_cfg(cfg, TRANSFORMER_ATTENTIONS, default_args)
return attention
def build_dataloader(cfg, default_args=None):
dataloader = build_from_cfg(cfg, DATALOADERS, default_args)
return dataloader
def build_brick(cfg, default_args=None):
brick = build_from_cfg(cfg, BRICKS, default_args)
return brick
def build_criterion(cfg):
criterion = build_from_cfg(cfg, CRITERIA, src='registry')
return criterion
def build_enhance_module(cfg, default_args=None):
enhance_module = build_from_cfg(cfg, ENHANCE_MODULES, default_args)
return enhance_module
def build_module(cfg, default_args=None):
util = build_from_cfg(cfg, UTILS, default_args)
return util
def build_rectificator(cfg, default_args=None):
rectificator = build_from_cfg(cfg, RECTIFICATORS, default_args)
return rectificator
def build_sampler(cfg, default_args=None):
sampler = build_from_cfg(cfg, SAMPLER, default_args)
return sampler
def build_metric(cfg, default_args=None):
metric = build_from_cfg(cfg, METRICS, default_args)
return metric
def build_component(cfg, default_args=None):
component = build_from_cfg(cfg, COMPONENT, default_args)
return component
def build_body(cfg, default_args=None):
body = build_from_cfg(cfg, BODIES, default_args)
return body
def build_position_encoder(cfg, default_args=None):
position_encoder = build_from_cfg(cfg, POSITION_ENCODERS, default_args)
return position_encoder
def build_sequence_encoder(cfg, default_args=None):
sequence_encoder = build_from_cfg(cfg, SEQUENCE_ENCODERS, default_args)
return sequence_encoder
def build_head(cfg, default_args=None):
head = build_from_cfg(cfg, HEADS, default_args)
return head
def build_lr_scheduler(cfg, default_args=None):
scheduler = build_from_cfg(cfg, LR_SCHEDULERS, default_args, 'registry')
return scheduler
def build_converter(cfg, default_args=None):
converter = build_from_cfg(cfg, CONVERTERS, default_args=default_args)
return converter
def build_backbone(cfg, default_args=None):
backbone = build_from_cfg(cfg, BACKBONES, default_args)
return backbone
