def build(task: str, cfg: CfgNode, seed: int = 0) -> TransformerBase:
r"""
Arguments
---------
task: str
task
cfg: CfgNode
node name: transformer
"""
assert task in TASK_TRANSFORMERS, "invalid task name"
MODULES = TASK_TRANSFORMERS[task]
names = cfg.names
modules = []
for name in names:
module = MODULES[name](seed=seed)
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
modules.append(module)
return modules
def build(task: str, cfg: CfgNode):
r"""
Builder function.
Arguments
---------
task: str
builder task name (track|vos)
cfg: CfgNode
buidler configuration
Returns
-------
torch.nn.Module
module built by builder
"""
if task in TASK_HEADS:
head_modules = TASK_HEADS[task]
else:
logger.error("no task model for task {}".format(task))
exit(-1)
name = cfg.name
head_module = head_modules[name]()
hps = head_module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
head_module.set_hps(hps)
head_module.update_params()
return head_module
def build(task: str, cfg: CfgNode, seed: int = 0) -> DatasetBase:
r"""
Arguments
---------
task: str
task name (track|vos)
cfg: CfgNode
node name: sampler
seed: int
seed for rng initialization
"""
assert task in TASK_SAMPLERS, "invalid task name"
MODULES = TASK_SAMPLERS[task]
submodules_cfg = cfg.submodules
dataset_cfg = submodules_cfg.dataset
datasets = dataset_builder.build(task, dataset_cfg)
if submodules_cfg.filter.name != "":
filter_cfg = submodules_cfg.filter
data_filter = filter_builder.build(task, filter_cfg)
else:
data_filter = None
name = cfg.name
module = MODULES[name](datasets, seed=seed, data_filter=data_filter)
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build(task: str, cfg: CfgNode, pipeline: PipelineBase):
r"""
Builder function.
Arguments
---------
task: str
builder task name (track|vos)
cfg: CfgNode
buidler configuration,
node nams: tester
Returns
-------
TesterBase
tester built by builder
"""
assert task in TASK_TESTERS, "no tester for task {}".format(task)
MODULES = TASK_TESTERS[task]
names = cfg.names
testers = []
# tester for multiple experiments
for name in names:
tester = MODULES[name](pipeline)
hps = tester.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
tester.set_hps(hps)
tester.update_params()
testers.append(tester)
return testers
def build(task: str, cfg: CfgNode, seed: int = 0) -> DatapipelineBase:
r"""
Arguments
---------
task: str
task name (track|vos)
cfg: CfgNode
node name: data
seed: int
seed for rng initialization
"""
assert task in TASK_DATAPIPELINES, "invalid task name"
MODULES = TASK_DATAPIPELINES[task]
sampler = build_sampler(task, cfg.sampler, seed=seed)
transformers = build_transformer(task, cfg.transformer, seed=seed)
target = build_target(task, cfg.target)
pipeline = []
pipeline.extend(transformers)
pipeline.append(target)
cfg = cfg.datapipeline
name = cfg.name
module = MODULES[name](sampler, pipeline)
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build(task: str, cfg: CfgNode) -> DatasetBase:
assert task in TASK_FILTERS, "invalid task name"
MODULES = TASK_FILTERS[task]
name = cfg.name
module = MODULES[name]()
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build(task: str, cfg: CfgNode, model: nn.Module) -> OptimizerBase:
r"""
Arguments
---------
task: str
task name (track|vos)
cfg: CfgNode
node name: optim
"""
name = cfg.name
module = OPTIMIZERS[name](cfg, model)
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build_sat_model(task: str,
cfg: CfgNode,
gml_extractor=None,
joint_encoder=None,
decoder=None,
loss: ModuleBase = None):
r"""
Builder function for SAT.
Arguments
---------
task: str
builder task name
cfg: CfgNode
buidler configuration
gml_extractor: torch.nn.Module
feature extractor for global modeling loop
joint_encoder: torch.nn.Module
joint encoder
decoder: torch.nn.Module
decoder for SAT
loss: torch.nn.Module
criterion module used by task module (for training). None in case other than training.
Returns
-------
torch.nn.Module
task module built by builder
"""
if task == "vos":
task_modules = TASK_TASKMODELS[task]
else:
logger.error("sat model builder could not build task {}".format(task))
exit(-1)
name = cfg.name
#SatVOS
task_module = task_modules[name](gml_extractor, joint_encoder, decoder,
loss)
hps = task_module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
task_module.set_hps(hps)
task_module.update_params()
return task_module
def build(
task: str,
cfg: CfgNode,
model: ModuleBase = None,
segmenter: ModuleBase = None,
tracker: ModuleBase = None,
):
r"""
Builder function.
Arguments
---------
task: str
task name
cfg: CfgNode
buidler configuration
model: ModuleBase
model instance for siamfcpp
segmenter: ModuleBase
segmenter instance for tracker
tracker: ModuleBase
model instance for tracker
Returns
-------
torch.nn.Module
module built by builder
"""
assert task in PIPELINES, "no pipeline for task {}".format(task)
pipelines = PIPELINES[task]
pipeline_name = cfg.name
if task == 'track':
pipeline = pipelines[pipeline_name](model)
elif task == 'vos':
pipeline = pipelines[pipeline_name](segmenter, tracker)
hps = pipeline.get_hps()
hps = merge_cfg_into_hps(cfg[pipeline_name], hps)
pipeline.set_hps(hps)
pipeline.update_params()
return pipeline
def build(task: str,
cfg: CfgNode,
backbone: ModuleBase,
head: ModuleBase,
loss: ModuleBase = None):
r"""
Builder function.
Arguments
---------
task: str
builder task name
cfg: CfgNode
buidler configuration
backbone: torch.nn.Module
backbone used by task module.
head: torch.nn.Module
head network used by task module.
loss: torch.nn.Module
criterion module used by task module (for training). None in case other than training.
Returns
-------
torch.nn.Module
task module built by builder
"""
if task in TASK_TASKMODELS:
task_modules = TASK_TASKMODELS[task]
else:
logger.error("no task model for task {}".format(task))
exit(-1)
if task == "track":
name = cfg.name
task_module = task_modules[name](backbone, head, loss)
hps = task_module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
task_module.set_hps(hps)
task_module.update_params()
return task_module
else:
logger.error("task model {} is not completed".format(task))
exit(-1)
def build(task: str, cfg: CfgNode) -> TargetBase:
r"""
Arguments
---------
task: str
task
cfg: CfgNode
node name: target
"""
assert task in TASK_TARGETS, "invalid task name"
MODULES = TASK_TARGETS[task]
name = cfg.name
module = MODULES[name]()
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build(task: str, cfg: CfgNode) -> GradModifierBase:
r"""
Arguments
---------
task: str
task name (track|vos)
cfg: CfgNode
node name: scheduler
seed: int
seed for rng initialization
"""
name = cfg.name
module = GRAD_MODIFIERS[name]()
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build(task: str, cfg: CfgNode, basemodel=None):
r"""
Builder function.
Arguments
---------
task: str
builder task name (track|vos)
cfg: CfgNode
buidler configuration
basemodel:
warp backbone into encoder if not None
Returns
-------
torch.nn.Module
module built by builder
"""
if task in TASK_BACKBONES:
modules = TASK_BACKBONES[task]
else:
logger.error("no backbone for task {}".format(task))
exit(-1)
name = cfg.name
assert name in modules, "backbone {} not registered for {}!".format(
name, task)
if basemodel:
module = modules[name](basemodel)
else:
module = modules[name]()
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
return module
def build(task: str, cfg: CfgNode) -> DatasetBase:
r"""
Arguments
---------
task: str
task name (track|vos)
cfg: CfgNode
node name: dataset
"""
assert task in TASK_DATASETS, "invalid task name"
dataset_modules = TASK_DATASETS[task]
names = cfg.names
modules = []
for name in names:
module = dataset_modules[name]()
hps = module.get_hps()
hps = merge_cfg_into_hps(cfg[name], hps)
module.set_hps(hps)
module.update_params()
modules.append(module)
return modules