def __call__(self, optimizer=None, epochs=None, steps=None):
"""Call lr scheduler class."""
params = obj2config(self.config).get("params", {})
logging.debug("Call LrScheduler. name={}, params={}".format(
self._cls.__name__, params))
if self._cls.__name__ == "CosineAnnealingLR":
if params.get("T_max", -1) == -1:
if params.get("by_epoch", True):
params["T_max"] = epochs
else:
params["T_max"] = epochs * steps
if self._cls.__name__ == "WarmupScheduler":
params["epochs"] = epochs
params["steps"] = steps
try:
if params and optimizer:
return self._cls(optimizer, **params)
elif optimizer:
return self._cls(optimizer)
else:
return self._cls(**params)
except Exception as ex:
logging.error(
"Failed to call LrScheduler name={}, params={}".format(
self._cls.__name__, params))
raise ex
def _do_horovod_fully_train(self):
pwd_dir = os.path.dirname(os.path.abspath(__file__))
cf_file = os.path.join(pwd_dir, 'cf.pickle')
cf_content = {'configs': ClassFactory.__configs__,
'registry': ClassFactory.__registry__,
'data': UserConfig().__data__,
'network_registry': NetworkFactory.__network_registry__,
'general': obj2config(General)}
with open(cf_file, 'wb') as f:
pickle.dump(cf_content, f)
cf_file_remote = os.path.join(self.task.local_base_path, 'cf.pickle')
FileOps.copy_file(cf_file, cf_file_remote)
if os.environ.get('DLS_TASK_NUMBER') is None:
# local cluster
worker_ips = '127.0.0.1'
if General.cluster.master_ip is not None and General.cluster.master_ip != '127.0.0.1':
worker_ips = General.cluster.master_ip
for ip in General.cluster.slaves:
worker_ips = worker_ips + ',' + ip
cmd = ['bash', '{}/horovod/run_cluster_horovod_train.sh'.format(pwd_dir),
str(self.world_device_size), cf_file_remote, worker_ips]
else:
# Roma
cmd = ['bash', '{}/horovod/run_horovod_train.sh'.format(pwd_dir),
str(self.world_device_size), cf_file_remote]
proc = subprocess.Popen(cmd, env=os.environ)
proc.wait()
def __call__(self):
"""Call loss cls."""
params = obj2config(self.config).get("params", {})
logging.debug("Call Loss. name={}, params={}".format(self._cls.__name__, params))
try:
if params:
cls_obj = self._cls(**params) if isclass(self._cls) else partial(self._cls, **params)
else:
cls_obj = self._cls() if isclass(self._cls) else partial(self._cls)
if vega.is_torch_backend() and TrainerConfig().cuda:
cls_obj = cls_obj.cuda()
return cls_obj
except Exception as ex:
logging.error("Failed to call Loss name={}, params={}".format(self._cls.__name__, params))
raise ex
def __init__(self, **kwargs):
super(Cityscapes, self).__init__(**kwargs)
config = obj2config(getattr(self.config, self.mode))
config.update(self.args)
self.args = config
self.root_dir = self.args['root_dir']
self.image_size = self.args.Rescale.size
self.list_file = self.args.list_file
self.batch_size = self.args.get('batch_size', 1)
self.num_parallel_batches = self.args.get('num_parallel_batches', 1)
self.drop_remainder = self.args.get('drop_remainder', False)
self.transforms = self._init_transforms()
self.root_dir = FileOps.download_dataset(self.root_dir)
self._init_data_files()
def __init__(self, hps=None, mode='train', **kwargs):
"""Construct method."""
super(Dataset, self).__init__()
if not hasattr(self, 'config'):
raise ValueError("Dataset class should has attr config.")
self.mode = mode
if self.mode == "test" and not hasattr(self.config, "test"):
self.mode = "val"
self.args = deepcopy(obj2config(getattr(self.config, self.mode)))
self._init_hps(hps)
self.train = self.mode in ["train", "val"]
self.num_images = self.args.get('num_images', 0)
self.batch_size = self.args.batch_size
self.world_size = 1
self.rank = 0
def __init__(self, search_space=None, **kwargs):
"""Init SearchAlgorithm."""
super(SearchAlgorithm, self).__init__()
# modify config by kwargs, using local scope
if self.config and kwargs:
self.config = self.config()
load_conf_from_desc(self.config, kwargs)
self.search_space = search_space
if hasattr(self.config, 'codec'):
self.codec = Codec(search_space, type=self.config.codec)
else:
self.codec = None
logging.debug("Config=%s", obj2config(self.config))
self.report = Report()
self.record = ReportRecord()
self.record.step_name = self.step_name
def __init__(self, metric_cfg=None):
"""Init Metrics."""
self.mdict = {}
metric_config = obj2config(self.config)
if not isinstance(metric_config, list):
metric_config = [metric_config]
for metric_item in metric_config:
ClassFactory.get_cls(ClassType.METRIC, self.config.type)
metric_name = metric_item.pop('type')
metric_class = ClassFactory.get_cls(ClassType.METRIC, metric_name)
if isfunction(metric_class):
metric_class = partial(metric_class, **metric_item.get("params", {}))
else:
metric_class = metric_class(**metric_item.get("params", {}))
self.mdict[metric_name] = metric_class
self.mdict = Config(self.mdict)
self.metric_results = dict()
def __call__(self,
model=None,
lr_scheduler=None,
epoch=None,
distributed=False):
"""Call Optimizer class.
:param model: model, used in torch case
:param lr_scheduler: learning rate scheduler, used in tf case
:param epoch: epoch of training, used in tf case
:param distributed: use distributed
:return: optimizer
"""
params = obj2config(self.config).get("params", {})
logging.debug("Call Optimizer. name={}, params={}".format(
self.optim_cls.__name__, params))
optimizer = None
try:
if vega.is_torch_backend():
learnable_params = [
param for param in model.parameters()
if param.requires_grad
]
optimizer = self.optim_cls(learnable_params, **params)
if distributed:
optimizer = hvd.DistributedOptimizer(
optimizer,
named_parameters=model.named_parameters(),
compression=hvd.Compression.none)
elif vega.is_tf_backend():
lr_scheduler.step(epoch)
params['learning_rate'] = lr_scheduler.get_lr()[0]
optimizer = self.optim_cls(**params)
if distributed:
optimizer = hvd.DistributedOptimizer(optimizer) if vega.is_gpu_device() else \
NPUDistributedOptimizer(optimizer)
return optimizer
except Exception as ex:
logging.error("Failed to call Optimizer name={}, params={}".format(
self.optim_cls.__name__, params))
raise ex
def __init__(self, hps=None, mode='train', **kwargs):
"""Construct method."""
super(Dataset, self).__init__()
self.args = dict()
self.mode = mode
if mode == "val" and not hasattr(self.config, "val"):
self.mode = "test"
# modify config from kwargs, `Cifar10(mode='test', data_path='/cache/datasets')`
if kwargs:
self.args = Config(kwargs)
if hasattr(self, 'config'):
config = obj2config(getattr(self.config, self.mode))
config.update(self.args)
self.args = config
self._init_hps(hps)
self.train = self.mode in ["train", "val"]
transforms_list = self._init_transforms()
self._transforms = Transforms(transforms_list)
if "transforms" in kwargs.keys():
self._transforms.__transform__ = kwargs["transforms"]
self.dataset_init()
self.sampler = self._init_sampler()
def __init__(self, args=None):
"""Init DistributedWorker."""
super(DistributedWorker, self).__init__()
# privates
DistributedWorker.__worker_id__ = DistributedWorker.__worker_id__ + 1
self._worker_id = DistributedWorker.__worker_id__
# publics
self.rank = 0
self.world_size = 1
self.worker_addr = ""
self.worker_nccl_port = 16666
self.timeout = int(float(General.worker.timeout) * 60 * 60)
self.__env_config__ = (copy.deepcopy(UserConfig().data),
copy.deepcopy(ClassFactory.__configs__),
copy.deepcopy(ClassFactory.__registry__))
self.__network_config__ = copy.deepcopy(
NetworkFactory.__network_registry__)
self.__general__ = obj2config(General)
self.__worker_device_folder__ = os.path.join(self.temp_path,
'.worker_device')
if not os.path.exists(self.__worker_device_folder__):
os.makedirs(self.__worker_device_folder__, exist_ok=True)
return
def build(self,
model=None,
optimizer=None,
loss=None,
lr_scheduler=None,
metrics=None,
hps=None,
callbacks=None,
train_loader=None,
valid_loader=None,
make_batch=None,
train_step=None,
valid_step=None,
model_fn=None,
train_input_fn=None,
valid_input_fn=None,
load_ckpt_flag=False,
checkpoint_file_name="checkpoint.pth",
model_pickle_file_name="model.pkl"):
"""Build the trainer by assembling the necessary components."""
# Intitialize hyperparameters by parameters or configurations
self._init_hps(hps)
logging.debug("Trainer Config: {}".format(obj2config(self.config)))
self.checkpoint_file_name = checkpoint_file_name
self.model_pickle_file_name = model_pickle_file_name
if vega.is_torch_backend():
self._init_step_functions(make_batch, train_step, valid_step)
elif vega.is_tf_backend():
self._init_estimator_fn(model_fn, train_input_fn, valid_input_fn)
self._init_tf_session()
self._init_distributed_setting()
self._init_cuda_setting()
self._init_tf_estimator()
self.do_validation = self.config.with_valid
self.model = self._init_model(model)
self.load_ckpt_flag = load_ckpt_flag
if self.load_ckpt_flag:
self.load_checkpoint()
else:
self._load_pretrained_model()
self.use_syncbn = self.config.syncbn
if self.use_syncbn and vega.is_torch_backend():
self.model = apex.parallel.convert_syncbn_model(self.model)
self.train_loader = self._init_dataloader(mode='train',
loader=train_loader)
self.valid_loader = self._init_dataloader(mode='val',
loader=valid_loader)
if vega.is_torch_backend():
self.optimizer = Optimizer()(model=self.model, distributed=self.distributed) \
if optimizer is None else optimizer
self.loss = Loss()() if loss is None else loss
self.lr_scheduler = LrScheduler()(
self.optimizer) if lr_scheduler is None else lr_scheduler
# Some trainer has different train batch size from valid batch
self.train_metrics = self._init_metrics(
metrics) if vega.is_torch_backend() else None
self.valid_metrics = self._init_metrics(metrics)
self._init_horovod_setting()
if self.use_amp and vega.is_torch_backend():
self.model, self.optimizer = amp.initialize(self.model,
self.optimizer,
opt_level='O1')
if self.callbacks is None:
self.callbacks = callbacks
# self.output_model_desc()
cur_working_dir = FileOps.join_path(self.local_output_path,
self.step_name)
FileOps.make_dir(cur_working_dir)
# Make sure Trainer has been built for training
self.has_built = True
