def __init__(self,
instence_id=0,
config_dir='./cfg',
):
self.root=root
print('root in :\n',os.path.join(self.root,'..'))
sys.path.append(os.path.join(sys.path[0],'../'))
print('workspace in:\n')
for i in sys.path:
print(i)
DatasetGenerator.__init__(self)
# super(NetworkGenerator,self).__init__()
super(Instence,self).__init__()
print('\n\n-----Instence Class Init-----\n\n')
#####################################################
#Dataloader
self.TrainSet=DatasetGenerator()
self.TrainSet.DefaultDataset(Mode='train')
self.Trainloader=DataLoader(
self.TrainSet,
self.BatchSize,
shuffle=True,
num_workers=self.worker_num,
collate_fn=self.TrainSet.detection_collate_fn
)
self.ValSet=DatasetGenerator()
self.ValSet.DefaultDataset(Mode='val')
self.Valloader=DataLoader(
self.ValSet,
self.BatchSize,
shuffle=True,
num_workers=self.worker_num,
collate_fn=self.ValSet.detection_collate_fn
)
def __init__(
self,
instence_id=0,
configfile='./cfg',
):
# ---------------------------------------------------------------------------- #
# workspace info #
# ---------------------------------------------------------------------------- #
self.root = root
self.configfile = configfile
print('root in :\n', os.path.join(self.root, '..'))
sys.path.append(os.path.join(sys.path[0], '../'))
print('workspace in:\n')
for i in sys.path:
print(i)
DatasetGenerator.__init__(self, configfile=configfile)
# super(Instence,self).__init__()
print('\n\n-----Instence Class Init-----\n\n')
# ---------------------------------------------------------------------------- #
# dataloader #
# ---------------------------------------------------------------------------- #
# ------------------------------ dataset object ------------------------------ #
transforms = []
transforms.append(ConvertCocoPolysToMask())
transforms.append(T.ToTensor())
transforms.append(T.RandomHorizontalFlip(0.5))
self.transform_compose = T.Compose(transforms)
# ---------------------------------------------------------------------------- #
# temp part #
# ---------------------------------------------------------------------------- #
if self.DefaultDataset:
self.datasets = DatasetGenerator(transforms=self.transform_compose,
configfile=configfile)
self.datasets.DefaultDatasetFunction()
self.trainset = _coco_remove_images_without_annotations(
self.datasets.trainset)
self.valset = self.datasets.valset
print('-----train&val set already done')
# ----------------------------- DataLoader object ---------------------------- #
if self.DistributedDataParallel:
self.train_sampler = torch.utils.data.distributed.DistributedSampler(
self.trainset)
self.test_sampler = torch.utils.data.distributed.DistributedSampler(
self.valset)
print("-----DistributedDataParallel Sampler build done")
self.model = torch.nn.parallel.DistributedDataParallel(
self.model, device_ids=self.gpu_id)
self.model_without_ddp = self.model.module
if not self.DistributedDataParallel:
self.train_sampler = torch.utils.data.RandomSampler(self.trainset)
self.test_sampler = torch.utils.data.SequentialSampler(self.valset)
print("-----DataSampler build done")
# ---------------------------------- Sampler --------------------------------- #
if self.aspect_ratio_factor >= 0:
self.group_ids = create_aspect_ratio_groups(
self.trainset, k=self.aspect_ratio_factor)
self.train_batch_sampler = GroupedBatchSampler(
self.train_sampler, self.group_ids, self.BatchSize)
else:
self.train_batch_sampler = torch.utils.data.BatchSampler(
self.train_sampler, self.BatchSize, drop_last=True)
# ---------------------------------- loader ---------------------------------- #
self.trainloader = torch.utils.data.DataLoader(
self.trainset,
batch_sampler=self.train_batch_sampler,
num_workers=self.worker_num,
collate_fn=self.collate_fn)
self.valloader = torch.utils.data.DataLoader(
self.valset,
batch_size=self.BatchSize,
sampler=self.test_sampler,
num_workers=self.worker_num,
collate_fn=self.collate_fn)
