def get_val_dataset(args):
dboxes = dboxes300_coco()
val_trans = SSDTransformer(dboxes, (300, 300), val=True)
val_annotate = os.path.join(args.data, "annotations/instances_val2017.json")
val_coco_root = os.path.join(args.data, "val2017")
val_coco = COCODetection(val_coco_root, val_annotate, val_trans)
return val_coco
def get_train_pytorch_loader(args, num_workers, default_boxes):
dataset = COCODetection(
args.train_coco_root, args.train_annotate,
SSDTransformer(default_boxes, args, (300, 300), val=False))
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
dataset)
else:
train_sampler = None
train_dataloader = DataLoader(dataset,
batch_size=args.batch_size,
shuffle=(train_sampler is None),
sampler=train_sampler,
drop_last=True,
num_workers=num_workers)
return train_dataloader
def get_train_dataset(args, transform):
train_coco = COCODetection(args.train_coco_root, args.train_annotate,
transform)
return train_coco
