os.makedirs(output_dir)
sampler_batch = sampler(train_size, args.batch_size)
dataset = roibatchLoader(roidb)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=args.batch_size,
sampler=sampler_batch,
num_workers=args.num_workers)
# initilize the network here.
if args.net == 'c3d':
tdcnn_demo = c3d_tdcnn(pretrained=True)
elif args.net == 'i3d':
tdcnn_demo = i3d_tdcnn(pretrained=True)
elif args.net == 'res34':
tdcnn_demo = resnet_tdcnn(depth=34, pretrained=True)
elif args.net == 'res50':
tdcnn_demo = resnet_tdcnn(depth=50, pretrained=True)
else:
print("network is not defined")
pdb.set_trace()
tdcnn_demo.create_architecture()
print(tdcnn_demo)
lr = args.lr
#tr_momentum = cfg.TRAIN.MOMENTUM
#tr_momentum = args.momentum
num_workers=args.num_workers)
# initilize the tensor holder here.
#video_data = torch.FloatTensor(1) #torch.FloatTensor(1, 3, 768, 112, 112)
#gt_twins = torch.FloatTensor(1)
device = torch.device("cuda:0" if args.cuda else "cpu")
if args.cuda:
cfg.CUDA = True
# initilize the network here.
if args.net == 'c3d':
tdcnn_demo = c3d_tdcnn(class_agnostic=cfg.AGNOSTIC, pretrained=True)
elif args.net == 'i3d':
tdcnn_demo = i3d_tdcnn(class_agnostic=cfg.AGNOSTIC, pretrained=True)
elif args.net == 'res34':
tdcnn_demo = resnet_tdcnn(depth=34,
class_agnostic=cfg.AGNOSTIC,
pretrained=True)
elif args.net == 'res50':
tdcnn_demo = resnet_tdcnn(depth=50,
class_agnostic=cfg.AGNOSTIC,
pretrained=True)
else:
print("network is not defined")
pdb.set_trace()
tdcnn_demo.create_architecture()
lr = cfg.TRAIN.LEARNING_RATE