args.pin_memory = False
if not args.disable_cuda and torch.cuda.is_available():
args.device = torch.device('cuda')
args.pin_memory = True
return args
args = cli()
print("Loading dataset...")
# load train data
preprocess = transforms.Compose([
transforms.Normalize(),
transforms.RandomApply(transforms.HFlip(), 0.5),
transforms.RescaleRelative(),
transforms.Crop(args.square_edge),
transforms.CenterPad(args.square_edge),
])
class rtpose_lightning(pl.LightningModule):
def __init__(self, args, preprocess, target_transforms, model):
super(rtpose_lightning, self).__init__()
self.args = args
self.preprocess = preprocess
self.model = model
self.target_transforms = target_transforms
def forward(self, x):
last_ckpt_path = os.path.join(trainer.checkpoint_callback.filepath, last_ckpt_name)
state_dict = torch.load(last_ckpt_path, map_location=lambda storage, loc: storage)['state_dict']
os.remove(last_ckpt_path)
state_dict = {k.replace('model.',''):v for k,v in state_dict.items()}
rtpose_model.load_state_dict(state_dict)
return rtpose_model
args = parse_args()
update_config(cfg, args)
print("Loading dataset...")
# load train data
preprocess = transforms.Compose([
transforms.Normalize(),
transforms.RandomApply(transforms.HFlip(), 0.5),
transforms.RescaleRelative(scale_range=(cfg.DATASET.SCALE_MIN, cfg.DATASET.SCALE_MAX)),
transforms.Crop(cfg.DATASET.IMAGE_SIZE),
transforms.CenterPad(cfg.DATASET.IMAGE_SIZE),
])
# model
rtpose_vgg = get_model(trunk='vgg19')
# load pretrained
use_vgg(rtpose_vgg)
class rtpose_lightning(pl.LightningModule):
def __init__(self, preprocess, target_transforms, model, optimizer):
super(rtpose_lightning, self).__init__()
self.preprocess = preprocess