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 self.model = model
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): _, saved_for_loss = self.model.forward(x)
args.device = torch.device('cuda') args.pin_memory = True args.writer = tb.SummaryWriter(log_dir=args.log_dir) if args.log_dir else None 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), # CHANGE args.square_edge (default 368) to larger for larger images! transforms.CenterPad(args.square_edge), ]) train_loader, val_loader, train_data, val_data = train_factory(args, preprocess, target_transforms=None) def build_names(): names = [] for j in range(1, 7): for k in range(1, 3): names.append('loss_stage%d_L%d' % (j, k)) return names def get_loss(saved_for_loss, heat_temp, vec_temp):