seed = int(sys.argv[1]) vis_dev = sys.argv[2] # os.environ['CUDA_DEVICE_ORDER'] = 'PCI_BUS_ID' os.environ["CUDA_VISIBLE_DEVICES"] = vis_dev pred_folder = 'dpn92cls_cce_{}_tuned'.format(seed) makedirs(pred_folder, exist_ok=True) # cudnn.benchmark = True models = [] snap_to_load = 'dpn92_cls_cce_{}_1_best'.format(seed) model = Dpn92_Unet_Double().cuda() model = nn.DataParallel(model).cuda() print("=> loading checkpoint '{}'".format(snap_to_load)) checkpoint = torch.load(path.join(models_folder, snap_to_load), map_location='cpu') loaded_dict = checkpoint['state_dict'] sd = model.state_dict() for k in model.state_dict(): if k in loaded_dict and sd[k].size() == loaded_dict[k].size(): sd[k] = loaded_dict[k] loaded_dict = sd model.load_state_dict(loaded_dict) print("loaded checkpoint '{}' (epoch {}, best_score {})".format( snap_to_load, checkpoint['epoch'], checkpoint['best_score']))
seed = int(sys.argv[1]) pre_file = sys.argv[2] post_file = sys.argv[3] loc_pred_file = sys.argv[4] cls_pred_file = sys.argv[5] pred_folder = 'dpn92cls_{}_tuned'.format(seed) makedirs(pred_folder, exist_ok=True) models = [] snap_to_load = 'dpn92_cls_cce_{}_tuned_best'.format(seed) model = Dpn92_Unet_Double(pretrained=None) model = nn.DataParallel(model) print("=> loading checkpoint '{}'".format(snap_to_load)) checkpoint = torch.load(path.join(models_folder, snap_to_load), map_location='cpu') loaded_dict = checkpoint['state_dict'] sd = model.state_dict() for k in model.state_dict(): if k in loaded_dict and sd[k].size() == loaded_dict[k].size(): sd[k] = loaded_dict[k] loaded_dict = sd model.load_state_dict(loaded_dict) print("loaded checkpoint '{}' (epoch {}, best_score {})".format( snap_to_load, checkpoint['epoch'], checkpoint['best_score']))