def load_net(self, net_name, set_net_version=None): net_path = os.path.join(self.trained_nets_path, net_name, 'model_dir', 'dynamic_net.pth') temp_opt = config.get_configurations() opt_path = os.path.join(self.trained_nets_path, net_name, 'config.txt') if os.path.exists(opt_path): opt = utils.read_config_and_arrange_opt(opt_path, temp_opt) else: opt = temp_opt self.main_widget.dynamic_model = InferenceModel(opt, set_net_version=set_net_version) self.main_widget.dynamic_model.load_network(net_path)
parser.add_argument('--num_of_images', default=num_of_images, type=int) inference_opt = parser.parse_args() network_name = inference_opt.network_name num_of_images = inference_opt.num_of_images networks_path = os.path.join('trained_nets', network_name) model_path = os.path.join(networks_path, 'model_dir', 'dynamic_net.pth') config_path = os.path.join(networks_path, 'config.txt') save_path = os.path.join('results', 'inference_results') if not os.path.exists(save_path): utils.make_dirs(save_path) opt = config.get_configurations(parser=parser) if os.path.exists(config_path): utils.read_config_and_arrange_opt(config_path, opt) dynamic_model = InferenceModel(opt) dynamic_model.load_network(model_path) dynamic_model.net.train() to_tensor = transforms.ToTensor() to_pil_image = transforms.ToPILImage() first_image = True input_tensor = torch.randn((128, dynamic_model.opt.z_size)).view(-1, dynamic_model.opt.z_size, 1, 1).to(dynamic_model.device) for alpha in tqdm(alphas): output_tensor = dynamic_model.forward_and_recover(input_tensor.requires_grad_(False), alpha=alpha) image_tensor = torchvision.utils.make_grid(output_tensor[:num_of_images, :, :, :].clamp(min=0.0, max=1), nrow=1) if first_image: