model = BayesianUniSkip('data/skip_thoughts', word_to_idx.keys()) for param in model.parameters(): param.requires_grad = False elif args.use_bert: model = BertModel.from_pretrained('bert-base-uncased') model.eval() elif args.use_gpt: model = OpenAIGPTModel.from_pretrained('openai-gpt') model.eval() else: model = RnnEncoder(dict_size=len(word_to_idx), embed_size=args.embed_size, hidden_dim=args.rnn_hidden_dim, drop_prob=0.5) generator = Generator() discriminator = Discriminator() dataloader = torch.utils.data.DataLoader(train_val_dataset, batch_size=1, shuffle=True) model = model.to(device) generator = generator.to(device) discriminator = discriminator.to(device) trainer = Trainer(dataloader, model, generator, discriminator, None, None, 1, device, None) print("Loading model files") trainer.load_model(args.model_path) print("Generating image from text") trainer.generate(args.text, args.output_file, args.count) print("Image saved to {}".format(args.output_file))