| from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
| import torch | |
| from PIL import Image | |
| model = VisionEncoderDecoderModel.from_pretrained("vit-gpt2-image-captioning") | |
| feature_extractor = ViTImageProcessor.from_pretrained("vit-gpt2-image-captioning") | |
| tokenizer = AutoTokenizer.from_pretrained("vit-gpt2-image-captioning") | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| model.to(device) | |
| max_length = 16 | |
| num_beams = 4 | |
| gen_kwargs = {'max_length': max_length, 'num_beams': num_beams} | |
| def generate(image): | |
| if image.mode != "RGB": | |
| image = image.convert(mode="RGB") | |
| pixel_values = feature_extractor(images=[image], return_tensors='pt').pixel_values | |
| pixel_values = pixel_values.to(device) | |
| output_ids = model.generate(pixel_values, **gen_kwargs) | |
| preds = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
| preds = [pred.strip() for pred in preds] | |
| return preds[0] | |
| def openAndGenerate(image_path): | |
| return generate(Image.open(image_path)) | |