from transformers import VisionEncoderDecoderModel, ViTFeatureExtractor, AutoTokenizer
model = VisionEncoderDecoderModel.from_pretrained("/Zayn/vit2distilgpt2") feature_extractor = ViTFeatureExtractor.from_pretrained("/Zayn/vit2distilgpt2") tokenizer = AutoTokenizer.from_pretrained("/Zayn/vit2distilgpt2")
device = torch.device("cuda" if torch.cuda.is_available() else "cpu") model.to(device)
max_length = 20 num_beams = 12 gen_kwargs = {"max_length": max_length, "num_beams": num_beams} def predict_step(image_paths): images = [] for image_path in image_paths: i_image = Image.open(image_path) if i_image.mode != "RGB": i_image = i_image.convert(mode="RGB")
images.append(i_image)
pixel_values = feature_extractor(images=images, 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
predict_step(['doctor.e16ba4e4.jpg'])