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from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer | |
from PIL import Image | |
import warnings | |
warnings.filterwarnings('ignore') | |
model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
feature_extractor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
tokenizer = AutoTokenizer.from_pretrained("nlpconnect/vit-gpt2-image-captioning") | |
max_length = 16 | |
num_beams = 4 | |
gen_kwargs = {"max_length": max_length, "num_beams": num_beams} | |
def predict_step(img_array): | |
i_image = Image.fromarray(img_array) | |
if i_image.mode != "RGB": | |
i_image = i_image.convert(mode="RGB") | |
pixel_values = feature_extractor(images=i_image, return_tensors="pt", do_normalize=True).pixel_values | |
output_ids = model.generate(pixel_values, **gen_kwargs) | |
pred = tokenizer.batch_decode(output_ids, skip_special_tokens=True) | |
pred = [p.strip() for p in pred] | |
return pred | |