vit_gpt2.py

from transformers import pipeline
from PIL import Image


def predict_step(img_array):
    image_to_text = pipeline("image-to-text", model="nlpconnect/vit-gpt2-image-captioning")
    i_image = Image.fromarray(img_array)

    if i_image.mode != "RGB":
        i_image = i_image.convert(mode="RGB")

    prediction = image_to_text(i_image)
    return prediction[0]['generated_text']

# from transformers import VisionEncoderDecoderModel, ViTImageProcessor, AutoTokenizer
# 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")
#
# 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