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from PIL import Image
from torchvision import transforms
from transformers import OFATokenizer, OFAModel
from generate import sequence_generator
mean, std = [0.5, 0.5, 0.5], [0.5, 0.5, 0.5]
resolution = 480
patch_resize_transform = transforms.Compose([
tokenizer = OFATokenizer.from_pretrained(ckpt_dir)
txt = " what does the image describe?"
inputs = tokenizer([txt], return_tensors="pt").input_ids
img = Image.open(path_to_image)
patch_img = patch_resize_transform(img).unsqueeze(0)
model = OFAModel.from_pretrained(ckpt_dir, use_cache=True)
generator = sequence_generator.SequenceGenerator(
data = {}
data["net_input"] = {"input_ids": inputs, 'patch_images': patch_img, 'patch_masks':torch.tensor([True])}
gen_output = generator.generate([model], data)
gen = [gen_output[i][0]["tokens"] for i in range(len(gen_output))]
model = OFAModel.from_pretrained(ckpt_dir, use_cache=False)
gen = model.generate(inputs, patch_images=patch_img, num_beams=5, no_repeat_ngram_size=3)
print(tokenizer.batch_decode(gen, skip_special_tokens=True))