import torch
from bros import BrosTokenizer, BrosModel


tokenizer = BrosTokenizer.from_pretrained("naver-clova-ocr/bros-base-uncased")
model = BrosModel.from_pretrained("naver-clova-ocr/bros-base-uncased")


width, height = 1280, 720

words = ["to", "the", "moon!"]
quads = [
    [638, 451, 863, 451, 863, 569, 638, 569],
    [877, 453, 1190, 455, 1190, 568, 876, 567],
    [632, 566, 1107, 566, 1107, 691, 632, 691],
]

bbox = []
for word, quad in zip(words, quads):
    n_word_tokens = len(tokenizer.tokenize(word))
    bbox.extend([quad] * n_word_tokens)

cls_quad = [0.0] * 8
sep_quad = [width, height] * 4
bbox = [cls_quad] + bbox + [sep_quad]

encoding = tokenizer(" ".join(words), return_tensors="pt")
input_ids = encoding["input_ids"]
attention_mask = encoding["attention_mask"]

bbox = torch.tensor([bbox])
bbox[:, :, [0, 2, 4, 6]] = bbox[:, :, [0, 2, 4, 6]] / width
bbox[:, :, [1, 3, 5, 7]] = bbox[:, :, [1, 3, 5, 7]] / height

outputs = model(input_ids=input_ids, bbox=bbox, attention_mask=attention_mask)
last_hidden_state = outputs.last_hidden_state

print("- last_hidden_state")
print(last_hidden_state)
print()
print("- last_hidden_state.shape")
print(last_hidden_state.shape)

- last_hidden_state
tensor([[[-0.0342,  0.2487, -0.2819,  ...,  0.1495,  0.0218,  0.0484],
         [ 0.0792, -0.0040, -0.0127,  ..., -0.0918,  0.0810,  0.0419],
         [ 0.0808, -0.0918,  0.0199,  ..., -0.0566,  0.0869, -0.1859],
         [ 0.0862,  0.0901,  0.0473,  ..., -0.1328,  0.0300, -0.1613],
         [-0.2925,  0.2539,  0.1348,  ...,  0.1988, -0.0148, -0.0982],
         [-0.4160,  0.2135, -0.0390,  ...,  0.6908, -0.2985,  0.1847]]],
       grad_fn=
   
    )

- last_hidden_state.shape
torch.Size([1, 6, 768])

   

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