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Merge pull request #672 from myhloli/add-layoutreader
feat：add layoutreader to sort blocks
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| # Copyright (c) Opendatalab. All rights reserved. | ||
| import torch | ||
| import gc | ||
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| def clean_memory(): | ||
| if torch.cuda.is_available(): | ||
| torch.cuda.empty_cache() | ||
| torch.cuda.ipc_collect() | ||
| gc.collect() |
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| @@ -0,0 +1,125 @@ | ||
| from collections import defaultdict | ||
| from typing import List, Dict | ||
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| import torch | ||
| from transformers import LayoutLMv3ForTokenClassification | ||
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| MAX_LEN = 510 | ||
| CLS_TOKEN_ID = 0 | ||
| UNK_TOKEN_ID = 3 | ||
| EOS_TOKEN_ID = 2 | ||
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| class DataCollator: | ||
| def __call__(self, features: List[dict]) -> Dict[str, torch.Tensor]: | ||
| bbox = [] | ||
| labels = [] | ||
| input_ids = [] | ||
| attention_mask = [] | ||
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| # clip bbox and labels to max length, build input_ids and attention_mask | ||
| for feature in features: | ||
| _bbox = feature["source_boxes"] | ||
| if len(_bbox) > MAX_LEN: | ||
| _bbox = _bbox[:MAX_LEN] | ||
| _labels = feature["target_index"] | ||
| if len(_labels) > MAX_LEN: | ||
| _labels = _labels[:MAX_LEN] | ||
| _input_ids = [UNK_TOKEN_ID] * len(_bbox) | ||
| _attention_mask = [1] * len(_bbox) | ||
| assert len(_bbox) == len(_labels) == len(_input_ids) == len(_attention_mask) | ||
| bbox.append(_bbox) | ||
| labels.append(_labels) | ||
| input_ids.append(_input_ids) | ||
| attention_mask.append(_attention_mask) | ||
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| # add CLS and EOS tokens | ||
| for i in range(len(bbox)): | ||
| bbox[i] = [[0, 0, 0, 0]] + bbox[i] + [[0, 0, 0, 0]] | ||
| labels[i] = [-100] + labels[i] + [-100] | ||
| input_ids[i] = [CLS_TOKEN_ID] + input_ids[i] + [EOS_TOKEN_ID] | ||
| attention_mask[i] = [1] + attention_mask[i] + [1] | ||
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| # padding to max length | ||
| max_len = max(len(x) for x in bbox) | ||
| for i in range(len(bbox)): | ||
| bbox[i] = bbox[i] + [[0, 0, 0, 0]] * (max_len - len(bbox[i])) | ||
| labels[i] = labels[i] + [-100] * (max_len - len(labels[i])) | ||
| input_ids[i] = input_ids[i] + [EOS_TOKEN_ID] * (max_len - len(input_ids[i])) | ||
| attention_mask[i] = attention_mask[i] + [0] * ( | ||
| max_len - len(attention_mask[i]) | ||
| ) | ||
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| ret = { | ||
| "bbox": torch.tensor(bbox), | ||
| "attention_mask": torch.tensor(attention_mask), | ||
| "labels": torch.tensor(labels), | ||
| "input_ids": torch.tensor(input_ids), | ||
| } | ||
| # set label > MAX_LEN to -100, because original labels may be > MAX_LEN | ||
| ret["labels"][ret["labels"] > MAX_LEN] = -100 | ||
| # set label > 0 to label-1, because original labels are 1-indexed | ||
| ret["labels"][ret["labels"] > 0] -= 1 | ||
| return ret | ||
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| def boxes2inputs(boxes: List[List[int]]) -> Dict[str, torch.Tensor]: | ||
| bbox = [[0, 0, 0, 0]] + boxes + [[0, 0, 0, 0]] | ||
| input_ids = [CLS_TOKEN_ID] + [UNK_TOKEN_ID] * len(boxes) + [EOS_TOKEN_ID] | ||
| attention_mask = [1] + [1] * len(boxes) + [1] | ||
| return { | ||
| "bbox": torch.tensor([bbox]), | ||
| "attention_mask": torch.tensor([attention_mask]), | ||
| "input_ids": torch.tensor([input_ids]), | ||
| } | ||
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| def prepare_inputs( | ||
| inputs: Dict[str, torch.Tensor], model: LayoutLMv3ForTokenClassification | ||
| ) -> Dict[str, torch.Tensor]: | ||
| ret = {} | ||
| for k, v in inputs.items(): | ||
| v = v.to(model.device) | ||
| if torch.is_floating_point(v): | ||
| v = v.to(model.dtype) | ||
| ret[k] = v | ||
| return ret | ||
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| def parse_logits(logits: torch.Tensor, length: int) -> List[int]: | ||
| """ | ||
| parse logits to orders | ||
| :param logits: logits from model | ||
| :param length: input length | ||
| :return: orders | ||
| """ | ||
| logits = logits[1 : length + 1, :length] | ||
| orders = logits.argsort(descending=False).tolist() | ||
| ret = [o.pop() for o in orders] | ||
| while True: | ||
| order_to_idxes = defaultdict(list) | ||
| for idx, order in enumerate(ret): | ||
| order_to_idxes[order].append(idx) | ||
| # filter idxes len > 1 | ||
| order_to_idxes = {k: v for k, v in order_to_idxes.items() if len(v) > 1} | ||
| if not order_to_idxes: | ||
| break | ||
| # filter | ||
| for order, idxes in order_to_idxes.items(): | ||
| # find original logits of idxes | ||
| idxes_to_logit = {} | ||
| for idx in idxes: | ||
| idxes_to_logit[idx] = logits[idx, order] | ||
| idxes_to_logit = sorted( | ||
| idxes_to_logit.items(), key=lambda x: x[1], reverse=True | ||
| ) | ||
| # keep the highest logit as order, set others to next candidate | ||
| for idx, _ in idxes_to_logit[1:]: | ||
| ret[idx] = orders[idx].pop() | ||
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| return ret | ||
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| def check_duplicate(a: List[int]) -> bool: | ||
| return len(a) != len(set(a)) |
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