456 lines
19 KiB
Python
456 lines
19 KiB
Python
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import os
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from typing import Any
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import numpy as np
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import pandas as pd
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import imagesize
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import itertools
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from PIL import Image
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import argparse
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import torch
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from utils.utils import read_ocr_result_from_txt, read_json, post_process_basic_ocr
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from utils.run_ocr import load_ocr_engine, process_img
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from lightning_modules.utils import sliding_windows
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class KVUProcess:
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def __init__(self, tokenizer_layoutxlm, feature_extractor, backbone_type, class_names, slice_interval, window_size, run_ocr, max_seq_length=512, mode=0):
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self.tokenizer_layoutxlm = tokenizer_layoutxlm
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self.feature_extractor = feature_extractor
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self.max_seq_length = max_seq_length
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self.backbone_type = backbone_type
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self.class_names = class_names
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self.slice_interval = slice_interval
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self.window_size = window_size
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self.run_ocr = run_ocr
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self.mode = mode
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self.pad_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids(tokenizer_layoutxlm._pad_token)
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self.cls_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids(tokenizer_layoutxlm._cls_token)
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self.sep_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids(tokenizer_layoutxlm._sep_token)
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self.unk_token_id_layoutxlm = tokenizer_layoutxlm.convert_tokens_to_ids(self.tokenizer_layoutxlm._unk_token)
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self.class_idx_dic = dict(
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[(class_name, idx) for idx, class_name in enumerate(self.class_names)]
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)
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self.ocr_engine = None
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if self.run_ocr == 1:
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self.ocr_engine = load_ocr_engine()
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def __call__(self, img_path: str, ocr_path: str) -> list:
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if (self.run_ocr == 1) or (not os.path.exists(ocr_path)):
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ocr_path = "tmp.txt"
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process_img(img_path, ocr_path, self.ocr_engine, export_img=False)
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lbboxes, lwords = read_ocr_result_from_txt(ocr_path)
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lwords = post_process_basic_ocr(lwords)
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bbox_windows = sliding_windows(lbboxes, self.window_size, self.slice_interval)
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word_windows = sliding_windows(lwords, self.window_size, self.slice_interval)
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assert len(bbox_windows) == len(word_windows), f"Shape of lbboxes and lwords after sliding window is not the same {len(bbox_windows)} # {len(word_windows)}"
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width, height = imagesize.get(img_path)
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images = [Image.open(img_path).convert("RGB")]
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image_features = torch.from_numpy(self.feature_extractor(images)['pixel_values'][0].copy())
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if self.mode == 0:
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output = self.preprocess(lbboxes, lwords,
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{'image': image_features, 'width': width, 'height': height, 'img_path': img_path},
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max_seq_length=self.max_seq_length)
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elif self.mode == 1:
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output = {}
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windows = []
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for i in range(len(bbox_windows)):
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_words = word_windows[i]
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_bboxes = bbox_windows[i]
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windows.append(
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self.preprocess(
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_bboxes, _words,
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{'image': image_features, 'width': width, 'height': height, 'img_path': img_path},
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max_seq_length=self.max_seq_length)
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)
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output['windows'] = windows
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elif self.mode == 2:
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output = {}
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windows = []
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output['doduments'] = self.preprocess(lbboxes, lwords,
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{'image': image_features, 'width': width, 'height': height, 'img_path': img_path},
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max_seq_length=2048)
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for i in range(len(bbox_windows)):
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_words = word_windows[i]
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_bboxes = bbox_windows[i]
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windows.append(
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self.preprocess(
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_bboxes, _words,
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{'image': image_features, 'width': width, 'height': height, 'img_path': img_path},
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max_seq_length=self.max_seq_length)
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)
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output['windows'] = windows
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else:
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raise ValueError(
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f"Not supported mode: {self.mode }"
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)
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return output
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def preprocess(self, bounding_boxes, words, feature_maps, max_seq_length):
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list_word_objects = []
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for bb, text in zip(bounding_boxes, words):
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boundingBox = [[bb[0], bb[1]], [bb[2], bb[1]], [bb[2], bb[3]], [bb[0], bb[3]]]
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tokens = self.tokenizer_layoutxlm.convert_tokens_to_ids(self.tokenizer_layoutxlm.tokenize(text))
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list_word_objects.append({
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"layoutxlm_tokens": tokens,
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"boundingBox": boundingBox,
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"text": text
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})
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(
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bbox,
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input_ids,
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attention_mask,
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are_box_first_tokens,
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box_to_token_indices,
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box2token_span_map,
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lwords,
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len_valid_tokens,
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len_non_overlap_tokens,
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len_list_tokens
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) = self.parser_words(list_word_objects, self.max_seq_length, feature_maps["width"], feature_maps["height"])
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assert len_list_tokens == len_valid_tokens + 2
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len_overlap_tokens = len_valid_tokens - len_non_overlap_tokens
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ntokens = max_seq_length if max_seq_length == 512 else len_valid_tokens + 2
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input_ids = input_ids[:ntokens]
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attention_mask = attention_mask[:ntokens]
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bbox = bbox[:ntokens]
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are_box_first_tokens = are_box_first_tokens[:ntokens]
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input_ids = torch.from_numpy(input_ids)
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attention_mask = torch.from_numpy(attention_mask)
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bbox = torch.from_numpy(bbox)
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are_box_first_tokens = torch.from_numpy(are_box_first_tokens)
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len_valid_tokens = torch.tensor(len_valid_tokens)
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len_overlap_tokens = torch.tensor(len_overlap_tokens)
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return_dict = {
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"img_path": feature_maps['img_path'],
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"words": lwords,
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"len_overlap_tokens": len_overlap_tokens,
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'len_valid_tokens': len_valid_tokens,
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"image": feature_maps['image'],
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"input_ids_layoutxlm": input_ids,
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"attention_mask_layoutxlm": attention_mask,
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"are_box_first_tokens": are_box_first_tokens,
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"bbox": bbox,
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}
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return return_dict
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def parser_words(self, words, max_seq_length, width, height):
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list_bbs = []
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list_words = []
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list_tokens = []
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cls_bbs = [0.0] * 8
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box2token_span_map = []
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box_to_token_indices = []
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lwords = [''] * max_seq_length
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cum_token_idx = 0
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len_valid_tokens = 0
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len_non_overlap_tokens = 0
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input_ids = np.ones(max_seq_length, dtype=int) * self.pad_token_id_layoutxlm
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bbox = np.zeros((max_seq_length, 8), dtype=np.float32)
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attention_mask = np.zeros(max_seq_length, dtype=int)
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are_box_first_tokens = np.zeros(max_seq_length, dtype=np.bool_)
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for word_idx, word in enumerate(words):
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this_box_token_indices = []
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tokens = word["layoutxlm_tokens"]
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bb = word["boundingBox"]
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text = word["text"]
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len_valid_tokens += len(tokens)
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if word_idx < self.slice_interval:
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len_non_overlap_tokens += len(tokens)
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if len(tokens) == 0:
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tokens.append(self.unk_token_id)
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if len(list_tokens) + len(tokens) > max_seq_length - 2:
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break
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box2token_span_map.append(
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[len(list_tokens) + 1, len(list_tokens) + len(tokens) + 1]
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) # including st_idx
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list_tokens += tokens
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# min, max clipping
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for coord_idx in range(4):
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bb[coord_idx][0] = max(0.0, min(bb[coord_idx][0], width))
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bb[coord_idx][1] = max(0.0, min(bb[coord_idx][1], height))
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bb = list(itertools.chain(*bb))
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bbs = [bb for _ in range(len(tokens))]
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texts = [text for _ in range(len(tokens))]
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for _ in tokens:
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cum_token_idx += 1
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this_box_token_indices.append(cum_token_idx)
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list_bbs.extend(bbs)
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list_words.extend(texts) ####
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box_to_token_indices.append(this_box_token_indices)
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sep_bbs = [width, height] * 4
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# For [CLS] and [SEP]
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list_tokens = (
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[self.cls_token_id_layoutxlm]
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+ list_tokens[: max_seq_length - 2]
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+ [self.sep_token_id_layoutxlm]
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)
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if len(list_bbs) == 0:
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# When len(json_obj["words"]) == 0 (no OCR result)
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list_bbs = [cls_bbs] + [sep_bbs]
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else: # len(list_bbs) > 0
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list_bbs = [cls_bbs] + list_bbs[: max_seq_length - 2] + [sep_bbs]
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# list_words = ['CLS'] + list_words[: max_seq_length - 2] + ['SEP'] ###
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# if len(list_words) < 510:
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# list_words.extend(['</p>' for _ in range(510 - len(list_words))])
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list_words = [self.tokenizer_layoutxlm._cls_token] + list_words[: max_seq_length - 2] + [self.tokenizer_layoutxlm._sep_token]
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len_list_tokens = len(list_tokens)
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input_ids[:len_list_tokens] = list_tokens
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attention_mask[:len_list_tokens] = 1
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bbox[:len_list_tokens, :] = list_bbs
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lwords[:len_list_tokens] = list_words
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# Normalize bbox -> 0 ~ 1
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bbox[:, [0, 2, 4, 6]] = bbox[:, [0, 2, 4, 6]] / width
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bbox[:, [1, 3, 5, 7]] = bbox[:, [1, 3, 5, 7]] / height
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if self.backbone_type in ("layoutlm", "layoutxlm"):
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bbox = bbox[:, [0, 1, 4, 5]]
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bbox = bbox * 1000
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bbox = bbox.astype(int)
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else:
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assert False
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st_indices = [
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indices[0]
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for indices in box_to_token_indices
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if indices[0] < max_seq_length
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]
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are_box_first_tokens[st_indices] = True
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return (
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bbox,
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input_ids,
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attention_mask,
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are_box_first_tokens,
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box_to_token_indices,
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box2token_span_map,
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lwords,
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len_valid_tokens,
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len_non_overlap_tokens,
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len_list_tokens
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)
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def parser_entity_extraction(self, parse_class, box_to_token_indices, max_seq_length):
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itc_labels = np.zeros(max_seq_length, dtype=int)
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stc_labels = np.ones(max_seq_length, dtype=np.int64) * max_seq_length
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classes_dic = parse_class
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for class_name in self.class_names:
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if class_name == "others":
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continue
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if class_name not in classes_dic:
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continue
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for word_list in classes_dic[class_name]:
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is_first, last_word_idx = True, -1
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for word_idx in word_list:
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if word_idx >= len(box_to_token_indices):
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break
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box2token_list = box_to_token_indices[word_idx]
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for converted_word_idx in box2token_list:
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if converted_word_idx >= max_seq_length:
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break # out of idx
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if is_first:
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itc_labels[converted_word_idx] = self.class_idx_dic[
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class_name
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]
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is_first, last_word_idx = False, converted_word_idx
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else:
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stc_labels[converted_word_idx] = last_word_idx
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last_word_idx = converted_word_idx
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return itc_labels, stc_labels
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def parser_entity_linking(self, parse_relation, itc_labels, box2token_span_map, max_seq_length):
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el_labels = np.ones(max_seq_length, dtype=int) * max_seq_length
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el_labels_from_key = np.ones(max_seq_length, dtype=int) * max_seq_length
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relations = parse_relation
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for relation in relations:
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if relation[0] >= len(box2token_span_map) or relation[1] >= len(
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box2token_span_map
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):
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continue
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if (
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box2token_span_map[relation[0]][0] >= max_seq_length
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or box2token_span_map[relation[1]][0] >= max_seq_length
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):
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continue
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word_from = box2token_span_map[relation[0]][0]
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word_to = box2token_span_map[relation[1]][0]
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# el_labels[word_to] = word_from
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if el_labels[word_to] != 512 and el_labels_from_key[word_to] != 512:
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continue
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if itc_labels[word_from] == 2 and itc_labels[word_to] == 3:
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el_labels_from_key[word_to] = word_from # pair of (key-value)
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if itc_labels[word_from] == 4 and (itc_labels[word_to] in (2, 3)):
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el_labels[word_to] = word_from # pair of (header, key) or (header-value)
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return el_labels, el_labels_from_key
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class DocumentKVUProcess(KVUProcess):
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def __init__(self, tokenizer_layoutxlm, feature_extractor, backbone_type, class_names, max_window_count, slice_interval, window_size, run_ocr, max_seq_length=512, mode=0):
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super().__init__(tokenizer_layoutxlm, feature_extractor, backbone_type, class_names, slice_interval, window_size, run_ocr, max_seq_length, mode)
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self.max_window_count = max_window_count
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||
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|
self.pad_token_id = self.pad_token_id_layoutxlm
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||
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|
self.cls_token_id = self.cls_token_id_layoutxlm
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|
self.sep_token_id = self.sep_token_id_layoutxlm
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||
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|
self.unk_token_id = self.unk_token_id_layoutxlm
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||
|
|
self.tokenizer = self.tokenizer_layoutxlm
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|
|
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||
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|
def __call__(self, img_path: str, ocr_path: str) -> list:
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||
|
|
if (self.run_ocr == 1) and (not os.path.exists(ocr_path)):
|
||
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|
ocr_path = "tmp.txt"
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||
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|
process_img(img_path, ocr_path, self.ocr_engine, export_img=False)
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|
|
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||
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lbboxes, lwords = read_ocr_result_from_txt(ocr_path)
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|
|
lwords = post_process_basic_ocr(lwords)
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||
|
|
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||
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|
width, height = imagesize.get(img_path)
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|
images = [Image.open(img_path).convert("RGB")]
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|
|
image_features = torch.from_numpy(self.feature_extractor(images)['pixel_values'][0].copy())
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||
|
|
output = self.preprocess(lbboxes, lwords,
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||
|
|
{'image': image_features, 'width': width, 'height': height, 'img_path': img_path},
|
||
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|
self.max_seq_length)
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||
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|
return output
|
||
|
|
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||
|
|
def preprocess(self, bounding_boxes, words, feature_maps, max_seq_length):
|
||
|
|
n_words = len(words)
|
||
|
|
output_dicts = {'windows': [], 'documents': []}
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|
|
n_empty_windows = 0
|
||
|
|
|
||
|
|
for i in range(self.max_window_count):
|
||
|
|
input_ids = np.ones(self.max_seq_length, dtype=int) * self.pad_token_id
|
||
|
|
bbox = np.zeros((self.max_seq_length, 8), dtype=np.float32)
|
||
|
|
attention_mask = np.zeros(self.max_seq_length, dtype=int)
|
||
|
|
are_box_first_tokens = np.zeros(self.max_seq_length, dtype=np.bool_)
|
||
|
|
|
||
|
|
if n_words == 0:
|
||
|
|
n_empty_windows += 1
|
||
|
|
output_dicts['windows'].append({
|
||
|
|
"image": feature_maps['image'],
|
||
|
|
"input_ids_layoutxlm": torch.from_numpy(input_ids),
|
||
|
|
"bbox": torch.from_numpy(bbox),
|
||
|
|
"words": [],
|
||
|
|
"attention_mask_layoutxlm": torch.from_numpy(attention_mask),
|
||
|
|
"are_box_first_tokens": torch.from_numpy(are_box_first_tokens),
|
||
|
|
})
|
||
|
|
continue
|
||
|
|
|
||
|
|
start_word_idx = i * self.window_size
|
||
|
|
stop_word_idx = min(n_words, (i+1)*self.window_size)
|
||
|
|
|
||
|
|
if start_word_idx >= stop_word_idx:
|
||
|
|
n_empty_windows += 1
|
||
|
|
output_dicts['windows'].append(output_dicts['windows'][-1])
|
||
|
|
continue
|
||
|
|
|
||
|
|
list_word_objects = []
|
||
|
|
for bb, text in zip(bounding_boxes[start_word_idx:stop_word_idx], words[start_word_idx:stop_word_idx]):
|
||
|
|
boundingBox = [[bb[0], bb[1]], [bb[2], bb[1]], [bb[2], bb[3]], [bb[0], bb[3]]]
|
||
|
|
tokens = self.tokenizer_layoutxlm.convert_tokens_to_ids(self.tokenizer_layoutxlm.tokenize(text))
|
||
|
|
list_word_objects.append({
|
||
|
|
"layoutxlm_tokens": tokens,
|
||
|
|
"boundingBox": boundingBox,
|
||
|
|
"text": text
|
||
|
|
})
|
||
|
|
|
||
|
|
(
|
||
|
|
bbox,
|
||
|
|
input_ids,
|
||
|
|
attention_mask,
|
||
|
|
are_box_first_tokens,
|
||
|
|
box_to_token_indices,
|
||
|
|
box2token_span_map,
|
||
|
|
lwords,
|
||
|
|
len_valid_tokens,
|
||
|
|
len_non_overlap_tokens,
|
||
|
|
len_list_layoutxlm_tokens
|
||
|
|
) = self.parser_words(list_word_objects, self.max_seq_length, feature_maps["width"], feature_maps["height"])
|
||
|
|
|
||
|
|
|
||
|
|
input_ids = torch.from_numpy(input_ids)
|
||
|
|
bbox = torch.from_numpy(bbox)
|
||
|
|
attention_mask = torch.from_numpy(attention_mask)
|
||
|
|
are_box_first_tokens = torch.from_numpy(are_box_first_tokens)
|
||
|
|
|
||
|
|
return_dict = {
|
||
|
|
"image": feature_maps['image'],
|
||
|
|
"input_ids_layoutxlm": input_ids,
|
||
|
|
"bbox": bbox,
|
||
|
|
"words": lwords,
|
||
|
|
"attention_mask_layoutxlm": attention_mask,
|
||
|
|
"are_box_first_tokens": are_box_first_tokens,
|
||
|
|
}
|
||
|
|
output_dicts["windows"].append(return_dict)
|
||
|
|
|
||
|
|
attention_mask = torch.cat([o['attention_mask_layoutxlm'] for o in output_dicts["windows"]])
|
||
|
|
are_box_first_tokens = torch.cat([o['are_box_first_tokens'] for o in output_dicts["windows"]])
|
||
|
|
if n_empty_windows > 0:
|
||
|
|
attention_mask[self.max_seq_length * (self.max_window_count - n_empty_windows):] = torch.from_numpy(np.zeros(self.max_seq_length * n_empty_windows, dtype=int))
|
||
|
|
are_box_first_tokens[self.max_seq_length * (self.max_window_count - n_empty_windows):] = torch.from_numpy(np.zeros(self.max_seq_length * n_empty_windows, dtype=np.bool_))
|
||
|
|
bbox = torch.cat([o['bbox'] for o in output_dicts["windows"]])
|
||
|
|
words = []
|
||
|
|
for o in output_dicts['windows']:
|
||
|
|
words.extend(o['words'])
|
||
|
|
|
||
|
|
return_dict = {
|
||
|
|
"attention_mask_layoutxlm": attention_mask,
|
||
|
|
"bbox": bbox,
|
||
|
|
"are_box_first_tokens": are_box_first_tokens,
|
||
|
|
"n_empty_windows": n_empty_windows,
|
||
|
|
"words": words
|
||
|
|
}
|
||
|
|
output_dicts['documents'] = return_dict
|
||
|
|
|
||
|
|
return output_dicts
|
||
|
|
|
||
|
|
|
||
|
|
|