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import os
import json
import torch
from torch.utils.data.dataset import Dataset
from torchvision import transforms
from PIL import Image
from layoutlmft.data.image_utils import Compose, RandomResizedCropAndInterpolationWithTwoPic
XFund_label2ids = {
"O":0,
'B-HEADER':1,
'I-HEADER':2,
'B-QUESTION':3,
'I-QUESTION':4,
'B-ANSWER':5,
'I-ANSWER':6,
}
class xfund_dataset(Dataset):
def box_norm(self, box, width, height):
def clip(min_num, num, max_num):
return min(max(num, min_num), max_num)
x0, y0, x1, y1 = box
x0 = clip(0, int((x0 / width) * 1000), 1000)
y0 = clip(0, int((y0 / height) * 1000), 1000)
x1 = clip(0, int((x1 / width) * 1000), 1000)
y1 = clip(0, int((y1 / height) * 1000), 1000)
assert x1 >= x0
assert y1 >= y0
return [x0, y0, x1, y1]
def get_segment_ids(self, bboxs):
segment_ids = []
for i in range(len(bboxs)):
if i == 0:
segment_ids.append(0)
else:
if bboxs[i - 1] == bboxs[i]:
segment_ids.append(segment_ids[-1])
else:
segment_ids.append(segment_ids[-1] + 1)
return segment_ids
def get_position_ids(self, segment_ids):
position_ids = []
for i in range(len(segment_ids)):
if i == 0:
position_ids.append(2)
else:
if segment_ids[i] == segment_ids[i - 1]:
position_ids.append(position_ids[-1] + 1)
else:
position_ids.append(2)
return position_ids
def load_data(
self,
data_file,
):
# re-org data format
total_data = {"id": [], "lines": [], "bboxes": [], "ner_tags": [], "image_path": []}
for i in range(len(data_file['documents'])):
width, height = data_file['documents'][i]['img']['width'], data_file['documents'][i]['img'][
'height']
cur_doc_lines, cur_doc_bboxes, cur_doc_ner_tags, cur_doc_image_path = [], [], [], []
for j in range(len(data_file['documents'][i]['document'])):
cur_item = data_file['documents'][i]['document'][j]
cur_doc_lines.append(cur_item['text'])
cur_doc_bboxes.append(self.box_norm(cur_item['box'], width=width, height=height))
cur_doc_ner_tags.append(cur_item['label'])
total_data['id'] += [len(total_data['id'])]
total_data['lines'] += [cur_doc_lines]
total_data['bboxes'] += [cur_doc_bboxes]
total_data['ner_tags'] += [cur_doc_ner_tags]
total_data['image_path'] += [data_file['documents'][i]['img']['fname']]
# tokenize text and get bbox/label
total_input_ids, total_bboxs, total_label_ids = [], [], []
for i in range(len(total_data['lines'])):
cur_doc_input_ids, cur_doc_bboxs, cur_doc_labels = [], [], []
for j in range(len(total_data['lines'][i])):
cur_input_ids = self.tokenizer(total_data['lines'][i][j], truncation=False, add_special_tokens=False, return_attention_mask=False)['input_ids']
if len(cur_input_ids) == 0: continue
cur_label = total_data['ner_tags'][i][j].upper()
if cur_label == 'OTHER':
cur_labels = ["O"] * len(cur_input_ids)
for k in range(len(cur_labels)):
cur_labels[k] = self.label2ids[cur_labels[k]]
else:
cur_labels = [cur_label] * len(cur_input_ids)
cur_labels[0] = self.label2ids['B-' + cur_labels[0]]
for k in range(1, len(cur_labels)):
cur_labels[k] = self.label2ids['I-' + cur_labels[k]]
assert len(cur_input_ids) == len([total_data['bboxes'][i][j]] * len(cur_input_ids)) == len(cur_labels)
cur_doc_input_ids += cur_input_ids
cur_doc_bboxs += [total_data['bboxes'][i][j]] * len(cur_input_ids)
cur_doc_labels += cur_labels
assert len(cur_doc_input_ids) == len(cur_doc_bboxs) == len(cur_doc_labels)
assert len(cur_doc_input_ids) > 0
total_input_ids.append(cur_doc_input_ids)
total_bboxs.append(cur_doc_bboxs)
total_label_ids.append(cur_doc_labels)
assert len(total_input_ids) == len(total_bboxs) == len(total_label_ids)
# split text to several slices because of over-length
input_ids, bboxs, labels = [], [], []
segment_ids, position_ids = [], []
image_path = []
for i in range(len(total_input_ids)):
start = 0
cur_iter = 0
while start < len(total_input_ids[i]):
end = min(start + 510, len(total_input_ids[i]))
input_ids.append([self.tokenizer.cls_token_id] + total_input_ids[i][start: end] + [self.tokenizer.sep_token_id])
bboxs.append([[0, 0, 0, 0]] + total_bboxs[i][start: end] + [[1000, 1000, 1000, 1000]])
labels.append([-100] + total_label_ids[i][start: end] + [-100])
cur_segment_ids = self.get_segment_ids(bboxs[-1])
cur_position_ids = self.get_position_ids(cur_segment_ids)
segment_ids.append(cur_segment_ids)
position_ids.append(cur_position_ids)
image_path.append(os.path.join(self.args.data_dir, "images", total_data['image_path'][i]))
start = end
cur_iter += 1
assert len(input_ids) == len(bboxs) == len(labels) == len(segment_ids) == len(position_ids)
assert len(segment_ids) == len(image_path)
res = {
'input_ids': input_ids,
'bbox': bboxs,
'labels': labels,
'segment_ids': segment_ids,
'position_ids': position_ids,
'image_path': image_path,
}
return res
def __init__(
self,
args,
tokenizer,
mode
):
self.args = args
self.mode = mode
self.cur_la = args.language
self.tokenizer = tokenizer
self.label2ids = XFund_label2ids
self.common_transform = Compose([
RandomResizedCropAndInterpolationWithTwoPic(
size=args.input_size, interpolation=args.train_interpolation,
),
])
self.patch_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(
mean=torch.tensor((0.5, 0.5, 0.5)),
std=torch.tensor((0.5, 0.5, 0.5)))
])
data_file = json.load(
open(os.path.join(args.data_dir, "{}.{}.json".format(self.cur_la, 'train' if mode == 'train' else 'val')),
'r'))
self.feature = self.load_data(data_file)
def __len__(self):
return len(self.feature['input_ids'])
def __getitem__(self, index):
input_ids = self.feature["input_ids"][index]
# attention_mask = self.feature["attention_mask"][index]
attention_mask = [1] * len(input_ids)
labels = self.feature["labels"][index]
bbox = self.feature["bbox"][index]
segment_ids = self.feature['segment_ids'][index]
position_ids = self.feature['position_ids'][index]
img = pil_loader(self.feature['image_path'][index])
for_patches, _ = self.common_transform(img, augmentation=False)
patch = self.patch_transform(for_patches)
assert len(input_ids) == len(attention_mask) == len(labels) == len(bbox) == len(segment_ids)
res = {
"input_ids": input_ids,
"attention_mask": attention_mask,
"labels": labels,
"bbox": bbox,
"segment_ids": segment_ids,
"position_ids": position_ids,
"images": patch,
}
return res
def pil_loader(path: str) -> Image.Image:
# open path as file to avoid ResourceWarning (https://github.com/python-pillow/Pillow/issues/835)
with open(path, 'rb') as f:
img = Image.open(f)
return img.convert('RGB') |