175 lines
6.3 KiB
Python
175 lines
6.3 KiB
Python
# copyright (c) 2024 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/lukas-blecher/LaTeX-OCR/blob/main/pix2tex/dataset/dataset.py
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"""
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import numpy as np
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import cv2
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import math
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import os
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import json
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import pickle
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import random
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import traceback
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import paddle
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from paddle.io import Dataset
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from .imaug.label_ops import LatexOCRLabelEncode
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from .imaug import transform, create_operators
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class LaTeXOCRDataSet(Dataset):
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def __init__(self, config, mode, logger, seed=None):
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super(LaTeXOCRDataSet, self).__init__()
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self.logger = logger
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self.mode = mode.lower()
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global_config = config["Global"]
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dataset_config = config[mode]["dataset"]
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loader_config = config[mode]["loader"]
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pkl_path = dataset_config.pop("data")
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self.data_dir = dataset_config["data_dir"]
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self.min_dimensions = dataset_config.pop("min_dimensions")
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self.max_dimensions = dataset_config.pop("max_dimensions")
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self.batchsize = dataset_config.pop("batch_size_per_pair")
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self.keep_smaller_batches = dataset_config.pop("keep_smaller_batches")
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self.max_seq_len = global_config.pop("max_seq_len")
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self.rec_char_dict_path = global_config.pop("rec_char_dict_path")
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self.tokenizer = LatexOCRLabelEncode(self.rec_char_dict_path)
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file = open(pkl_path, "rb")
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data = pickle.load(file)
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temp = {}
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for k in data:
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if (
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self.min_dimensions[0] <= k[0] <= self.max_dimensions[0]
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and self.min_dimensions[1] <= k[1] <= self.max_dimensions[1]
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):
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temp[k] = data[k]
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self.data = temp
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self.do_shuffle = loader_config["shuffle"]
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self.seed = seed
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if self.mode == "train" and self.do_shuffle:
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random.seed(self.seed)
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self.pairs = []
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for k in self.data:
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info = np.array(self.data[k], dtype=object)
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p = (
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paddle.randperm(len(info))
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if self.mode == "train" and self.do_shuffle
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else paddle.arange(len(info))
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)
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for i in range(0, len(info), self.batchsize):
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batch = info[p[i : i + self.batchsize]]
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if len(batch.shape) == 1:
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batch = batch[None, :]
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if len(batch) < self.batchsize and not self.keep_smaller_batches:
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continue
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self.pairs.append(batch)
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if self.do_shuffle:
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self.pairs = np.random.permutation(np.array(self.pairs, dtype=object))
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else:
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self.pairs = np.array(self.pairs, dtype=object)
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self.size = len(self.pairs)
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self.set_epoch_as_seed(self.seed, dataset_config)
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self.ops = create_operators(dataset_config["transforms"], global_config)
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self.ext_op_transform_idx = dataset_config.get("ext_op_transform_idx", 2)
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self.need_reset = True
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def set_epoch_as_seed(self, seed, dataset_config):
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if self.mode == "train":
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try:
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border_map_id = [
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index
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for index, dictionary in enumerate(dataset_config["transforms"])
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if "MakeBorderMap" in dictionary
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][0]
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shrink_map_id = [
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index
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for index, dictionary in enumerate(dataset_config["transforms"])
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if "MakeShrinkMap" in dictionary
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][0]
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dataset_config["transforms"][border_map_id]["MakeBorderMap"][
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"epoch"
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] = (seed if seed is not None else 0)
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dataset_config["transforms"][shrink_map_id]["MakeShrinkMap"][
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"epoch"
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] = (seed if seed is not None else 0)
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except Exception as E:
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print(E)
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return
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def shuffle_data_random(self):
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random.seed(self.seed)
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random.shuffle(self.data_lines)
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return
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def __getitem__(self, idx):
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batch = self.pairs[idx]
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eqs, ims = batch.T
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try:
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max_width, max_height, max_length = 0, 0, 0
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images_transform = []
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for file_name in ims:
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img_path = os.path.join(self.data_dir, file_name)
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data = {
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"img_path": img_path,
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}
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with open(data["img_path"], "rb") as f:
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img = f.read()
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data["image"] = img
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item = transform(data, self.ops)
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images_transform.append(np.array(item[0]))
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image_concat = np.concatenate(images_transform, axis=0)[:, np.newaxis, :, :]
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images_transform = image_concat.astype(np.float32)
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labels, attention_mask, max_length = self.tokenizer(list(eqs))
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if self.max_seq_len < max_length:
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rnd_idx = (
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np.random.randint(self.__len__())
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if self.mode == "train"
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else (idx + 1) % self.__len__()
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)
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return self.__getitem__(rnd_idx)
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return (images_transform, labels, attention_mask)
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except:
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self.logger.error(
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"When parsing line {}, error happened with msg: {}".format(
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data["img_path"], traceback.format_exc()
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)
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)
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outs = None
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if outs is None:
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# during evaluation, we should fix the idx to get same results for many times of evaluation.
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rnd_idx = (
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np.random.randint(self.__len__())
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if self.mode == "train"
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else (idx + 1) % self.__len__()
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)
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return self.__getitem__(rnd_idx)
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return outs
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def __len__(self):
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return self.size
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