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dataset.py
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dataset.py
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import torch
import torch.nn.functional as F
from torch.nn.utils.rnn import pad_sequence
import numpy as np
import imagesize
import logging
import glob
import os
from os.path import join
from collections import defaultdict
import pickle
import cv2
from transformers import PreTrainedTokenizerFast
from tqdm.auto import tqdm
from pix2tex.utils.utils import in_model_path
from pix2tex.dataset.transforms import train_transform, test_transform
from multiprocessing import Pool
from functools import partial
import math
class Im2LatexDataset:
keep_smaller_batches = False
shuffle = True
batchsize = 16
max_dimensions = (1024, 512)
min_dimensions = (32, 32)
max_seq_len = 1024
pad_token = "[PAD]"
bos_token = "[BOS]"
eos_token = "[EOS]"
pad_token_id = 0
bos_token_id = 1
eos_token_id = 2
transform = train_transform
data = defaultdict(lambda: [])
def __init__(self, equations=None, images=None, tokenizer=None, shuffle=True, batchsize=16, max_seq_len=1024,
max_dimensions=(1024, 512), min_dimensions=(32, 32), pad=False, keep_smaller_batches=False, test=False):
"""Generates a torch dataset from pairs of `equations` and `images`.
Args:
equations (str, optional): Path to equations. Defaults to None.
images (str, optional): Directory where images are saved. Defaults to None.
tokenizer (str, optional): Path to saved tokenizer. Defaults to None.
shuffle (bool, opitonal): Defaults to True.
batchsize (int, optional): Defaults to 16.
max_seq_len (int, optional): Defaults to 1024.
max_dimensions (tuple(int, int), optional): Maximal dimensions the model can handle
min_dimensions (tuple(int, int), optional): Minimal dimensions the model can handle
pad (bool): Pad the images to `max_dimensions`. Defaults to False.
keep_smaller_batches (bool): Whether to also return batches with smaller size than `batchsize`. Defaults to False.
test (bool): Whether to use the test transformation or not. Defaults to False.
"""
if images is not None and equations is not None:
assert tokenizer is not None
self.images = [path.replace('\\', '/')
for path in glob.glob(join(images, '*.png'))]
self.sample_size = len(self.images)
eqs = open(equations, 'r').read().split('\n')
self.indices = [int(os.path.basename(img).split('.')[0])
for img in self.images]
self.tokenizer = PreTrainedTokenizerFast(tokenizer_file=tokenizer)
self.shuffle = shuffle
self.batchsize = batchsize
self.max_seq_len = max_seq_len
self.max_dimensions = max_dimensions
self.min_dimensions = min_dimensions
self.pad = pad
self.keep_smaller_batches = keep_smaller_batches
self.test = test
# check the image dimension for every image and group them together
try:
for i, im in tqdm(enumerate(self.images), total=len(self.images)):
width, height = imagesize.get(im)
if min_dimensions[0] <= width <= max_dimensions[0] and min_dimensions[1] <= height <= max_dimensions[1]:
self.data[(width, height)].append(
(eqs[self.indices[i]], im))
except KeyboardInterrupt:
pass
self.data = dict(self.data)
self._get_size()
iter(self)
def __len__(self):
return self.size
def __iter__(self):
self.i = 0
self.transform = test_transform if self.test else train_transform
self.pairs = []
for k in self.data:
info = np.array(self.data[k], dtype=object)
p = torch.randperm(
len(info)) if self.shuffle else torch.arange(len(info))
for i in range(0, len(info), self.batchsize):
batch = info[p[i:i+self.batchsize]]
if len(batch.shape) == 1:
batch = batch[None, :]
if len(batch) < self.batchsize and not self.keep_smaller_batches:
continue
self.pairs.append(batch)
if self.shuffle:
self.pairs = np.random.permutation(
np.array(self.pairs, dtype=object))
else:
self.pairs = np.array(self.pairs, dtype=object)
self.size = len(self.pairs)
return self
def __next__(self):
if self.i >= self.size:
raise StopIteration
self.i += 1
return self.prepare_data(self.pairs[self.i-1])
def prepare_data(self, batch):
"""loads images into memory
Args:
batch (numpy.array[[str, str]]): array of equations and image path pairs
Returns:
tuple(torch.tensor, torch.tensor): data in memory
"""
eqs, ims = batch.T
tok = self.tokenizer(list(eqs), return_token_type_ids=False)
# pad with bos and eos token
for k, p in zip(tok, [[self.bos_token_id, self.eos_token_id], [1, 1]]):
tok[k] = pad_sequence([torch.LongTensor(
[p[0]]+x+[p[1]]) for x in tok[k]], batch_first=True, padding_value=self.pad_token_id)
# check if sequence length is too long
if self.max_seq_len < tok['attention_mask'].shape[1]:
return next(self)
# images = []
# for path in list(ims):
# im = cv2.imread(path)
# if im is None:
# print(path, 'not found!')
# continue
# im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
# if not self.test:
# # sometimes convert to bitmask
# if np.random.random() < .04:
# im[im != 255] = 0
# images.append(self.transform(image=im)['image'][:1])
# images = self.pool.map(self.im_transfrom, ims)
images = [ele[0]
for ele in self.pool.map(partial(self.im_transform,), ims)]
# output_pool, images = [], []
# batch_size = math.ceil(len(ims)/self.num_workers)
# for i in range(self.num_workers):
# batch_datum = ims[i*batch_size:(i+1)*batch_size]
# output_pool.append(self.pool.apply_async(
# func=self.im_transform, args=(batch_datum,)))
# for i in output_pool:
# images.extend(i.get())
try:
images = torch.cat(images).float().unsqueeze(1)
except RuntimeError:
logging.critical('Images not working: %s' % (' '.join(list(ims))))
return None, None
if self.pad:
h, w = images.shape[2:]
images = F.pad(
images, (0, self.max_dimensions[0]-w, 0, self.max_dimensions[1]-h), value=1)
return tok, images
def __getstate__(self):
self_dict = self.__dict__.copy()
del self_dict['pool']
return self_dict
def im_transform(self, ims, **kwargs):
images = []
ims = [ims] if isinstance(ims, str) else ims
for path in ims:
im = cv2.imread(path)
if im is None:
print(path, 'not found!')
raise FileNotFoundError
im = cv2.cvtColor(im, cv2.COLOR_BGR2RGB)
if not self.test:
# sometimes convert to bitmask
if np.random.random() < .04:
im[im != 255] = 0
im = self.transform(image=im)['image'][:1]
images.append(im)
print(im[0].shape)
return images
def _get_size(self):
self.size = 0
for k in self.data:
div, mod = divmod(len(self.data[k]), self.batchsize)
self.size += div # + (1 if mod > 0 else 0)
def load(self, filename, args=[], **kwargs):
"""returns a pickled version of a dataset
Args:
filename (str): Path to dataset
"""
if not os.path.exists(filename):
with in_model_path():
tempf = os.path.join('..', filename)
if os.path.exists(tempf):
filename = os.path.realpath(tempf)
with open(filename, 'rb') as file:
x = pickle.load(file)
x.num_workers = kwargs.get('num_workers', 1)
x.pool = Pool(processes=x.num_workers)
return x
def combine(self, x):
"""Combine Im2LatexDataset with another Im2LatexDataset
Args:
x (Im2LatexDataset): Dataset to absorb
"""
for key in x.data.keys():
if key in self.data.keys():
self.data[key].extend(x.data[key])
self.data[key] = list(set(self.data[key]))
else:
self.data[key] = x.data[key]
self._get_size()
iter(self)
def save(self, filename):
"""save a pickled version of a dataset
Args:
filename (str): Path to dataset
"""
with open(filename, 'wb') as file:
pickle.dump(self, file)
def update(self, **kwargs):
for k in ['batchsize', 'shuffle', 'pad', 'keep_smaller_batches', 'test', 'max_seq_len']:
if k in kwargs:
setattr(self, k, kwargs[k])
if 'max_dimensions' in kwargs or 'min_dimensions' in kwargs:
if 'max_dimensions' in kwargs:
self.max_dimensions = kwargs['max_dimensions']
if 'min_dimensions' in kwargs:
self.min_dimensions = kwargs['min_dimensions']
temp = {}
for k in self.data:
if self.min_dimensions[0] <= k[0] <= self.max_dimensions[0] and self.min_dimensions[1] <= k[1] <= self.max_dimensions[1]:
temp[k] = self.data[k]
self.data = temp
if 'tokenizer' in kwargs:
tokenizer_file = kwargs['tokenizer']
if not os.path.exists(tokenizer_file):
with in_model_path():
tokenizer_file = os.path.realpath(tokenizer_file)
self.tokenizer = PreTrainedTokenizerFast(
tokenizer_file=tokenizer_file)
self._get_size()
iter(self)
def generate_tokenizer(equations, output, vocab_size):
from tokenizers import Tokenizer, pre_tokenizers
from tokenizers.models import BPE
from tokenizers.trainers import BpeTrainer
tokenizer = Tokenizer(BPE())
tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=False)
trainer = BpeTrainer(special_tokens=[
"[PAD]", "[BOS]", "[EOS]"], vocab_size=vocab_size, show_progress=True)
tokenizer.train(equations, trainer)
tokenizer.save(path=output, pretty=False)
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Train model', add_help=False)
parser.add_argument('-i', '--images', type=str, nargs='+',
default=None, help='Image folders')
parser.add_argument('-e', '--equations', type=str,
nargs='+', default=None, help='equations text files')
parser.add_argument('-t', '--tokenizer', default=None,
help='Pretrained tokenizer file')
parser.add_argument('-o', '--out', type=str,
required=True, help='output file')
parser.add_argument('-s', '--vocab-size', default=8000,
type=int, help='vocabulary size when training a tokenizer')
args = parser.parse_args()
if args.tokenizer is None:
with in_model_path():
args.tokenizer = os.path.realpath(
os.path.join('dataset', 'tokenizer.json'))
if args.images is None and args.equations is not None:
print('Generate tokenizer')
generate_tokenizer(args.equations, args.out, args.vocab_size)
elif args.images is not None and args.equations is not None:
print('Generate dataset')
dataset = None
for images, equations in zip(args.images, args.equations):
if dataset is None:
dataset = Im2LatexDataset(equations, images, args.tokenizer)
else:
dataset.combine(Im2LatexDataset(
equations, images, args.tokenizer))
dataset.update(batchsize=1, keep_smaller_batches=True)
dataset.save(args.out)
else:
print('Not defined')