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dataset.py
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dataset.py
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import os
import sys
import re
import six
import math
import lmdb
import torch
from natsort import natsorted
from PIL import Image
import numpy as np
from torch.utils.data import Dataset, ConcatDataset, Subset
from torch._utils import _accumulate
import torchvision.transforms as transforms
class Batch_Balanced_Dataset(object):
def __init__(self, opt):
"""
Modulate the data ratio in the batch.
For example, when select_data is "MJ-ST" and batch_ratio is "0.5-0.5",
the 50% of the batch is filled with MJ and the other 50% of the batch is filled with ST.
"""
log = open(f'./saved_models/{opt.exp_name}/log_dataset.txt', 'a')
dashed_line = '-' * 80
print(dashed_line)
log.write(dashed_line + '\n')
print(f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}')
log.write(f'dataset_root: {opt.train_data}\nopt.select_data: {opt.select_data}\nopt.batch_ratio: {opt.batch_ratio}\n')
assert len(opt.select_data) == len(opt.batch_ratio)
_AlignCollate = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD)
self.data_loader_list = []
self.dataloader_iter_list = []
batch_size_list = []
Total_batch_size = 0
for selected_d, batch_ratio_d in zip(opt.select_data, opt.batch_ratio):
_batch_size = max(round(opt.batch_size * float(batch_ratio_d)), 1)
print(dashed_line)
log.write(dashed_line + '\n')
_dataset, _dataset_log = hierarchical_dataset(root=opt.train_data, opt=opt, select_data=[selected_d])
total_number_dataset = len(_dataset)
log.write(_dataset_log)
"""
The total number of data can be modified with opt.total_data_usage_ratio.
ex) opt.total_data_usage_ratio = 1 indicates 100% usage, and 0.2 indicates 20% usage.
See 4.2 section in our paper.
"""
number_dataset = int(total_number_dataset * float(opt.total_data_usage_ratio))
dataset_split = [number_dataset, total_number_dataset - number_dataset]
indices = range(total_number_dataset)
_dataset, _ = [Subset(_dataset, indices[offset - length:offset])
for offset, length in zip(_accumulate(dataset_split), dataset_split)]
selected_d_log = f'num total samples of {selected_d}: {total_number_dataset} x {opt.total_data_usage_ratio} (total_data_usage_ratio) = {len(_dataset)}\n'
selected_d_log += f'num samples of {selected_d} per batch: {opt.batch_size} x {float(batch_ratio_d)} (batch_ratio) = {_batch_size}'
print(selected_d_log)
log.write(selected_d_log + '\n')
batch_size_list.append(str(_batch_size))
Total_batch_size += _batch_size
_data_loader = torch.utils.data.DataLoader(
_dataset, batch_size=_batch_size,
shuffle=True,
num_workers=int(opt.workers),
collate_fn=_AlignCollate, pin_memory=True)
self.data_loader_list.append(_data_loader)
self.dataloader_iter_list.append(iter(_data_loader))
Total_batch_size_log = f'{dashed_line}\n'
batch_size_sum = '+'.join(batch_size_list)
Total_batch_size_log += f'Total_batch_size: {batch_size_sum} = {Total_batch_size}\n'
Total_batch_size_log += f'{dashed_line}'
opt.batch_size = Total_batch_size
print(Total_batch_size_log)
log.write(Total_batch_size_log + '\n')
log.close()
def get_batch(self):
balanced_batch_images = []
balanced_batch_texts = []
for i, data_loader_iter in enumerate(self.dataloader_iter_list):
try:
image, text = data_loader_iter.next()
balanced_batch_images.append(image)
balanced_batch_texts += text
except StopIteration:
self.dataloader_iter_list[i] = iter(self.data_loader_list[i])
image, text = self.dataloader_iter_list[i].next()
balanced_batch_images.append(image)
balanced_batch_texts += text
except ValueError:
pass
balanced_batch_images = torch.cat(balanced_batch_images, 0)
return balanced_batch_images, balanced_batch_texts
def hierarchical_dataset(root, opt, select_data='/'):
""" select_data='/' contains all sub-directory of root directory """
dataset_list = []
dataset_log = f'dataset_root: {root}\t dataset: {select_data[0]}'
print(dataset_log)
dataset_log += '\n'
for dirpath, dirnames, filenames in os.walk(root+'/'):
if not dirnames:
select_flag = False
for selected_d in select_data:
if selected_d in dirpath:
select_flag = True
break
if select_flag:
dataset = LmdbDataset(dirpath, opt)
sub_dataset_log = f'sub-directory:\t/{os.path.relpath(dirpath, root)}\t num samples: {len(dataset)}'
print(sub_dataset_log)
dataset_log += f'{sub_dataset_log}\n'
dataset_list.append(dataset)
concatenated_dataset = ConcatDataset(dataset_list)
return concatenated_dataset, dataset_log
class LmdbDataset(Dataset):
def __init__(self, root, opt):
self.root = root
self.opt = opt
self.env = lmdb.open(root, max_readers=32, readonly=True, lock=False, readahead=False, meminit=False)
if not self.env:
print('cannot create lmdb from %s' % (root))
sys.exit(0)
with self.env.begin(write=False) as txn:
nSamples = int(txn.get('num-samples'.encode()))
self.nSamples = nSamples
if self.opt.data_filtering_off:
# for fast check or benchmark evaluation with no filtering
self.filtered_index_list = [index + 1 for index in range(self.nSamples)]
else:
""" Filtering part
If you want to evaluate IC15-2077 & CUTE datasets which have special character labels,
use --data_filtering_off and only evaluate on alphabets and digits.
see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L190-L192
And if you want to evaluate them with the model trained with --sensitive option,
use --sensitive and --data_filtering_off,
see https://github.com/clovaai/deep-text-recognition-benchmark/blob/dff844874dbe9e0ec8c5a52a7bd08c7f20afe704/test.py#L137-L144
"""
self.filtered_index_list = []
for index in range(self.nSamples):
index += 1 # lmdb starts with 1
label_key = 'label-%09d'.encode() % index
label = txn.get(label_key).decode('utf-8')
if len(label) > self.opt.batch_max_length:
# print(f'The length of the label is longer than max_length: length
# {len(label)}, {label} in dataset {self.root}')
continue
# By default, images containing characters which are not in opt.character are filtered.
# You can add [UNK] token to `opt.character` in utils.py instead of this filtering.
out_of_char = f'[^{self.opt.character}]'
if re.search(out_of_char, label.lower()):
continue
self.filtered_index_list.append(index)
self.nSamples = len(self.filtered_index_list)
def __len__(self):
return self.nSamples
def __getitem__(self, index):
assert index <= len(self), 'index range error'
index = self.filtered_index_list[index]
with self.env.begin(write=False) as txn:
label_key = 'label-%09d'.encode() % index
label = txn.get(label_key).decode('utf-8')
img_key = 'image-%09d'.encode() % index
imgbuf = txn.get(img_key)
buf = six.BytesIO()
buf.write(imgbuf)
buf.seek(0)
try:
if self.opt.rgb:
img = Image.open(buf).convert('RGB') # for color image
else:
img = Image.open(buf).convert('L')
except IOError:
print(f'Corrupted image for {index}')
# make dummy image and dummy label for corrupted image.
if self.opt.rgb:
img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
else:
img = Image.new('L', (self.opt.imgW, self.opt.imgH))
label = '[dummy_label]'
if not self.opt.sensitive:
label = label.lower()
# We only train and evaluate on alphanumerics (or pre-defined character set in train.py)
out_of_char = f'[^{self.opt.character}]'
label = re.sub(out_of_char, '', label)
return (img, label)
class RawDataset(Dataset):
def __init__(self, root, opt):
self.opt = opt
self.image_path_list = []
for dirpath, dirnames, filenames in os.walk(root):
for name in filenames:
_, ext = os.path.splitext(name)
ext = ext.lower()
if ext == '.jpg' or ext == '.jpeg' or ext == '.png':
self.image_path_list.append(os.path.join(dirpath, name))
self.image_path_list = natsorted(self.image_path_list)
self.nSamples = len(self.image_path_list)
def __len__(self):
return self.nSamples
def __getitem__(self, index):
try:
if self.opt.rgb:
img = Image.open(self.image_path_list[index]).convert('RGB') # for color image
else:
img = Image.open(self.image_path_list[index]).convert('L')
except IOError:
print(f'Corrupted image for {index}')
# make dummy image and dummy label for corrupted image.
if self.opt.rgb:
img = Image.new('RGB', (self.opt.imgW, self.opt.imgH))
else:
img = Image.new('L', (self.opt.imgW, self.opt.imgH))
return (img, self.image_path_list[index])
class ResizeNormalize(object):
def __init__(self, size, interpolation=Image.BICUBIC):
self.size = size
self.interpolation = interpolation
self.toTensor = transforms.ToTensor()
def __call__(self, img):
img = img.resize(self.size, self.interpolation)
img = self.toTensor(img)
img.sub_(0.5).div_(0.5)
return img
class NormalizePAD(object):
def __init__(self, max_size, PAD_type='right'):
self.toTensor = transforms.ToTensor()
self.max_size = max_size
self.max_width_half = math.floor(max_size[2] / 2)
self.PAD_type = PAD_type
def __call__(self, img):
img = self.toTensor(img)
img.sub_(0.5).div_(0.5)
c, h, w = img.size()
Pad_img = torch.FloatTensor(*self.max_size).fill_(0)
Pad_img[:, :, :w] = img # right pad
if self.max_size[2] != w: # add border Pad
Pad_img[:, :, w:] = img[:, :, w - 1].unsqueeze(2).expand(c, h, self.max_size[2] - w)
return Pad_img
class AlignCollate(object):
def __init__(self, imgH=32, imgW=100, keep_ratio_with_pad=False):
self.imgH = imgH
self.imgW = imgW
self.keep_ratio_with_pad = keep_ratio_with_pad
def __call__(self, batch):
batch = filter(lambda x: x is not None, batch)
images, labels = zip(*batch)
if self.keep_ratio_with_pad: # same concept with 'Rosetta' paper
resized_max_w = self.imgW
input_channel = 3 if images[0].mode == 'RGB' else 1
transform = NormalizePAD((input_channel, self.imgH, resized_max_w))
resized_images = []
for image in images:
w, h = image.size
ratio = w / float(h)
if math.ceil(self.imgH * ratio) > self.imgW:
resized_w = self.imgW
else:
resized_w = math.ceil(self.imgH * ratio)
resized_image = image.resize((resized_w, self.imgH), Image.BICUBIC)
resized_images.append(transform(resized_image))
# resized_image.save('./image_test/%d_test.jpg' % w)
image_tensors = torch.cat([t.unsqueeze(0) for t in resized_images], 0)
else:
transform = ResizeNormalize((self.imgW, self.imgH))
image_tensors = [transform(image) for image in images]
image_tensors = torch.cat([t.unsqueeze(0) for t in image_tensors], 0)
return image_tensors, labels
def tensor2im(image_tensor, imtype=np.uint8):
image_numpy = image_tensor.cpu().float().numpy()
if image_numpy.shape[0] == 1:
image_numpy = np.tile(image_numpy, (3, 1, 1))
image_numpy = (np.transpose(image_numpy, (1, 2, 0)) + 1) / 2.0 * 255.0
return image_numpy.astype(imtype)
def save_image(image_numpy, image_path):
image_pil = Image.fromarray(image_numpy)
image_pil.save(image_path)