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文章目录
- 前言
- 一、CRNN模型介绍
- 1.模型结构
- 2.CTCLoss
- beam search
- 二、使用pytorch实现crnn
- 数据集
前言
文本识别是图像领域的一个常见任务，场景文字识别OCR任务中，需要先检测出图像中文字位置，再对检测出的文字进行识别，文本介绍的CRNN模型可用于后者， 对检测出的文字进行识别。
An End-to-End Trainable Neural Network for Image-Based Sequence Recognition and Its Application to Scene Text Recognition
原论文地址：论文地址
一、CRNN模型介绍
1.模型结构
CRNN模型结合了CNN模型与RNN模型，CNN用于提取图像特征，RNN将CNN提取的特征进行处理得到输出，对应最终的标签。
CRNN包含三层，卷积层，循环层和转录层，由于每张图像中英文单词的长度不一致，但是经过CNN之后提取的特征长度是一定的，所以就需要一个转录层处理，得到最终结果。
该图为模型的大体结构。
输入模型的是一张图像，其shape是(1,32,100) (channel,width,height),
经过一个卷积神经网络之后，其shape变成(512,1,24)(new_channel,new_height,new_width),把channel和height这两个维度合并，合并后shape(512,24),再将这两个维度交换位置，(24,512)(new_width,new_height*new_channel),由于后续需要将提取的特征输入循环神经网络，这个24就相当于是时间步了，24个时间步。输出特征图shape是(24,512)可以理解为，把原图分成24列，每一列用512维的特征向量表示。如下图所示
将24个特征向量输入进循环神经网络，论文中循环神经网络层是两个LSTM堆叠而成的，经过后就得到24个时间步的输出，再经过全连接层以及softmax层得到一个概率矩阵，形状为(T,num_class),T是时间步，num_class是要分类的类别数，是0-9数字以及a-z字母组合，还有一个blank标识符，总共37类。时间步输出是24个，但是图片中字符数不一定都是24，长短不一，经过转录层将其处理。
2.CTCLoss
如果使用传统的loss function，需要对齐训练样本，有24个时间步，就需要有24个对应的标签，在该任务中显然不合适，除非可以把图片中的每一个字符都单独检测出来，一个字符对应一个标签，则需要很强大的文字检测算法，CTCLoss不需要对齐样本。
还是24个时间步得到24个标签，再进行一个β变换，才得到最终标签。24个时间步可以看作原图中分成24列，每一列输出一个标签，有时一个字母占据好几列，例如字母S占据三列，则这三列输出类别都应该是S，有的列没有字母，则输出空白类别，可以这么理解。得到最终类别时将连续重复的字符去重(空白符两侧的相同字符不去重，因为真实标签中可能存在连续重复字符，例如green，中的两个连续的e不应该去重，则生成标签的时候就该是类似e-e这种，则不会去重),最终去除空白符即可得到最终标签。
β变换定义如下
T代表时间步，长度，由于对连续重复字符去重，则处理后的长度一定小于T
举几个β变换的例子，空白用-表示
可以看出若想要输出state，不止一条路径可以实现输出state.
经过LSTM后的结果需要送入转录层处理，设LSTM的输出标签序列为x，输出标签为l的概率为：
表示经过β变换后为l的路径集合
对于每一条路径有
表示该路径第t个时间步取得该标签的一个概率，连乘起来就是取得该路径的概率。
CTCLoss的优化目标是使得最大，所以
,使得该loss最小化，来更新前面lstm以及cnn的参数，由于CTCLoss计算有些复杂，暂不讨论。Pytorch中提供了CTCLoss的计算接口，我们直接使用即可。
from torch.nn import CTCLoss
beam search
训练阶段使用CTCLoss更新参数，测试阶段如果使用暴力解法，算出每条路径的一个概率，最终取最大概率的一个路径，时间复杂度非常大，如果有37个类别，序列长度是24，那么路径总和是,这只是一个样本的路径数 。所以就需要用到beam search来优化计算过程。
二、使用pytorch实现crnn
数据集
将好几个数据集合并并做了相关处理，得到八千多张图片
import os
import torch
from torch.utils.data import Dataset
from PIL import Image
import numpy as np
class Synth90kDataset(Dataset):
CHARS = '0123456789abcdefghijklmnopqrstuvwxyz'
CHAR2LABEL = {char: i + 1 for i, char in enumerate(CHARS)}
LABEL2CHAR = {label: char for char, label in CHAR2LABEL.items()}
def __init__(self, root_dir=None,image_dir = None, mode=None, file_names=None, img_height=32, img_width=100):
if mode == "train":
file_names, texts = self._load_from_raw_files(root_dir, mode)
else:
texts = None
self.root_dir = root_dir
self.image_dir = image_dir
self.file_names = file_names
self.texts = texts
self.img_height = img_height
self.img_width = img_width
def _load_from_raw_files(self, root_dir, mode):
paths_file = None
if mode == 'train':
paths_file = 'train.txt'
elif mode == 'test':
paths_file = 'test.txt'
file_names = []
texts = []
with open(os.path.join(root_dir, paths_file), 'r') as fr:
for line in fr.readlines():
file_name, ext = line.strip().split('.')
text = file_name.split('_')[-1].lower()
file_names.append(file_name + "." + ext)
texts.append(text)
return file_names, texts
def __len__(self):
return len(self.file_names)
def __getitem__(self, index):
file_name = self.file_names[index]
file_path = os.path.join(self.image_dir,file_name)
image = Image.open(file_path).convert('L') # grey-scale
image = image.resize((self.img_width, self.img_height), resample=Image.BILINEAR)
image = np.array(image)
image = image.reshape((1, self.img_height, self.img_width))
image = (image / 127.5) - 1.0
image = torch.FloatTensor(image)
if self.texts:
text = self.texts[index]
target = [self.CHAR2LABEL[c] for c in text]
target_length = [len(target)]
target = torch.LongTensor(target)
target_length = torch.LongTensor(target_length)
# 如果DataLoader不设置collate_fn,则此处返回值为迭代DataLoader时取到的值
return image, target, target_length
else:
return image
def synth90k_collate_fn(batch):
# zip(*batch)拆包
images, targets, target_lengths = zip(*batch)
# stack就是向量堆叠的意思。一定是扩张一个维度，然后在扩张的维度上，把多个张量纳入仅一个张量。想象向上摞面包片，摞的操作即是stack，0轴即按块stack
images = torch.stack(images, 0)
# cat是指向量拼接的意思。一定不扩张维度，想象把两个长条向量cat成一个更长的向量。
targets = torch.cat(targets, 0)
target_lengths = torch.cat(target_lengths, 0)
# 此处返回的数据即使train_loader每次取到的数据，迭代train_loader，每次都会取到三个值，即此处返回值。
return images, targets, target_lengths
if __name__ == '__main__':
from torch.utils.data import DataLoader
from config import train_config as config
img_width = config['img_width']
img_height = config['img_height']
data_dir = config['data_dir']
train_batch_size = config['train_batch_size']
cpu_workers = config['cpu_workers']
train_dataset = Synth90kDataset(root_dir=data_dir, mode='train',
img_height=img_height, img_width=img_width)
train_loader = DataLoader(
dataset=train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=cpu_workers,
collate_fn=synth90k_collate_fn)
model.py
import torch.nn as nn
class CRNN(nn.Module):
def __init__(self, img_channel, img_height, img_width, num_class,
map_to_seq_hidden=64, rnn_hidden=256, leaky_relu=False):
super(CRNN, self).__init__()
self.cnn, (output_channel, output_height, output_width) = \
self._cnn_backbone(img_channel, img_height, img_width, leaky_relu)
self.map_to_seq = nn.Linear(output_channel * output_height, map_to_seq_hidden)
self.rnn1 = nn.LSTM(map_to_seq_hidden, rnn_hidden, bidirectional=True)
# 如果接双向lstm输出，则要 *2,固定用法
self.rnn2 = nn.LSTM(2 * rnn_hidden, rnn_hidden, bidirectional=True)
self.dense = nn.Linear(2 * rnn_hidden, num_class)
# CNN主干网络
def _cnn_backbone(self, img_channel, img_height, img_width, leaky_relu):
assert img_height % 16 == 0
assert img_width % 4 == 0
# 超参设置
channels = [img_channel, 64, 128, 256, 256, 512, 512, 512]
kernel_sizes = [3, 3, 3, 3, 3, 3, 2]
strides = [1, 1, 1, 1, 1, 1, 1]
paddings = [1, 1, 1, 1, 1, 1, 0]
cnn = nn.Sequential()
def conv_relu(i, batch_norm=False):
# shape of input: (batch, input_channel, height, width)
input_channel = channels[i]
output_channel = channels[i+1]
cnn.add_module(
f'conv{i}',
nn.Conv2d(input_channel, output_channel, kernel_sizes[i], strides[i], paddings[i])
)
if batch_norm:
cnn.add_module(f'batchnorm{i}', nn.BatchNorm2d(output_channel))
relu = nn.LeakyReLU(0.2, inplace=True) if leaky_relu else nn.ReLU(inplace=True)
cnn.add_module(f'relu{i}', relu)
# size of image: (channel, height, width) = (img_channel, img_height, img_width)
conv_relu(0)
cnn.add_module('pooling0', nn.MaxPool2d(kernel_size=2, stride=2))
# (64, img_height // 2, img_width // 2)
conv_relu(1)
cnn.add_module('pooling1', nn.MaxPool2d(kernel_size=2, stride=2))
# (128, img_height // 4, img_width // 4)
conv_relu(2)
conv_relu(3)
cnn.add_module(
'pooling2',
nn.MaxPool2d(kernel_size=(2, 1))
) # (256, img_height // 8, img_width // 4)
conv_relu(4, batch_norm=True)
conv_relu(5, batch_norm=True)
cnn.add_module(
'pooling3',
nn.MaxPool2d(kernel_size=(2, 1))
) # (512, img_height // 16, img_width // 4)
conv_relu(6) # (512, img_height // 16 - 1, img_width // 4 - 1)
output_channel, output_height, output_width = \
channels[-1], img_height // 16 - 1, img_width // 4 - 1
return cnn, (output_channel, output_height, output_width)
# CNN+LSTM前向计算
def forward(self, images):
# shape of images: (batch, channel, height, width)
conv = self.cnn(images)
batch, channel, height, width = conv.size()
conv = conv.view(batch, channel * height, width)
conv = conv.permute(2, 0, 1) # (width, batch, feature)
# 卷积接全连接。全连接输入形状为(width, batch, channel*height)，
# 输出形状为(width, batch, hidden_layer)，分别对应时序长度，batch，特征数，符合LSTM输入要求
seq = self.map_to_seq(conv)
recurrent, _ = self.rnn1(seq)
recurrent, _ = self.rnn2(recurrent)
output = self.dense(recurrent)
return output # shape: (seq_len, batch, num_class)
train.py
import os
import cv2
import torch
from torch.utils.data import DataLoader
import torch.optim as optim
from torch.nn import CTCLoss
from dataset import Synth90kDataset, synth90k_collate_fn
from model import CRNN
from evaluate import evaluate
from config import train_config as config
def train_batch(crnn, data, optimizer, criterion, device):
crnn.train()
images, targets, target_lengths = [d.to(device) for d in data]
logits = crnn(images)
log_probs = torch.nn.functional.log_softmax(logits, dim=2)
batch_size = images.size(0)
input_lengths = torch.LongTensor([logits.size(0)] * batch_size)
target_lengths = torch.flatten(target_lengths)
loss = criterion(log_probs, targets, input_lengths, target_lengths)
optimizer.zero_grad()
loss.backward()
optimizer.step()
return loss.item()
def main():
epochs = config['epochs']
train_batch_size = config['train_batch_size']
lr = config['lr']
show_interval = config['show_interval']
valid_interval = config['valid_interval']
save_interval = config['save_interval']
cpu_workers = config['cpu_workers']
reload_checkpoint = config['reload_checkpoint']
img_width = config['img_width']
img_height = config['img_height']
data_dir = config['data_dir']
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
print(f'device: {device}')
train_dataset = Synth90kDataset(root_dir=data_dir,image_dir='../data/images', mode='train',
img_height=img_height, img_width=img_width)
train_loader = DataLoader(
dataset=train_dataset,
batch_size=train_batch_size,
shuffle=True,
num_workers=cpu_workers,
collate_fn=synth90k_collate_fn)
num_class = len(Synth90kDataset.LABEL2CHAR) + 1
crnn = CRNN(1, img_height, img_width, num_class,
map_to_seq_hidden=config['map_to_seq_hidden'],
rnn_hidden=config['rnn_hidden'],
leaky_relu=config['leaky_relu'])
if reload_checkpoint:
crnn.load_state_dict(torch.load(reload_checkpoint, map_location=device))
crnn.to(device)
optimizer = optim.RMSprop(crnn.parameters(), lr=lr)
criterion = CTCLoss(reduction='sum')
criterion.to(device)
assert save_interval % valid_interval == 0 or valid_interval % save_interval ==0
i = 1
for epoch in range(1, epochs + 1):
print(f'epoch: {epoch}')
tot_train_loss = 0.
tot_train_count = 0
for train_data in train_loader:
loss = train_batch(crnn, train_data, optimizer, criterion, device)
train_size = train_data[0].size(0)
tot_train_loss += loss
tot_train_count += train_size
if i % show_interval == 0:
print('train_batch_loss[', i, ']: ', loss / train_size)
if i % save_interval == 0:
save_model_path = os.path.join(config["checkpoints_dir"],"crnn.pt")
torch.save(crnn.state_dict(), save_model_path)
print('save model at ', save_model_path)
i += 1
print('train_loss: ', tot_train_loss / tot_train_count)
if __name__ == '__main__':
main()
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