零食网站建设需求分析,wordpress搜索页制作,wordpress首页内容怎么修改,个人网站可以做导航最近接触了一个仪表盘识别的项目#xff0c;简单调研以后发现可以用CRNN来做。但是手边缺少仪表盘数据集#xff0c;就先用ICDAR2013试了一下。 结果遇到了一系列坑。为了不使读者和自己在以后的日子继续遭罪。我把正确的代码发到下面了。 1#xff09;超参数请不要调整简单调研以后发现可以用CRNN来做。但是手边缺少仪表盘数据集就先用ICDAR2013试了一下。 结果遇到了一系列坑。为了不使读者和自己在以后的日子继续遭罪。我把正确的代码发到下面了。 1超参数请不要调整CRNN前期训练极其离谱需要良好的调参loss才会慢慢下降。 我给出了一个训练曲线可以看到确实贼几把怪七拐八拐的。
2千万不要用百度开源的那个ctc
网络代码
#crnn.py
import torch.nn as nn
import torch.nn.functional as Fclass BidirectionalLSTM(nn.Module):# Inputs hidden units Outdef __init__(self, nIn, nHidden, nOut):super(BidirectionalLSTM, self).__init__()self.rnn nn.LSTM(nIn, nHidden, bidirectionalTrue)self.embedding nn.Linear(nHidden * 2, nOut)def forward(self, input):recurrent, _ self.rnn(input)T, b, h recurrent.size()t_rec recurrent.view(T * b, h)output self.embedding(t_rec) # [T * b, nOut]output output.view(T, b, -1)return outputclass CRNN(nn.Module):# 32 1 37 256def __init__(self, imgH, nc, nclass, nh, n_rnn2, leakyReluFalse):super(CRNN, self).__init__()assert imgH % 16 0, imgH has to be a multiple of 16ks [3, 3, 3, 3, 3, 3, 2]ps [1, 1, 1, 1, 1, 1, 0]ss [1, 1, 1, 1, 1, 1, 1]nm [64, 128, 256, 256, 512, 512, 512]cnn nn.Sequential()def convRelu(i, batchNormalizationFalse):nIn nc if i 0 else nm[i - 1]nOut nm[i]cnn.add_module(conv{0}.format(i),nn.Conv2d(nIn, nOut, ks[i], ss[i], ps[i]))if batchNormalization:cnn.add_module(batchnorm{0}.format(i), nn.BatchNorm2d(nOut))if leakyRelu:cnn.add_module(relu{0}.format(i),nn.LeakyReLU(0.2, inplaceTrue))else:cnn.add_module(relu{0}.format(i), nn.ReLU(True))convRelu(0)cnn.add_module(pooling{0}.format(0), nn.MaxPool2d(2, 2)) # 64x16x64convRelu(1)cnn.add_module(pooling{0}.format(1), nn.MaxPool2d(2, 2)) # 128x8x32convRelu(2, True)convRelu(3)cnn.add_module(pooling{0}.format(2),nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 256x4x16convRelu(4, True)convRelu(5)cnn.add_module(pooling{0}.format(3),nn.MaxPool2d((2, 2), (2, 1), (0, 1))) # 512x2x16convRelu(6, True) # 512x1x16self.cnn cnnself.rnn nn.Sequential(BidirectionalLSTM(512, nh, nh),BidirectionalLSTM(nh, nh, nclass))def forward(self, input):# conv features#print(---forward propagation---)conv self.cnn(input)b, c, h, w conv.size()assert h 1, the height of conv must be 1conv conv.squeeze(2) # b *512 * widthconv conv.permute(2, 0, 1) # [w, b, c]output F.log_softmax(self.rnn(conv), dim2)return output训练
#train.py
import os
import torch
import cv2
import numpy as np
from torchvision import transforms
from torch.utils.data import Dataset, DataLoader
from torch.nn.utils.rnn import pad_sequenceimport crnn
import time
import re
import matplotlib.pyplot as plt
dic{ :0,a:1,b:2,c:3,d:4,e:5,f:6,g:7,h:8,i:9,j:10,k:11,l:12,m:13,n:14,o:15,p:16,q:17,r:18,s:19,t:20,u:21,v:22,w:23,x:24,y:25,z:26,A:27,B:28,C:29,D:30,E:31,F:32,G:33,H:34,I:35,J:36,K:37,L:38,M:39,N:40,O:41,P:42,Q:43,R:44,S:45,T:46,U:47,V:48,W:49,X:50,Y:51,Z:52}STR abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ
n_class53
label_sourcesrE:\machine_learning\instrument\icdar_2013\Challenge2_Test_Task1_GT
image_sourcesrE:\machine_learning\instrument\icdar_2013\Challenge2_Test_Task12_Images
use_gpu True
learning_rate 0.0001
max_epoch 100
batch_size 20
# 调整图像大小和归一化操作
class resizeAndNormalize():def __init__(self, size, interpolationcv2.INTER_LINEAR):# 注意对于opencv,size的格式是(w,h)self.size sizeself.interpolation interpolation# ToTensor属于类 Convert a PIL Image or numpy.ndarray to tensor.self.toTensor transforms.ToTensor()def __call__(self, image):# (x,y) 对于opencv来说图像宽对应x轴高对应y轴image cv2.resize(image, self.size, interpolationself.interpolation)# 转为tensor的数据结构image self.toTensor(image)# 对图像进行归一化操作#image image.sub_(0.5).div_(0.5)return imagedef load_data(label_folder,image_folder,label_suffix_name.txt,image_suffix_name.jpg):image_file,label_file,num_file[],[],[]for parent_folder, _, file_names in os.walk(label_folder):# 遍历当前子文件夹中的所有文件for file_name in file_names:# 只处理图片文件# if file_name.endswith((jpg, jpeg, png, gif)):#提取jpg、jpeg等格式的文件到指定目录if file_name.endswith((label_suffix_name)): # 提取json格式的文件到指定目录# 构造源文件路径和目标文件路径a,bfile_name.split(gt_)c,db.split(label_suffix_name)image_nameimage_folder \\ c image_suffix_nameif os.path.exists(image_name):label_name label_folder \\ file_nametxtopen(label_name,rb)txtltxt.readlines()for line in range(len(txtl)):image_file.append(image_name)label_file.append(label_name)num_file.append(line)return image_file,label_file,num_filedef zl2lable(zl):label_list[]for str in zl:label_list.append(dic[str])return label_listclass NewDataSet(Dataset):def __init__(self, label_source,image_source,trainTrue):super(NewDataSet, self).__init__()self.image_file,self.label_file,self.num_file load_data(label_source,image_source)def __len__(self):return len(self.image_file)def __getitem__(self, index):txt open(self.label_file[index], rb)imgcv2.imread(self.image_file[index],cv2.IMREAD_GRAYSCALE)wordL txt.readlines()wordstr(wordL[self.num_file[index]])pl re.findall(r\d,word)zl re.findall(r[a-zA-Z], word)[1] #1#img tensorx1, y1, x2, y2 pl[:4]img img[int(y1):int(y2),int(x1):int(x2), ](height, width)img.shape# 由于crnn网络输入图像的高为32故需要resize原始图像的heightsize_height 32# ratio 32 / float(height)size_width 100transform resizeAndNormalize((size_width, size_height))# 图像预处理imageTensor transform(img)#label tensorl zl2lable(zl)labelTensor torch.IntTensor(l)return imageTensor,labelTensorclass CRNNDataSet(Dataset):def __init__(self, imageRoot, labelRoot):self.image_root imageRootself.image_dict self.readfile(labelRoot)self.image_name [fileName for fileName, _ in self.image_dict.items()]def __getitem__(self, index):image_path os.path.join(self.image_root, self.image_name[index])keys self.image_dict.get(self.image_name[index])label [int(x) for x in keys]image cv2.imread(image_path, cv2.IMREAD_GRAYSCALE)# if image is None:# return None,None(height, width) image.shape# 由于crnn网络输入图像的高为32故需要resize原始图像的heightsize_height 32ratio 32 / float(height)size_width int(ratio * width)transform resizeAndNormalize((size_width, size_height))# 图像预处理image transform(image)# 标签格式转换为IntTensorlabel torch.IntTensor(label)return image, labeldef __len__(self):return len(self.image_name)def readfile(self, fileName):res []with open(fileName, r) as f:lines f.readlines()for line in lines:res.append(line.strip())dic {}total 0for line in res:part line.split( )# 由于会存在训练过程中取图像的时候图像不存在导致异常所以在初始化的时候就判断图像是否存在if not os.path.exists(os.path.join(self.image_root, part[0])):print(os.path.join(self.image_root, part[0]))total 1else:dic[part[0]] part[1:]print(total)return dictrainData NewDataSet(label_sources,image_sources)trainLoader DataLoader(datasettrainData, batch_size1, shuffleTrue, num_workers0)# valData CRNNDataSet(imageRootD:\BaiduNetdiskDownload\Synthetic_Chinese_String_Dataset\images\\,
# labelRootD:\BaiduNetdiskDownload\Synthetic_Chinese_String_Dataset\lables\data_t.txt)
#
# valLoader DataLoader(datasetvalData, batch_size1, shuffleTrue, num_workers1)#
# def decode(preds):
# pred []
# for i in range(len(preds)):
# if preds[i] ! 5989 and ((i 5989) or (i ! 5989 and preds[i] ! preds[i - 1])):
# pred.append(int(preds[i]))
# return pred
#
#
def toSTR(l):str_l[]if isinstance(l, int):l[l]for i in range(len(l)):str_l.append(STR[l[i]])return str_l
def toRES(l):new_l[]new_str for i in range(len(l)):if(l[i] ):new_str continueelif new_str!l[i]:new_l.append(l[i])new_strl[i]return new_ldef val(modeltorch.load(pytorch-crnn.pth)):# 将模式切换为验证评估模式loss_func torch.nn.CTCLoss(blank0, reductionmean)model.eval()test_n10for i, (data, label) in enumerate(trainLoader):if(itest_n):break;output model(data.cuda())pred_labeloutput.max(2)[1]input_lengths torch.IntTensor([output.size(0)] * int(output.size(1)))target_lengths torch.IntTensor([label.size(1)] * int(label.size(0)))# forward(self, log_probs, targets, input_lengths, target_lengths)#log_probs output.log_softmax(2).requires_grad_()targets label.cuda()loss loss_func(output.cpu(), targets.cpu(), input_lengths, target_lengths)pred_lnp.array(pred_label.cpu().squeeze()).tolist()label_lnp.array(targets.cpu().squeeze()).tolist()print(i,:,loss,pred:,toRES(toSTR(pred_l)),label_l,toSTR(label_l))def train():model crnn.CRNN(32, 1, n_class, 256)if torch.cuda.is_available() and use_gpu:model.cuda()loss_func torch.nn.CTCLoss(blank0,reductionmean)optimizer torch.optim.Adam(model.parameters(), lrlearning_rate,betas(0.9, 0.999))lossTotal 0.0k 0printInterval 100start_time time.time()loss_list[]total_list[]for epoch in range(max_epoch):n0data_list []label_list []label_len[]for i, (data, label) in enumerate(trainLoader):#data_list.append(data)label_list.append(label)label_len.append(label.size(1))nn1if n%batch_size!0:continuekk1datatorch.cat(data_list, dim0)data_list.clear()label torch.cat(label_list, dim1).squeeze(0)label_list.clear()target_lengthstorch.tensor(np.array(label_len))label_len.clear()# 开启训练模式model.train()if torch.cuda.is_available and use_gpu:data data.cuda()loss_func loss_func.cuda()label label.cuda()output model(data)log_probs output# example 建议使用这样貌似直接把output送进去loss fun也没发现什么问题#log_probs output.log_softmax(2).requires_grad_()targets label.cuda()input_lengths torch.IntTensor([output.size(0)] * int(output.size(1)))# forward(self, log_probs, targets, input_lengths, target_lengths)#targets torch.zeros(targets.shape)loss loss_func(log_probs.cpu(), targets, input_lengths, target_lengths)/batch_sizelossTotal float(loss)print(epoch:,epoch,num:,i,loss:,float(loss))loss_list.append(float(loss))if k % printInterval 0:print([%d/%d] [%d/%d] loss:%f % (epoch, max_epoch, i 1, len(trainLoader), lossTotal / printInterval))total_list.append( lossTotal / printInterval)lossTotal 0.0torch.save(model, pytorch-crnn.pth)optimizer.zero_grad()loss.backward()optimizer.step()plt.figure()plt.plot(loss_list)plt.savefig(loss.jpg)plt.clf()plt.figure()plt.plot(total_list)plt.savefig(total.jpg)end_time time.time()print(takes {}s.format((end_time - start_time)))return modelif __name__ __main__:train()测试结果如下 最后给一些参考文献 https://www.cnblogs.com/azheng333/p/7449515.html https://blog.csdn.net/wzw12315/article/details/106643182
另外给出数据集和我训练好的模型 链接https://pan.baidu.com/s/1-jTA22bLKv2ut_1EJ1WMKA?pwdjvk8 提取码jvk8
