license_plate_recognition/plate_recognition/plateNet.py

import torch.nn as nn
import torch
import torch.nn.functional as F

class myNet_ocr(nn.Module):
    def __init__(self,cfg=None,num_classes=78,export=False):
        super(myNet_ocr, self).__init__()
        if cfg is None:
            cfg =[32,32,64,64,'M',128,128,'M',196,196,'M',256,256]
            # cfg =[32,32,'M',64,64,'M',128,128,'M',256,256]
        self.feature = self.make_layers(cfg, True)
        self.export = export
        # self.classifier = nn.Linear(cfg[-1], num_classes)
        # self.loc =  nn.MaxPool2d((2, 2), (5, 1), (0, 1),ceil_mode=True)
        # self.loc =  nn.AvgPool2d((2, 2), (5, 2), (0, 1),ceil_mode=False)
        self.loc =  nn.MaxPool2d((5, 2), (1, 1),(0,1),ceil_mode=False)
        self.newCnn=nn.Conv2d(cfg[-1],num_classes,1,1)
        # self.newBn=nn.BatchNorm2d(num_classes)
    def make_layers(self, cfg, batch_norm=False):
        layers = []
        in_channels = 3
        for i in range(len(cfg)):
            if i == 0:
                conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1)
                if batch_norm:
                    layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)]
                else:
                    layers += [conv2d, nn.ReLU(inplace=True)]
                in_channels = cfg[i]
            else :
                if cfg[i] == 'M':
                    layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)]
                else:
                    conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=(1,1),stride =1)
                    if batch_norm:
                        layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)]
                    else:
                        layers += [conv2d, nn.ReLU(inplace=True)]
                    in_channels = cfg[i]
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.feature(x)
        x=self.loc(x)
        x=self.newCnn(x)
        # x=self.newBn(x)
        if self.export:
            conv = x.squeeze(2) # b *512 * width
            conv = conv.transpose(2,1)  # [w, b, c]
            # conv =conv.argmax(dim=2)
            return conv
        else:
            b, c, h, w = x.size()
            assert h == 1, "the height of conv must be 1"
            conv = x.squeeze(2) # b *512 * width
            conv = conv.permute(2, 0, 1)  # [w, b, c]
            # output = F.log_softmax(self.rnn(conv), dim=2)
            output = torch.softmax(conv, dim=2)
            return output

myCfg = [32,'M',64,'M',96,'M',128,'M',256]
class myNet(nn.Module):
    def __init__(self,cfg=None,num_classes=3):
        super(myNet, self).__init__()
        if cfg is None:
            cfg = myCfg
        self.feature = self.make_layers(cfg, True)
        self.classifier = nn.Linear(cfg[-1], num_classes)
    def make_layers(self, cfg, batch_norm=False):
        layers = []
        in_channels = 3
        for i in range(len(cfg)):
            if i == 0:
                conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1)
                if batch_norm:
                    layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)]
                else:
                    layers += [conv2d, nn.ReLU(inplace=True)]
                in_channels = cfg[i]
            else :
                if cfg[i] == 'M':
                    layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)]
                else:
                    conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=1,stride =1)
                    if batch_norm:
                        layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)]
                    else:
                        layers += [conv2d, nn.ReLU(inplace=True)]
                    in_channels = cfg[i]
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.feature(x)
        x = nn.AvgPool2d(kernel_size=3, stride=1)(x)
        x = x.view(x.size(0), -1)
        y = self.classifier(x)
        return y
    
    
class MyNet_color(nn.Module):
    def __init__(self, class_num=6):
        super(MyNet_color, self).__init__()
        self.class_num = class_num
        self.backbone = nn.Sequential(
            nn.Conv2d(in_channels=3, out_channels=16, kernel_size=(5, 5), stride=(1, 1)),  # 0
            torch.nn.BatchNorm2d(16),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=(2, 2)),
            nn.Dropout(0),
            nn.Flatten(),
            nn.Linear(480, 64),
            nn.Dropout(0),
            nn.ReLU(),
            nn.Linear(64, class_num),
            nn.Dropout(0),
            nn.Softmax(1)
        )

    def forward(self, x):
        logits = self.backbone(x)

        return logits


class myNet_ocr_color(nn.Module):
    def __init__(self,cfg=None,num_classes=78,export=False,color_num=None):
        super(myNet_ocr_color, self).__init__()
        if cfg is None:
            cfg =[32,32,64,64,'M',128,128,'M',196,196,'M',256,256]
            # cfg =[32,32,'M',64,64,'M',128,128,'M',256,256]
        self.feature = self.make_layers(cfg, True)
        self.export = export
        self.color_num=color_num
        self.conv_out_num=12  #颜色第一个卷积层输出通道12
        if self.color_num:
            self.conv1=nn.Conv2d(cfg[-1],self.conv_out_num,kernel_size=3,stride=2)
            self.bn1=nn.BatchNorm2d(self.conv_out_num)
            self.relu1=nn.ReLU(inplace=True)
            self.gap =nn.AdaptiveAvgPool2d(output_size=1)
            self.color_classifier=nn.Conv2d(self.conv_out_num,self.color_num,kernel_size=1,stride=1)
            self.color_bn = nn.BatchNorm2d(self.color_num)
            self.flatten = nn.Flatten()
        self.loc =  nn.MaxPool2d((5, 2), (1, 1),(0,1),ceil_mode=False)
        self.newCnn=nn.Conv2d(cfg[-1],num_classes,1,1)
        # self.newBn=nn.BatchNorm2d(num_classes)
    def make_layers(self, cfg, batch_norm=False):
        layers = []
        in_channels = 3
        for i in range(len(cfg)):
            if i == 0:
                conv2d =nn.Conv2d(in_channels, cfg[i], kernel_size=5,stride =1)
                if batch_norm:
                    layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)]
                else:
                    layers += [conv2d, nn.ReLU(inplace=True)]
                in_channels = cfg[i]
            else :
                if cfg[i] == 'M':
                    layers += [nn.MaxPool2d(kernel_size=3, stride=2,ceil_mode=True)]
                else:
                    conv2d = nn.Conv2d(in_channels, cfg[i], kernel_size=3, padding=(1,1),stride =1)
                    if batch_norm:
                        layers += [conv2d, nn.BatchNorm2d(cfg[i]), nn.ReLU(inplace=True)]
                    else:
                        layers += [conv2d, nn.ReLU(inplace=True)]
                    in_channels = cfg[i]
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.feature(x)
        if self.color_num:
            x_color=self.conv1(x)
            x_color=self.bn1(x_color)
            x_color =self.relu1(x_color)
            x_color = self.color_classifier(x_color)
            x_color = self.color_bn(x_color)
            x_color =self.gap(x_color)
            x_color = self.flatten(x_color) 
        x=self.loc(x)
        x=self.newCnn(x)
       
        if self.export:
            conv = x.squeeze(2) # b *512 * width
            conv = conv.transpose(2,1)  # [w, b, c]
            if self.color_num:
                return conv,x_color
            return conv
        else:
            b, c, h, w = x.size()
            assert h == 1, "the height of conv must be 1"
            conv = x.squeeze(2) # b *512 * width
            conv = conv.permute(2, 0, 1)  # [w, b, c]
            output = F.log_softmax(conv, dim=2)
            if self.color_num:
                return output,x_color
            return output


if __name__ == '__main__':
    x = torch.randn(1,3,48,216)
    model = myNet_ocr(num_classes=78,export=True)
    out = model(x)
    print(out.shape)