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]
            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)
            # output = torch.softmax(conv, dim=2)
           
            return output

if __name__ == '__main__':
    x = torch.randn(1,3,48,168)
    cfg =[32,'M',64,'M',128,'M',256]
    model = myNet_ocr(num_classes=78,export=True,cfg=cfg)
    # print(model)
    out = model(x)
    print(out.shape)