PytorchOCR/torchocr/networks/architectures/RecModel.py

# -*- coding: utf-8 -*-
# @Time    : 2020/5/16 11:18
# @Author  : zhoujun
from torch import nn

from torchocr.networks.backbones.RecMobileNetV3 import MobileNetV3
from torchocr.networks.backbones.RecResNetvd import ResNet
from torchocr.networks.necks.RNN import SequenceEncoder, Im2Seq

from torchocr.networks.heads.RecCTCHead import CTC

backbone_dict = {'MobileNetV3': MobileNetV3, 'ResNet': ResNet}
neck_dict = {'PPaddleRNN': SequenceEncoder, 'None': Im2Seq}
head_dict = {'CTC': CTC}


class RecModel(nn.Module):
    def __init__(self, config):
        super().__init__()
        assert 'in_channels' in config, 'in_channels must in model config'
        backbone_type = config.backbone.pop('type')
        assert backbone_type in backbone_dict, f'backbone.type must in {backbone_dict}'
        self.backbone = backbone_dict[backbone_type](config.in_channels, **config.backbone)

        neck_type = config.neck.pop('type')
        assert neck_type in neck_dict, f'neck.type must in {neck_dict}'
        self.neck = neck_dict[neck_type](self.backbone.out_channels, **config.neck)

        head_type = config.head.pop('type')
        assert head_type in head_dict, f'head.type must in {head_dict}'
        self.head = head_dict[head_type](self.neck.out_channels, **config.head)

        self.name = f'RecModel_{backbone_type}_{neck_type}_{head_type}'

    def forward(self, x):
        x = self.backbone(x)
        x = self.neck(x)
        features = x
        x = self.head(x)
        # return features,x
        return x