| from torch import nn | |
| from .RNN import SequenceEncoder, Im2Seq, Im2Im | |
| from .RecMv1_enhance import MobileNetV1Enhance | |
| from .RecCTCHead import CTCHead | |
| backbone_dict = {"MobileNetV1Enhance":MobileNetV1Enhance} | |
| neck_dict = {'SequenceEncoder': SequenceEncoder, 'Im2Seq': Im2Seq,'None':Im2Im} | |
| head_dict = {'CTCHead':CTCHead} | |
| 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 load_3rd_state_dict(self, _3rd_name, _state): | |
| self.backbone.load_3rd_state_dict(_3rd_name, _state) | |
| self.neck.load_3rd_state_dict(_3rd_name, _state) | |
| self.head.load_3rd_state_dict(_3rd_name, _state) | |
| def forward(self, x): | |
| x = self.backbone(x) | |
| x = self.neck(x) | |
| x = self.head(x) | |
| return x | |
| def encode(self, x): | |
| x = self.backbone(x) | |
| x = self.neck(x) | |
| x = self.head.ctc_encoder(x) | |
| return x | |