46 lines
1.6 KiB
Python
Executable File
46 lines
1.6 KiB
Python
Executable File
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
|