| import torch |
| from torch import nn |
|
|
| from opendet.modeling.backbones import build_backbone |
| from opendet.modeling.necks import build_neck |
| from opendet.modeling.heads import build_head |
|
|
| __all__ = ['BaseDetector'] |
|
|
|
|
| class BaseDetector(nn.Module): |
|
|
| def __init__(self, config): |
| """the module for OCR. |
| |
| args: |
| config (dict): the super parameters for module. |
| """ |
| super(BaseDetector, self).__init__() |
| in_channels = config.get('in_channels', 3) |
| self.use_wd = config.get('use_wd', True) |
|
|
| |
| if 'Backbone' not in config or config['Backbone'] is None: |
| self.use_backbone = False |
| else: |
| self.use_backbone = True |
| config['Backbone']['in_channels'] = in_channels |
| self.backbone = build_backbone(config['Backbone']) |
| in_channels = self.backbone.out_channels |
|
|
| |
| if 'Neck' not in config or config['Neck'] is None: |
| self.use_neck = False |
| else: |
| self.use_neck = True |
| config['Neck']['in_channels'] = in_channels |
| self.neck = build_neck(config['Neck']) |
| in_channels = self.neck.out_channels |
|
|
| |
| if 'Head' not in config or config['Head'] is None: |
| self.use_head = False |
| else: |
| self.use_head = True |
| config['Head']['in_channels'] = in_channels |
| self.head = build_head(config['Head']) |
|
|
| @torch.jit.ignore |
| def no_weight_decay(self): |
| if self.use_wd: |
| if hasattr(self.backbone, 'no_weight_decay'): |
| no_weight_decay = self.backbone.no_weight_decay() |
| else: |
| no_weight_decay = {} |
| if hasattr(self.head, 'no_weight_decay'): |
| no_weight_decay.update(self.head.no_weight_decay()) |
| return no_weight_decay |
| else: |
| return {} |
|
|
| def forward(self, x, data=None): |
| if self.use_backbone: |
| x = self.backbone(x) |
| if self.use_neck: |
| x = self.neck(x) |
| if self.use_head: |
| x = self.head(x, data=data) |
| return x |
|
|
