| import torch.nn as nn |
|
|
| from lib.models.backbones.backbone_selector import BackboneSelector |
| from lib.models.modules.decoder_block import DeepLabHead |
| from lib.models.modules.projection import ProjectionHead |
|
|
|
|
| class DeepLabV3Contrast(nn.Module): |
| def __init__(self, configer): |
| super(DeepLabV3Contrast, self).__init__() |
|
|
| self.configer = configer |
| self.num_classes = self.configer.get('data', 'num_classes') |
| self.backbone = BackboneSelector(configer).get_backbone() |
| self.proj_dim = self.configer.get('contrast', 'proj_dim') |
|
|
| |
| if "wide_resnet38" in self.configer.get('network', 'backbone'): |
| in_channels = [2048, 4096] |
| else: |
| in_channels = [1024, 2048] |
|
|
| self.proj_head = ProjectionHead(dim_in=in_channels[1], proj_dim=self.proj_dim) |
|
|
| self.decoder = DeepLabHead(num_classes=self.num_classes, bn_type=self.configer.get('network', 'bn_type')) |
|
|
| for modules in [self.proj_head, self.decoder]: |
| for m in modules.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight.data) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def forward(self, x_, with_embed=False, is_eval=False): |
| x = self.backbone(x_) |
|
|
| embedding = self.proj_head(x[-1]) |
|
|
| x = self.decoder(x[-4:]) |
|
|
| return {'embed': embedding, 'seg_aux': x[1], 'seg': x[0]} |
|
|
| class DeepLabV3(nn.Module): |
| def __init__(self, configer): |
| super(DeepLabV3, self).__init__() |
|
|
| self.configer = configer |
| self.num_classes = self.configer.get('data', 'num_classes') |
| self.backbone = BackboneSelector(configer).get_backbone() |
|
|
| self.decoder = DeepLabHead(num_classes=self.num_classes, bn_type=self.configer.get('network', 'bn_type')) |
|
|
| for m in self.decoder.modules(): |
| if isinstance(m, nn.Conv2d): |
| nn.init.kaiming_normal_(m.weight.data) |
| if m.bias is not None: |
| m.bias.data.zero_() |
|
|
| def forward(self, x_): |
| x = self.backbone(x_) |
|
|
| x = self.decoder(x[-4:]) |
|
|
| return x[1], x[0] |
|
|
