dk - edit

net/models.py

@@ -0,0 +1,76 @@
+import torch
+from torch import nn
+from torch.nn import init
+from torchvision import models
+from net.utils import ClassBlock
+from torch.nn import functional as F
+
+
+class Backbone_nFC(nn.Module):
+    def __init__(self, class_num, model_name='resnet50_nfc'):
+        super(Backbone_nFC, self).__init__()
+        self.model_name = model_name
+        self.backbone_name = model_name.split('_')[0]
+        self.class_num = class_num
+
+        model_ft = getattr(models, self.backbone_name)(pretrained=True)
+        if 'resnet' in self.backbone_name:
+            model_ft.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+            model_ft.fc = nn.Sequential()
+            self.features = model_ft
+            self.num_ftrs = 2048
+        elif 'densenet' in self.backbone_name:
+            model_ft.features.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+            model_ft.fc = nn.Sequential()
+            self.features = model_ft.features
+            self.num_ftrs = 1024
+        else:
+            raise NotImplementedError
+        
+        for c in range(self.class_num):
+            self.__setattr__('class_%d' % c, ClassBlock(input_dim=self.num_ftrs, class_num=1, activ='sigmoid') )
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        pred_label = [self.__getattr__('class_%d' % c)(x) for c in range(self.class_num)]
+        pred_label = torch.cat(pred_label, dim=1)
+        return pred_label
+
+
+class Backbone_nFC_Id(nn.Module):
+    def __init__(self, class_num, id_num, model_name='resnet50_nfc_id'):
+        super(Backbone_nFC_Id, self).__init__()
+        self.model_name = model_name
+        self.backbone_name = model_name.split('_')[0]
+        self.class_num = class_num
+        self.id_num = id_num
+        
+        model_ft = getattr(models, self.backbone_name)(pretrained=True)
+        if 'resnet' in self.backbone_name:
+            model_ft.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+            model_ft.fc = nn.Sequential()
+            self.features = model_ft
+            self.num_ftrs = 2048
+        elif 'densenet' in self.backbone_name:
+            model_ft.features.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+            model_ft.fc = nn.Sequential()
+            self.features = model_ft.features
+            self.num_ftrs = 1024
+        else:
+            raise NotImplementedError
+        
+        for c in range(self.class_num+1):
+            if c == self.class_num:
+                self.__setattr__('class_%d' % c, ClassBlock(self.num_ftrs, class_num=self.id_num, activ='none'))
+            else:
+                self.__setattr__('class_%d' % c, ClassBlock(self.num_ftrs, class_num=1, activ='sigmoid'))
+
+    def forward(self, x):
+        x = self.features(x)
+        x = x.view(x.size(0), -1)
+        pred_label = [self.__getattr__('class_%d' % c)(x) for c in range(self.class_num)]
+        pred_label = torch.cat(pred_label, dim=1)
+        pred_id = self.__getattr__('class_%d' % self.class_num)(x)
+        return pred_label, pred_id
+