| | import torch |
| | import torch.nn as nn |
| | import torchvision.models as models |
| |
|
| |
|
| | class ResClassifier(nn.Module): |
| | def __init__(self, class_num=14): |
| | super(ResClassifier, self).__init__() |
| | self.fc1 = nn.Sequential( |
| | nn.Linear(128, 64), |
| | nn.BatchNorm1d(64, affine=True), |
| | nn.ReLU(inplace=True), |
| | nn.Dropout() |
| | ) |
| | self.fc2 = nn.Sequential( |
| | nn.Linear(64, 64), |
| | nn.BatchNorm1d(64, affine=True), |
| | nn.ReLU(inplace=True), |
| | nn.Dropout() |
| | ) |
| | self.fc3 = nn.Linear(64, class_num) |
| |
|
| | def forward(self, x): |
| | fc1_emb = self.fc1(x) |
| | fc2_emb = self.fc2(fc1_emb) |
| | logit = self.fc3(fc2_emb) |
| | return logit |
| |
|
| | class CC_model(nn.Module): |
| | def __init__(self, num_classes1=14, num_classes2=None): |
| |
|
| | if num_classes2 is None: |
| | num_classes2 = num_classes1 |
| |
|
| | super(CC_model, self).__init__() |
| | assert num_classes1 == num_classes2 |
| | self.num_classes = num_classes1 |
| | self.model_resnet = models.resnet50(weights='ResNet50_Weights.DEFAULT') |
| | num_ftrs = self.model_resnet.fc.in_features |
| | self.model_resnet.fc = nn.Identity() |
| | self.classification_fc = nn.Linear(num_ftrs, num_classes1) |
| | self.dr = nn.Linear(num_ftrs, 128) |
| | self.fc1 = ResClassifier(num_classes1) |
| | self.fc2 = ResClassifier(num_classes1) |
| |
|
| | def forward(self, x, detach_feature=False): |
| |
|
| | with torch.no_grad(): |
| | feature = self.model_resnet(x) |
| | res_out = self.classification_fc(feature) |
| | if detach_feature: |
| | feature = feature.detach() |
| | dr_feature = self.dr(feature) |
| | out1 = self.fc1(dr_feature) |
| | out2 = self.fc2(dr_feature) |
| | output_mean = (out1 + out2) / 2 |
| | return dr_feature, output_mean |
| |
|
| | |
