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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):
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)
return output_mean
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