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Configuration error
Configuration error
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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_classes=14):
super(CC_model, self).__init__()
self.num_classes = num_classes
self.model_resnet = models.resnet50(weights='ResNet50_Weights.DEFAULT')
# Modify final layers
num_ftrs = self.model_resnet.fc.in_features
self.model_resnet.fc = nn.Identity() # Remove ResNet's default final layer
self.classification_fc = nn.Linear(num_ftrs, num_classes)
self.dr = nn.Linear(num_ftrs, 128) # Feature reduction (for embeddings)
self.fc1 = ResClassifier(num_classes)
self.fc2 = ResClassifier(num_classes)
def forward(self, x):
feature = self.model_resnet(x)
class_logits = self.classification_fc(feature) # Correct classification output
return class_logits # Ensure output shape is [batch_size, 14]
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