81 lines
3.5 KiB
Python
81 lines
3.5 KiB
Python
import torch.nn as nn
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import timm
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# class ModifiedXception(nn.Module):
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# def __init__(self, num_classes):
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# super(ModifiedXception, self).__init__()
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# # Load Xception pre-trained model (full model, not just features)
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# self.base_model = timm.create_model(
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# 'xception',
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# pretrained=True
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# )
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# # Replace the default global pooling with AdaptiveAvgPool2d
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# self.base_model.global_pool = nn.AdaptiveAvgPool2d(output_size=1) # Output size of 1x1 spatially
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# # Replace the final fully connected layer with Identity to get features
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# self.base_model.fc = nn.Identity() # Output will be 2048 (Xception's default feature size)
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# # Custom head: Linear from 2048 to 1370, additional 1370 layer, then to num_classes
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# self.custom_head = nn.Sequential(
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# nn.Linear(2048, 1025), # From Xception’s 2048 features to 1370
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# nn.ReLU(), # Activation
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# nn.Dropout(0.6), # Dropout for regularization
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# nn.Linear(1025, num_classes) # Final output layer
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# # nn.Softmax(dim = 1) # Sigmoid for binary/multi-label classification
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# )
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# def forward(self, x):
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# # Pass through the base Xception model (up to global pooling)
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# x = self.base_model.forward_features(x) # Get feature maps
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# x = self.base_model.global_pool(x) # Apply AdaptiveAvgPool2d (output: [B, 2048, 1, 1])
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# x = x.flatten(1) # Flatten to [B, 2048]
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# # x = self.base_model.fc(x) # Identity layer (still [B, 2048])
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# output = self.custom_head(x) # Custom head processing
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# return output
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class ModifiedXception(nn.Module):
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def __init__(self, num_classes):
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super(ModifiedXception, self).__init__()
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# 加載 Xception 預訓練模型，去掉最後一層 (fc 層)
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self.base_model = timm.create_model('xception', pretrained=True)
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self.base_model.fc = nn.Identity() # 移除原來的 fully connected 層
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# 新增全局平均池化層、隱藏層和輸出層
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GAP_Output = 2048
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self.global_avg_pool = nn.AdaptiveAvgPool1d(2048) # 全局平均池化
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self.hidden_layer = nn.Linear(2048, 1025) # 隱藏層，輸入大小取決於 Xception 的輸出大小
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self.output_layer = nn.Linear(1025, num_classes) # 輸出層，依據分類數目設定
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# 激活函數與 dropout
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self.relu = nn.ReLU()
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self.softmax = nn.Softmax(1)
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self.dropout = nn.Dropout(0.6)
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def forward(self, x):
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x = self.base_model(x) # Xception 主體
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x = self.global_avg_pool(x) # 全局平均池化
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x = self.relu(self.hidden_layer(x)) # 隱藏層 + ReLU
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x = self.dropout(x) # Dropout
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x = self.output_layer(x) # 輸出層
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return x
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class Model_module():
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def __init__(self):
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self.conv1 = nn.Conv2d(in_channels = 3, out_channels = 32, kernel_size = 3, padding = 1)
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self.conv2 = nn.Conv2d(in_channels = 64, out_channels = 128, kernel_size = 3, padding = 1)
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self.conv3 = nn.Conv2d(in_channels = 128, out_channels = 128, kernel_size = 3, padding = 1)
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self.relu = nn.ReLU()
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self.sigmoid = nn.Sigmoid()
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self.max_Pool = nn.MaxPool2d(2, 2)
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self.fc1 = nn.Linear()
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self.fc2 = nn.Linear()
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pass
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def forward(self, input):
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pass |