model
unknown
python
5 months ago
2.1 kB
3
Indexable
import torch import torch.nn as nn import torch.nn.functional as F class CNN3D(nn.Module): """3D Convolutional Neural Network""" def __init__(self, width=128, height=128, depth=64): super(CNN3D, self).__init__() # 第一层卷积 self.conv1 = nn.Conv3d(in_channels=1, out_channels=64, kernel_size=3, padding=1) # padding=1 保持 shape self.pool1 = nn.MaxPool3d(kernel_size=2) self.bn1 = nn.BatchNorm3d(64) # 第二层卷积 self.conv2 = nn.Conv3d(in_channels=64, out_channels=64, kernel_size=3, padding=1) self.pool2 = nn.MaxPool3d(kernel_size=2) self.bn2 = nn.BatchNorm3d(64) # 第三层卷积 self.conv3 = nn.Conv3d(in_channels=64, out_channels=128, kernel_size=3, padding=1) self.pool3 = nn.MaxPool3d(kernel_size=2) self.bn3 = nn.BatchNorm3d(128) # 第四层卷积 self.conv4 = nn.Conv3d(in_channels=128, out_channels=256, kernel_size=3, padding=1) self.pool4 = nn.MaxPool3d(kernel_size=2) self.bn4 = nn.BatchNorm3d(256) # 全局平均池化 self.global_avg_pool = nn.AdaptiveAvgPool3d(output_size=1) # 全连接层 self.fc1 = nn.Linear(256, 512) # 输入通道数与前一层输出一致 self.dropout = nn.Dropout(0.3) self.fc2 = nn.Linear(512, 1) # 二分类输出 def forward(self, x): x = F.relu(self.conv1(x)) x = self.pool1(x) x = self.bn1(x) x = F.relu(self.conv2(x)) x = self.pool2(x) x = self.bn2(x) x = F.relu(self.conv3(x)) x = self.pool3(x) x = self.bn3(x) x = F.relu(self.conv4(x)) x = self.pool4(x) x = self.bn4(x) x = self.global_avg_pool(x) # (batch_size, 256, 1, 1, 1) x = torch.flatten(x, start_dim=1) # 展平成 (batch_size, 256) x = F.relu(self.fc1(x)) x = self.dropout(x) x = torch.sigmoid(self.fc2(x)) # 输出 sigmoid 概率 return x
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