Tue Oct 24 16:49:00 CST 2023 inscode

.inscode

 run = "npm i && npm run dev"
+language = "node"
 
 [env]
 PATH = "/root/${PROJECT_DIR}/.config/npm/node_global/bin:/root/${PROJECT_DIR}/node_modules/.bin:${PATH}"
 XDG_CONFIG_HOME = "/root/.config"
-npm_config_prefix = "/root/${PROJECT_DIR}/.config/npm/node_global"
\ No newline at end of file
+npm_config_prefix = "/root/${PROJECT_DIR}/.config/npm/node_global"
+
+[debugger]
+program = "main.js"

build/index.js

@@ -33,3 +33,51 @@ if (process.env.npm_config_preview || rawArgv.includes('--preview')) {
 } else {
   run(`vue-cli-service build ${args}`)
 }
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch import Tensor
+
+class CNN_Transformer(nn.Module):
+    
+    def __init__(self, num_classes):
+        super(CNN_Transformer, self).__init__()
+        
+        # Define CNN layers
+        self.conv1 = nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
+        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, stride=1, padding=1)
+        self.fc1 = nn.Linear(64 * 16 * 16, 128)
+        
+        # Define Transformer layers
+        self.embedding = nn.Linear(128, 512)
+        self.layer_norm = nn.LayerNorm(512)
+        self.self_attn = nn.MultiheadAttention(512, 8, dropout=0.1)
+        self.dropout1 = nn.Dropout(0.1)
+        self.linear1 = nn.Linear(512, 256)
+        self.dropout2 = nn.Dropout(0.5)
+        self.linear2 = nn.Linear(256, 128)
+        
+        # Define output layer
+        self.out = nn.Linear(128, num_classes)
+
+    def forward(self, x: Tensor) -> Tensor:
+        # Process input with CNN
+        x = F.relu(self.conv1(x))
+        x = F.relu(self.conv2(x))
+        x = x.view(-1, 64 * 16 * 16)
+        x = F.relu(self.fc1(x))
+        
+        # Process output with Transformer
+        x = self.embedding(x)
+        x = self.layer_norm(x.permute(1, 0, 2))
+        x, _ = self.self_attn(x, x, x)
+        x = self.dropout1(x)
+        x = F.relu(self.linear1(x))
+        x = self.dropout2(x)
+        x = F.relu(self.linear2(x))
+        
+        # Process output with output layer
+        x = self.out(x)
+        return x
+