server cleanup

.vscode/settings.json

@@ -1,5 +0,0 @@
-{
-    "python.analysis.extraPaths": [
-        "./api/src/yolo"
-    ]
-}

api/Dockerfile

@@ -12,8 +12,7 @@ COPY requirements.txt .
 
 RUN pip install --no-cache-dir -r requirements.txt
 
-COPY src ./src
-COPY start_server.sh ./start_server.sh
+COPY src/production/. .
 COPY build_models ./build_models
 
 RUN chmod +x start_server.sh

api/src/production/bird_cnn_production.py

@@ -0,0 +1,76 @@
+import torch.nn as nn
+import torch.nn.functional as F
+
+class  SeparableConvolution(nn.Module):
+    def __init__(self, c_in, c_out, kernel_size):
+        super().__init__()
+        self.depthwise = nn.Conv2d(c_in, c_in, kernel_size, groups=c_in, padding=kernel_size//2)
+        self.bn1 = nn.BatchNorm2d(c_in)
+        self.pointwise = nn.Conv2d(c_in, c_out, kernel_size=1)
+        self.bn2 = nn.BatchNorm2d(c_out)
+
+    def forward(self, x):
+        x = self.depthwise(x)
+        x = self.bn1(x)
+        x = F.relu(x)
+
+        x = self.pointwise(x)
+        x = self.bn2(x)
+        x = F.relu(x)
+
+        return x
+    
+class SkipBlock(nn.Module):
+    def __init__(self, c_in, c_out, kernel_size=3):
+        super().__init__()
+        self.conv = nn.Sequential(
+            nn.Conv2d(c_in, c_out, kernel_size, padding=kernel_size//2),
+            nn.BatchNorm2d(c_out),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(c_out, c_out, kernel_size, padding=kernel_size//2),
+            nn.BatchNorm2d(c_out),
+            nn.ReLU(inplace=True),
+            nn.Conv2d(c_out, c_out, kernel_size, padding=kernel_size//2),
+            nn.BatchNorm2d(c_out),
+            nn.ReLU(inplace=True)
+        )
+        self.conv_skip = nn.Sequential(
+            nn.Conv2d(c_in, c_out, 1),
+            nn.BatchNorm2d(c_out),
+            nn.ReLU(inplace=True)
+        )
+
+    def forward(self, x):
+        return(F.relu(self.conv_skip(x) + self.conv(x), inplace=True))
+
+
+class Bird_CNN(nn.Module):
+    def __init__(self, c_in, c_hidden, c_out):
+        super().__init__()
+
+        self.model = nn.Sequential(
+            nn.Conv2d(c_in, c_hidden, kernel_size=3, padding=1),
+            nn.BatchNorm2d(c_hidden),
+            nn.ReLU(inplace=True),
+
+            SkipBlock(c_in=c_hidden, c_out=c_hidden),
+            SkipBlock(c_in=c_hidden, c_out=c_hidden),
+            SkipBlock(c_in=c_hidden, c_out=c_hidden),
+            SkipBlock(c_in=c_hidden, c_out=c_hidden),
+            
+            SkipBlock(c_in=c_hidden, c_out=c_hidden*2),
+            SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
+            SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
+            SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
+
+            nn.Conv2d(c_hidden*2, c_hidden*4, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
+
+            nn.AdaptiveAvgPool2d((1, 1)),
+            nn.Flatten(),
+            nn.Linear(c_hidden*4, c_out),
+            nn.Dropout(0.3)
+        )
+
+    def forward(self, x):
+        return self.model(x)

api/src/production/server.py

@@ -17,13 +17,8 @@ import torch.nn.functional as F
 
 import threading
 
-import sys
-
-sys.path.insert(1, './src/yolo')
 from yolo_model_production import convert_prediction, Yolo_model
-
-sys.path.insert(2, './src/bird_cnn')
-from bird_cnn import Bird_CNN
+from bird_cnn_production import Bird_CNN
 
 BUILD_PATH = "./build_models"
 IMAGE_SIZE_CNN = 64

api/src/production/start_server.bat

@@ -0,0 +1 @@
+python -m uvicorn server:app --reload

api/src/production/start_server.sh

@@ -0,0 +1 @@
+uvicorn server:app --host 0.0.0.0 --port 8000 --reload

api/start_server.bat

@@ -1 +0,0 @@
-python -m uvicorn src.server:app --reload

api/start_server.sh

@@ -1 +0,0 @@
-uvicorn src.server:app --host 0.0.0.0 --port 8000 --reload