changed cnn architecture

bird_cnn/bird_cnn.py

@@ -27,14 +27,25 @@ class  SeparableConvolution(nn.Module):
 class SkipBlock(nn.Module):
     def __init__(self, c_in, c_out, kernel_size=3):
         super().__init__()
-        self.conv = SeparableConvolution(c_in=c_in, c_out=c_out, kernel_size=kernel_size)
+        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.BatchNorm2d(c_out),
+            nn.ReLU(inplace=True)
         )
 
     def forward(self, x):
-        return(F.relu(self.conv_skip(x) + self.conv(x)))
+        return(F.relu(self.conv_skip(x) + self.conv(x), inplace=True))
 
 
 class Bird_CNN(nn.Module):
@@ -44,7 +55,7 @@ class Bird_CNN(nn.Module):
         self.model = nn.Sequential(
             nn.Conv2d(c_in, c_hidden, kernel_size=3, padding=1),
             nn.BatchNorm2d(c_hidden),
-            nn.ReLU(),
+            nn.ReLU(inplace=True),
 
             SkipBlock(c_in=c_hidden, c_out=c_hidden),
             SkipBlock(c_in=c_hidden, c_out=c_hidden),
@@ -56,7 +67,8 @@ class Bird_CNN(nn.Module):
             SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
             SkipBlock(c_in=c_hidden*2, c_out=c_hidden*2),
 
-            SeparableConvolution(c_in=c_hidden*2, c_out=c_hidden*4, kernel_size=3),
+            nn.Conv2d(c_hidden*2, c_hidden*4, kernel_size=3, padding=1),
+            nn.ReLU(inplace=True),
 
             nn.AdaptiveAvgPool2d((1, 1)),
             nn.Flatten(),

bird_cnn/server.py

@@ -14,7 +14,7 @@ from bird_cnn import Bird_CNN
 import threading
 
 BUILD_PATH = "./build_models"
-IMAGE_SIZE = 256
+IMAGE_SIZE = 64
 
 sem = threading.Semaphore(1) #adjust to performance
 
@@ -35,25 +35,13 @@ transform = transforms.Compose([
 
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 
-model =Bird_CNN(c_in=3, c_hidden=64, c_out=7)
+model = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
 full_path = os.path.join(BUILD_PATH, "bird_cnn")
 model.load_state_dict(torch.load(full_path, map_location=torch.device(device)))
 model.to(device)
 model.eval()
 
-app = FastAPI()
+app = FastAPI(root_path="/cnn-api")
-
-origins = [
-    "*"
-]
-
-app.add_middleware(
-    CORSMiddleware,
-    allow_origins=origins,
-    allow_credentials=True,
-    allow_methods=["*"],
-    allow_headers=["*"],
-)
 
 @app.post("/predict")
 async def predict(file: UploadFile = File(...)):

bird_cnn/train_bird.py

@@ -1,7 +1,9 @@
 import torch
 import torch.nn as nn
+import torchvision
 from torchvision import datasets, transforms
 from torch.utils.data import DataLoader, random_split
+import matplotlib.pyplot as plt
 
 from bird_cnn import Bird_CNN, sample, trainCNN
 
@@ -10,7 +12,7 @@ from enum import Enum
 from PIL import Image
 
 SAVE_PATH = "./saved_models"
-IMAGE_SIZE = 256
+IMAGE_SIZE = 64
 
 class bird_species(Enum):
     Common_Kingfisher = 0
@@ -34,13 +36,14 @@ val_size = len(dataset) - train_size
 
 train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
 
-train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
+
 
 device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
 print("Using device", device)
 
-model = Bird_CNN(c_in=3, c_hidden=64, c_out=7)
+model = Bird_CNN(c_in=3, c_hidden=16, c_out=7)
 model.to(device)
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
 loss_module = nn.CrossEntropyLoss()

bird_cnn/train_mnist.py

@@ -27,13 +27,13 @@ val_dataset = datasets.MNIST(
     transform=transform
 )
 
-train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True)
+train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
-val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False)
+val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
 
 device = torch.device("cpu") if not torch.cuda.is_available() else torch.device("cuda:0")
 print("Using device", device)
 
-model = Bird_CNN(c_in=1, c_hidden=8, c_out=10)
+model = Bird_CNN(c_in=1, c_hidden=4, c_out=10)
 model.to(device)
 optimizer = torch.optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-4)
 loss_module = nn.CrossEntropyLoss()

cnn_website/index.html

@@ -3,9 +3,9 @@
 
 <head>
   <meta charset="UTF-8" />
-  <link rel="icon" type="image/svg+xml" href="/favicon.svg" />
+  <link rel="icon" type="image/svg+xml" href="/generated_two_no_background.png" />
   <meta name="viewport" content="width=device-width, initial-scale=1.0" />
-  <title>cnn_website</title>
+  <title>Birdy</title>
 </head>
 
 <body>

cnn_website/src/Bird_CNN.jsx

@@ -4,8 +4,8 @@ import './Bird_CNN.css';
 
 function Bird_CNN() {
     const apiUrl = process.env.NODE_ENV === "development"
-        ? "https://api.marvinkrausser.com"
+        ? "https://marvinkrausser.com/api"
-        : "https://api.marvinkrausser.com";
+        : "https://marvinkrausser.com/api";
 
     const [file, setFile] = useState(null);
     const [birdClass, setBirdClass] = useState(null);
@@ -54,17 +54,24 @@ function Bird_CNN() {
         const formData = new FormData();
         formData.append("file", file);
 
-        try {
         setLoading(true);
+
+        try {
             const response = await fetch(`${apiUrl}/predict`, {
                 method: "POST",
                 body: formData,
             });
-
+        } catch (e) {
+            setError(true);
+            return;
+        }
+        finally {
             setLoading(false);
+        }
 
         if (!response.ok) {
             setError(true);
+            return;
         }
         else {
             setError(false);
@@ -74,10 +81,6 @@ function Bird_CNN() {
         setBirdClass(result["class"]);
         const confidence = result["confidence"];
         setConfidence(`${Math.round(confidence * 100)}%`);
-        }
-        catch (error) {
-            console.error("Upload failed:", error);
-        }
     };
 
     return (