working server

bird_cnn/bird_cnn.py

@@ -16,6 +16,7 @@ class Bird_CNN(nn.Module):
 
             nn.Conv2d(c_hidden, c_hidden, kernel_size, padding=kernel_size//2),
             nn.ReLU(),
+
             nn.Flatten(),
             nn.Linear(c_hidden * img_height * img_width, c_out)
         )

bird_cnn/main.py

@@ -10,6 +10,7 @@ from enum import Enum
 from PIL import Image
 
 SAVE_PATH = "./saved_models"
+#IMAGE_SIZE = (1141, 850)
 IMAGE_SIZE = (300, 300)
 
 class bird_species(Enum):
@@ -28,11 +29,10 @@ transform = transforms.Compose([
 
 dataset = datasets.ImageFolder("data/train", transform=transform)
 
-train_size = int(0.3 * len(dataset))
+train_size = int(0.8 * len(dataset))
-val_size = int(len(dataset) * 0.3)
+val_size = len(dataset) - train_size
-throw_away = len(dataset) - val_size - train_size
 
-train_dataset, val_dataset, _ = random_split(dataset, [train_size, val_size, throw_away])
+train_dataset, val_dataset = random_split(dataset, [train_size, val_size])
 
 train_loader = DataLoader(train_dataset, batch_size=32, shuffle=True)
 val_loader = DataLoader(val_dataset, batch_size=32, shuffle=False)
@@ -48,7 +48,7 @@ loss_module = nn.CrossEntropyLoss()
 trainCNN(model, optimizer, loss_module, train_loader, val_loader, device, 50, SAVE_PATH=SAVE_PATH, save=True)
 exit()
 
-image_path = "test.jpg"
+image_path = "test1.jpg"
 
 image = Image.open(image_path).convert("RGB")
 image = transform(image)

bird_cnn/server.py

@@ -0,0 +1,61 @@
+from enum import Enum
+import os
+
+import torch
+from torchvision import transforms
+from fastapi import FastAPI, File, UploadFile
+from PIL import Image
+import io
+import torch.nn.functional as F
+
+from bird_cnn import Bird_CNN
+
+SAVE_PATH = "./saved_models"
+#IMAGE_SIZE = (1141, 850)
+IMAGE_SIZE = (300, 300)
+
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+class bird_species(Enum):
+    Common_Kingfisher = 0
+    CommonMyna = 1
+    House_Crow = 2
+    Indian_Peacock = 3
+    Indian_Pitta = 4
+    Ruddy_Shelduck = 5
+    Sarus_Crane = 6
+
+transform = transforms.Compose([
+    transforms.Resize(IMAGE_SIZE),
+    transforms.ToTensor()
+])
+
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+model = Bird_CNN(c_in=3, c_hidden=15, c_out=7, kernel_size=3, img_width=IMAGE_SIZE[0], img_height=IMAGE_SIZE[1])
+full_path = os.path.join(SAVE_PATH, "bird_cnn", "bird_cnn")
+model.load_state_dict(torch.load(full_path, weights_only=False))
+model.to(device)
+model.eval()
+
+app = FastAPI()
+
+@app.post("/predict")
+async def predict(file: UploadFile = File(...)):
+    image_bytes = await file.read()
+
+    image = Image.open(io.BytesIO(image_bytes)).convert("RGB")
+    image = transform(image).unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        pred = model(image)
+        probs = F.softmax(pred, dim=1)
+        confidence, cls = torch.max(probs, dim=1)
+
+    return {
+        "class": bird_species(cls.item()).name,
+        "confidence": confidence.item()
+    }

bird_cnn/util.py

@@ -0,0 +1,22 @@
+from PIL import Image
+import os
+
+folder = "data/train"
+
+widths = []
+heights = []
+
+for root, _, files in os.walk(folder):
+    for file in files:
+        if file.endswith((".jpg", ".png", ".jpeg")):
+            path = os.path.join(root, file)
+            img = Image.open(path)
+            w, h = img.size
+            widths.append(w)
+            heights.append(h)
+
+avg_w = sum(widths) / len(widths)
+avg_h = sum(heights) / len(heights)
+
+print("Average width:", avg_w)
+print("Average height:", avg_h)