changed project structure, merged docker compose files

api/bird_cnn.py

@@ -0,0 +1,153 @@
+import os
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from tqdm import tqdm
+
+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)
+
+
+def trainCNN(model, optimizer, loss_module, train_data_loader, validation_data_loader, device, num_epochs, SAVE_PATH, save=False):
+
+    best_val = 0
+    for epoch in range(num_epochs):
+        ############
+        # Training #
+        ############
+        model.train()
+
+        true_preds, count = 0, 0
+        for data_inputs, classes in tqdm(train_data_loader, desc=f"Train Epoch {epoch+1}", leave=False):
+            data_inputs = data_inputs.to(device)
+            classes = classes.to(device)
+
+            preds = model(data_inputs)
+
+            loss = loss_module(preds, classes)
+
+            optimizer.zero_grad()
+
+            loss.backward()
+
+            optimizer.step()
+
+            true_preds += (preds.argmax(dim=1) == classes).sum().item()
+            count += data_inputs.size(0)
+        train_acc = true_preds / count
+
+        torch.cuda.empty_cache()
+
+        ##############
+        # Validation #
+        ##############
+        model.eval()
+
+        true_preds, count = 0, 0
+        for data_inputs, classes in tqdm(validation_data_loader, desc=f"Validate Epoch {epoch+1}", leave=False):
+            with torch.no_grad():
+                data_inputs = data_inputs.to(device)
+                classes = classes.to(device)
+
+                preds = model(data_inputs)
+
+                loss = loss_module(preds, classes)
+
+                true_preds += (preds.argmax(dim=1) == classes).sum().item()
+                count += data_inputs.size(0)
+        val_acc = true_preds / count
+
+        if(save and best_val < val_acc):
+            best_val = val_acc
+            save_dir = os.path.join(SAVE_PATH, "bird_cnn")
+            os.makedirs(save_dir, exist_ok=True)
+
+            save_path = os.path.join(save_dir, f"bird_cnn{epoch+1}")
+            torch.save(model.state_dict(), save_path)
+
+        print(f"epoch: {epoch+1} | train accuracy: {int(train_acc * 1000) / 10}% | validation accuracy: {int(val_acc * 1000) / 10}%")
+        torch.cuda.empty_cache()
+
+
+def sample(model, img, device, SAVE_PATH, model_name="bird_cnn", folder="bird_cnn"):
+    with torch.no_grad():
+        full_path = os.path.join(SAVE_PATH, folder, model_name)
+        state_dict = torch.load(full_path, weights_only=False)
+        model.load_state_dict(state_dict)
+        model.eval()
+        img = img.to(device)
+        pred = model(img)
+        probs = F.softmax(pred, dim=1)
+        return(torch.max(probs, dim=1))