breakthrough_unet_v6_simple.py

"""
Breakthrough U-Net V6 (Simplified): Memory-efficient model designed to exceed V5's 0.9341 Dice and achieve 0.95+ with target 0.99.
Key innovations:
- Enhanced multi-scale feature fusion
- Improved attention mechanisms
- Better skip connections
- Advanced activation functions
- Optimized for memory efficiency
"""

import torch
import torch.nn as nn
import torch.nn.functional as F
import math

class Mish(nn.Module):
    """Mish activation function for better gradient flow."""

    def __init__(self):
        super(Mish, self).__init__()

    def forward(self, x):
        return x * torch.tanh(F.softplus(x))

class ChannelSpatialAttention(nn.Module):
    """Combined channel and spatial attention for V6."""
    
    def __init__(self, in_channels, reduction=8):
        super().__init__()
        
        # Channel attention
        self.avg_pool = nn.AdaptiveAvgPool2d(1)
        self.max_pool = nn.AdaptiveMaxPool2d(1)
        
        self.channel_mlp = nn.Sequential(
            nn.Conv2d(in_channels, max(8, in_channels // reduction), 1),
            Mish(),
            nn.Conv2d(max(8, in_channels // reduction), in_channels, 1)
        )
        
        # Spatial attention
        self.spatial_conv = nn.Sequential(
            nn.Conv2d(2, 1, 7, padding=3),
            nn.Sigmoid()
        )
        
    def forward(self, x):
        # Channel attention
        avg_out = self.channel_mlp(self.avg_pool(x))
        max_out = self.channel_mlp(self.max_pool(x))
        channel_att = torch.sigmoid(avg_out + max_out)
        x = x * channel_att
        
        # Spatial attention
        avg_out = torch.mean(x, dim=1, keepdim=True)
        max_out, _ = torch.max(x, dim=1, keepdim=True)
        spatial_input = torch.cat([avg_out, max_out], dim=1)
        spatial_att = self.spatial_conv(spatial_input)
        x = x * spatial_att
        
        return x

class EnhancedMultiScaleFusion(nn.Module):
    """Enhanced multi-scale feature fusion for V6."""
    
    def __init__(self, in_channels, out_channels):
        super().__init__()
        
        # Multi-scale convolutions
        branch_channels = out_channels // 4
        
        self.conv_1x1 = nn.Conv2d(in_channels, branch_channels, 1)
        self.conv_3x3 = nn.Conv2d(in_channels, branch_channels, 3, padding=1)
        self.conv_5x5 = nn.Conv2d(in_channels, branch_channels, 5, padding=2)
        self.conv_dilated = nn.Conv2d(in_channels, branch_channels, 3, padding=2, dilation=2)
        
        # Global context
        self.global_pool = nn.AdaptiveAvgPool2d(1)
        self.global_conv = nn.Conv2d(in_channels, branch_channels, 1)
        
        # Fusion
        total_channels = branch_channels * 5
        self.fusion = nn.Sequential(
            nn.Conv2d(total_channels, out_channels, 3, padding=1),
            nn.BatchNorm2d(out_channels),
            Mish()
        )
        
        # Attention
        self.attention = ChannelSpatialAttention(out_channels)

        # Pre-instantiate Mish activations for JIT compatibility
        self.mish_1x1 = Mish()
        self.mish_3x3 = Mish()
        self.mish_5x5 = Mish()
        self.mish_dilated = Mish()
        self.mish_global = Mish()

    def forward(self, x):
        B, C, H, W = x.shape

        # 🔬 RESEARCH: CPU-optimized multi-scale processing
        # Process features in order of computational efficiency
        feat_1x1 = self.mish_1x1(self.conv_1x1(x))
        feat_3x3 = self.mish_3x3(self.conv_3x3(x))

        # 🔬 RESEARCH: Always use full processing for stability (conditional optimization removed)
        feat_5x5 = self.mish_5x5(self.conv_5x5(x))
        feat_dilated = self.mish_dilated(self.conv_dilated(x))

        # Global context
        global_feat = self.global_pool(x)
        global_conv = self.mish_global(self.global_conv(global_feat))
        global_upsampled = F.interpolate(global_conv, size=(H, W), mode='bilinear', align_corners=False)

        # Concatenate all features
        all_features = torch.cat([feat_1x1, feat_3x3, feat_5x5, feat_dilated, global_upsampled], dim=1)

        # Fuse features
        fused = self.fusion(all_features)

        # Apply attention
        output = self.attention(fused)

        return output

class V6ResidualBlock(nn.Module):
    """Enhanced residual block for V6."""
    
    def __init__(self, in_channels, out_channels, use_attention=True):
        super().__init__()
        
        # Main pathway
        self.main_path = EnhancedMultiScaleFusion(in_channels, out_channels)
        
        # Residual refinement
        self.refine = nn.Sequential(
            nn.Conv2d(out_channels, out_channels, 3, padding=1, groups=out_channels),
            nn.BatchNorm2d(out_channels),
            Mish(),
            nn.Conv2d(out_channels, out_channels, 1),
            nn.BatchNorm2d(out_channels)
        )
        
        # Skip connection
        self.skip = None
        if in_channels != out_channels:
            self.skip = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1),
                nn.BatchNorm2d(out_channels)
            )
        
        # Final activation
        self.final_activation = Mish()
        
    def forward(self, x):
        identity = x
        
        # Main processing
        out = self.main_path(x)
        
        # Residual refinement
        out = out + self.refine(out)
        
        # Skip connection
        if self.skip is not None:
            identity = self.skip(identity)
        
        if identity.shape == out.shape:
            out = out + identity
        
        out = self.final_activation(out)
        
        return out

class V6Down(nn.Module):
    """V6 downsampling block."""
    
    def __init__(self, in_channels, out_channels):
        super().__init__()
        self.pool = nn.MaxPool2d(2)
        self.conv = V6ResidualBlock(in_channels, out_channels)
    
    def forward(self, x):
        x = self.pool(x)
        return self.conv(x)

class V6Up(nn.Module):
    """V6 upsampling block."""
    
    def __init__(self, in_channels, out_channels, bilinear=True):
        super().__init__()
        
        if bilinear:
            self.up = nn.Upsample(scale_factor=2, mode='bilinear', align_corners=True)
            self.conv = V6ResidualBlock(in_channels, out_channels, use_attention=True)
        else:
            self.up = nn.ConvTranspose2d(in_channels, in_channels // 2, kernel_size=2, stride=2)
            self.conv = V6ResidualBlock(in_channels, out_channels, use_attention=True)
    
    def forward(self, x1, x2):
        x1 = self.up(x1)
        
        # Handle size mismatch
        diffY = x2.size()[2] - x1.size()[2]
        diffX = x2.size()[3] - x1.size()[3]
        
        x1 = F.pad(x1, [diffX // 2, diffX - diffX // 2,
                        diffY // 2, diffY - diffY // 2])
        
        x = torch.cat([x2, x1], dim=1)
        return self.conv(x)

class BreakthroughUNetV6(nn.Module):
    """
    Breakthrough U-Net V6: Simplified model designed to exceed V5's 0.9341 Dice and achieve 0.95+ with target 0.99.
    
    Key innovations:
    - Enhanced multi-scale feature fusion
    - Combined channel and spatial attention
    - Improved residual connections
    - Mish activation for better gradients
    - Memory-efficient architecture
    """
    
    def __init__(self, n_channels=3, n_classes=1, base_channels=32, bilinear=True):
        super(BreakthroughUNetV6, self).__init__()
        self.n_channels = n_channels
        self.n_classes = n_classes
        self.bilinear = bilinear
        
        # Channel progression for V6
        c1, c2, c3, c4, c5 = base_channels, base_channels*2, base_channels*4, base_channels*8, base_channels*16
        
        # Encoder
        self.inc = V6ResidualBlock(n_channels, c1, use_attention=False)
        self.down1 = V6Down(c1, c2)
        self.down2 = V6Down(c2, c3)
        self.down3 = V6Down(c3, c4)
        
        factor = 2 if bilinear else 1
        self.down4 = V6Down(c4, c5 // factor)
        
        # Decoder
        self.up1 = V6Up(c5, c4 // factor, bilinear)
        self.up2 = V6Up(c4, c3 // factor, bilinear)
        self.up3 = V6Up(c3, c2 // factor, bilinear)
        self.up4 = V6Up(c2, c1, bilinear)
        
        # Enhanced output head
        self.outc = nn.Sequential(
            EnhancedMultiScaleFusion(c1, c1),
            nn.Conv2d(c1, c1 // 2, 3, padding=1),
            nn.BatchNorm2d(c1 // 2),
            Mish(),
            nn.Conv2d(c1 // 2, n_classes, 1)
        )
        
        # Initialize weights
        self._initialize_weights()
    
    def _initialize_weights(self):
        """Advanced weight initialization for V6."""
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                if m.bias is not None:
                    nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
    
    def forward(self, x):
        # Encoder
        x1 = self.inc(x)
        x2 = self.down1(x1)
        x3 = self.down2(x2)
        x4 = self.down3(x3)
        x5 = self.down4(x4)
        
        # Decoder
        x = self.up1(x5, x4)
        x = self.up2(x, x3)
        x = self.up3(x, x2)
        x = self.up4(x, x1)
        
        # Output
        logits = self.outc(x)
        return logits

def get_breakthrough_v6_model(base_channels=32, n_channels=3, n_classes=1):
    """Get the breakthrough U-Net V6 model."""
    return BreakthroughUNetV6(
        n_channels=n_channels,
        n_classes=n_classes,
        base_channels=base_channels,
        bilinear=True
    )

def analyze_v6_models():
    """Analyze different V6 model configurations."""
    
    print("🚀 BREAKTHROUGH U-NET V6 ANALYSIS")
    print("=" * 60)
    print("🎯 Target: Exceed V5's 0.9341 Dice and achieve 0.95+ with target 0.99")
    
    configs = [
        ("V6-24", 24),
        ("V6-28", 28),
        ("V6-32", 32),
        ("V6-36", 36),
    ]
    
    results = []
    
    for name, base_channels in configs:
        model = get_breakthrough_v6_model(base_channels=base_channels)
        
        # Count parameters
        total_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
        
        # Calculate efficiency metrics
        v1_params = 17262977
        v5_params = 4290977
        efficiency_vs_v1 = total_params / v1_params
        efficiency_vs_v5 = total_params / v5_params
        
        # Test forward pass
        x = torch.randn(1, 3, 256, 256)
        with torch.no_grad():
            output = model(x)
        
        result = {
            'name': name,
            'base_channels': base_channels,
            'params': total_params,
            'efficiency_v1': efficiency_vs_v1,
            'efficiency_v5': efficiency_vs_v5,
            'output_shape': output.shape
        }
        results.append(result)
        
        print(f"{name}: {total_params:,} params ({efficiency_vs_v1:.3f}x vs V1, {efficiency_vs_v5:.2f}x vs V5)")
    
    print("\n🎯 V6 MODEL RECOMMENDATIONS:")
    print("-" * 50)
    
    for result in results:
        if result['efficiency_v1'] < 1.0:
            efficiency_grade = "🟢 Efficient vs V1"
        else:
            efficiency_grade = "🟡 Larger than V1"
        
        print(f"{result['name']}: {result['params']:,} params - {efficiency_grade}")
        
        if result['name'] == 'V6-32':
            print(f"  👑 RECOMMENDED: Optimal balance for 0.95+ Dice target")
        elif result['name'] == 'V6-36':
            print(f"  🚀 MAXIMUM: Highest capacity for 0.99 breakthrough")
    
    return results

if __name__ == "__main__":
    print("🚀 Testing Breakthrough U-Net V6...")
    
    # Analyze different configurations
    results = analyze_v6_models()
    
    print(f"\n💡 V6 BREAKTHROUGH INNOVATIONS:")
    print("- Enhanced multi-scale feature fusion")
    print("- Combined channel and spatial attention")
    print("- Improved residual connections")
    print("- Mish activation for better gradients")
    print("- Memory-efficient architecture")
    print("- Advanced weight initialization")
    
    print(f"\n🎯 TARGET: Exceed V5's 0.9341 Dice and achieve 0.95+ with target 0.99!")
    print(f"🚀 EXPECTED: V6-32 should achieve 0.95+ Dice, V6-36 targeting 0.99!")