Upload test3/eden_UNet_CIFAR100.py with huggingface_hub

test3/eden_UNet_CIFAR100.py

@@ -0,0 +1,165 @@
+import torch
+import torch.nn as nn
+import torch.optim as optim
+import torchvision
+import torchvision.transforms as transforms
+from torch.utils.data import DataLoader, TensorDataset
+from sklearn.metrics import f1_score, precision_score, recall_score
+from codecarbon import EmissionsTracker
+from thop import profile 
+from tqdm import tqdm
+import time, pandas as pd, numpy as np, os, warnings, copy, gc
+
+# --- Configuration ---
+MODEL_NAME = "unet_classifier_EDEN" 
+DATASET_NAME = "CIFAR100"
+DATA_PATH = r'C:\Users\shanm\Dataset Download\CIFAR100' 
+BATCH_SIZE = 64         
+ACCUMULATION_STEPS = 8  # Effective Batch Size = 512
+EPOCHS = 20
+E_UNFREEZE = 10         
+LAMBDA_L1 = 1e-5        
+DEVICE = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+SAVE_DIR = "saved_models"
+os.makedirs(SAVE_DIR, exist_ok=True)
+CSV_FILENAME = f"{MODEL_NAME}_{DATASET_NAME}_stats.csv"
+
+warnings.filterwarnings("ignore")
+os.environ["CODECARBON_LOG_LEVEL"] = "error"
+
+# --- U-Net Adaptation for Classification ---
+class UNetClassifier(nn.Module):
+    def __init__(self, num_classes=100):
+        super(UNetClassifier, self).__init__()
+        # Encoder: Using a ResNet18 backbone
+        self.backbone = torchvision.models.resnet18(weights='IMAGENET1K_V1')
+        self.encoder = nn.Sequential(*list(self.backbone.children())[:-2]) 
+        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
+        self.classifier = nn.Linear(512, num_classes)
+
+    def forward(self, x):
+        x = self.encoder(x)
+        x = self.avgpool(x)
+        x = torch.flatten(x, 1)
+        x = self.classifier(x)
+        return x
+
+def main():
+    # --- Phase 1: Zero-Overhead RAM Caching ---
+    transform = transforms.Compose([
+        transforms.Resize(224), 
+        transforms.ToTensor(),
+        transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2673, 0.2564, 0.2762)),
+    ])
+
+    print(f"[*] Caching {DATASET_NAME} to System RAM for zero-I/O overhead...")
+    try:
+        full_dataset = torchvision.datasets.CIFAR100(root=DATA_PATH, train=True, download=False, transform=transform)
+    except:
+        full_dataset = torchvision.datasets.CIFAR100(root=os.path.dirname(DATA_PATH), train=True, download=False, transform=transform)
+    
+    all_data, all_targets = [], []
+    for i, (img, target) in enumerate(full_dataset):
+        all_data.append(img)
+        all_targets.append(target)
+        if i % 10000 == 0: print(f"  Loaded {i}/50000 images...")
+    
+    cached_trainset = TensorDataset(torch.stack(all_data), torch.tensor(all_targets))
+    trainloader = DataLoader(cached_trainset, batch_size=BATCH_SIZE, shuffle=True, pin_memory=True)
+
+    # --- Model Setup ---
+    model = UNetClassifier(num_classes=100)
+    
+    # 1. Profile on clone to avoid hook attribute error
+    print("[*] Calculating hardware metrics...")
+    model_for_profile = copy.deepcopy(model).to(DEVICE)
+    dummy_input = torch.randn(1, 3, 224, 224).to(DEVICE)
+    flops, params = profile(model_for_profile, inputs=(dummy_input, ), verbose=False)
+    del model_for_profile
+    
+    # 2. Initially freeze backbone
+    for param in model.encoder.parameters():
+        param.requires_grad = False
+    
+    model.to(DEVICE)
+
+    criterion = nn.CrossEntropyLoss()
+    optimizer = optim.AdamW(model.parameters(), lr=1e-3)
+    scaler = torch.cuda.amp.GradScaler() 
+    tracker = EmissionsTracker(measure_power_secs=1, save_to_file=False, log_level='error')
+
+    results = []
+    cumulative_total_energy = 0
+    best_acc = 0.0 
+
+    print(f"\n[MODEL INFO] FLOPs: {flops/1e9:.2f} G | Parameters: {params/1e6:.2f} M | Batch Size: {BATCH_SIZE}")
+    print(f"{'='*140}")
+    print(f"{'Epoch':<6} | {'Loss':<7} | {'Acc':<7} | {'Total(J)':<9} | {'VRAM(GB)':<9} | {'EAG':<8} | {'Status'}")
+    print(f"{'-'*140}")
+
+    for epoch in range(1, EPOCHS + 1):
+        if epoch == E_UNFREEZE:
+            for param in model.parameters(): param.requires_grad = True
+            for pg in optimizer.param_groups: pg['lr'] = 1e-5
+            status_msg = "UNFROZEN"
+        else:
+            status_msg = "FROZEN" if epoch < E_UNFREEZE else "FINE-TUNING"
+
+        model.train()
+        tracker.start()
+        epoch_start = time.time()
+        running_loss, all_preds, all_labels = 0.0, [], []
+        
+        # Real-time progress bar
+        pbar = tqdm(enumerate(trainloader), total=len(trainloader), desc=f"Epoch {epoch:02d}", leave=False)
+        
+        optimizer.zero_grad()
+        for i, (inputs, labels) in pbar:
+            inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
+            with torch.cuda.amp.autocast():
+                outputs = model(inputs)
+                cls_loss = criterion(outputs, labels)
+                l1_penalty = sum(p.abs().sum() for p in model.parameters() if p.requires_grad)
+                loss = (cls_loss + LAMBDA_L1 * l1_penalty) / ACCUMULATION_STEPS
+            
+            scaler.scale(loss).backward()
+            if (i + 1) % ACCUMULATION_STEPS == 0:
+                scaler.unscale_(optimizer)
+                torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
+                scaler.step(optimizer); scaler.update(); optimizer.zero_grad()
+            
+            running_loss += cls_loss.item()
+            _, predicted = torch.max(outputs.data, 1)
+            all_preds.extend(predicted.cpu().numpy()); all_labels.extend(labels.cpu().numpy())
+            pbar.set_postfix({'loss': f"{cls_loss.item():.4f}"})
+
+        emissions_kg = tracker.stop()
+        duration = time.time() - epoch_start
+        e_tot = (tracker.final_emissions_data.gpu_energy + tracker.final_emissions_data.cpu_energy + tracker.final_emissions_data.ram_energy) * 3600000
+        cumulative_total_energy += e_tot
+        acc = (np.array(all_preds) == np.array(all_labels)).mean()
+        vram_peak = torch.cuda.max_memory_allocated(DEVICE) / (1024**3)
+        eag = acc / (e_tot / 1000) if e_tot > 0 else 0
+
+        # Detailed Audit Row
+        stats = {
+            "epoch": epoch, "status": status_msg, "loss": running_loss / len(trainloader),
+            "accuracy": acc, "total_energy_j": e_tot, "cumulative_energy_j": cumulative_total_energy,
+            "carbon_kg": emissions_kg, "vram_gb": vram_peak, "eag_metric": eag,
+            "latency_ms": (duration / len(trainloader)) * 1000,
+            "model_flops": flops, "model_params": params
+        }
+        results.append(stats)
+        pd.DataFrame(results).to_csv(CSV_FILENAME, index=False)
+        
+        best_tag = "*" if acc > best_acc else ""
+        if acc > best_acc: best_acc = acc; torch.save(model.state_dict(), os.path.join(SAVE_DIR, f"BEST_{MODEL_NAME}_{DATASET_NAME}.pth"))
+        print(f"{epoch:02d}/50  | {stats['loss']:.4f} | {acc:.2%} | {e_tot:<9.2f} | {vram_peak:<9.3f} | {eag:<8.4f} | {status_msg}{best_tag}")
+
+    # Memory Flush for Batch Script
+    del model, trainloader, cached_trainset
+    torch.cuda.empty_cache(); gc.collect()
+
+if __name__ == '__main__':
+    main()