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aac87ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 | 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
import time
import pandas as pd
import numpy as np
import os
import warnings
from datetime import timedelta
# --- Configuration ---
MODEL_NAME = "alexnet_EDEN"
DATASET_NAME = "CIFAR10"
DATA_PATH = r'C:\Users\shanm\Dataset Download\CIFAR10'
BATCH_SIZE = 128
ACCUMULATION_STEPS = 4 # Simulates a larger batch size of 512 for energy stability
EPOCHS = 15
E_UNFREEZE = 10 # When to unfreeze the backbone (EDEN Phase 2)
LAMBDA_L1 = 1e-5 # Sparsity penalty (EDEN Phase 2)
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"
def main():
# --- Phase 1: Zero-Overhead Initialization (RAM Caching) ---
transform = transforms.Compose([
transforms.Resize(224), # AlexNet pre-trained expects 224x224
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
])
print(f"[*] Caching {DATASET_NAME} to System RAM for zero-I/O overhead...")
full_dataset = torchvision.datasets.CIFAR10(root=DATA_PATH, train=True, download=False, transform=transform)
# Load all data into memory tensors
all_data = []
all_targets = []
for img, target in full_dataset:
all_data.append(img)
all_targets.append(target)
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 (Transfer Learning) ---
# Using IMAGENET1K_V1 as per EDEN Algorithm Phase 1
model = torchvision.models.alexnet(weights='IMAGENET1K_V1')
model.classifier[6] = nn.Linear(4096, 10) # 10 classes for CIFAR-10
# Initially freeze backbone
for param in model.features.parameters():
param.requires_grad = False
model.to(DEVICE)
# Calculate FLOPs & Parameters
dummy_input = torch.randn(1, 3, 224, 224).to(DEVICE)
flops, params = profile(model, inputs=(dummy_input, ), verbose=False)
criterion = nn.CrossEntropyLoss()
optimizer = optim.AdamW(model.parameters(), lr=1e-3)
scaler = torch.cuda.amp.GradScaler() # For Automated Mixed Precision (AMP)
results = []
cumulative_total_energy = 0
total_start_time = time.time()
best_acc = 0.0
tracker = EmissionsTracker(measure_power_secs=1, save_to_file=False, log_level='error')
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):
# --- Phase 2: Progressive Unfreezing ---
if epoch == E_UNFREEZE:
for param in model.features.parameters():
param.requires_grad = True
# Reduce LR for fine-tuning
for param_group in optimizer.param_groups:
param_group['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.time()
running_loss, all_preds, all_labels, grad_norms = 0.0, [], [], []
optimizer.zero_grad()
for i, (inputs, labels) in enumerate(trainloader):
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
# Automated Mixed Precision Forward Pass
with torch.cuda.amp.autocast():
outputs = model(inputs)
cls_loss = criterion(outputs, labels)
# Sparse Training Penalty (L1)
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()
# Gradient Accumulation
if (i + 1) % ACCUMULATION_STEPS == 0:
scaler.unscale_(optimizer)
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
grad_norms.append(grad_norm.item())
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())
emissions_kg = tracker.stop()
duration = time.time() - epoch_start_time
# Energy Metrics (kWh to Joules)
e_gpu = tracker.final_emissions_data.gpu_energy * 3600000
e_cpu = tracker.final_emissions_data.cpu_energy * 3600000
e_ram = tracker.final_emissions_data.ram_energy * 3600000
total_energy = e_gpu + e_cpu + e_ram
cumulative_total_energy += total_energy
acc = (np.array(all_preds) == np.array(all_labels)).mean()
f1 = f1_score(all_labels, all_preds, average='macro')
vram_peak = torch.cuda.max_memory_allocated(DEVICE) / (1024**3)
eag = acc / (total_energy / 1000) if total_energy > 0 else 0
# CSV Logging
epoch_stats = {
"epoch": epoch,
"status": status_msg,
"loss": running_loss / len(trainloader),
"accuracy": acc,
"f1_score": f1,
"precision": precision_score(all_labels, all_preds, average='macro', zero_division=0),
"recall": recall_score(all_labels, all_preds, average='macro', zero_division=0),
"energy_gpu_j": e_gpu,
"energy_cpu_j": e_cpu,
"energy_ram_j": e_ram,
"total_energy_j": total_energy,
"cumulative_energy_j": cumulative_total_energy,
"carbon_kg": emissions_kg,
"vram_gb": vram_peak,
"latency_ms": (duration / len(trainloader)) * 1000,
"eag_metric": eag,
"grad_norm": np.mean(grad_norms) if grad_norms else 0,
"model_flops": flops,
"model_params": params,
"batch_size": BATCH_SIZE,
"accumulation_steps": ACCUMULATION_STEPS,
"effective_batch_size": BATCH_SIZE * ACCUMULATION_STEPS
}
results.append(epoch_stats)
pd.DataFrame(results).to_csv(CSV_FILENAME, index=False)
if acc > best_acc:
best_acc = acc
torch.save(model.state_dict(), os.path.join(SAVE_DIR, f"BEST_{MODEL_NAME}.pth"))
best_tag = "*"
else:
best_tag = ""
print(f"{epoch:02d}/50 | {epoch_stats['loss']:.4f} | {acc:.2%} | {total_energy:<9.2f} | {vram_peak:<9.3f} | {eag:<8.4f} | {status_msg}{best_tag}")
print(f"{'='*140}\n[FINISH] Results saved to {CSV_FILENAME}")
if __name__ == '__main__':
main() |