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96de042 | 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 | 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 = "densenet121_EDEN"
DATASET_NAME = "CIFAR10"
DATA_PATH = r'C:\Users\shanm\Dataset Download\CIFAR10'
BATCH_SIZE = 64 # Reduced for DenseNet's VRAM usage; compensated by Accumulation
ACCUMULATION_STEPS = 8 # Effective Batch Size = 512
EPOCHS = 15
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"
def main():
# --- Phase 1: Zero-Overhead Initialization (RAM Caching) ---
transform = transforms.Compose([
transforms.Resize(224), # DenseNet-121 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)
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 (EDEN Phase 1) ---
model = torchvision.models.densenet121(weights='IMAGENET1K_V1')
# DenseNet's classification head is called 'classifier'
num_ftrs = model.classifier.in_features
model.classifier = nn.Linear(num_ftrs, 10)
# Initially freeze backbone (all layers except the classifier)
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()
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
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)
with torch.cuda.amp.autocast():
outputs = model(inputs)
cls_loss = criterion(outputs, labels)
# Sparse Training Penalty (L1) - Encouraging low-energy weight structures
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)
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
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
}
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] DenseNet-121 Stats saved to {CSV_FILENAME}")
if __name__ == '__main__':
main() |