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adcfcfe | 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 | 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
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, ssl
# --- 1. SSL BYPASS (VIT-AP Network Fix) ---
ssl._create_default_https_context = ssl._create_unverified_context
# --- Configuration ---
MODEL_NAME = "inceptionV3_EDEN"
DATASET_NAME = "CIFAR100"
DATA_PATH = r'C:\Users\shanm\Dataset Download\CIFAR100'
BATCH_SIZE = 32
ACCUMULATION_STEPS = 16
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"
def main():
# --- Phase 1: Data Loading ---
transform = transforms.Compose([
transforms.Resize(299),
transforms.ToTensor(),
transforms.Normalize((0.5071, 0.4867, 0.4408), (0.2673, 0.2564, 0.2762)),
])
print(f"[*] Loading {DATASET_NAME}...")
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)
trainloader = DataLoader(full_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4, pin_memory=True)
# --- Model Setup ---
print("[*] Initializing Pre-trained InceptionV3...")
model = torchvision.models.inception_v3(weights='IMAGENET1K_V1')
# Update both primary and auxiliary heads for 100 classes
model.AuxLogits.fc = nn.Linear(model.AuxLogits.fc.in_features, 100)
model.fc = nn.Linear(model.fc.in_features, 100)
print("[*] Calculating hardware metrics...")
model_for_profile = copy.deepcopy(model).to(DEVICE)
dummy_input = torch.randn(1, 3, 299, 299).to(DEVICE)
flops, params = profile(model_for_profile, inputs=(dummy_input, ), verbose=False)
del model_for_profile
# Initially freeze backbone
for name, param in model.named_parameters():
if "fc" not in name:
param.requires_grad = False
model.to(DEVICE)
criterion = nn.CrossEntropyLoss()
optimizer = optim.AdamW(model.parameters(), lr=1e-3)
scaler = torch.cuda.amp.GradScaler()
# --- 2. Variable Initialization (Scope Fix) ---
results = []
cumulative_total_energy = 0
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):
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, aux_outputs = model(inputs)
cls_loss = criterion(outputs, labels) + 0.4 * criterion(aux_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_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()
vram_peak = torch.cuda.max_memory_allocated(DEVICE) / (1024**3)
eag = acc / (total_energy / 1000) if total_energy > 0 else 0
# Detailed Audit Row
stats = {
"epoch": epoch, "status": status_msg, "loss": running_loss / len(trainloader),
"accuracy": acc, "total_energy_j": total_energy, "cumulative_total_energy_j": cumulative_total_energy,
"carbon_kg": emissions_kg, "vram_gb": vram_peak, "eag_metric": eag,
"model_flops": flops, "model_params": params
}
results.append(stats)
pd.DataFrame(results).to_csv(CSV_FILENAME, index=False)
best_tag = ""
if acc > best_acc:
best_acc = acc
torch.save(model.state_dict(), os.path.join(SAVE_DIR, f"BEST_{MODEL_NAME}_{DATASET_NAME}.pth"))
best_tag = "*"
print(f"{epoch:02d}/{EPOCHS:02d} | {stats['loss']:.4f} | {acc:.2%} | {total_energy:<9.2f} | {vram_peak:<9.3f} | {eag:<8.4f} | {status_msg}{best_tag}")
# Memory Flush
del model, trainloader
torch.cuda.empty_cache(); gc.collect()
if __name__ == '__main__':
main() |