File size: 7,188 Bytes
a169a1b | 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 | 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, random_split
from sklearn.metrics import f1_score, precision_score, recall_score
from codecarbon import EmissionsTracker
from thop import profile
import time, pandas as pd, numpy as np, os, warnings, copy, gc
from datetime import timedelta
# --- Configuration ---
MODEL_NAME = "resnet18_EDEN"
DATASET_NAME = "CustomImageNet300"
# Path to the folder containing your 300 class folders directly
DATA_PATH = r'C:\Users\shanm\Dataset Download\custom image net'
BATCH_SIZE = 128
ACCUMULATION_STEPS = 4 # 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"
def main():
# --- Phase 1: High-Resolution Initialization ---
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
print(f"[*] Loading {DATASET_NAME} from disk (80/20 Random Split)...")
# Load from root since your folders are flat
full_dataset = torchvision.datasets.ImageFolder(root=DATA_PATH, transform=transform)
# Split into 80% Train, 20% Val
train_size = int(0.8 * len(full_dataset))
val_size = len(full_dataset) - train_size
train_dataset, _ = random_split(
full_dataset, [train_size, val_size],
generator=torch.Generator().manual_seed(42)
)
# DataLoader with 4 workers to keep the 1080 Ti busy
trainloader = DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=4, pin_memory=True)
# --- Model Setup ---
model = torchvision.models.resnet18(weights='IMAGENET1K_V1')
model.fc = nn.Linear(model.fc.in_features, 300) # Match your 300 classes
# 1. Profile on clone to avoid thop/pytorch hook conflicts
print("[*] Calculating hardware metrics (FLOPs/Params)...")
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 EDEN Phase 2
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()
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 | Classes: 300")
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.time()
running_loss, all_preds, all_labels = 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)
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(), max_norm=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())
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
# Full Audit Row for .csv
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_total_energy_j": cumulative_total_energy,
"carbon_kg": emissions_kg, "vram_gb": vram_peak,
"latency_ms": (duration / len(trainloader)) * 1000,
"eag_metric": eag, "model_flops": flops, "model_params": params
}
results.append(epoch_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 | {epoch_stats['loss']:.4f} | {acc:.2%} | {total_energy:<9.2f} | {vram_peak:<9.3f} | {eag:<8.4f} | {status_msg}{best_tag}")
# Explicit memory cleanup for overnight batch safety
del model, trainloader
torch.cuda.empty_cache()
gc.collect()
print(f"{'='*140}\n[FINISH] ResNet-18 on ImageNet300 complete.")
if __name__ == '__main__':
main() |