File size: 7,256 Bytes
af09f06 | 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 | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
from torchvision.datasets import ImageFolder
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
import pandas as pd
import numpy as np
import os
import warnings
from datetime import timedelta
# --- Configuration ---
MODEL_NAME = "vgg16"
DATASET_NAME = "CustomImageNet300"
DATA_PATH = r'C:\Users\shanm\Dataset Download\custom image net'
BATCH_SIZE = 32
EPOCHS = 50
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():
# 1. Data Loading
transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(224),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
])
if not os.path.exists(DATA_PATH):
print(f"[ERROR] Dataset path not found: {DATA_PATH}")
return
trainset = ImageFolder(root=DATA_PATH, transform=transform)
trainloader = DataLoader(trainset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2, pin_memory=True)
# 2. Model setup
model = torchvision.models.vgg16(weights=None)
model.classifier[6] = nn.Linear(4096, 300)
model.to(DEVICE)
dummy_input = torch.randn(1, 3, 224, 224).to(DEVICE)
flops, params = profile(model, inputs=(dummy_input, ), verbose=False)
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.9, weight_decay=5e-4)
scheduler = optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=EPOCHS)
results = []
cumulative_total_energy = 0
total_start_time = time.time()
best_acc = 0.0
print(f"\n[MODEL INFO] FLOPs: {flops/1e9:.2f} G | Parameters: {params/1e6:.2f} M")
print("="*125)
print(f"TRAINING {MODEL_NAME.upper()} ON {DATASET_NAME}")
print("-" * 125)
try:
for epoch in range(1, EPOCHS + 1):
# START TRACKER FOR THIS EPOCH
tracker = EmissionsTracker(measure_power_secs=1, save_to_file=False, log_level='error')
tracker.start()
model.train()
epoch_start_time = time.time()
running_loss, all_preds, all_labels, grad_norms = 0.0, [], [], []
pbar = tqdm(enumerate(trainloader), total=len(trainloader), desc=f"Epoch {epoch}/{EPOCHS}")
for i, (inputs, labels) in pbar:
inputs, labels = inputs.to(DEVICE), labels.to(DEVICE)
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=100)
grad_norms.append(grad_norm.item())
optimizer.step()
running_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
pbar.set_postfix({'loss': f'{running_loss/(i+1):.4f}'})
all_preds.extend(predicted.cpu().numpy())
all_labels.extend(labels.cpu().numpy())
scheduler.step()
duration = time.time() - epoch_start_time
# STOP TRACKER TO POPULATE final_emissions_data
emissions_kg = tracker.stop()
# --- CALCULATIONS (Now safe because tracker is stopped) ---
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) if torch.cuda.is_available() else 0
elapsed_total = time.time() - total_start_time
avg_per_epoch = elapsed_total / epoch
eta = str(timedelta(seconds=int(avg_per_epoch * (EPOCHS - epoch))))
# --- ALL REQUESTED STATS ---
epoch_stats = {
"epoch": epoch,
"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),
"epoch_energy_gpu_j": e_gpu,
"epoch_energy_cpu_j": e_cpu,
"epoch_energy_ram_j": e_ram,
"epoch_total_energy_j": total_energy,
"cumulative_total_energy_j": cumulative_total_energy,
"carbon_emissions_kg": emissions_kg,
"vram_peak_gb": vram_peak,
"avg_power_gpu_w": tracker.final_emissions_data.gpu_power,
"avg_power_cpu_w": tracker.final_emissions_data.cpu_power,
"avg_power_ram_w": tracker.final_emissions_data.ram_power,
"latency_ms": (duration / len(trainloader)) * 1000,
"avg_grad_norm": np.mean(grad_norms),
"eag_metric": acc / (total_energy / 1000) if total_energy > 0 else 0,
"it_per_sec": len(trainloader) / duration,
"total_iterations": len(trainloader),
"epoch_duration_sec": duration,
"cumulative_time_sec": elapsed_total,
"model_flops": flops,
"model_parameters": params
}
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}_{DATASET_NAME}.pth"))
best_msg = " (Best Saved!)"
else:
best_msg = ""
print(f"\nEpoch {epoch:02d} Summary: Loss: {epoch_stats['loss']:.4f} | Acc: {acc:.2%} | Energy: {total_energy:.2f}J | VRAM: {vram_peak:.2f}GB | ETA: {eta}{best_msg}\n")
print("-" * 125)
except Exception as e:
print(f"\n[CRASH] Error: {e}")
import traceback
traceback.print_exc()
finally:
print(f"\n[SUCCESS] Training Complete. Results saved to {CSV_FILENAME}")
if __name__ == '__main__':
main() |