diff --git a/.gitattributes b/.gitattributes index a6344aac8c09253b3b630fb776ae94478aa0275b..bcc11434d96d1ceff00c6b54b9b49fe2ff48e9b9 100644 --- a/.gitattributes +++ b/.gitattributes @@ -33,3 +33,12 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text *.zip filter=lfs diff=lfs merge=lfs -text *.zst filter=lfs diff=lfs merge=lfs -text *tfevents* filter=lfs diff=lfs merge=lfs -text +confusion_matrix_custom.png filter=lfs diff=lfs merge=lfs -text +confusion_matrix_resnet18.png filter=lfs diff=lfs merge=lfs -text +wandb/run-20250717_042842-06r19hoq/run-06r19hoq.wandb filter=lfs diff=lfs merge=lfs -text +wandb/run-20250718_001150-vs4r5xs7/run-vs4r5xs7.wandb filter=lfs diff=lfs merge=lfs -text +wandb/run-20250718_005715-s89rop1c/run-s89rop1c.wandb filter=lfs diff=lfs merge=lfs -text +wandb/run-20250718_014549-myiqhu6f/run-myiqhu6f.wandb filter=lfs diff=lfs merge=lfs -text +wandb/run-20250718_025207-slendl5x/run-slendl5x.wandb filter=lfs diff=lfs merge=lfs -text 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100644 index 0000000000000000000000000000000000000000..d1b4038e34a0828389afa27b5ac5314f51b348aa Binary files /dev/null and b/__pycache__/eval.cpython-313.pyc differ diff --git a/__pycache__/train.cpython-313.pyc b/__pycache__/train.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ef269e1087c5f5721bfc22ab60de7e12b3e21795 Binary files /dev/null and b/__pycache__/train.cpython-313.pyc differ diff --git a/__pycache__/wandb_utils.cpython-313.pyc b/__pycache__/wandb_utils.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..818bce665317bfa00d4302aadba8eddc18923425 Binary files /dev/null and b/__pycache__/wandb_utils.cpython-313.pyc differ diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..f22d15747b2f9241b360074c49dad1f7482e3f5d --- /dev/null +++ b/app.py @@ -0,0 +1,151 @@ +import gradio as gr +import torch +import torch.nn.functional as F +from torchvision import transforms +from PIL import Image +import numpy as np +from models.custom_cnn import create_custom_cnn +from models.resnet18 import load_resnet18 +from utils.data_loader import get_cifar10_info + +# CIFAR-10 preprocessing +CIFAR10_MEAN = (0.4914, 0.4822, 0.4465) +CIFAR10_STD = (0.2023, 0.1994, 0.2010) + +def preprocess_image(image): + """Preprocess uploaded image for CIFAR-10 model.""" + transform = transforms.Compose([ + transforms.Resize((32, 32)), + transforms.ToTensor(), + transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD) + ]) + + if image.mode != 'RGB': + image = image.convert('RGB') + + return transform(image).unsqueeze(0) + +def predict_image(image, model_choice="CustomCNN"): + """ + Predict class label for uploaded image. + + Args: + image: PIL Image uploaded by user + model_choice: Which model to use for prediction + + Returns: + str: Formatted prediction result + """ + try: + # Load model + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + + if model_choice == "CustomCNN": + model = create_custom_cnn() + model_path = "best_model_custom.pth" + else: + model = load_resnet18() + model_path = "best_model_resnet18.pth" + + # Load trained weights + try: + checkpoint = torch.load(model_path, map_location=device) + model.load_state_dict(checkpoint['model_state_dict']) + except FileNotFoundError: + return f"āŒ Model weights not found: {model_path}\nTrain the model first!" + + model.to(device) + model.eval() + + # Preprocess image + input_tensor = preprocess_image(image).to(device) + + # Make prediction + with torch.no_grad(): + outputs = model(input_tensor) + probabilities = F.softmax(outputs, dim=1) + confidence, predicted = torch.max(probabilities, 1) + + # Get class info + cifar10_info = get_cifar10_info() + class_names = cifar10_info['class_names'] + predicted_class = class_names[predicted.item()] + confidence_score = confidence.item() * 100 + + # Format result with top-3 predictions + top3_prob, top3_indices = torch.topk(probabilities, 3) + + result = f"šŸŽÆ **Prediction: {predicted_class}** ({confidence_score:.1f}% confidence)\n\n" + result += f"šŸ“Š **Top 3 Predictions:**\n" + + for i in range(3): + class_name = class_names[top3_indices[0][i].item()] + prob = top3_prob[0][i].item() * 100 + result += f"{i+1}. {class_name}: {prob:.1f}%\n" + + result += f"\nšŸ”§ **Model:** {model_choice}\n" + result += f"šŸ“± **Device:** {device}" + + return result + + except Exception as e: + return f"āŒ **Error:** {str(e)}\n\nPlease ensure the image is valid and model is trained." + +def create_gradio_interface(): + """Create and launch Gradio interface.""" + + # Custom CSS for better styling + css = """ + .gradio-container { + font-family: 'Segoe UI', Tahoma, Geneva, Verdana, sans-serif; + } + .output-text { + font-family: 'Courier New', monospace; + font-size: 14px; + } + """ + + # Create interface + interface = gr.Interface( + fn=predict_image, + inputs=[ + gr.Image(type="pil", label="Upload Image", height=300), + gr.Dropdown( + choices=["CustomCNN", "ResNet18"], + value="CustomCNN", + label="Select Model" + ) + ], + outputs=gr.Textbox( + label="Prediction Result", + lines=10, + elem_classes=["output-text"] + ), + title="šŸ§  CIFAR-10 CNN Benchmark", + description=""" + Upload an image to test our trained models! + + **Models:** + - **CustomCNN**: Lightweight 3M parameter model + - **ResNet18**: Standard 11M parameter baseline + + **Note:** Images will be resized to 32x32 pixels (CIFAR-10 format) + + **CIFAR-10 Classes:** airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck + """, + css=css, + theme=gr.themes.Soft(), + allow_flagging="never" + ) + + return interface + +if __name__ == "__main__": + # Create and launch interface + demo = create_gradio_interface() + demo.launch( + server_name="0.0.0.0", + server_port=7860, + share=True, + show_error=True + ) \ No newline at end of file diff --git a/best_model_custom.pth b/best_model_custom.pth new file mode 100644 index 0000000000000000000000000000000000000000..f2cf57f7aea6a26ed2c19f764fce1cc8b8fb3b94 --- /dev/null +++ b/best_model_custom.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4c270dbebadfa9bd400caf6fb6a094e26509122cf5577076a8c9a264542a3ccf +size 28032771 diff --git a/best_model_customcnn.pth b/best_model_customcnn.pth new file mode 100644 index 0000000000000000000000000000000000000000..f5b920ab20b3a8b973652c4db0991223f5a3471a --- /dev/null +++ b/best_model_customcnn.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a77f4a3d40ddc06d5d194006f7f974c85e306b64287838857c628cfbdde71763 +size 28033128 diff --git a/best_model_resnet18.pth b/best_model_resnet18.pth new file mode 100644 index 0000000000000000000000000000000000000000..f4b7fede84d78db36ed4a84dfc650b320b388416 --- /dev/null +++ b/best_model_resnet18.pth @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:890d6e5f67ced601d8fe8770cba3ad89c2c14270920070ecc5b6a48e408c0114 +size 134315407 diff --git a/confusion_matrix_custom.png b/confusion_matrix_custom.png new file mode 100644 index 0000000000000000000000000000000000000000..3df05bf31b8ff22fd8c4d86159bb84c293265cf1 --- /dev/null +++ b/confusion_matrix_custom.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:bb216a45e9cbc1a8e6dffd5c9a4b959200ae7a3c10e2d0ef6689524fa2c124f6 +size 257092 diff --git a/confusion_matrix_resnet18.png b/confusion_matrix_resnet18.png new file mode 100644 index 0000000000000000000000000000000000000000..920896e3a2af4d9b79c80a2bc9ef81744fc84145 --- /dev/null +++ b/confusion_matrix_resnet18.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2a7ec82048988584c221cd2b7c5faa4e7a99a618e40d6b81ebf9bf0b5c78ab31 +size 261803 diff --git a/eval.py b/eval.py new file mode 100644 index 0000000000000000000000000000000000000000..cc5d5e0d87324e378c00dc608c64ffad499b50ad --- /dev/null +++ b/eval.py @@ -0,0 +1,169 @@ +import torch +import torch.nn as nn +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import time +from sklearn.metrics import confusion_matrix, classification_report +from utils.data_loader import get_cifar10_info + +def evaluate(model, test_loader, device='cuda'): + """ + Evaluate model on test set with comprehensive metrics. + + Args: + model: Trained PyTorch model + test_loader: Test data loader + device: Device to run evaluation on + + Returns: + dict: Evaluation results including accuracy, loss, predictions + """ + model.to(device) + model.eval() + + test_loss = 0.0 + correct = 0 + total = 0 + all_predictions = [] + all_targets = [] + + criterion = nn.CrossEntropyLoss() + + with torch.no_grad(): + for inputs, targets in test_loader: + inputs, targets = inputs.to(device), targets.to(device) + outputs = model(inputs) + loss = criterion(outputs, targets) + + test_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + all_predictions.extend(predicted.cpu().numpy()) + all_targets.extend(targets.cpu().numpy()) + + test_acc = 100.0 * correct / total + test_loss /= len(test_loader) + + return { + 'test_accuracy': test_acc, + 'test_loss': test_loss, + 'predictions': np.array(all_predictions), + 'targets': np.array(all_targets) + } + +def plot_confusion_matrix(y_true, y_pred, save_path=None): + """ + Plot confusion matrix heatmap. + + Args: + y_true: True labels + y_pred: Predicted labels + save_path: Path to save plot (optional) + """ + cifar10_info = get_cifar10_info() + class_names = cifar10_info['class_names'] + + cm = confusion_matrix(y_true, y_pred) + + plt.figure(figsize=(10, 8)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + xticklabels=class_names, yticklabels=class_names) + plt.title('Confusion Matrix', fontsize=16) + plt.ylabel('True Label', fontsize=12) + plt.xlabel('Predicted Label', fontsize=12) + plt.xticks(rotation=45) + plt.yticks(rotation=0) + plt.tight_layout() + + if save_path: + plt.savefig(save_path, dpi=300, bbox_inches='tight') + + plt.show() + return cm + +def benchmark_model(model, test_loader, device='cuda', num_samples=100): + """ + Benchmark model performance metrics. + + Args: + model: PyTorch model to benchmark + test_loader: Test data loader + device: Device to run benchmark on + num_samples: Number of samples for inference timing + + Returns: + dict: Benchmark results + """ + model.to(device) + model.eval() + + # Parameter count + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + model_size_mb = total_params * 4 / (1024 * 1024) # Float32 + + # Inference timing + sample_inputs = [] + sample_count = 0 + + for inputs, _ in test_loader: + if sample_count >= num_samples: + break + batch_size = min(num_samples - sample_count, inputs.size(0)) + sample_inputs.append(inputs[:batch_size]) + sample_count += batch_size + + test_input = torch.cat(sample_inputs, dim=0)[:num_samples].to(device) + + # Warmup runs + with torch.no_grad(): + for _ in range(10): + _ = model(test_input) + + # Timing runs + torch.cuda.synchronize() if device == 'cuda' else None + start_time = time.time() + + with torch.no_grad(): + for _ in range(10): + _ = model(test_input) + + torch.cuda.synchronize() if device == 'cuda' else None + end_time = time.time() + + avg_inference_time = (end_time - start_time) / 10 / num_samples * 1000 # ms per sample + + return { + 'total_params': total_params, + 'trainable_params': trainable_params, + 'model_size_mb': model_size_mb, + 'inference_time_ms': avg_inference_time, + 'throughput_samples_per_sec': 1000 / avg_inference_time + } + +def print_evaluation_report(eval_results, benchmark_results, model_name): + """Print comprehensive evaluation report.""" + print(f"\n{'='*50}") + print(f"EVALUATION REPORT: {model_name}") + print(f"{'='*50}") + + print(f"šŸ“Š Test Performance:") + print(f" Accuracy: {eval_results['test_accuracy']:.2f}%") + print(f" Loss: {eval_results['test_loss']:.4f}") + + print(f"\nšŸ”§ Model Statistics:") + print(f" Parameters: {benchmark_results['total_params']:,}") + print(f" Model Size: {benchmark_results['model_size_mb']:.2f} MB") + print(f" Inference Time: {benchmark_results['inference_time_ms']:.2f} ms/sample") + print(f" Throughput: {benchmark_results['throughput_samples_per_sec']:.0f} samples/sec") + + # Classification report + cifar10_info = get_cifar10_info() + class_names = cifar10_info['class_names'] + + print(f"\nšŸ“ˆ Per-Class Performance:") + print(classification_report(eval_results['targets'], eval_results['predictions'], + target_names=class_names, digits=3)) \ No newline at end of file diff --git a/examples/9977.png b/examples/9977.png new file mode 100644 index 0000000000000000000000000000000000000000..0142e9989362371ea6ff8c217109cb1cb17d165a Binary files /dev/null and b/examples/9977.png differ diff --git a/examples/9992.png b/examples/9992.png new file mode 100644 index 0000000000000000000000000000000000000000..a00292205994b754195a991ed7e1ae66264fab0f Binary files /dev/null and b/examples/9992.png differ diff --git a/main.py b/main.py new file mode 100644 index 0000000000000000000000000000000000000000..1722be47ab0c3742b49812c3c76c651eede9463d --- /dev/null +++ b/main.py @@ -0,0 +1,212 @@ +#!/usr/bin/env python3 +""" +CIFAR-10 CNN Benchmark - Production Pipeline +FAANG-level deep learning experiment orchestration +""" + +import torch +import torch.nn as nn +from pathlib import Path +import argparse +import json +from datetime import datetime + +# Local imports +from models.custom_cnn import create_custom_cnn +from models.resnet18 import load_resnet18 +from utils.data_loader import get_cifar10_loaders +from train import train, create_optimizer, create_scheduler +from eval import evaluate, benchmark_model, print_evaluation_report, plot_confusion_matrix +from wandb_utils import WandbLogger, create_hyperparameter_sweep, run_hyperparameter_sweep + +def run_single_experiment(model_name: str, config: dict, use_wandb: bool = True): + """Run a single training experiment with comprehensive evaluation.""" + print(f"\nšŸš€ Starting {model_name} experiment...") + + # Initialize model + if model_name.lower() == 'custom': + model = create_custom_cnn() + model_display_name = "custom" + elif model_name.lower() == 'resnet18': + model = load_resnet18() + model_display_name = "ResNet18" + else: + raise ValueError(f"Unknown model: {model_name}") + + # Load data + train_loader, val_loader, test_loader = get_cifar10_loaders( + batch_size=config['batch_size'], + num_workers=config.get('num_workers', 4) + ) + + # Setup training components + optimizer = create_optimizer( + model, + opt_type=config['optimizer'], + lr=config['learning_rate'], + weight_decay=config['weight_decay'] + ) + scheduler = create_scheduler( + optimizer, + scheduler_type=config['scheduler'], + num_epochs=config['epochs'] + ) + criterion = nn.CrossEntropyLoss() + + # Train model + device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') + history = train( + model, train_loader, val_loader, optimizer, criterion, scheduler, + num_epochs=config['epochs'], device=device, + use_wandb=use_wandb, model_name=model_display_name + ) + + # Comprehensive evaluation + print(f"\nšŸ“Š Evaluating {model_display_name}...") + + # Load best model + checkpoint = torch.load(f'best_model_{model_name.lower()}.pth', map_location=device) + model.load_state_dict(checkpoint['model_state_dict']) + + # Test evaluation + eval_results = evaluate(model, test_loader, device) + benchmark_results = benchmark_model(model, test_loader, device) + + # Print comprehensive report + print_evaluation_report(eval_results, benchmark_results, model_display_name) + + # Plot confusion matrix + plot_confusion_matrix( + eval_results['targets'], + eval_results['predictions'], + save_path=f'confusion_matrix_{model_name.lower()}.png' + ) + + # Save experiment results + results = { + 'model': model_display_name, + 'config': config, + 'timestamp': datetime.now().isoformat(), + 'evaluation': { + 'test_accuracy': eval_results['test_accuracy'], + 'test_loss': eval_results['test_loss'] + }, + 'benchmark': benchmark_results, + 'training_history': history + } + + with open(f'results_{model_name.lower()}.json', 'w') as f: + json.dump(results, f, indent=2) + + return results + +def run_model_comparison(config: dict, use_wandb: bool = True): + """Run comparative analysis between CustomCNN and ResNet18.""" + print("\nšŸ”¬ Starting Model Comparison Analysis...") + + models = ['custom', 'resnet18'] + all_results = {} + + for model_name in models: + print(f"\n{'='*60}") + print(f"Training {model_name.upper()}") + print(f"{'='*60}") + + results = run_single_experiment(model_name, config, use_wandb) + all_results[model_name] = results + + # Generate comparison report + print(f"\n{'='*60}") + print("šŸ“ˆ MODEL COMPARISON SUMMARY") + print(f"{'='*60}") + + for model_name, results in all_results.items(): + eval_results = results['evaluation'] + benchmark_results = results['benchmark'] + + print(f"\n{results['model']}:") + print(f" Test Accuracy: {eval_results['test_accuracy']:.2f}%") + print(f" Parameters: {benchmark_results['total_params']:,}") + print(f" Model Size: {benchmark_results['model_size_mb']:.2f} MB") + print(f" Inference Time: {benchmark_results['inference_time_ms']:.2f} ms") + print(f" Throughput: {benchmark_results['throughput_samples_per_sec']:.0f} samples/sec") + + # Determine winner + custom_acc = all_results['custom']['evaluation']['test_accuracy'] + resnet_acc = all_results['resnet18']['evaluation']['test_accuracy'] + + if custom_acc > resnet_acc: + print(f"\nšŸ† Winner: CustomCNN ({custom_acc:.2f}% vs {resnet_acc:.2f}%)") + else: + print(f"\nšŸ† Winner: ResNet18 ({resnet_acc:.2f}% vs {custom_acc:.2f}%)") + + # Save comparison results + with open('model_comparison.json', 'w') as f: + json.dump(all_results, f, indent=2) + + return all_results + +def run_hyperparameter_sweep(model_name: str, count: int = 20): + """Run automated hyperparameter optimization.""" + print(f"\nšŸŽÆ Starting Hyperparameter Sweep for {model_name}...") + + sweep_config = create_hyperparameter_sweep() + + def train_fn(): + import wandb + config = wandb.config + + # Convert wandb config to dict + config_dict = { + 'learning_rate': config.learning_rate, + 'batch_size': config.batch_size, + 'weight_decay': config.weight_decay, + 'optimizer': config.optimizer, + 'scheduler': config.scheduler, + 'epochs': 50 # Changed from 50 to 50 + } + + run_single_experiment(model_name, config_dict, use_wandb=True) + + run_hyperparameter_sweep(train_fn, sweep_config, count=count) + +def main(): + parser = argparse.ArgumentParser(description='CIFAR-10 CNN Benchmark Pipeline') + parser.add_argument('--mode', choices=['single', 'compare', 'sweep'], default='compare', + help='Experiment mode') + parser.add_argument('--model', choices=['custom', 'resnet18'], default='custom', + help='Model for single experiment') + parser.add_argument('--no-wandb', action='store_true', help='Disable W&B logging') + parser.add_argument('--epochs', type=int, default=50, help='Number of epochs') # Changed from 100 to 50 + parser.add_argument('--lr', type=float, default=0.001, help='Learning rate') + parser.add_argument('--batch_size', type=int, default=128, help='Batch size') + parser.add_argument('--sweep_count', type=int, default=20, help='Number of sweep runs') + + args = parser.parse_args() + + # Default configuration + config = { + 'epochs': args.epochs, + 'learning_rate': args.lr, + 'batch_size': args.batch_size, + 'weight_decay': 1e-4, + 'optimizer': 'adamw', + 'scheduler': 'cosine', + 'num_workers': 4 + } + + # Create output directory + Path('outputs').mkdir(exist_ok=True) + + # Run experiment based on mode + if args.mode == 'single': + run_single_experiment(args.model, config, use_wandb=not args.no_wandb) + elif args.mode == 'compare': + run_model_comparison(config, use_wandb=not args.no_wandb) + elif args.mode == 'sweep': + run_hyperparameter_sweep(args.model, count=args.sweep_count) + + print("\nāœ… Experiment completed! Check outputs/ directory for results.") + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/model_comparison.json b/model_comparison.json new file mode 100644 index 0000000000000000000000000000000000000000..8cfb12209b0dd3ffb9527348891bd8ad88630e56 --- /dev/null +++ b/model_comparison.json @@ -0,0 +1,574 @@ +{ + "custom": { + "model": "custom", + "config": { + "epochs": 50, + "learning_rate": 0.001, + "batch_size": 128, + "weight_decay": 0.0001, + "optimizer": "adamw", + "scheduler": "cosine", + "num_workers": 4 + }, + "timestamp": "2025-07-18T04:13:55.384588", + "evaluation": { + "test_accuracy": 90.64, + "test_loss": 0.40045145286034933 + }, + "benchmark": { + "total_params": 2332106, + "trainable_params": 2332106, + "model_size_mb": 8.896278381347656, + "inference_time_ms": 0.03669309616088868, + "throughput_samples_per_sec": 27253.083130823507 + }, + "training_history": { + "train_loss": [ + 1.4149173482913862, + 1.0162051956761966, + 0.8482165988534689, 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a/models/__pycache__/resnet18.cpython-313.pyc b/models/__pycache__/resnet18.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..ae9dc8b0ca04ef78b21281e0eaf98c1546374593 Binary files /dev/null and b/models/__pycache__/resnet18.cpython-313.pyc differ diff --git a/models/custom_cnn.py b/models/custom_cnn.py new file mode 100644 index 0000000000000000000000000000000000000000..0e64fd23195c820d07e289e835f084e1b390d63d --- /dev/null +++ b/models/custom_cnn.py @@ -0,0 +1,84 @@ +import torch +import torch.nn as nn +import torch.nn.functional as F + +class CustomCNN(nn.Module): + """ + Custom CNN optimized for CIFAR-10 with ~3-4M parameters. + Designed to compete with ResNet18 while being lightweight. + """ + + def __init__(self, num_classes=10, dropout_rate=0.4): + super(CustomCNN, self).__init__() + + # Feature extractor with efficient blocks + self.features = nn.Sequential( + # Block 1: 32x32 -> 32x32 + self._conv_block(3, 64, stride=1), + self._conv_block(64, 64, stride=1), + + # Block 2: 32x32 -> 16x16 + self._conv_block(64, 128, stride=2), + self._conv_block(128, 128, stride=1), + + # Block 3: 16x16 -> 8x8 + self._conv_block(128, 256, stride=2), + self._conv_block(256, 256, stride=1), + + # Block 4: 8x8 -> 4x4 (deep feature extraction) + self._conv_block(256, 512, stride=2), + ) + + # Global pooling and classifier + self.avgpool = nn.AdaptiveAvgPool2d((1, 1)) + self.dropout = nn.Dropout(dropout_rate) + self.classifier = nn.Linear(512, num_classes) + + # Initialize weights + self._initialize_weights() + + def _conv_block(self, in_channels, out_channels, stride=1): + """Efficient conv block with BatchNorm and ReLU.""" + return nn.Sequential( + nn.Conv2d(in_channels, out_channels, kernel_size=3, + stride=stride, padding=1, bias=False), + nn.BatchNorm2d(out_channels), + nn.ReLU(inplace=True) + ) + + def _initialize_weights(self): + """Initialize weights using He initialization for ReLU.""" + for m in self.modules(): + if isinstance(m, nn.Conv2d): + nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif isinstance(m, nn.BatchNorm2d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + elif isinstance(m, nn.Linear): + nn.init.normal_(m.weight, 0, 0.01) + nn.init.constant_(m.bias, 0) + + def forward(self, x): + x = self.features(x) + x = self.avgpool(x) + x = torch.flatten(x, 1) + x = self.dropout(x) + x = self.classifier(x) + return x + + def get_model_info(self): + """Return model architecture info.""" + total_params = sum(p.numel() for p in self.parameters()) + trainable_params = sum(p.numel() for p in self.parameters() if p.requires_grad) + + return { + 'total_params': total_params, + 'trainable_params': trainable_params, + 'model_size_mb': total_params * 4 / (1024 * 1024), # Assuming float32 + 'architecture': '7-layer CNN with BatchNorm and Dropout' + } + +# Factory function for easy instantiation +def create_custom_cnn(num_classes=10, dropout_rate=0.4): + """Create and return a CustomCNN instance.""" + return CustomCNN(num_classes=num_classes, dropout_rate=dropout_rate) diff --git a/models/resnet18.py b/models/resnet18.py new file mode 100644 index 0000000000000000000000000000000000000000..75432ca05587cf5adf0241f5b0faf3815bed7685 --- /dev/null +++ b/models/resnet18.py @@ -0,0 +1,56 @@ +import torch +import torch.nn as nn +from torchvision import models + +def load_resnet18(num_classes=10, pretrained=False): + """ + Load ResNet18 modified for CIFAR-10 classification. + + Args: + num_classes: Number of output classes (default: 10 for CIFAR-10) + pretrained: Whether to use ImageNet pretrained weights (default: False for fair comparison) + + Returns: + Modified ResNet18 model + """ + # Load ResNet18 without pretrained weights for fair comparison + weights = models.ResNet18_Weights.IMAGENET1K_V1 if pretrained else None + model = models.resnet18(weights=weights) + + # Replace final layer for CIFAR-10 (10 classes) + model.fc = nn.Linear(model.fc.in_features, num_classes) + + # Initialize the new classifier layer properly + nn.init.normal_(model.fc.weight, 0, 0.01) + nn.init.constant_(model.fc.bias, 0) + + return model + +def get_resnet18_info(): + """Return ResNet18 model information.""" + model = load_resnet18() + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + return { + 'total_params': total_params, + 'trainable_params': trainable_params, + 'model_size_mb': total_params * 4 / (1024 * 1024), + 'architecture': 'ResNet18 with modified classifier', + 'original_fc_features': 512, + 'modified_fc_classes': 10 + } + +def freeze_backbone(model, freeze=True): + """ + Freeze/unfreeze ResNet18 backbone for transfer learning experiments. + + Args: + model: ResNet18 model + freeze: Whether to freeze backbone parameters + """ + for name, param in model.named_parameters(): + if 'fc' not in name: # Don't freeze the final classifier + param.requires_grad = not freeze + + return model \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..76aea1bf8c7db76bf2f2ac82f9a2e7a92b0c2510 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,24 @@ +# Core ML/DL Dependencies +torch>=2.0.0 +torchvision>=0.15.0 +numpy>=1.21.0 +scikit-learn>=1.0.0 + +# Experiment Tracking & Visualization +wandb>=0.15.0 +matplotlib>=3.5.0 +seaborn>=0.11.0 + +# UI & Demo +gradio>=4.0.0 + +# Development & Code Quality +pytest>=7.0.0 +black>=22.0.0 +flake8>=4.0.0 +mypy>=0.950 + +# Optional: For advanced features +tensorboard>=2.10.0 # Alternative to W&B +pillow>=9.0.0 # Image processing +tqdm>=4.64.0 # Progress bars \ No newline at end of file diff --git a/results_custom.json b/results_custom.json new file mode 100644 index 0000000000000000000000000000000000000000..cd592f13e57bab1527b936818548b5994e276590 --- /dev/null +++ b/results_custom.json @@ -0,0 +1,286 @@ +{ + "model": "custom", + "config": { + "epochs": 50, + "learning_rate": 0.001, + "batch_size": 128, + "weight_decay": 0.0001, + "optimizer": "adamw", + "scheduler": "cosine", + "num_workers": 4 + }, + "timestamp": "2025-07-18T04:13:55.384588", + "evaluation": { + "test_accuracy": 90.64, + "test_loss": 0.40045145286034933 + }, + "benchmark": { + "total_params": 2332106, + "trainable_params": 2332106, + "model_size_mb": 8.896278381347656, + "inference_time_ms": 0.03669309616088868, + "throughput_samples_per_sec": 27253.083130823507 + }, + "training_history": { + "train_loss": [ + 1.4149173482913862, + 1.0162051956761966, + 0.8482165988534689, + 0.728303463621573, + 0.6363477440212261, + 0.5701356856152415, + 0.5242524116554044, + 0.48185200515118515, + 0.4417327832938595, + 0.4158814721740782, + 0.3859495721672746, + 0.36533536635000596, + 0.3332995446398854, + 0.31937127585776826, + 0.30104608948088507, + 0.2783441687087444, + 0.2632080921056596, + 0.24968139340423726, + 0.22971883236261254, + 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Production-grade training pipeline with optional W&B integration. + + Args: + model: PyTorch model to train + train_loader: Training data loader + val_loader: Validation data loader + optimizer: Optimizer (AdamW/SGD) + criterion: Loss function (CrossEntropyLoss) + scheduler: Learning rate scheduler + num_epochs: Number of training epochs + device: Device to train on + use_wandb: Whether to use W&B logging + model_name: Name of the model for logging + + Returns: + dict: Training history with losses and accuracies + """ + model.to(device) + best_val_acc = 0.0 + history = defaultdict(list) + + # Initialize W&B if enabled + logger = None + if use_wandb: + logger = WandbLogger() + config = { + 'epochs': num_epochs, + 'device': str(device), + 'optimizer': optimizer.__class__.__name__, + 'scheduler': scheduler.__class__.__name__, + 'learning_rate': optimizer.param_groups[0]['lr'] + } + logger.init_experiment(config, model, model_name) + + print(f"šŸš€ Training {model_name} on {device} for {num_epochs} epochs...") + print("-" * 60) + + for epoch in range(num_epochs): + epoch_start = time.time() + + # Training phase + model.train() + train_loss, train_acc = _train_epoch(model, train_loader, optimizer, criterion, device) + + # Validation phase + model.eval() + val_loss, val_acc, val_preds, val_targets = _validate_epoch(model, val_loader, criterion, device) + + # Update scheduler + scheduler.step() + + # Log metrics + metrics = { + 'train_loss': train_loss, + 'train_acc': train_acc, + 'val_loss': val_loss, + 'val_acc': val_acc, + 'lr': optimizer.param_groups[0]['lr'] + } + + for key, value in metrics.items(): + history[key].append(value) + + # W&B logging + if logger: + logger.log_metrics({ + 'epoch': epoch, + 'train_loss': train_loss, + 'train_accuracy': train_acc, + 'val_loss': val_loss, + 'val_accuracy': val_acc, + 'learning_rate': optimizer.param_groups[0]['lr'] + }, step=epoch) + + # Log confusion matrix every 20 epochs + if (epoch + 1) % 20 == 0: + logger.log_confusion_matrix(val_targets, val_preds, epoch) + + # Save best model + is_best = val_acc > best_val_acc + if is_best: + best_val_acc = val_acc + checkpoint = { + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + 'val_acc': val_acc, + 'val_loss': val_loss + } + torch.save(checkpoint, f'best_model_{model_name.lower()}.pth') + + # Only log best model checkpoint to W&B + if logger: + logger.log_model_checkpoint(model, optimizer, epoch, + {'val_accuracy': val_acc, 'val_loss': val_loss}, + is_best=True) + + # Print progress + epoch_time = time.time() - epoch_start + print(f"Epoch {epoch+1:3d}/{num_epochs} | " + f"Train: {train_loss:.4f}/{train_acc:.2f}% | " + f"Val: {val_loss:.4f}/{val_acc:.2f}% | " + f"LR: {optimizer.param_groups[0]['lr']:.2e} | " + f"Time: {epoch_time:.1f}s") + + if logger: + logger.finish() + + print(f"\nšŸŽÆ Best validation accuracy: {best_val_acc:.2f}%") + return dict(history) + +def _train_epoch(model, train_loader, optimizer, criterion, device): + """Single training epoch with metrics.""" + running_loss = 0.0 + correct = 0 + total = 0 + + for batch_idx, (inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(device), targets.to(device) + + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() + + running_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + return running_loss / len(train_loader), 100.0 * correct / total + +def _validate_epoch(model, val_loader, criterion, device): + """Single validation epoch with predictions.""" + running_loss = 0.0 + correct = 0 + total = 0 + all_preds = [] + all_targets = [] + + with torch.no_grad(): + for inputs, targets in val_loader: + inputs, targets = inputs.to(device), targets.to(device) + outputs = model(inputs) + loss = criterion(outputs, targets) + + running_loss += loss.item() + _, predicted = outputs.max(1) + total += targets.size(0) + correct += predicted.eq(targets).sum().item() + + all_preds.extend(predicted.cpu().numpy()) + all_targets.extend(targets.cpu().numpy()) + + return (running_loss / len(val_loader), 100.0 * correct / total, + all_preds, all_targets) + +def create_optimizer(model, opt_type='adamw', lr=0.001, weight_decay=1e-4): + """Create optimizer with best practices.""" + if opt_type.lower() == 'adamw': + return torch.optim.AdamW(model.parameters(), lr=lr, weight_decay=weight_decay) + elif opt_type.lower() == 'sgd': + return torch.optim.SGD(model.parameters(), lr=lr, momentum=0.9, weight_decay=weight_decay) + else: + raise ValueError(f"Unsupported optimizer: {opt_type}") + +def create_scheduler(optimizer, scheduler_type='cosine', num_epochs=50): # Changed default from 100 to 50 + """Create learning rate scheduler.""" + if scheduler_type.lower() == 'cosine': + return torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=num_epochs) + elif scheduler_type.lower() == 'step': + return torch.optim.lr_scheduler.StepLR(optimizer, step_size=30, gamma=0.1) + else: + raise ValueError(f"Unsupported scheduler: {scheduler_type}") + +# CLI interface for hyperparameter sweeps +def main(): + parser = argparse.ArgumentParser(description='Train CIFAR-10 models with W&B') + parser.add_argument('--model', choices=['custom', 'resnet18'], default='custom') + parser.add_argument('--epochs', type=int, default=50) # Changed from 100 to 50 + parser.add_argument('--lr', type=float, default=0.001) + parser.add_argument('--batch_size', type=int, default=128) + parser.add_argument('--no-wandb', action='store_true', help='Disable W&B logging') + parser.add_argument('--sweep', action='store_true', help='Run hyperparameter sweep') + + args = parser.parse_args() + + # Import models + if args.model == 'custom': + from models.custom_cnn import create_custom_cnn + model = create_custom_cnn() + model_name = "CustomCNN" + else: + from models.resnet18 import load_resnet18 + model = load_resnet18() + model_name = "ResNet18" + + # Load data + from utils.data_loader import get_cifar10_loaders + train_loader, val_loader, test_loader = get_cifar10_loaders(batch_size=args.batch_size) + + # Create optimizer and scheduler + optimizer = create_optimizer(model, lr=args.lr) + scheduler = create_scheduler(optimizer, num_epochs=args.epochs) + criterion = nn.CrossEntropyLoss() + + # Train model + if args.sweep: + from wandb_utils import create_hyperparameter_sweep, run_hyperparameter_sweep + sweep_config = create_hyperparameter_sweep() + + def train_fn(): + # W&B will set hyperparameters + train(model, train_loader, val_loader, optimizer, criterion, + scheduler, args.epochs, model_name=model_name) + + run_hyperparameter_sweep(train_fn, sweep_config) + else: + train(model, train_loader, val_loader, optimizer, criterion, scheduler, + args.epochs, use_wandb=not args.no_wandb, model_name=model_name) + +if __name__ == "__main__": + main() \ No newline at end of file diff --git a/utils/__pycache__/data_loader.cpython-313.pyc b/utils/__pycache__/data_loader.cpython-313.pyc new file mode 100644 index 0000000000000000000000000000000000000000..4ad9c107c5dc6ac302ca21db44f05de6f86776ce Binary files /dev/null and b/utils/__pycache__/data_loader.cpython-313.pyc differ diff --git a/utils/data_loader.py b/utils/data_loader.py new file mode 100644 index 0000000000000000000000000000000000000000..974b691ef2d1a1dff243de37385d747c276cd359 --- /dev/null +++ b/utils/data_loader.py @@ -0,0 +1,156 @@ +import torch +from torch.utils.data import DataLoader, Dataset, random_split +from torchvision import transforms +import os +import pandas as pd +from PIL import Image + +class KaggleCIFAR10Dataset(Dataset): + """Custom dataset for CIFAR-10 in train/test + CSV format.""" + + def __init__(self, data_dir, is_train=True, transform=None): + self.data_dir = data_dir + self.is_train = is_train + self.transform = transform + + # Class name mapping (same for train and test) + self.class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck'] + self.label_to_idx = {name: idx for idx, name in enumerate(self.class_names)} + + # Common root for labels and image folders + labels_root = os.path.join(data_dir, 'raw') + + if is_train: + labels_path = os.path.join(labels_root, 'trainLabels.csv') + self.image_dir = os.path.join(labels_root, 'train') + else: + labels_path = os.path.join(labels_root, 'testLabels.csv') + self.image_dir = os.path.join(labels_root, 'test') + + self.labels_df = pd.read_csv(labels_path) + + def __len__(self): + return len(self.labels_df) + + def __getitem__(self, idx): + row = self.labels_df.iloc[idx] + image_id = row['id'] + label_name = row['label'] + + image_path = os.path.join(self.image_dir, f"{image_id}.png") + image = Image.open(image_path).convert('RGB') + label = self.label_to_idx[label_name] + + if self.transform: + image = self.transform(image) + + return image, label + + +def get_cifar10_loaders(data_dir='./data', batch_size=64, num_workers=4, val_split=0.1): + """ + Load CIFAR-10 from custom directory layout with augmentation and splits. + + Args: + data_dir: Directory containing 'raw/train', 'raw/test', and both CSV files + batch_size: Batch size for loaders + num_workers: Worker processes for DataLoader + val_split: Fraction of training data used for validation + + Returns: + (train_loader, val_loader, test_loader) + """ + CIFAR10_MEAN = (0.4914, 0.4822, 0.4465) + CIFAR10_STD = (0.2023, 0.1994, 0.2010) + + train_transform = transforms.Compose([ + transforms.RandomCrop(32, padding=4), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD) + ]) + + eval_transform = transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize(CIFAR10_MEAN, CIFAR10_STD) + ]) + + # Full training dataset + full_train_dataset = KaggleCIFAR10Dataset( + data_dir=data_dir, + is_train=True, + transform=train_transform + ) + + # Split into train and validation + train_size = int((1 - val_split) * len(full_train_dataset)) + val_size = len(full_train_dataset) - train_size + + train_subset, val_subset = random_split( + full_train_dataset, + [train_size, val_size], + generator=torch.Generator().manual_seed(42) + ) + + # Use eval transforms for validation set + val_dataset = KaggleCIFAR10Dataset( + data_dir=data_dir, + is_train=True, + transform=eval_transform + ) + val_subset.dataset = val_dataset + + # Load labeled test dataset + test_dataset = KaggleCIFAR10Dataset( + data_dir=data_dir, + is_train=False, + transform=eval_transform + ) + + # DataLoaders + train_loader = DataLoader( + train_subset, + batch_size=batch_size, + shuffle=True, + num_workers=num_workers, + pin_memory=torch.cuda.is_available(), + persistent_workers=True if num_workers > 0 else False + ) + + val_loader = DataLoader( + val_subset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=torch.cuda.is_available(), + persistent_workers=True if num_workers > 0 else False + ) + + test_loader = DataLoader( + test_dataset, + batch_size=batch_size, + shuffle=False, + num_workers=num_workers, + pin_memory=torch.cuda.is_available(), + persistent_workers=True if num_workers > 0 else False + ) + + print(f"šŸ“Š Dataset loaded successfully!") + print(f" Training samples: {len(train_subset)}") + print(f" Validation samples: {len(val_subset)}") + print(f" Test samples: {len(test_dataset)}") + + return train_loader, val_loader, test_loader + + +def get_cifar10_info(): + """Return CIFAR-10 dataset information.""" + return { + 'num_classes': 10, + 'class_names': ['airplane', 'automobile', 'bird', 'cat', 'deer', + 'dog', 'frog', 'horse', 'ship', 'truck'], + 'input_shape': (3, 32, 32), + 'mean': (0.4914, 0.4822, 0.4465), + 'std': (0.2023, 0.1994, 0.2010) + } diff --git a/utils/visualizations.py b/utils/visualizations.py new file mode 100644 index 0000000000000000000000000000000000000000..e85bee5a7adb399de99d42bff0370f0f3fc7b102 --- /dev/null +++ b/utils/visualizations.py @@ -0,0 +1,154 @@ +import matplotlib.pyplot as plt +import numpy as np +import os + +def plot_training_curves(train_acc, val_acc, train_loss, val_loss, model_name, save_dir='plots'): + """ + Plot and save training/validation curves. + + Args: + train_acc: List of training accuracies + val_acc: List of validation accuracies + train_loss: List of training losses + val_loss: List of validation losses + model_name: Name of the model for plot titles and filenames + save_dir: Directory to save plots + """ + os.makedirs(save_dir, exist_ok=True) + + epochs = range(1, len(train_acc) + 1) + + # Set up the plotting style + plt.style.use('seaborn-v0_8') + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6)) + + # Plot accuracy curves + ax1.plot(epochs, train_acc, 'b-', label='Training Accuracy', linewidth=2) + ax1.plot(epochs, val_acc, 'r-', label='Validation Accuracy', linewidth=2) + ax1.set_title(f'{model_name} - Accuracy Curves', fontsize=14, fontweight='bold') + ax1.set_xlabel('Epochs', fontsize=12) + ax1.set_ylabel('Accuracy (%)', fontsize=12) + ax1.legend(fontsize=11) + ax1.grid(True, alpha=0.3) + ax1.set_ylim([0, 100]) + + # Add best accuracy annotations + best_train_acc = max(train_acc) + best_val_acc = max(val_acc) + best_train_epoch = train_acc.index(best_train_acc) + 1 + best_val_epoch = val_acc.index(best_val_acc) + 1 + + ax1.annotate(f'Best: {best_val_acc:.2f}%', + xy=(best_val_epoch, best_val_acc), + xytext=(10, 10), textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.3', facecolor='red', alpha=0.7), + arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) + + # Plot loss curves + ax2.plot(epochs, train_loss, 'b-', label='Training Loss', linewidth=2) + ax2.plot(epochs, val_loss, 'r-', label='Validation Loss', linewidth=2) + ax2.set_title(f'{model_name} - Loss Curves', fontsize=14, fontweight='bold') + ax2.set_xlabel('Epochs', fontsize=12) + ax2.set_ylabel('Loss', fontsize=12) + ax2.legend(fontsize=11) + ax2.grid(True, alpha=0.3) + + # Add minimum loss annotation + min_val_loss = min(val_loss) + min_loss_epoch = val_loss.index(min_val_loss) + 1 + ax2.annotate(f'Min: {min_val_loss:.4f}', + xy=(min_loss_epoch, min_val_loss), + xytext=(10, 10), textcoords='offset points', + bbox=dict(boxstyle='round,pad=0.3', facecolor='red', alpha=0.7), + arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) + + plt.tight_layout() + + # Save plot + save_path = os.path.join(save_dir, f'{model_name.lower()}_training_curves.png') + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.show() + + print(f"šŸ“Š Training curves saved to: {save_path}") + return fig + +def plot_model_comparison(models_history, save_dir='plots'): + """ + Compare training curves of multiple models. + + Args: + models_history: Dict with model names as keys and history dicts as values + save_dir: Directory to save plots + """ + os.makedirs(save_dir, exist_ok=True) + + plt.style.use('seaborn-v0_8') + fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6)) + + colors = ['blue', 'red', 'green', 'orange', 'purple'] + + for i, (model_name, history) in enumerate(models_history.items()): + epochs = range(1, len(history['val_acc']) + 1) + color = colors[i % len(colors)] + + # Plot validation accuracy + ax1.plot(epochs, history['val_acc'], color=color, + label=f'{model_name}', linewidth=2, marker='o', markersize=3) + + # Plot validation loss + ax2.plot(epochs, history['val_loss'], color=color, + label=f'{model_name}', linewidth=2, marker='o', markersize=3) + + # Configure accuracy plot + ax1.set_title('Model Comparison - Validation Accuracy', fontsize=14, fontweight='bold') + ax1.set_xlabel('Epochs', fontsize=12) + ax1.set_ylabel('Validation Accuracy (%)', fontsize=12) + ax1.legend(fontsize=11) + ax1.grid(True, alpha=0.3) + ax1.set_ylim([0, 100]) + + # Configure loss plot + ax2.set_title('Model Comparison - Validation Loss', fontsize=14, fontweight='bold') + ax2.set_xlabel('Epochs', fontsize=12) + ax2.set_ylabel('Validation Loss', fontsize=12) + ax2.legend(fontsize=11) + ax2.grid(True, alpha=0.3) + + plt.tight_layout() + + # Save comparison plot + save_path = os.path.join(save_dir, 'model_comparison.png') + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.show() + + print(f"šŸ“Š Model comparison saved to: {save_path}") + return fig + +def plot_learning_rate_schedule(lr_history, model_name, save_dir='plots'): + """ + Plot learning rate schedule over epochs. + + Args: + lr_history: List of learning rates per epoch + model_name: Name of the model + save_dir: Directory to save plots + """ + os.makedirs(save_dir, exist_ok=True) + + epochs = range(1, len(lr_history) + 1) + + plt.figure(figsize=(10, 6)) + plt.plot(epochs, lr_history, 'g-', linewidth=2) + plt.title(f'{model_name} - Learning Rate Schedule', fontsize=14, fontweight='bold') + plt.xlabel('Epochs', fontsize=12) + plt.ylabel('Learning Rate', fontsize=12) + plt.grid(True, alpha=0.3) + plt.yscale('log') + + # Save plot + save_path = os.path.join(save_dir, f'{model_name.lower()}_lr_schedule.png') + plt.savefig(save_path, dpi=300, bbox_inches='tight') + plt.show() + + print(f"šŸ“Š LR schedule saved to: {save_path}") + return plt.gcf() \ No newline at end of file diff --git a/wandb/run-20250717_042842-06r19hoq/files/config.yaml b/wandb/run-20250717_042842-06r19hoq/files/config.yaml new file mode 100644 index 0000000000000000000000000000000000000000..368fb21890406f39cc4e5c331da62ee4a916f6be --- /dev/null +++ b/wandb/run-20250717_042842-06r19hoq/files/config.yaml @@ -0,0 +1,83 @@ +_wandb: + value: + cli_version: 0.21.0 + e: + wwsyhap7oz1qssew84oaomf0lpswl335: + codePath: main.py + codePathLocal: main.py + cpu_count: 4 + cpu_count_logical: 8 + cudaVersion: "12.7" + disk: + /: + total: "255230791680" + used: "157923209216" + email: shreshthkapai@gmail.com + executable: C:\Users\Legion\Miniconda3\envs\ML\python.exe + git: + remote: https://github.com/shreshthkapai/cnn_benchmark.git + gpu: NVIDIA GeForce GTX 1650 + gpu_count: 1 + gpu_nvidia: + - architecture: Turing + cudaCores: 1024 + memoryTotal: "4294967296" + name: NVIDIA GeForce GTX 1650 + uuid: GPU-fbcd7647-fb67-66f5-b8c7-1a4198b7e4fa + host: DESKTOP-EIHJJJL + memory: + total: "8506298368" + os: Windows-11-10.0.26100-SP0 + program: C:\Users\Legion\desktop\cnn_benchmark\main.py + python: CPython 3.13.5 + root: C:\Users\Legion\desktop\cnn_benchmark + startedAt: "2025-07-16T22:58:42.840536Z" + writerId: wwsyhap7oz1qssew84oaomf0lpswl335 + m: [] + python_version: 3.13.5 + t: + "1": + - 1 + - 5 + - 41 + - 53 + - 105 + "2": + - 1 + - 5 + - 41 + - 53 + - 105 + "3": + - 1 + - 2 + - 13 + - 16 + - 61 + - 62 + "4": 3.13.5 + "5": 0.21.0 + "8": + - 3 + "12": 0.21.0 + "13": windows-amd64 +architecture: + value: CustomCNN +device: + value: cpu +epochs: + value: 100 +learning_rate: + value: 0.001 +model_name: + value: CustomCNN +model_size_mb: + value: 8.896278381347656 +optimizer: + value: AdamW +scheduler: + value: CosineAnnealingLR +total_params: + value: 2332106 +trainable_params: + value: 2332106 diff --git a/wandb/run-20250717_042842-06r19hoq/files/media/graph/graph_0_summary_132b1dd32ead095f5a49.graph.json b/wandb/run-20250717_042842-06r19hoq/files/media/graph/graph_0_summary_132b1dd32ead095f5a49.graph.json new file mode 100644 index 0000000000000000000000000000000000000000..d62494996388e296fb116a9d12557710a7eaf1b9 --- /dev/null +++ b/wandb/run-20250717_042842-06r19hoq/files/media/graph/graph_0_summary_132b1dd32ead095f5a49.graph.json @@ -0,0 +1 @@ +{"format": "torch", "nodes": [{"name": "features.0.0", "id": 2287480190496, "class_name": "Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)", "parameters": [["weight", [64, 3, 3, 3]]], "output_shape": [[128, 64, 32, 32]], "num_parameters": [1728]}, {"name": 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epochs... +------------------------------------------------------------ +Epoch 1/100 | Train: 1.4263/47.28% | Val: 1.2500/55.26% | LR: 1.00e-03 | Time: 688.8s +Epoch 2/100 | Train: 0.9965/64.49% | Val: 1.0144/64.68% | LR: 9.99e-04 | Time: 554.9s +Epoch 3/100 | Train: 0.8147/71.34% | Val: 0.8488/71.12% | LR: 9.98e-04 | Time: 493.9s +Epoch 4/100 | Train: 0.6970/75.81% | Val: 0.7762/73.08% | LR: 9.96e-04 | Time: 442.6s +Epoch 5/100 | Train: 0.6207/78.49% | Val: 0.6689/77.54% | LR: 9.94e-04 | Time: 425.4s +Epoch 6/100 | Train: 0.5577/80.68% | Val: 0.6680/77.22% | LR: 9.91e-04 | Time: 445.9s +Epoch 7/100 | Train: 0.5138/82.33% | Val: 0.6256/79.04% | LR: 9.88e-04 | Time: 444.6s +Epoch 8/100 | Train: 0.4757/83.72% | Val: 0.6287/79.50% | LR: 9.84e-04 | Time: 443.6s +Epoch 9/100 | Train: 0.4378/84.72% | Val: 0.4669/83.58% | LR: 9.80e-04 | Time: 446.4s +Epoch 10/100 | Train: 0.4089/86.04% | Val: 0.5060/82.66% | LR: 9.76e-04 | Time: 444.0s +Epoch 11/100 | Train: 0.3825/86.71% | Val: 0.4139/85.20% | LR: 9.70e-04 | Time: 444.0s +Epoch 12/100 | Train: 0.3571/87.85% | Val: 0.4525/84.94% | LR: 9.65e-04 | Time: 442.7s +Epoch 13/100 | Train: 0.3396/88.30% | Val: 0.4341/85.46% | LR: 9.59e-04 | Time: 443.9s +Epoch 14/100 | Train: 0.3161/89.11% | Val: 0.4940/84.54% | LR: 9.52e-04 | Time: 443.9s +Epoch 15/100 | Train: 0.3031/89.43% | Val: 0.3860/87.34% | LR: 9.46e-04 | Time: 444.4s +Epoch 16/100 | Train: 0.2818/90.13% | Val: 0.4627/85.04% | LR: 9.38e-04 | Time: 444.2s +Epoch 17/100 | Train: 0.2702/90.67% | Val: 0.3989/87.02% | LR: 9.30e-04 | Time: 441.9s +Epoch 18/100 | Train: 0.2493/91.35% | Val: 0.3635/87.86% | LR: 9.22e-04 | Time: 445.1s +Epoch 19/100 | Train: 0.2414/91.60% | Val: 0.4147/87.10% | LR: 9.14e-04 | Time: 444.3s +Epoch 20/100 | Train: 0.2253/92.17% | Val: 0.3490/88.34% | LR: 9.05e-04 | Time: 448.1s +Epoch 21/100 | Train: 0.2160/92.56% | Val: 0.4976/84.96% | LR: 8.95e-04 | Time: 446.3s +Epoch 22/100 | Train: 0.2005/92.99% | Val: 0.3767/88.18% | LR: 8.85e-04 | Time: 445.7s +Epoch 23/100 | Train: 0.1937/93.28% | Val: 0.3992/87.66% | LR: 8.75e-04 | Time: 426.4s +Epoch 24/100 | Train: 0.1825/93.52% | Val: 0.3954/88.22% | LR: 8.64e-04 | Time: 446.3s +Epoch 25/100 | Train: 0.1690/94.03% | Val: 0.3595/89.06% | LR: 8.54e-04 | Time: 445.6s +Epoch 26/100 | Train: 0.1590/94.44% | Val: 0.3949/88.24% | LR: 8.42e-04 | Time: 446.7s +Epoch 27/100 | Train: 0.1583/94.37% | Val: 0.3753/88.96% | LR: 8.31e-04 | Time: 445.8s +Epoch 28/100 | Train: 0.1425/95.03% | Val: 0.3435/89.68% | LR: 8.19e-04 | Time: 445.8s +Epoch 29/100 | Train: 0.1382/95.12% | Val: 0.4103/88.12% | LR: 8.06e-04 | Time: 446.9s +Epoch 30/100 | Train: 0.1358/95.16% | Val: 0.3981/88.36% | LR: 7.94e-04 | Time: 444.6s +Epoch 31/100 | Train: 0.1203/95.72% | Val: 0.3894/88.74% | LR: 7.81e-04 | Time: 445.1s +Epoch 32/100 | Train: 0.1173/95.90% | Val: 0.3978/89.02% | LR: 7.68e-04 | Time: 444.5s +Epoch 33/100 | Train: 0.1121/96.00% | Val: 0.4003/88.84% | LR: 7.55e-04 | Time: 445.3s +Epoch 34/100 | Train: 0.1015/96.43% | Val: 0.3812/89.66% | LR: 7.41e-04 | Time: 446.5s +Epoch 35/100 | Train: 0.0982/96.53% | Val: 0.3925/89.56% | LR: 7.27e-04 | Time: 447.3s +Epoch 36/100 | Train: 0.0908/96.82% | Val: 0.3888/89.32% | LR: 7.13e-04 | Time: 445.4s +Epoch 37/100 | Train: 0.0928/96.70% | Val: 0.3925/90.04% | LR: 6.99e-04 | Time: 433.2s +Epoch 38/100 | Train: 0.0833/97.05% | Val: 0.3851/89.82% | LR: 6.84e-04 | Time: 442.6s +Epoch 39/100 | Train: 0.0799/97.23% | Val: 0.4012/90.10% | LR: 6.69e-04 | Time: 439.2s +Epoch 40/100 | Train: 0.0789/97.12% | Val: 0.3925/89.76% | LR: 6.55e-04 | Time: 414.8s +Epoch 41/100 | Train: 0.0707/97.46% | Val: 0.3593/90.86% | LR: 6.39e-04 | Time: 426.1s +Epoch 42/100 | Train: 0.0702/97.56% | Val: 0.3883/89.76% | LR: 6.24e-04 | Time: 439.5s +Epoch 43/100 | Train: 0.0646/97.77% | Val: 0.4134/89.82% | LR: 6.09e-04 | Time: 446.1s +Epoch 44/100 | Train: 0.0642/97.75% | Val: 0.3782/90.18% | LR: 5.94e-04 | Time: 445.1s +Epoch 45/100 | Train: 0.0610/97.93% | Val: 0.3908/89.98% | LR: 5.78e-04 | Time: 423.6s +Epoch 46/100 | Train: 0.0576/97.94% | Val: 0.4269/89.86% | LR: 5.63e-04 | Time: 443.9s +Epoch 47/100 | Train: 0.0510/98.27% | Val: 0.4067/90.46% | LR: 5.47e-04 | Time: 443.9s +Epoch 48/100 | Train: 0.0476/98.34% | Val: 0.3866/90.96% | LR: 5.31e-04 | Time: 444.3s +Epoch 49/100 | Train: 0.0445/98.49% | Val: 0.4426/90.34% | LR: 5.16e-04 | Time: 445.6s +Epoch 50/100 | Train: 0.0439/98.44% | Val: 0.3987/90.38% | LR: 5.00e-04 | Time: 444.7s +Epoch 51/100 | Train: 0.0396/98.73% | Val: 0.4131/90.62% | LR: 4.84e-04 | Time: 443.5s +Epoch 52/100 | Train: 0.0397/98.64% | Val: 0.4082/90.96% | LR: 4.69e-04 | Time: 442.0s +Epoch 53/100 | Train: 0.0366/98.74% | Val: 0.4078/90.94% | LR: 4.53e-04 | Time: 442.1s +Epoch 54/100 | Train: 0.0385/98.68% | Val: 0.4498/90.38% | LR: 4.37e-04 | Time: 441.6s +Epoch 55/100 | Train: 0.0342/98.84% | Val: 0.4282/90.56% | LR: 4.22e-04 | Time: 441.1s +Epoch 56/100 | Train: 0.0326/98.90% | Val: 0.4451/90.56% | LR: 4.06e-04 | Time: 437.2s +Epoch 57/100 | Train: 0.0279/99.06% | Val: 0.4428/90.86% | LR: 3.91e-04 | Time: 424.6s +Epoch 58/100 | Train: 0.0271/99.10% | Val: 0.4333/90.58% | LR: 3.76e-04 | Time: 427.0s +Epoch 59/100 | Train: 0.0277/99.03% | Val: 0.4483/90.60% | LR: 3.61e-04 | Time: 442.3s +Epoch 60/100 | Train: 0.0265/99.07% | Val: 0.4467/90.60% | LR: 3.45e-04 | Time: 443.3s +Epoch 61/100 | Train: 0.0237/99.21% | Val: 0.4419/91.16% | LR: 3.31e-04 | Time: 443.0s +Epoch 62/100 | Train: 0.0234/99.21% | Val: 0.4439/90.46% | LR: 3.16e-04 | Time: 464.5s +Epoch 63/100 | Train: 0.0198/99.37% | Val: 0.4616/90.80% | LR: 3.01e-04 | Time: 474.4s +Epoch 64/100 | Train: 0.0177/99.42% | Val: 0.4360/90.92% | LR: 2.87e-04 | Time: 444.6s +Epoch 65/100 | Train: 0.0178/99.44% | Val: 0.4398/91.00% | LR: 2.73e-04 | Time: 444.0s +Epoch 66/100 | Train: 0.0188/99.40% | Val: 0.4573/91.22% | LR: 2.59e-04 | Time: 445.7s +Epoch 67/100 | Train: 0.0164/99.45% | Val: 0.4541/91.30% | LR: 2.45e-04 | Time: 437.4s +Epoch 68/100 | Train: 0.0165/99.49% | Val: 0.4793/90.92% | LR: 2.32e-04 | Time: 435.2s +Epoch 69/100 | Train: 0.0165/99.46% | Val: 0.4639/91.12% | LR: 2.19e-04 | Time: 444.4s +Epoch 70/100 | Train: 0.0138/99.58% | Val: 0.4675/90.68% | LR: 2.06e-04 | Time: 444.3s +Epoch 71/100 | Train: 0.0135/99.54% | Val: 0.4640/91.08% | LR: 1.94e-04 | Time: 444.8s +Epoch 72/100 | Train: 0.0117/99.63% | Val: 0.4833/91.30% | LR: 1.81e-04 | Time: 469.8s +Epoch 73/100 | Train: 0.0111/99.65% | Val: 0.4804/90.96% | LR: 1.69e-04 | Time: 452.3s +Epoch 74/100 | Train: 0.0114/99.65% | Val: 0.4615/91.60% | LR: 1.58e-04 | Time: 426.1s +Epoch 75/100 | Train: 0.0097/99.72% | Val: 0.4715/91.26% | LR: 1.46e-04 | Time: 424.5s +Epoch 76/100 | Train: 0.0105/99.68% | Val: 0.4694/91.62% | LR: 1.36e-04 | Time: 425.6s +Epoch 77/100 | Train: 0.0083/99.75% | Val: 0.4695/91.24% | LR: 1.25e-04 | Time: 427.2s +Epoch 78/100 | Train: 0.0080/99.76% | Val: 0.4773/91.18% | LR: 1.15e-04 | Time: 427.5s +Epoch 79/100 | Train: 0.0074/99.78% | Val: 0.4529/91.66% | LR: 1.05e-04 | Time: 424.3s +Epoch 80/100 | Train: 0.0076/99.78% | Val: 0.4566/91.30% | LR: 9.55e-05 | Time: 425.2s +Epoch 81/100 | Train: 0.0068/99.80% | Val: 0.4476/91.64% | LR: 8.65e-05 | Time: 424.2s +Epoch 82/100 | Train: 0.0067/99.81% | Val: 0.4565/91.52% | LR: 7.78e-05 | Time: 424.5s +Epoch 83/100 | Train: 0.0057/99.86% | Val: 0.4623/91.62% | LR: 6.96e-05 | Time: 425.2s +Epoch 84/100 | Train: 0.0060/99.85% | Val: 0.4523/91.44% | LR: 6.18e-05 | Time: 423.6s +Epoch 85/100 | Train: 0.0058/99.84% | Val: 0.4586/91.40% | LR: 5.45e-05 | Time: 425.0s +Epoch 86/100 | Train: 0.0052/99.86% | Val: 0.4588/91.48% | LR: 4.76e-05 | Time: 425.5s +Epoch 87/100 | Train: 0.0047/99.87% | Val: 0.4550/91.32% | LR: 4.11e-05 | Time: 423.3s +Epoch 88/100 | Train: 0.0051/99.86% | Val: 0.4538/91.34% | LR: 3.51e-05 | Time: 423.2s +Epoch 89/100 | Train: 0.0052/99.84% | Val: 0.4567/91.56% | LR: 2.96e-05 | Time: 426.4s +Epoch 90/100 | Train: 0.0049/99.85% | Val: 0.4543/91.42% | LR: 2.45e-05 | Time: 424.0s +Epoch 91/100 | Train: 0.0042/99.91% | Val: 0.4672/91.54% | LR: 1.99e-05 | Time: 423.7s +Epoch 92/100 | Train: 0.0043/99.87% | Val: 0.4621/91.62% | LR: 1.57e-05 | Time: 424.6s +Epoch 93/100 | Train: 0.0038/99.92% | Val: 0.4639/91.60% | LR: 1.20e-05 | Time: 424.1s +Epoch 94/100 | Train: 0.0039/99.91% | Val: 0.4578/91.40% | LR: 8.86e-06 | Time: 424.6s +Epoch 95/100 | Train: 0.0042/99.90% | Val: 0.4663/91.46% | LR: 6.16e-06 | Time: 425.7s +Epoch 96/100 | Train: 0.0039/99.89% | Val: 0.4600/91.44% | LR: 3.94e-06 | Time: 422.5s +Epoch 97/100 | Train: 0.0042/99.88% | Val: 0.4623/91.62% | LR: 2.22e-06 | Time: 423.3s +Epoch 98/100 | Train: 0.0040/99.90% | Val: 0.4649/91.54% | LR: 9.87e-07 | Time: 423.9s +Epoch 99/100 | Train: 0.0043/99.90% | Val: 0.4625/91.60% | LR: 2.47e-07 | Time: 422.6s +Epoch 100/100 | Train: 0.0039/99.89% | Val: 0.4649/91.52% | LR: 0.00e+00 | Time: 423.9s diff --git a/wandb/run-20250717_042842-06r19hoq/files/requirements.txt 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files /dev/null and b/wandb/run-20250718_001150-vs4r5xs7/files/media/images/confusion_matrix_39_c51878a573e7397c245b.png differ diff --git a/wandb/run-20250718_001150-vs4r5xs7/files/output.log b/wandb/run-20250718_001150-vs4r5xs7/files/output.log new file mode 100644 index 0000000000000000000000000000000000000000..db08617feba4af1dcaeb7a9757bd2b79dbdcab9d --- /dev/null +++ b/wandb/run-20250718_001150-vs4r5xs7/files/output.log @@ -0,0 +1,53 @@ +wandb: logging graph, to disable use `wandb.watch(log_graph=False)` +šŸš€ Training CustomCNN on cuda for 50 epochs... +------------------------------------------------------------ +Epoch 1/50 | Train: 1.4377/46.60% | Val: 1.3395/53.40% | LR: 9.99e-04 | Time: 254.5s +Epoch 2/50 | Train: 1.0022/63.95% | Val: 0.8815/68.20% | LR: 9.96e-04 | Time: 133.9s +Epoch 3/50 | Train: 0.8269/70.64% | Val: 0.7499/73.38% | LR: 9.91e-04 | Time: 65.1s +Epoch 4/50 | Train: 0.7065/75.32% | Val: 0.7545/73.74% | LR: 9.84e-04 | Time: 72.0s +Epoch 5/50 | Train: 0.6231/78.44% | Val: 0.8905/73.22% | LR: 9.76e-04 | Time: 28.7s +Epoch 6/50 | Train: 0.5634/80.57% | Val: 0.5721/80.42% | LR: 9.65e-04 | Time: 30.6s +Epoch 7/50 | Train: 0.5146/82.15% | Val: 0.5392/81.46% | LR: 9.52e-04 | Time: 30.5s +Epoch 8/50 | Train: 0.4782/83.44% | Val: 0.5748/79.96% | LR: 9.38e-04 | Time: 30.1s +Epoch 9/50 | Train: 0.4418/84.86% | Val: 0.5654/81.44% | LR: 9.22e-04 | Time: 31.2s +Epoch 10/50 | Train: 0.4094/85.76% | Val: 0.5252/83.16% | LR: 9.05e-04 | Time: 30.5s +Epoch 11/50 | Train: 0.3839/86.78% | Val: 0.4837/83.48% | LR: 8.85e-04 | Time: 30.8s +Epoch 12/50 | Train: 0.3596/87.53% | Val: 0.4123/85.56% | LR: 8.64e-04 | Time: 30.3s +Epoch 13/50 | Train: 0.3343/88.44% | Val: 0.4390/85.12% | LR: 8.42e-04 | Time: 29.8s +Epoch 14/50 | Train: 0.3136/89.23% | Val: 0.3795/86.96% | LR: 8.19e-04 | Time: 30.4s +Epoch 15/50 | Train: 0.2931/89.91% | Val: 0.4262/86.18% | LR: 7.94e-04 | Time: 29.3s +Epoch 16/50 | Train: 0.2774/90.26% | Val: 0.3854/86.94% | LR: 7.68e-04 | Time: 29.0s +Epoch 17/50 | Train: 0.2579/91.06% | Val: 0.3839/87.62% | LR: 7.41e-04 | Time: 29.6s +Epoch 18/50 | Train: 0.2427/91.53% | Val: 0.3406/88.50% | LR: 7.13e-04 | Time: 29.5s +Epoch 19/50 | Train: 0.2298/92.06% | Val: 0.3633/88.86% | LR: 6.84e-04 | Time: 29.5s +Epoch 20/50 | Train: 0.2122/92.56% | Val: 0.3388/89.00% | LR: 6.55e-04 | Time: 33.0s +Epoch 21/50 | Train: 0.1964/93.12% | Val: 0.3935/87.56% | LR: 6.24e-04 | Time: 28.9s +Epoch 22/50 | Train: 0.1834/93.61% | Val: 0.3369/89.00% | LR: 5.94e-04 | Time: 28.9s +Epoch 23/50 | Train: 0.1725/94.03% | Val: 0.3968/87.30% | LR: 5.63e-04 | Time: 28.9s +Epoch 24/50 | Train: 0.1626/94.27% | Val: 0.3287/89.74% | LR: 5.31e-04 | Time: 30.1s +Epoch 25/50 | Train: 0.1530/94.62% | Val: 0.3462/89.54% | LR: 5.00e-04 | Time: 131.4s +Epoch 26/50 | Train: 0.1341/95.20% | Val: 0.3402/89.96% | LR: 4.69e-04 | Time: 68.9s +Epoch 27/50 | Train: 0.1269/95.59% | Val: 0.3460/89.26% | LR: 4.37e-04 | Time: 28.9s +Epoch 28/50 | Train: 0.1181/95.88% | Val: 0.3417/90.22% | LR: 4.06e-04 | Time: 29.4s +Epoch 29/50 | Train: 0.1057/96.22% | Val: 0.3538/89.76% | LR: 3.76e-04 | Time: 29.0s +Epoch 30/50 | Train: 0.0985/96.49% | Val: 0.3729/89.46% | LR: 3.45e-04 | Time: 29.1s +Epoch 31/50 | Train: 0.0934/96.76% | Val: 0.3492/90.12% | LR: 3.16e-04 | Time: 29.1s +Epoch 32/50 | Train: 0.0831/97.13% | Val: 0.3630/89.92% | LR: 2.87e-04 | Time: 29.2s +Epoch 33/50 | Train: 0.0751/97.44% | Val: 0.3596/90.32% | LR: 2.59e-04 | Time: 29.4s +Epoch 34/50 | Train: 0.0700/97.58% | Val: 0.3494/90.66% | LR: 2.32e-04 | Time: 29.5s +Epoch 35/50 | Train: 0.0650/97.77% | Val: 0.3769/90.80% | LR: 2.06e-04 | Time: 29.6s +Epoch 36/50 | Train: 0.0591/97.97% | Val: 0.3650/90.48% | LR: 1.81e-04 | Time: 29.0s +Epoch 37/50 | Train: 0.0547/98.15% | Val: 0.3599/90.78% | LR: 1.58e-04 | Time: 29.1s +Epoch 38/50 | Train: 0.0494/98.31% | Val: 0.3569/90.76% | LR: 1.36e-04 | Time: 29.4s +Epoch 39/50 | Train: 0.0451/98.48% | Val: 0.3625/91.14% | LR: 1.15e-04 | Time: 30.5s +Epoch 40/50 | Train: 0.0408/98.72% | Val: 0.3622/91.06% | LR: 9.55e-05 | Time: 32.6s +Epoch 41/50 | Train: 0.0385/98.74% | Val: 0.3755/90.80% | LR: 7.78e-05 | Time: 30.0s +Epoch 42/50 | Train: 0.0364/98.85% | Val: 0.3603/90.98% | LR: 6.18e-05 | Time: 29.2s +Epoch 43/50 | Train: 0.0349/98.92% | Val: 0.3659/91.10% | LR: 4.76e-05 | Time: 28.8s +Epoch 44/50 | Train: 0.0326/98.98% | Val: 0.3597/91.18% | LR: 3.51e-05 | Time: 29.0s +Epoch 45/50 | Train: 0.0322/99.00% | Val: 0.3619/91.16% | LR: 2.45e-05 | Time: 28.4s +Epoch 46/50 | Train: 0.0306/99.04% | Val: 0.3592/91.16% | LR: 1.57e-05 | Time: 28.7s +Epoch 47/50 | Train: 0.0300/99.13% | Val: 0.3618/91.20% | LR: 8.86e-06 | Time: 29.0s +Epoch 48/50 | Train: 0.0292/99.13% | Val: 0.3569/91.26% | LR: 3.94e-06 | Time: 29.0s +Epoch 49/50 | Train: 0.0290/99.14% | Val: 0.3627/91.28% | LR: 9.87e-07 | Time: 29.0s +Epoch 50/50 | Train: 0.0286/99.10% | Val: 0.3608/91.26% | LR: 0.00e+00 | Time: 28.5s diff --git 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0.5510/81.10% | LR: 9.52e-04 | Time: 30.0s +Epoch 8/50 | Train: 0.4809/83.51% | Val: 0.5080/81.94% | LR: 9.38e-04 | Time: 29.6s +Epoch 9/50 | Train: 0.4448/84.72% | Val: 0.5029/83.82% | LR: 9.22e-04 | Time: 29.3s +Epoch 10/50 | Train: 0.4113/85.78% | Val: 0.4641/83.98% | LR: 9.05e-04 | Time: 30.4s +Epoch 11/50 | Train: 0.3837/86.82% | Val: 0.4840/83.18% | LR: 8.85e-04 | Time: 29.2s +Epoch 12/50 | Train: 0.3577/87.68% | Val: 0.4262/86.00% | LR: 8.64e-04 | Time: 30.1s +Epoch 13/50 | Train: 0.3385/88.40% | Val: 0.4140/86.32% | LR: 8.42e-04 | Time: 30.5s +Epoch 14/50 | Train: 0.3153/89.13% | Val: 0.4601/85.48% | LR: 8.19e-04 | Time: 29.2s +Epoch 15/50 | Train: 0.2957/89.79% | Val: 0.3796/87.20% | LR: 7.94e-04 | Time: 29.6s +Epoch 16/50 | Train: 0.2778/90.44% | Val: 0.3789/87.10% | LR: 7.68e-04 | Time: 29.2s +Epoch 17/50 | Train: 0.2555/91.06% | Val: 0.4423/86.14% | LR: 7.41e-04 | Time: 28.8s +Epoch 18/50 | Train: 0.2464/91.37% | Val: 0.3798/87.92% | LR: 7.13e-04 | Time: 30.1s +Epoch 19/50 | Train: 0.2283/92.00% | Val: 0.3942/86.82% | LR: 6.84e-04 | Time: 29.4s +Epoch 20/50 | Train: 0.2058/92.88% | Val: 0.3527/89.06% | LR: 6.55e-04 | Time: 33.4s +Epoch 21/50 | Train: 0.1958/93.12% | Val: 0.3724/88.10% | LR: 6.24e-04 | Time: 29.4s +Epoch 22/50 | Train: 0.1866/93.40% | Val: 0.3669/88.92% | LR: 5.94e-04 | Time: 29.1s +Epoch 23/50 | Train: 0.1698/94.05% | Val: 0.4119/88.40% | LR: 5.63e-04 | Time: 29.1s +Epoch 24/50 | Train: 0.1596/94.31% | Val: 0.3616/88.98% | LR: 5.31e-04 | Time: 29.1s +Epoch 25/50 | Train: 0.1493/94.71% | Val: 0.3953/88.12% | LR: 5.00e-04 | Time: 29.3s +Epoch 26/50 | Train: 0.1387/95.08% | Val: 0.3714/89.14% | LR: 4.69e-04 | Time: 29.7s +Epoch 27/50 | Train: 0.1261/95.49% | Val: 0.3769/89.48% | LR: 4.37e-04 | Time: 30.0s +Epoch 28/50 | Train: 0.1157/95.89% | Val: 0.3801/89.22% | LR: 4.06e-04 | Time: 29.6s +Epoch 29/50 | Train: 0.1030/96.42% | Val: 0.3846/89.30% | LR: 3.76e-04 | Time: 29.7s +Epoch 30/50 | Train: 0.0980/96.57% | Val: 0.3950/89.16% | LR: 3.45e-04 | Time: 30.3s +Epoch 31/50 | Train: 0.0931/96.67% | Val: 0.3545/90.06% | LR: 3.16e-04 | Time: 30.1s +Epoch 32/50 | Train: 0.0827/97.11% | Val: 0.3699/89.98% | LR: 2.87e-04 | Time: 28.6s +Epoch 33/50 | Train: 0.0756/97.46% | Val: 0.3700/90.00% | LR: 2.59e-04 | Time: 28.5s +Epoch 34/50 | Train: 0.0697/97.62% | Val: 0.3820/90.00% | LR: 2.32e-04 | Time: 28.5s +Epoch 35/50 | Train: 0.0636/97.89% | Val: 0.3737/89.76% | LR: 2.06e-04 | Time: 28.5s +Epoch 36/50 | Train: 0.0596/97.98% | Val: 0.3775/90.38% | LR: 1.81e-04 | Time: 29.1s +Epoch 37/50 | Train: 0.0524/98.23% | Val: 0.3818/90.36% | LR: 1.58e-04 | Time: 28.6s +Epoch 38/50 | Train: 0.0518/98.26% | Val: 0.3821/90.42% | LR: 1.36e-04 | Time: 29.1s +Epoch 39/50 | Train: 0.0463/98.46% | Val: 0.3750/90.68% | LR: 1.15e-04 | Time: 29.1s +Epoch 40/50 | Train: 0.0423/98.68% | Val: 0.3811/90.20% | LR: 9.55e-05 | Time: 30.2s +Epoch 41/50 | Train: 0.0397/98.66% | Val: 0.3843/90.58% | LR: 7.78e-05 | Time: 28.5s +Epoch 42/50 | Train: 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https://github.com/shreshthkapai/cnn_benchmark.git + gpu: NVIDIA GeForce GTX 1650 + gpu_count: 1 + gpu_nvidia: + - architecture: Turing + cudaCores: 1024 + memoryTotal: "4294967296" + name: NVIDIA GeForce GTX 1650 + uuid: GPU-fbcd7647-fb67-66f5-b8c7-1a4198b7e4fa + host: DESKTOP-EIHJJJL + memory: + total: "8506298368" + os: Windows-11-10.0.26100-SP0 + program: C:\Users\Legion\desktop\cnn_benchmark\main.py + python: CPython 3.13.5 + root: C:\Users\Legion\desktop\cnn_benchmark + startedAt: "2025-07-17T20:15:49.283044Z" + writerId: vyuzbn0qrw5fre8l432eiyjjmq3rpp26 + m: [] + python_version: 3.13.5 + t: + "1": + - 1 + - 5 + - 41 + - 53 + - 105 + "2": + - 1 + - 5 + - 41 + - 53 + - 105 + "3": + - 1 + - 2 + - 13 + - 16 + - 61 + - 62 + "4": 3.13.5 + "5": 0.21.0 + "8": + - 3 + "12": 0.21.0 + "13": windows-amd64 +architecture: + value: ResNet +device: + value: cuda +epochs: + value: 50 +learning_rate: + value: 0.001 +model_name: + value: ResNet18 +model_size_mb: + value: 42.654579162597656 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+wandb: logging graph, to disable use `wandb.watch(log_graph=False)` +šŸš€ Training ResNet18 on cuda for 50 epochs... +------------------------------------------------------------ +Epoch 1/50 | Train: 1.5246/44.36% | Val: 1.3138/53.20% | LR: 9.99e-04 | Time: 259.5s +Epoch 2/50 | Train: 1.1778/57.95% | Val: 1.1739/59.68% | LR: 9.96e-04 | Time: 163.0s +Epoch 3/50 | Train: 1.0334/63.40% | Val: 0.9762/66.22% | LR: 9.91e-04 | Time: 155.2s +Epoch 4/50 | Train: 0.9254/67.43% | Val: 0.9484/66.60% | LR: 9.84e-04 | Time: 181.7s +Epoch 5/50 | Train: 0.8518/70.20% | Val: 0.8076/70.88% | LR: 9.76e-04 | Time: 164.8s +Epoch 6/50 | Train: 0.7933/72.30% | Val: 0.8082/71.52% | LR: 9.65e-04 | Time: 171.9s +Epoch 7/50 | Train: 0.7466/74.13% | Val: 0.6922/75.72% | LR: 9.52e-04 | Time: 160.9s +Epoch 8/50 | Train: 0.7080/75.23% | Val: 0.7285/75.06% | LR: 9.38e-04 | Time: 150.5s +Epoch 9/50 | Train: 0.6665/76.72% | Val: 0.6983/75.58% | LR: 9.22e-04 | Time: 76.0s +Epoch 10/50 | Train: 0.6355/77.77% | Val: 0.6308/77.70% | LR: 9.05e-04 | Time: 66.7s +Epoch 11/50 | Train: 0.6057/78.84% | Val: 0.6374/77.50% | LR: 8.85e-04 | Time: 111.5s +Epoch 12/50 | Train: 0.5872/79.58% | Val: 0.6203/78.44% | LR: 8.64e-04 | Time: 39.3s +Epoch 13/50 | Train: 0.5605/80.45% | Val: 0.5931/78.80% | LR: 8.42e-04 | Time: 155.3s +Epoch 14/50 | Train: 0.5362/81.26% | Val: 0.5803/79.92% | LR: 8.19e-04 | Time: 66.7s +Epoch 15/50 | Train: 0.5193/81.91% | Val: 0.5878/80.04% | LR: 7.94e-04 | Time: 26.3s +Epoch 16/50 | Train: 0.4934/82.80% | Val: 0.5457/80.86% | LR: 7.68e-04 | Time: 53.4s +Epoch 17/50 | Train: 0.4831/83.13% | Val: 0.5618/81.62% | LR: 7.41e-04 | Time: 24.1s +Epoch 18/50 | Train: 0.4612/83.81% | Val: 0.5495/81.10% | LR: 7.13e-04 | Time: 22.8s +Epoch 19/50 | Train: 0.4398/84.66% | Val: 0.5617/80.76% | LR: 6.84e-04 | Time: 22.8s +Epoch 20/50 | Train: 0.4263/85.06% | Val: 0.5765/80.78% | LR: 6.55e-04 | Time: 23.6s +Epoch 21/50 | Train: 0.4131/85.47% | Val: 0.5254/82.16% | LR: 6.24e-04 | Time: 25.2s +Epoch 22/50 | Train: 0.3929/86.05% | Val: 0.5304/82.66% | LR: 5.94e-04 | Time: 26.2s +Epoch 23/50 | Train: 0.3804/86.64% | Val: 0.5231/82.88% | LR: 5.63e-04 | Time: 26.4s +Epoch 24/50 | Train: 0.3585/87.39% | Val: 0.5381/81.58% | LR: 5.31e-04 | Time: 26.2s +Epoch 25/50 | Train: 0.3497/87.59% | Val: 0.5230/82.88% | LR: 5.00e-04 | Time: 29.0s +Epoch 26/50 | Train: 0.3332/88.33% | Val: 0.5063/83.84% | LR: 4.69e-04 | Time: 27.6s +Epoch 27/50 | Train: 0.3179/88.79% | Val: 0.5035/84.02% | LR: 4.37e-04 | Time: 195.2s +Epoch 28/50 | Train: 0.3072/89.19% | Val: 0.5059/83.52% | LR: 4.06e-04 | Time: 31.3s +Epoch 29/50 | Train: 0.2976/89.52% | Val: 0.5044/83.90% | LR: 3.76e-04 | Time: 23.7s +Epoch 30/50 | Train: 0.2842/89.97% | Val: 0.4960/84.22% | LR: 3.45e-04 | Time: 25.5s +Epoch 31/50 | Train: 0.2677/90.34% | Val: 0.4855/84.60% | LR: 3.16e-04 | Time: 25.5s +Epoch 32/50 | Train: 0.2608/90.80% | Val: 0.5100/84.34% | LR: 2.87e-04 | Time: 25.4s +Epoch 33/50 | Train: 0.2429/91.45% | Val: 0.5074/84.24% | LR: 2.59e-04 | Time: 23.9s +Epoch 34/50 | Train: 0.2324/91.69% | Val: 0.4955/84.78% | LR: 2.32e-04 | Time: 25.7s +Epoch 35/50 | Train: 0.2224/92.26% | Val: 0.5147/84.64% | LR: 2.06e-04 | Time: 24.1s +Epoch 36/50 | Train: 0.2143/92.47% | Val: 0.5190/85.18% | LR: 1.81e-04 | Time: 25.8s +Epoch 37/50 | Train: 0.2050/92.66% | Val: 0.5128/84.62% | LR: 1.58e-04 | Time: 24.8s +Epoch 38/50 | Train: 0.1930/93.14% | Val: 0.5175/85.10% | LR: 1.36e-04 | Time: 23.6s +Epoch 39/50 | Train: 0.1856/93.40% | Val: 0.5142/85.30% | LR: 1.15e-04 | Time: 26.1s +Epoch 40/50 | Train: 0.1794/93.57% | Val: 0.5174/85.48% | LR: 9.55e-05 | Time: 26.8s +Epoch 41/50 | Train: 0.1684/94.05% | Val: 0.5306/85.04% | LR: 7.78e-05 | Time: 24.2s +Epoch 42/50 | Train: 0.1669/94.02% | Val: 0.5208/85.12% | LR: 6.18e-05 | Time: 24.5s +Epoch 43/50 | Train: 0.1585/94.38% | Val: 0.5301/84.82% | LR: 4.76e-05 | Time: 26.2s +Epoch 44/50 | Train: 0.1614/94.22% | Val: 0.5267/85.12% | LR: 3.51e-05 | Time: 24.2s +Epoch 45/50 | Train: 0.1554/94.43% | Val: 0.5256/85.16% | LR: 2.45e-05 | Time: 24.0s +Epoch 46/50 | Train: 0.1490/94.68% | Val: 0.5315/85.14% | LR: 1.57e-05 | Time: 24.5s +Epoch 47/50 | Train: 0.1454/94.90% | Val: 0.5241/85.40% | LR: 8.86e-06 | Time: 23.9s +Epoch 48/50 | Train: 0.1441/94.88% | Val: 0.5275/85.26% | LR: 3.94e-06 | Time: 23.8s +Epoch 49/50 | Train: 0.1414/95.12% | Val: 0.5300/85.24% | LR: 9.87e-07 | Time: 24.2s +Epoch 50/50 | Train: 0.1449/94.94% | Val: 0.5295/85.26% | LR: 0.00e+00 | Time: 24.4s diff --git a/wandb/run-20250718_014549-myiqhu6f/files/requirements.txt b/wandb/run-20250718_014549-myiqhu6f/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..cdcf2c7afa19d3da6bbc19e09d9933ed3c5c0bd7 --- /dev/null +++ b/wandb/run-20250718_014549-myiqhu6f/files/requirements.txt @@ -0,0 +1,120 @@ +aiofiles==24.1.0 +alembic==1.16.2 +altair==5.5.0 +annotated-types==0.7.0 +anyio==4.9.0 +attrs==25.3.0 +audioop-lts==0.2.1 +blinker==1.9.0 +Bottleneck==1.4.2 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LR: 9.99e-04 | Time: 230.8s +Epoch 2/50 | Train: 0.9993/64.06% | Val: 1.1472/62.46% | LR: 9.96e-04 | Time: 104.1s +Epoch 3/50 | Train: 0.8190/71.20% | Val: 0.7731/72.78% | LR: 9.91e-04 | Time: 29.3s +Epoch 4/50 | Train: 0.7009/75.58% | Val: 0.6820/75.28% | LR: 9.84e-04 | Time: 29.3s +Epoch 5/50 | Train: 0.6199/78.56% | Val: 0.6450/77.84% | LR: 9.76e-04 | Time: 29.5s +Epoch 6/50 | Train: 0.5615/80.63% | Val: 0.6340/78.84% | LR: 9.65e-04 | Time: 29.4s +Epoch 7/50 | Train: 0.5082/82.46% | Val: 0.4925/82.72% | LR: 9.52e-04 | Time: 29.9s +Epoch 8/50 | Train: 0.4793/83.57% | Val: 0.5754/80.40% | LR: 9.38e-04 | Time: 29.3s +Epoch 9/50 | Train: 0.4434/84.66% | Val: 0.5939/80.36% | LR: 9.22e-04 | Time: 29.2s +Epoch 10/50 | Train: 0.4048/86.14% | Val: 0.4558/84.14% | LR: 9.05e-04 | Time: 30.0s +Epoch 11/50 | Train: 0.3787/87.04% | Val: 0.4767/84.08% | LR: 8.85e-04 | Time: 29.0s +Epoch 12/50 | Train: 0.3600/87.37% | Val: 0.4351/84.58% | LR: 8.64e-04 | Time: 29.7s +Epoch 13/50 | Train: 0.3372/88.24% | Val: 0.3863/86.56% | LR: 8.42e-04 | Time: 29.9s +Epoch 14/50 | Train: 0.3123/89.19% | Val: 0.3804/86.78% | LR: 8.19e-04 | Time: 30.2s +Epoch 15/50 | Train: 0.2940/89.84% | Val: 0.3817/86.54% | LR: 7.94e-04 | Time: 28.8s +Epoch 16/50 | Train: 0.2741/90.40% | Val: 0.3585/87.76% | LR: 7.68e-04 | Time: 29.7s +Epoch 17/50 | Train: 0.2570/91.10% | Val: 0.3989/87.02% | LR: 7.41e-04 | Time: 29.1s +Epoch 18/50 | Train: 0.2417/91.46% | Val: 0.4114/87.04% | LR: 7.13e-04 | Time: 29.4s +Epoch 19/50 | Train: 0.2254/92.03% | Val: 0.4162/87.24% | LR: 6.84e-04 | Time: 28.9s +Epoch 20/50 | Train: 0.2111/92.77% | Val: 0.3731/87.74% | LR: 6.55e-04 | Time: 32.7s +Epoch 21/50 | Train: 0.1981/93.11% | Val: 0.3692/88.20% | LR: 6.24e-04 | Time: 30.0s +Epoch 22/50 | Train: 0.1832/93.52% | Val: 0.3252/89.92% | LR: 5.94e-04 | Time: 29.9s +Epoch 23/50 | Train: 0.1707/93.98% | Val: 0.3494/89.28% | LR: 5.63e-04 | Time: 29.0s +Epoch 24/50 | Train: 0.1607/94.38% | Val: 0.3736/88.42% | LR: 5.31e-04 | Time: 29.2s +Epoch 25/50 | Train: 0.1490/94.70% | Val: 0.3874/88.54% | LR: 5.00e-04 | Time: 29.0s +Epoch 26/50 | Train: 0.1346/95.34% | Val: 0.3378/90.14% | LR: 4.69e-04 | Time: 29.7s +Epoch 27/50 | Train: 0.1264/95.56% | Val: 0.3457/89.32% | LR: 4.37e-04 | Time: 29.6s +Epoch 28/50 | Train: 0.1148/96.02% | Val: 0.3232/90.58% | LR: 4.06e-04 | Time: 29.6s +Epoch 29/50 | Train: 0.1082/96.24% | Val: 0.3281/90.72% | LR: 3.76e-04 | Time: 29.9s +Epoch 30/50 | Train: 0.0955/96.73% | Val: 0.3170/90.28% | LR: 3.45e-04 | Time: 29.2s +Epoch 31/50 | Train: 0.0888/96.85% | Val: 0.3430/90.58% | LR: 3.16e-04 | Time: 29.4s +Epoch 32/50 | Train: 0.0826/97.18% | Val: 0.3309/90.86% | LR: 2.87e-04 | Time: 29.7s +Epoch 33/50 | Train: 0.0756/97.45% | Val: 0.3348/91.02% | LR: 2.59e-04 | Time: 30.1s +Epoch 34/50 | Train: 0.0704/97.53% | Val: 0.3635/90.50% | LR: 2.32e-04 | Time: 28.9s +Epoch 35/50 | Train: 0.0639/97.86% | Val: 0.3570/90.76% | LR: 2.06e-04 | Time: 30.5s +Epoch 36/50 | Train: 0.0587/98.05% | Val: 0.3476/90.84% | LR: 1.81e-04 | Time: 29.6s +Epoch 37/50 | Train: 0.0563/98.10% | Val: 0.3529/90.68% | LR: 1.58e-04 | Time: 29.5s +Epoch 38/50 | Train: 0.0505/98.30% | Val: 0.3491/91.04% | LR: 1.36e-04 | Time: 30.6s +Epoch 39/50 | Train: 0.0468/98.50% | Val: 0.3351/91.18% | LR: 1.15e-04 | Time: 29.7s +Epoch 40/50 | Train: 0.0415/98.62% | Val: 0.3549/90.92% | LR: 9.55e-05 | Time: 30.8s +Epoch 41/50 | Train: 0.0390/98.72% | Val: 0.3458/90.96% | LR: 7.78e-05 | Time: 31.7s +Epoch 42/50 | Train: 0.0369/98.87% | Val: 0.3465/91.36% | LR: 6.18e-05 | Time: 33.1s +Epoch 43/50 | Train: 0.0364/98.82% | Val: 0.3425/91.26% | LR: 4.76e-05 | Time: 32.4s +Epoch 44/50 | Train: 0.0339/98.90% | Val: 0.3446/91.28% | LR: 3.51e-05 | Time: 29.1s +Epoch 45/50 | Train: 0.0311/99.06% | Val: 0.3451/91.64% | LR: 2.45e-05 | Time: 30.4s +Epoch 46/50 | Train: 0.0317/99.01% | Val: 0.3422/91.46% | LR: 1.57e-05 | Time: 30.3s +Epoch 47/50 | Train: 0.0300/99.12% | Val: 0.3469/91.44% | LR: 8.86e-06 | Time: 30.7s +Epoch 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0.7054/74.86% | LR: 9.38e-04 | Time: 27.3s +Epoch 9/50 | Train: 0.6625/76.95% | Val: 0.6213/77.42% | LR: 9.22e-04 | Time: 26.0s +Epoch 10/50 | Train: 0.6338/77.78% | Val: 0.6891/75.88% | LR: 9.05e-04 | Time: 25.4s +Epoch 11/50 | Train: 0.6026/78.98% | Val: 0.6197/77.98% | LR: 8.85e-04 | Time: 25.9s +Epoch 12/50 | Train: 0.5758/79.76% | Val: 0.6279/77.94% | LR: 8.64e-04 | Time: 25.1s +Epoch 13/50 | Train: 0.5572/80.59% | Val: 0.5852/79.48% | LR: 8.42e-04 | Time: 25.8s +Epoch 14/50 | Train: 0.5360/81.26% | Val: 0.5720/80.06% | LR: 8.19e-04 | Time: 25.6s +Epoch 15/50 | Train: 0.5101/82.36% | Val: 0.5639/80.52% | LR: 7.94e-04 | Time: 27.3s +Epoch 16/50 | Train: 0.4927/82.65% | Val: 0.5432/81.12% | LR: 7.68e-04 | Time: 24.9s +Epoch 17/50 | Train: 0.4709/83.36% | Val: 0.5190/81.82% | LR: 7.41e-04 | Time: 25.6s +Epoch 18/50 | Train: 0.4563/84.06% | Val: 0.5302/81.36% | LR: 7.13e-04 | Time: 24.1s +Epoch 19/50 | Train: 0.4394/84.60% | Val: 0.5305/82.00% | LR: 6.84e-04 | Time: 24.0s +Epoch 20/50 | Train: 0.4184/85.22% | Val: 0.5138/82.28% | LR: 6.55e-04 | Time: 29.3s +Epoch 21/50 | Train: 0.4038/85.87% | Val: 0.5045/82.28% | LR: 6.24e-04 | Time: 63.1s +Epoch 22/50 | Train: 0.3881/86.27% | Val: 0.5120/82.90% | LR: 5.94e-04 | Time: 27.3s +Epoch 23/50 | Train: 0.3766/86.81% | Val: 0.4806/83.24% | LR: 5.63e-04 | Time: 26.4s +Epoch 24/50 | Train: 0.3583/87.41% | Val: 0.4726/83.80% | LR: 5.31e-04 | Time: 25.8s +Epoch 25/50 | Train: 0.3401/88.08% | Val: 0.4617/84.10% | LR: 5.00e-04 | Time: 28.1s +Epoch 26/50 | Train: 0.3252/88.50% | Val: 0.5056/83.46% | LR: 4.69e-04 | Time: 23.5s +Epoch 27/50 | Train: 0.3184/88.76% | Val: 0.4751/84.04% | LR: 4.37e-04 | Time: 25.6s +Epoch 28/50 | Train: 0.3035/89.19% | Val: 0.4916/84.44% | LR: 4.06e-04 | Time: 26.2s +Epoch 29/50 | Train: 0.2918/89.59% | Val: 0.4652/84.64% | LR: 3.76e-04 | Time: 26.5s +Epoch 30/50 | Train: 0.2756/90.20% | Val: 0.4645/84.90% | LR: 3.45e-04 | Time: 25.9s +Epoch 31/50 | Train: 0.2633/90.67% | Val: 0.4959/84.78% | LR: 3.16e-04 | Time: 25.9s +Epoch 32/50 | Train: 0.2490/91.09% | Val: 0.4686/85.14% | LR: 2.87e-04 | Time: 28.9s +Epoch 33/50 | Train: 0.2385/91.59% | Val: 0.4813/85.00% | LR: 2.59e-04 | Time: 24.3s +Epoch 34/50 | Train: 0.2295/91.88% | Val: 0.4905/84.88% | LR: 2.32e-04 | Time: 25.2s +Epoch 35/50 | Train: 0.2174/92.32% | Val: 0.5038/85.22% | LR: 2.06e-04 | Time: 25.9s +Epoch 36/50 | Train: 0.2083/92.51% | Val: 0.5075/85.10% | LR: 1.81e-04 | Time: 24.9s +Epoch 37/50 | Train: 0.1980/92.89% | Val: 0.4937/85.70% | LR: 1.58e-04 | Time: 26.4s +Epoch 38/50 | Train: 0.1920/93.10% | Val: 0.5001/85.24% | LR: 1.36e-04 | Time: 24.2s +Epoch 39/50 | Train: 0.1782/93.57% | Val: 0.5115/84.82% | LR: 1.15e-04 | Time: 24.9s +Epoch 40/50 | Train: 0.1770/93.82% | Val: 0.5081/85.42% | LR: 9.55e-05 | Time: 25.4s +Epoch 41/50 | Train: 0.1682/94.01% | Val: 0.5071/85.80% | LR: 7.78e-05 | Time: 25.9s +Epoch 42/50 | Train: 0.1602/94.31% | Val: 0.5074/85.56% | LR: 6.18e-05 | Time: 26.7s +Epoch 43/50 | Train: 0.1558/94.41% | Val: 0.5116/85.98% | LR: 4.76e-05 | Time: 26.9s +Epoch 44/50 | Train: 0.1524/94.56% | Val: 0.5152/85.78% | LR: 3.51e-05 | Time: 27.5s +Epoch 45/50 | Train: 0.1488/94.71% | Val: 0.5157/85.78% | LR: 2.45e-05 | Time: 24.1s +Epoch 46/50 | Train: 0.1457/94.85% | Val: 0.5089/85.64% | LR: 1.57e-05 | Time: 22.8s +Epoch 47/50 | Train: 0.1476/94.76% | Val: 0.5072/85.90% | LR: 8.86e-06 | Time: 23.0s +Epoch 48/50 | Train: 0.1414/95.01% | Val: 0.5112/85.76% | LR: 3.94e-06 | Time: 22.8s +Epoch 49/50 | Train: 0.1415/94.98% | Val: 0.5138/85.58% | LR: 9.87e-07 | Time: 23.0s +Epoch 50/50 | Train: 0.1396/95.06% | Val: 0.5134/85.76% | LR: 0.00e+00 | Time: 22.8s diff --git a/wandb/run-20250718_041408-zryhujpe/files/requirements.txt b/wandb/run-20250718_041408-zryhujpe/files/requirements.txt new file mode 100644 index 0000000000000000000000000000000000000000..cdcf2c7afa19d3da6bbc19e09d9933ed3c5c0bd7 --- /dev/null +++ b/wandb/run-20250718_041408-zryhujpe/files/requirements.txt @@ -0,0 +1,120 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wandb.init called with sweep_config: {} +config: {'epochs': 50, 'device': 'cuda', 'optimizer': 'AdamW', 'scheduler': 'CosineAnnealingLR', 'learning_rate': 0.001, 'model_name': 'ResNet18', 'total_params': 11181642, 'trainable_params': 11181642, 'model_size_mb': 42.654579162597656, 'architecture': 'ResNet', '_wandb': {}} +2025-07-18 04:14:08,114 INFO MainThread:5644 [wandb_init.py:init():871] starting backend +2025-07-18 04:14:08,114 INFO MainThread:5644 [wandb_init.py:init():874] sending inform_init request +2025-07-18 04:14:08,123 INFO MainThread:5644 [wandb_init.py:init():882] backend started and connected +2025-07-18 04:14:08,163 INFO MainThread:5644 [wandb_init.py:init():953] updated telemetry +2025-07-18 04:14:08,171 INFO MainThread:5644 [wandb_init.py:init():977] communicating run to backend with 90.0 second timeout +2025-07-18 04:14:10,234 INFO MainThread:5644 [wandb_init.py:init():1029] starting run threads in backend +2025-07-18 04:14:10,687 INFO MainThread:5644 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[wandb_run.py:_footer_history_summary_info():3903] rendering history +2025-07-18 04:42:51,595 INFO MainThread:5644 [wandb_run.py:_footer_history_summary_info():3935] rendering summary +2025-07-18 04:42:51,599 INFO MainThread:5644 [wandb_run.py:_footer_sync_info():3864] logging synced files diff --git a/wandb/run-20250718_041408-zryhujpe/run-zryhujpe.wandb b/wandb/run-20250718_041408-zryhujpe/run-zryhujpe.wandb new file mode 100644 index 0000000000000000000000000000000000000000..dc4b5d78d7fcb8405550808e98559008c56a0fde --- /dev/null +++ b/wandb/run-20250718_041408-zryhujpe/run-zryhujpe.wandb @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:e2191c26ca7c3855982ea585c35bc76930282e62fa4af7a5b68208b946264d5a +size 10416370 diff --git a/wandb_utils.py b/wandb_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..204b0b8802fe628547f0fa3f107e88f1a7f1b77f --- /dev/null +++ b/wandb_utils.py @@ -0,0 +1,190 @@ +import wandb +import torch +import torch.nn as nn +import numpy as np +from typing import Dict, Any, Optional +import matplotlib.pyplot as plt +import seaborn as sns +from sklearn.metrics import confusion_matrix +from utils.data_loader import get_cifar10_info + +class WandbLogger: + """Minimal yet powerful W&B integration for FAANG-level ML projects.""" + + def __init__(self, project: str = "cifar10-benchmark", entity: Optional[str] = None): + self.project = project + self.entity = entity + self.run = None + + def init_experiment(self, config: Dict[str, Any], model: nn.Module, model_name: str): + """Initialize W&B run with model architecture tracking.""" + # Auto-detect model stats for config + total_params = sum(p.numel() for p in model.parameters()) + trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) + + enhanced_config = { + **config, + 'model_name': model_name, + 'total_params': total_params, + 'trainable_params': trainable_params, + 'model_size_mb': total_params * 4 / (1024 ** 2), + 'architecture': str(model.__class__.__name__) + } + + self.run = wandb.init( + project=self.project, + entity=self.entity, + config=enhanced_config, + name=f"{model_name}-{wandb.util.generate_id()}" + ) + + # Log model architecture + wandb.watch(model, log_freq=100, log_graph=True) + return self.run + + def log_metrics(self, metrics: Dict[str, float], step: int): + """Log training metrics with automatic prefixing.""" + wandb.log(metrics, step=step) + + def log_confusion_matrix(self, y_true: np.ndarray, y_pred: np.ndarray, epoch: int): + """Log confusion matrix as W&B image.""" + cifar10_info = get_cifar10_info() + cm = confusion_matrix(y_true, y_pred) + + plt.figure(figsize=(8, 6)) + sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', + xticklabels=cifar10_info['class_names'], + yticklabels=cifar10_info['class_names']) + plt.title(f'Confusion Matrix - Epoch {epoch}') + plt.tight_layout() + + wandb.log({ + "confusion_matrix": wandb.Image(plt), + "epoch": epoch + }) + plt.close() + + def log_model_checkpoint(self, model: nn.Module, optimizer, epoch: int, + metrics: Dict[str, float], is_best: bool = False): + """Log model checkpoint with metadata.""" + checkpoint = { + 'epoch': epoch, + 'model_state_dict': model.state_dict(), + 'optimizer_state_dict': optimizer.state_dict(), + **metrics + } + + filename = f"model_epoch_{epoch}.pth" + torch.save(checkpoint, filename) + + artifact = wandb.Artifact( + name=f"model-{self.run.id}", + type="model", + metadata={"epoch": epoch, "is_best": is_best, **metrics} + ) + artifact.add_file(filename) + wandb.log_artifact(artifact) + + def finish(self): + """Cleanup W&B run.""" + if self.run: + wandb.finish() + +def create_hyperparameter_sweep(): + """FAANG-level hyperparameter sweep configuration.""" + return { + 'method': 'bayes', + 'metric': {'name': 'val_accuracy', 'goal': 'maximize'}, + 'parameters': { + 'learning_rate': {'distribution': 'log_uniform', 'min': 1e-5, 'max': 1e-2}, + 'batch_size': {'values': [32, 64, 128]}, + 'weight_decay': {'distribution': 'log_uniform', 'min': 1e-6, 'max': 1e-3}, + 'optimizer': {'values': ['adamw', 'sgd']}, + 'scheduler': {'values': ['cosine', 'step']}, + 'dropout_rate': {'distribution': 'uniform', 'min': 0.1, 'max': 0.5} + } + } + +def run_hyperparameter_sweep(train_fn, sweep_config: Dict[str, Any], count: int = 20): + """Execute hyperparameter sweep with W&B.""" + sweep_id = wandb.sweep(sweep_config, project="cifar10-benchmark") + wandb.agent(sweep_id, train_fn, count=count) + +# Integration with existing training loop +def enhanced_train_step(model, train_loader, val_loader, optimizer, criterion, + scheduler, num_epochs, device, logger: WandbLogger): + """Enhanced training with W&B logging.""" + model.to(device) + best_val_acc = 0.0 + + for epoch in range(num_epochs): + # Training + model.train() + train_loss, train_acc = 0.0, 0.0 + + for batch_idx, (inputs, targets) in enumerate(train_loader): + inputs, targets = inputs.to(device), targets.to(device) + + optimizer.zero_grad() + outputs = model(inputs) + loss = criterion(outputs, targets) + loss.backward() + optimizer.step() + + train_loss += loss.item() + train_acc += (outputs.argmax(1) == targets).float().mean().item() + + # Validation + model.eval() + val_loss, val_acc = 0.0, 0.0 + all_preds, all_targets = [], [] + + with torch.no_grad(): + for inputs, targets in val_loader: + inputs, targets = inputs.to(device), targets.to(device) + outputs = model(inputs) + loss = criterion(outputs, targets) + + val_loss += loss.item() + val_acc += (outputs.argmax(1) == targets).float().mean().item() + + all_preds.extend(outputs.argmax(1).cpu().numpy()) + all_targets.extend(targets.cpu().numpy()) + + # Normalize metrics + train_loss /= len(train_loader) + train_acc /= len(train_loader) + val_loss /= len(val_loader) + val_acc /= len(val_loader) + + scheduler.step() + + # Log to W&B + logger.log_metrics({ + 'epoch': epoch, + 'train_loss': train_loss, + 'train_accuracy': train_acc * 100, + 'val_loss': val_loss, + 'val_accuracy': val_acc * 100, + 'learning_rate': optimizer.param_groups[0]['lr'] + }, step=epoch) + + # Log confusion matrix every 10 epochs + if (epoch + 1) % 10 == 0: + logger.log_confusion_matrix(all_targets, all_preds, epoch) + + # Save best model + is_best = val_acc > best_val_acc + if is_best: + best_val_acc = val_acc + logger.log_model_checkpoint( + model, optimizer, epoch, + {'val_accuracy': val_acc, 'val_loss': val_loss}, + is_best=True + ) + + print(f"Epoch {epoch+1}/{num_epochs} | " + f"Train: {train_loss:.4f}/{train_acc:.3f} | " + f"Val: {val_loss:.4f}/{val_acc:.3f}") + + return best_val_acc \ No newline at end of file