diff --git a/.gitignore b/.gitignore index 308af3ccf..f516b4165 100755 --- a/.gitignore +++ b/.gitignore @@ -1,2 +1,5 @@ __pycache__/ .cache/ +out +.DS_Store +*.pth \ No newline at end of file diff --git a/.python-version b/.python-version new file mode 100644 index 000000000..e4fba2183 --- /dev/null +++ b/.python-version @@ -0,0 +1 @@ +3.12 diff --git a/.vscode/launch.json b/.vscode/launch.json new file mode 100644 index 000000000..9f899083d --- /dev/null +++ b/.vscode/launch.json @@ -0,0 +1,14 @@ +{ + "version": "0.2.0", + "configurations": [ + { + "name": "Python: Debug Current File", + "type": "debugpy", + "request": "launch", + "program": "${file}", + "console": "integratedTerminal", + "cwd": "${workspaceFolder}", + "justMyCode": true + } + ] +} diff --git a/SAGEMAKER_SETUP.md b/SAGEMAKER_SETUP.md new file mode 100644 index 000000000..cf1b7b9e8 --- /dev/null +++ b/SAGEMAKER_SETUP.md @@ -0,0 +1,170 @@ +# SageMaker Image Generation Setup Guide + +## What You Have Now + +✅ **requirements.txt** - All Python dependencies exported from your uv setup +✅ **launch_sagemaker_generation.py** - SageMaker launcher script +✅ **AWS credentials** - Configured via SSO (profile: Timothe) +✅ **S3 bucket** - stylegan2-generated-images_v2 +✅ **IAM Role** - arn:aws:iam::183295429625:role/service-role/AmazonSageMaker-ExecutionRole-20241001T141781 + +## How It Works + +1. **ScriptProcessor** uploads your entire repo to SageMaker +2. Installs dependencies from requirements.txt +3. Runs generate.py on GPU instances (ml.g4dn.xlarge) +4. Each instance generates a portion of the 1M images +5. Images are continuously uploaded to S3 as they're generated + +## Step-by-Step Instructions + +### Step 1: Install SageMaker Python SDK + +```bash +uv pip install sagemaker boto3 +``` + +### Step 2: (Optional) Upload Your Trained Model to S3 + +If you have a custom trained model (not using NVIDIA's pretrained): + +```bash +# Upload pre-trained VGG checkpoint to S3 +aws s3 cp /home/sagemaker-user/stylegan2-ada-pytorch/dex_age_classifier.pth \ +s3://emobot-prod-workspace-bucket/usecases/emobot-research/datasets/stylegan2-generated-images_v2/models/vgg.pth + +# Upload weight vector +aws s3 cp /home/sagemaker-user/stylegan2-ada-pytorch/weight.npy \ +s3://emobot-prod-workspace-bucket/usecases/emobot-research/datasets/stylegan2-generated-images_v2/models/weight.npy +``` + +### Step 3: Run a Test Job (RECOMMENDED) + +Start with a small test to verify everything works: + +```bash +python launch_sagemaker_generation.py --test +``` + +This will: +- Generate only 100 images +- Use 1 instance +- Run for max 1 hour +- Help verify your setup works + +### Step 4: Launch Full Production Job + +Once the test succeeds, launch the full job: + +```bash +# Using NVIDIA's pretrained FFHQ model (no upload needed) +python launch_sagemaker_generation.py \ + --total-images 1000000 \ + --instances 10 \ + --role arn:aws:iam::183295429625:role/service-role/AmazonSageMaker-ExecutionRole-20241001T141781 + +# OR with your custom model +python launch_sagemaker_generation.py \ + --total-images 1000000 \ + --instances 10 \ + --role arn:aws:iam::183295429625:role/service-role/AmazonSageMaker-ExecutionRole-20241001T141781 \ + --model-s3 s3://stylegan2-generated-images_v2/models/model.pkl + +# OR with weight vector modulation +python launch_sagemaker_generation.py \ + --total-images 1000000 \ + --instances 10 \ + --role arn:aws:iam::183295429625:role/service-role/AmazonSageMaker-ExecutionRole-20241001T141781 \ + --weight-vector-s3 s3://stylegan2-generated-images_v2/models/weight.npy +``` + +### Step 5: Monitor Progress + +Monitor your job at: +https://console.aws.amazon.com/sagemaker/home?region=eu-west-3#/processing-jobs + +Or via CLI: +```bash +aws sagemaker list-processing-jobs --profile Timothe +``` + +### Step 6: Access Generated Images + +Images will be uploaded to: +``` +s3://stylegan2-generated-images_v2/generated_images_YYYYMMDD_HHMMSS/ +``` + +Download them: +```bash +aws s3 sync s3://stylegan2-generated-images_v2/generated_images_YYYYMMDD_HHMMSS/ ./downloaded_images/ --profile Timothe +``` + +## Configuration Options + +### Instance Types + +- **ml.g4dn.xlarge** (1 GPU, 16 GB RAM) - ~$0.736/hour - Default +- **ml.g4dn.2xlarge** (1 GPU, 32 GB RAM) - ~$0.94/hour - More memory +- **ml.g4dn.4xlarge** (1 GPU, 64 GB RAM) - ~$1.50/hour - If you need more RAM + +### Cost Estimation + +For 1M images with default settings: +- 10 instances × ml.g4dn.xlarge × 24 hours = ~$176 +- Adjust instance_count and max_runtime based on your needs + +### Parallelization Strategy + +The script splits images evenly across instances: +- **10 instances** → 100K images each +- **20 instances** → 50K images each (faster but more expensive) +- **5 instances** → 200K images each (cheaper but slower) + +## Customizing Generation + +Edit `launch_sagemaker_generation.py` to customize: + +```python +launch_image_generation( + total_images=1_000_000, + instance_count=10, + truncation_psi=0.7, # Adjust truncation + noise_mode='random', # or 'const', 'none' + alphas="-5,0,5", # For weight modulation + style_range=(0, 17), # Style block range + max_runtime_hours=24, +) +``` + +## Troubleshooting + +### Issue: "Could not auto-detect SageMaker role" +**Solution:** Pass the role explicitly with `--role` flag + +### Issue: "Access Denied" when uploading to S3 +**Solution:** Check your IAM role has S3 write permissions: +```bash +aws iam get-role-policy --role-name AmazonSageMaker-ExecutionRole-20241001T141781 --policy-name S3Access --profile Timothe +``` + +### Issue: Job fails with "CUDA out of memory" +**Solution:** Reduce batch size or use larger instance (ml.g4dn.2xlarge) + +### Issue: Dependencies fail to install +**Solution:** The requirements.txt is comprehensive. If issues persist, you may need to create a custom Docker image. + +## Next Steps After Setup + +1. ✅ Run test job with `--test` flag +2. ✅ Verify images appear in S3 +3. ✅ Launch full production job +4. ✅ Monitor progress in SageMaker console +5. ✅ Download and use generated images + +## Important Notes + +- **Continuous Upload**: Images upload to S3 as they're generated (no need to wait for job completion) +- **Resumability**: If a job fails, you can restart with different seed ranges +- **Cost Control**: Set `max_runtime_hours` to prevent runaway costs +- **Region**: All resources are in eu-west-3 (Paris) diff --git a/age_estimator.py b/age_estimator.py new file mode 100644 index 000000000..6a768400a --- /dev/null +++ b/age_estimator.py @@ -0,0 +1,34 @@ +import torch +import torch.nn.functional as F +from vgg import VGG + + +class AgePredictor: + def __init__(self, vgg_path: str): + self.age_net = VGG() + ckpt = torch.load(vgg_path, map_location="cpu")['state_dict'] + ckpt = {k.replace('-', '_'): v for k, v in ckpt.items()} + self.age_net.load_state_dict(ckpt) + self.age_net.cuda() + self.age_net.eval() + self.min_age = 0 + self.max_age = 100 + + def __get_predicted_age(self, age_pb): + predict_age_pb = F.softmax(age_pb) + predict_age = torch.zeros(age_pb.size(0)).type_as(predict_age_pb) + for i in range(age_pb.size(0)): + for j in range(age_pb.size(1)): + predict_age[i] += j * predict_age_pb[i][j] + return predict_age + + def extract_age(self, x): + x = F.interpolate(x, size=(224, 224), mode='bilinear', align_corners=False) # shape: [1, 3, 224, 224] + predict_age_pb = self.age_net(x)['fc8'] + predicted_age = self.__get_predicted_age(predict_age_pb) + return predicted_age + + def __call__(self, img: torch.tensor) -> float | None: + """Allow instance to be called directly.""" + return self.extract_age(img)[0] + \ No newline at end of file diff --git a/alpha_patch_work.png b/alpha_patch_work.png new file mode 100644 index 000000000..b7bd28aeb Binary files /dev/null and b/alpha_patch_work.png differ diff --git a/analyze_color_by_style_range.py b/analyze_color_by_style_range.py new file mode 100644 index 000000000..b80fa35fe --- /dev/null +++ b/analyze_color_by_style_range.py @@ -0,0 +1,347 @@ +""" +Analyze and compare color shifts across different style ranges. + +This script loads images generated with different style ranges and alpha values, +then creates comparison plots showing how each style range affects color metrics. +""" + +import os +import re +from pathlib import Path +import argparse + +import numpy as np +import matplotlib.pyplot as plt +from PIL import Image +import seaborn as sns + + +def load_images_by_style_range(image_dir: str, pattern: str = "seed*_alpha*_styles*.png") -> dict: + """ + Load all generated images and organize by style range, seed, and alpha. + + Returns: + Dict with structure: {style_range: {seed: {alpha: image_array}}} + """ + image_dir = Path(image_dir) + images = {} + + # Pattern to extract seed, alpha, and style range from filename + regex = re.compile(r'seed(\d+)_alpha(-?\d+)_styles(\d+)-(\d+)\.png') + + for img_path in sorted(image_dir.glob(pattern)): + match = regex.match(img_path.name) + if match: + seed = int(match.group(1)) + alpha = int(match.group(2)) + style_start = int(match.group(3)) + style_end = int(match.group(4)) + style_range = (style_start, style_end) + + img = np.array(Image.open(img_path)) + + if style_range not in images: + images[style_range] = {} + if seed not in images[style_range]: + images[style_range][seed] = {} + images[style_range][seed][alpha] = { + 'image': img, + 'path': str(img_path) + } + + return images + + +def compute_color_statistics(image: np.ndarray) -> dict: + """Compute RGB color statistics for an image.""" + stats_dict = {} + + for i, channel in enumerate(['R', 'G', 'B']): + channel_data = image[:, :, i].flatten() + stats_dict[f'rgb_{channel}_mean'] = np.mean(channel_data) + stats_dict[f'rgb_{channel}_std'] = np.std(channel_data) + + stats_dict['overall_brightness'] = np.mean(image) + + return stats_dict + + +def analyze_style_range_data(style_range_data: dict, reference_alpha: int = 0) -> dict: + """ + Analyze all images for a single style range across seeds and alphas. + + Args: + style_range_data: Dict mapping seed -> {alpha -> image data} + reference_alpha: Alpha value to use as reference + + Returns: + Dict with analysis results aggregated across seeds + """ + first_seed = next(iter(style_range_data.keys())) + alphas = sorted(style_range_data[first_seed].keys()) + + reference_alpha = 0 + + results = { + 'alphas': alphas, + 'rgb_mean_shift': [], + 'brightness_shift': [], + 'rgb_R_mean': [], + 'rgb_G_mean': [], + 'rgb_B_mean': [], + 'rgb_R_std': [], + 'rgb_G_std': [], + 'rgb_B_std': [], + } + + # For each alpha, aggregate across all seeds + for alpha in alphas: + alpha_rgb_shift = [] + alpha_brightness_shift = [] + alpha_stats = {key: [] for key in ['rgb_R_mean', 'rgb_G_mean', 'rgb_B_mean', + 'rgb_R_std', 'rgb_G_std', 'rgb_B_std']} + + for seed, seed_data in style_range_data.items(): + img = seed_data[alpha]['image'] + ref_img = seed_data[reference_alpha]['image'] + + stats_current = compute_color_statistics(img) + stats_ref = compute_color_statistics(ref_img) + + rgb_shift = np.sqrt( + (stats_current['rgb_R_mean'] - stats_ref['rgb_R_mean'])**2 + + (stats_current['rgb_G_mean'] - stats_ref['rgb_G_mean'])**2 + + (stats_current['rgb_B_mean'] - stats_ref['rgb_B_mean'])**2 + ) + alpha_rgb_shift.append(rgb_shift) + + brightness_shift = stats_current['overall_brightness'] - stats_ref['overall_brightness'] + alpha_brightness_shift.append(brightness_shift) + + for key in alpha_stats.keys(): + alpha_stats[key].append(stats_current[key]) + + results['rgb_mean_shift'].append(np.mean(alpha_rgb_shift)) + results['brightness_shift'].append(np.mean(alpha_brightness_shift)) + + for key in alpha_stats.keys(): + results[key].append(np.mean(alpha_stats[key])) + + return results + + +def get_style_range_label(style_range: tuple[int, int]) -> str: + """Get a descriptive label for a style range.""" + start, end = style_range + if start <= 8 and end <= 8: + return f"Coarse ({start}-{end})" + elif start >= 4 and end <= 12: + return f"Middle ({start}-{end})" + elif start >= 12: + return f"Fine ({start}-{end})" + else: + return f"Blocks {start}-{end}" + + +def plot_comparison_by_style_range(all_results: dict, output_dir: str): + """ + Create comparison plots showing different style ranges. + + Args: + all_results: Dict mapping style_range -> analysis results + output_dir: Directory to save plots + """ + output_dir = Path(output_dir) + output_dir.mkdir(exist_ok=True) + + sns.set_style("whitegrid") + + style_colors = {} + color_palette = sns.color_palette("husl", len(all_results)) + for idx, style_range in enumerate(sorted(all_results.keys())): + style_colors[style_range] = color_palette[idx] + + alphas = next(iter(all_results.values()))['alphas'] + ref_idx = min(range(len(alphas)), key=lambda i: abs(alphas[i])) + + # fig1. Overview plot with 2 key metrics + fig, axes = plt.subplots(1, 2, figsize=(14, 5)) + + # Plot 1: RGB Mean Shift + ax = axes[0] + for style_range in sorted(all_results.keys()): + results = all_results[style_range] + label = get_style_range_label(style_range) + ax.plot(results['alphas'], results['rgb_mean_shift'], + marker='s', linewidth=2, label=label, color=style_colors[style_range]) + ax.set_xlabel('Alpha', fontsize=12) + ax.set_ylabel('RGB Mean Shift (Euclidean)', fontsize=12) + ax.set_title('RGB Color Mean Shift vs Alpha', fontsize=14, fontweight='bold') + ax.legend() + ax.grid(True, alpha=0.3) + ax.axhline(y=0, color='black', linestyle='-', alpha=0.3, linewidth=1) + + # Plot 2: Brightness Shift + ax = axes[1] + for style_range in sorted(all_results.keys()): + results = all_results[style_range] + label = get_style_range_label(style_range) + ax.plot(results['alphas'], results['brightness_shift'], + marker='d', linewidth=2, label=label, color=style_colors[style_range]) + ax.set_xlabel('Alpha', fontsize=12) + ax.set_ylabel('Brightness Shift', fontsize=12) + ax.set_title('Overall Brightness Change vs Alpha', fontsize=14, fontweight='bold') + ax.legend() + ax.grid(True, alpha=0.3) + ax.axhline(y=0, color='black', linestyle='-', alpha=0.3, linewidth=1) + + plt.suptitle('Color Analysis by Style Range (Averaged Across Seeds)', + fontsize=16, y=0.995, fontweight='bold') + plt.tight_layout() + plt.savefig(output_dir / 'color_analysis_by_style_range_overview.png', dpi=150, bbox_inches='tight') + plt.close() + + # fig2. RGB Channel Analysis - 2 rows: means and proportions for each channel + fig, axes = plt.subplots(2, 3, figsize=(18, 10)) + + rgb_channels = ['R', 'G', 'B'] + channel_colors = {'R': '#E63946', 'G': '#06D6A0', 'B': '#118AB2'} + channel_names = {'R': 'Red', 'G': 'Green', 'B': 'Blue'} + + # Row 0: Mean plots for each channel + for col_idx, channel in enumerate(rgb_channels): + ax = axes[0, col_idx] + for style_range in sorted(all_results.keys()): + results = all_results[style_range] + label = get_style_range_label(style_range) + vals = np.array(results[f'rgb_{channel}_mean']) + ref_val = vals[ref_idx] + pct_change = (vals / ref_val - 1) * 100 + ax.plot(results['alphas'], pct_change, + marker='o', linewidth=2, label=label, color=style_colors[style_range]) + + ax.set_xlabel('Alpha', fontsize=11) + ax.set_ylabel(f'{channel} Channel Mean Change (%)', fontsize=11) + ax.set_title(f'{channel} Channel Mean vs Alpha', fontsize=12, fontweight='bold', color=channel_colors[channel]) + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + ax.axhline(y=0, color='black', linestyle='-', alpha=0.3, linewidth=1) + + # Row 1: Proportion plots for each channel (R/(R+G+B), G/(R+G+B), B/(R+G+B)) + for col_idx, channel in enumerate(rgb_channels): + ax = axes[1, col_idx] + + for style_range in sorted(all_results.keys()): + results = all_results[style_range] + label = get_style_range_label(style_range) + + r_vals = np.array(results['rgb_R_mean']) + g_vals = np.array(results['rgb_G_mean']) + b_vals = np.array(results['rgb_B_mean']) + total = r_vals + g_vals + b_vals + + # Calculate proportion for current channel + if channel == 'R': + proportion = r_vals / total + elif channel == 'G': + proportion = g_vals / total + else: # 'B' + proportion = b_vals / total + + ref_proportion = proportion[ref_idx] + + # Convert to percentage change + pct_change = (proportion / ref_proportion - 1) * 100 + + ax.plot(results['alphas'], pct_change, + marker='D', linewidth=2, label=label, color=style_colors[style_range]) + + ax.set_xlabel('Alpha', fontsize=11) + ax.set_ylabel(f'{channel_names[channel]} Proportion Change (%)', fontsize=11) + ax.set_title(f'{channel_names[channel]} Proportion vs Alpha\n({channel} / (R+G+B))', + fontsize=12, fontweight='bold', color=channel_colors[channel]) + ax.legend(fontsize=8) + ax.grid(True, alpha=0.3) + ax.axhline(y=0, color='black', linestyle='-', alpha=0.3, linewidth=1) + + plt.suptitle('RGB Channel Analysis by Style Range (% Change)', + fontsize=16, y=0.995, fontweight='bold') + plt.tight_layout() + plt.savefig(output_dir / 'rgb_analysis_by_style_range.png', dpi=150, bbox_inches='tight') + plt.close() + + print(f'\nComparison plots saved to {output_dir}/') + + +def print_summary_statistics(all_results: dict): + """Print summary statistics about RGB color shifts for each style range.""" + print("\n" + "="*70) + print("RGB COLOR SHIFT COMPARISON BY STYLE RANGE") + print("="*70) + + for style_range in sorted(all_results.keys()): + results = all_results[style_range] + label = get_style_range_label(style_range) + + print(f"\n{label}:") + print(f" Alpha range: {min(results['alphas'])} to {max(results['alphas'])}") + print(f" Max RGB shift: {max(results['rgb_mean_shift']):.2f}") + print(f" Max brightness shift: {max(results['brightness_shift']):.2f}") + + print("\n" + "="*70) + + +def main(): + parser = argparse.ArgumentParser(description='Compare color shifts across different style ranges') + parser.add_argument('--image-dir', type=str, required=True, + help='Directory containing generated images') + parser.add_argument('--output-dir', type=str, default=None, + help='Directory to save analysis plots (default: image-dir/analysis_by_range)') + parser.add_argument('--reference-alpha', type=int, default=0, + help='Alpha value to use as reference (default: 0)') + + args = parser.parse_args() + + # Set output directory + if args.output_dir is None: + args.output_dir = os.path.join(args.image_dir, 'analysis_by_range') + + print(f"Loading images from: {args.image_dir}") + images = load_images_by_style_range(args.image_dir) + + if not images: + print("Error: No images found matching the pattern 'seed*_alpha*_styles*.png'") + return + + print(f"Found {len(images)} style ranges:") + for style_range in sorted(images.keys()): + num_seeds = len(images[style_range]) + num_alphas = len(next(iter(images[style_range].values()))) + print(f" {get_style_range_label(style_range)}: {num_seeds} seeds × {num_alphas} alphas") + + # Analyze each style range + print("\nAnalyzing color statistics by style range...") + all_results = {} + for style_range, style_data in images.items(): + print(f" Processing {get_style_range_label(style_range)}...") + all_results[style_range] = analyze_style_range_data(style_data, args.reference_alpha) + + # Print summary + print_summary_statistics(all_results) + + # Create visualizations + print(f"\nGenerating comparison plots...") + plot_comparison_by_style_range(all_results, args.output_dir) + + print(f"\nAnalysis complete! Check {args.output_dir}/ for visualizations.") + + +if __name__ == '__main__': + main() + + +# Basic usage +# uv run python analyze_color_by_style_range.py --image-dir out/ + +# Specify output directory and reference alpha +# uv run python analyze_color_by_style_range.py --image-dir out/ --output-dir analysis_results/ --reference-alpha 0 diff --git a/create_alpha_patchwork.py b/create_alpha_patchwork.py new file mode 100644 index 000000000..438bfe173 --- /dev/null +++ b/create_alpha_patchwork.py @@ -0,0 +1,218 @@ +""" +Create a patchwork visualization of generated images across different alpha values. + +For each alpha value, select n=25 images and arrange them in a 5x5 grid. +Then arrange these grids in a larger grid (e.g., 5x5) to show 25 different alpha values. +This helps visualize colorimetric differences and brightness across alpha values. +""" + +import os +import click +import numpy as np +from PIL import Image +import matplotlib.pyplot as plt +from pathlib import Path + + +def get_alpha_dirs(base_dir): + """Get all alpha_* directories and sort them by alpha value.""" + alpha_dirs = [] + for item in os.listdir(base_dir): + if item.startswith("alpha_"): + try: + alpha_value = float(item.replace("alpha_", "")) + alpha_path = os.path.join(base_dir, item) + if os.path.isdir(alpha_path): + alpha_dirs.append((alpha_value, alpha_path)) + except ValueError: + continue + + # Sort by alpha value + alpha_dirs.sort(key=lambda x: x[0]) + return alpha_dirs + + +def get_images_from_dir(dir_path, max_images=25): + """Get up to max_images PNG files from a directory.""" + images = [] + for file in sorted(os.listdir(dir_path)): + if file.endswith(".png"): + images.append(os.path.join(dir_path, file)) + if len(images) >= max_images: + break + return images + + +def create_grid_from_images(image_paths, grid_size=5): + """Create a single grid image from a list of image paths.""" + if not image_paths: + return None + + # Load first image to get dimensions + first_img = Image.open(image_paths[0]) + img_width, img_height = first_img.size + + # Create a blank canvas for the grid + grid_img = Image.new( + "RGB", (img_width * grid_size, img_height * grid_size), color="black" + ) + + # Place images in grid + for idx, img_path in enumerate(image_paths[: grid_size * grid_size]): + if idx >= grid_size * grid_size: + break + + row = idx // grid_size + col = idx % grid_size + + img = Image.open(img_path) + grid_img.paste(img, (col * img_width, row * img_height)) + + return grid_img + + +@click.command() +@click.option( + "--input-dir", + type=click.Path(exists=True), + required=True, + help="Directory containing alpha_* subdirectories with generated images", +) +@click.option( + "--output-path", + type=click.Path(), + required=True, + help="Output path for the patchwork image", +) +@click.option( + "--images-per-alpha", + type=int, + default=25, + help="Number of images to sample per alpha value (default: 25)", +) +@click.option( + "--grid-size", + type=int, + default=5, + help="Size of the grid for each alpha patch (default: 5x5)", +) +@click.option( + "--max-alphas", + type=int, + default=25, + help="Maximum number of alpha values to include (default: 25)", +) +@click.option( + "--patchwork-cols", + type=int, + default=5, + help="Number of columns in the main patchwork grid (default: 5)", +) +def create_patchwork( + input_dir, output_path, images_per_alpha, grid_size, max_alphas, patchwork_cols +): + """Create a patchwork visualization of images across different alpha values.""" + + print(f"Scanning directory: {input_dir}") + alpha_dirs = get_alpha_dirs(input_dir) + + if not alpha_dirs: + print(f"No alpha_* directories found in {input_dir}") + return + + print(f"Found {len(alpha_dirs)} alpha directories") + + # Limit to max_alphas + alpha_dirs = alpha_dirs[:max_alphas] + print(f"Using {len(alpha_dirs)} alpha values") + + # Create grids for each alpha + alpha_grids = [] + alpha_values = [] + + for alpha_value, alpha_path in alpha_dirs: + print(f"Processing alpha={alpha_value}...") + + # Get images from this alpha directory + image_paths = get_images_from_dir(alpha_path, max_images=images_per_alpha) + + if len(image_paths) < images_per_alpha: + print( + f" Warning: Only found {len(image_paths)} images (expected {images_per_alpha})" + ) + + if not image_paths: + print(f" Skipping alpha={alpha_value} (no images found)") + continue + + # Create grid for this alpha + grid = create_grid_from_images(image_paths, grid_size=grid_size) + if grid: + alpha_grids.append(grid) + alpha_values.append(alpha_value) + print( + f" Created {grid_size}x{grid_size} grid with {len(image_paths)} images" + ) + + if not alpha_grids: + print("No grids created. Exiting.") + return + + print(f"\nCreating final patchwork with {len(alpha_grids)} grids...") + + # Calculate patchwork dimensions + patchwork_rows = (len(alpha_grids) + patchwork_cols - 1) // patchwork_cols + + # Create final patchwork with matplotlib for better control and labels + fig_width = patchwork_cols * 4 + fig_height = patchwork_rows * 4 + + fig, axes = plt.subplots( + patchwork_rows, patchwork_cols, figsize=(fig_width, fig_height) + ) + + # Handle case where there's only one row or column + if patchwork_rows == 1 and patchwork_cols == 1: + axes = np.array([[axes]]) + elif patchwork_rows == 1: + axes = axes.reshape(1, -1) + elif patchwork_cols == 1: + axes = axes.reshape(-1, 1) + + # Plot each grid + for idx, (grid, alpha_val) in enumerate(zip(alpha_grids, alpha_values)): + row = idx // patchwork_cols + col = idx % patchwork_cols + + ax = axes[row, col] + ax.imshow(grid) + ax.set_title(f"α = {alpha_val}", fontsize=14, fontweight="bold") + ax.axis("off") + + # Hide unused subplots + for idx in range(len(alpha_grids), patchwork_rows * patchwork_cols): + row = idx // patchwork_cols + col = idx % patchwork_cols + axes[row, col].axis("off") + + plt.suptitle( + f"Alpha Value Patchwork ({grid_size}×{grid_size} images per alpha)", + fontsize=16, + fontweight="bold", + y=0.995, + ) + plt.tight_layout() + + # Save the figure + print(f"Saving patchwork to: {output_path}/alpha_patch_work") + plt.savefig(output_path, dpi=150, bbox_inches="tight") + plt.close() + + print(f"✓ Patchwork created successfully!") + print(f" - {len(alpha_grids)} alpha values") + print(f" - {grid_size}×{grid_size} images per alpha") + print(f" - {patchwork_rows}×{patchwork_cols} patchwork grid") + + +if __name__ == "__main__": + create_patchwork() diff --git a/generate.py b/generate.py index f7f961931..6f0170fe7 100755 --- a/generate.py +++ b/generate.py @@ -10,6 +10,7 @@ import os import re +import warnings from typing import List, Optional import click @@ -17,11 +18,28 @@ import numpy as np import PIL.Image import torch +import matplotlib.pyplot as plt import legacy +from age_estimator import AgePredictor -#---------------------------------------------------------------------------- +# Suppress CUDA kernel compilation warnings +warnings.filterwarnings('ignore', message='Failed to build CUDA kernels for upfirdn2d') +# ---------------------------------------------------------------------------- + + +def float_range(s: str) -> List[float]: + + parts = s.split(":") + if len(parts) == 3: + start, stop, num = float(parts[0]), float(parts[1]), int(parts[2]) + return list(np.round(np.linspace(start, stop, num), decimals=1)) + else: + raise ValueError( + f"Linspace format requires exactly 3 values (start:stop:num), got {len(parts)}" + ) + def num_range(s: str) -> List[int]: '''Accept either a comma separated list of numbers 'a,b,c' or a range 'a-c' and return as a list of ints.''' @@ -32,26 +50,92 @@ def num_range(s: str) -> List[int]: vals = s.split(',') return [int(x) for x in vals] -#---------------------------------------------------------------------------- + +# ---------------------------------------------------------------------------- + @click.command() @click.pass_context -@click.option('--network', 'network_pkl', help='Network pickle filename', required=True) -@click.option('--seeds', type=num_range, help='List of random seeds') -@click.option('--trunc', 'truncation_psi', type=float, help='Truncation psi', default=1, show_default=True) -@click.option('--class', 'class_idx', type=int, help='Class label (unconditional if not specified)') -@click.option('--noise-mode', help='Noise mode', type=click.Choice(['const', 'random', 'none']), default='const', show_default=True) -@click.option('--projected-w', help='Projection result file', type=str, metavar='FILE') -@click.option('--outdir', help='Where to save the output images', type=str, required=True, metavar='DIR') +@click.option("--network", "network_pkl", help="Network pickle filename", required=True) +@click.option( + "--vgg-path", + help="Path to vgg age predictor", + type=str, + metavar="FILE", +) +@click.option("--seeds", type=num_range, help="List of random seeds") +@click.option( + "--trunc", + "truncation_psi", + type=float, + help="Truncation psi", + default=1, + show_default=True, +) +@click.option( + "--class", + "class_idx", + type=int, + help="Class label (unconditional if not specified)", +) +@click.option( + "--noise-mode", + help="Noise mode", + type=click.Choice(["const", "random", "none"]), + default="const", + show_default=True, +) +@click.option("--projected-w", help="Projection result file", type=str, metavar="FILE") +@click.option( + "--outdir", + help="Where to save the output images", + type=str, + required=True, + metavar="DIR", +) +@click.option( + "--weight-vector", + help="Path to weight vector file (.npy)", + type=str, + metavar="FILE", +) + +@click.option( + "--alphas", + type=float_range, + help='Alpha values for weight modulation (e.g., "-10,0,10" or "-10-10")', + default="-5, -2, -1, 0, 1, 2, 5", + show_default=True, +) +@click.option( + "--style-range", + type=(int, int), + help="Range of style blocks to apply weight vector (start, end). Coarse: 0-8, Middle: 4-12, Fine: 12-17. Default: all (0, 17)", + default=(0, 17), + show_default=True, +) +@click.option( + "--create-composite", + type=bool, + help="Bolean to create or not a composite grid of the generated images", + default=False, + show_default=True, +) + def generate_images( ctx: click.Context, network_pkl: str, + vgg_path: str, seeds: Optional[List[int]], truncation_psi: float, noise_mode: str, outdir: str, class_idx: Optional[int], - projected_w: Optional[str] + projected_w: Optional[str], + weight_vector: Optional[str], + alphas: List[int], + style_range: tuple, + create_composite: bool = False, ): """Generate images using pretrained network pickle. @@ -76,54 +160,232 @@ def generate_images( # Render an image from projected W python generate.py --outdir=out --projected_w=projected_w.npz \\ --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/metfaces.pkl + + \b + # (Tim) Generate FFHQ images with weight modulation + python generate.py --outdir=stylegan2-ada-pytorch/out --trunc=0.7 --seeds=600-605 \\ + --weight-vector=weight.npy --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl + + \b + # (Tim) Generate with weight modulation on coarse styles only (pose, face shape, eyeglasses) + python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\ + --weight-vector=weight.npy --style-range 0 8 --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl + + \b + # (Tim) Generate with weight modulation on middle styles (facial features, hair style, eyes) + python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\ + --weight-vector=weight.npy --style-range 4 12 --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl + + \b + # (Tim) Generate with weight modulation on fine styles (color scheme, microstructure) + python generate.py --outdir=out --trunc=0.7 --seeds=600-605 \\ + --weight-vector=weight.npy --style-range 12 17 --network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl """ print('Loading networks from "%s"...' % network_pkl) - device = torch.device('cuda') + device = torch.device("cuda") with dnnlib.util.open_url(network_pkl) as f: - G = legacy.load_network_pkl(f)['G_ema'].to(device) # type: ignore + G = legacy.load_network_pkl(f)["G_ema"].to(device) # type: ignore os.makedirs(outdir, exist_ok=True) # Synthesize the result of a W projection. if projected_w is not None: if seeds is not None: - print ('warn: --seeds is ignored when using --projected-w') + print("warn: --seeds is ignored when using --projected-w") print(f'Generating images from projected W "{projected_w}"') - ws = np.load(projected_w)['w'] - ws = torch.tensor(ws, device=device) # pylint: disable=not-callable + ws = np.load(projected_w)["w"] + ws = torch.tensor(ws, device=device) # pylint: disable=not-callable assert ws.shape[1:] == (G.num_ws, G.w_dim) for idx, w in enumerate(ws): img = G.synthesis(w.unsqueeze(0), noise_mode=noise_mode) img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) - img = PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/proj{idx:02d}.png') + img = PIL.Image.fromarray(img[0].cpu().numpy(), "RGB").save( + f"{outdir}/proj{idx:02d}.png" + ) return if seeds is None: - ctx.fail('--seeds option is required when not using --projected-w') + ctx.fail("--seeds option is required when not using --projected-w") # Labels. label = torch.zeros([1, G.c_dim], device=device) if G.c_dim != 0: if class_idx is None: - ctx.fail('Must specify class label with --class when using a conditional network') + ctx.fail( + "Must specify class label with --class when using a conditional network" + ) label[:, class_idx] = 1 else: if class_idx is not None: - print ('warn: --class=lbl ignored when running on an unconditional network') + print("warn: --class=lbl ignored when running on an unconditional network") + + # Load weight vector if specified. + weight_vec = None + if weight_vector is not None: + print(f'Loading weight vector from "{weight_vector}"') + weight_vec = torch.tensor(np.load(weight_vector), device=device) + print(f"Weight vector shape: {weight_vec.shape}") + + # Validate style range + start_idx, end_idx = style_range + if not (0 <= start_idx <= 17 and 0 <= end_idx <= 17): + ctx.fail("Style range indices must be between 0 and 17") + if start_idx > end_idx: + ctx.fail("Style range start must be <= end") + print( + f"Applying weight vector to style blocks {start_idx} to {end_idx} (inclusive)" + ) + + # Initialize age predictor if using weight vector + age_predictor = None + if weight_vector is not None: + print("Initializing age predictor...") + age_predictor = AgePredictor(vgg_path=vgg_path) # Generate images. + all_images = [] # Store images for composite: list of lists (one per seed) + for seed_idx, seed in enumerate(seeds): - print('Generating image for seed %d (%d/%d) ...' % (seed, seed_idx, len(seeds))) + print("Generating image for seed %d (%d/%d) ..." % (seed, seed_idx, len(seeds))) z = torch.from_numpy(np.random.RandomState(seed).randn(1, G.z_dim)).to(device) - img = G(z, label, truncation_psi=truncation_psi, noise_mode=noise_mode) - img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) - PIL.Image.fromarray(img[0].cpu().numpy(), 'RGB').save(f'{outdir}/seed{seed:04d}.png') + w = G.mapping(z, label, truncation_psi=truncation_psi) + + if weight_vec is not None: + start_idx, end_idx = style_range + + # First, check age with alpha=0 if age estimator is available + if age_predictor is not None: + img = G.synthesis(w, noise_mode=noise_mode) + estimated_age = age_predictor(img) + + if estimated_age is None: + print(f" ⚠️ Seed {seed}: No face detected, skipping...") + continue + elif estimated_age <= 20: + print(f" ⚠️ Seed {seed}: Age {estimated_age:.1f} <= 20, skipping...") + continue + else: + print(f" ✓ Seed {seed}: Age {estimated_age:.1f} > 20, proceeding...") + + # If age check passed (or not applicable), generate all alphas + seed_images = [] + for alpha in alphas: + # Clone w to avoid modifying the original + w_modified = w.clone() + # Apply weight vector only to the specified range of style blocks + w_modified[:, start_idx : end_idx + 1, :] += alpha * weight_vec[ + start_idx : end_idx + 1, : + ].unsqueeze(0) + assert w_modified.shape[1:] == (G.num_ws, G.w_dim) + img = G.synthesis(w_modified, noise_mode=noise_mode) + img = ( + (img.permute(0, 2, 3, 1) * 127.5 + 128) + .clamp(0, 255) + .to(torch.uint8) + ) + img_array = img[0].cpu().numpy() + pil_img = PIL.Image.fromarray(img_array, "RGB") + + # Create subdirectory for this alpha value + alpha_dir = os.path.join(outdir, f"alpha_{alpha}") + os.makedirs(alpha_dir, exist_ok=True) + + pil_img.save( + f"{alpha_dir}/seed{seed:04d}_styles{start_idx}-{end_idx}.png" + ) + seed_images.append(img_array) + all_images.append(seed_images) + else: + # Generate image without weight modulation + img = G.synthesis(w, truncation_psi=truncation_psi, noise_mode=noise_mode) + img = (img.permute(0, 2, 3, 1) * 127.5 + 128).clamp(0, 255).to(torch.uint8) + PIL.Image.fromarray(img[0].cpu().numpy(), "RGB").save( + f"{outdir}/seed{seed:04d}.png" + ) + + # Create composite image if weight modulation was used + if create_composite and weight_vec is not None and len(all_images) > 0: + print("Creating composite image...") + num_rows = len(all_images) + num_cols = len(alphas) + start_idx, end_idx = style_range + # Create matplotlib figure + fig, axes = plt.subplots( + num_rows, num_cols, figsize=(num_cols * 3, num_rows * 3) + ) -#---------------------------------------------------------------------------- + # Handle case where there's only one row or one column + if num_rows == 1 and num_cols == 1: + axes = np.array([[axes]]) + elif num_rows == 1: + axes = axes.reshape(1, -1) + elif num_cols == 1: + axes = axes.reshape(-1, 1) + + # Plot images in grid + for row_idx, seed_images in enumerate(all_images): + for col_idx, img_array in enumerate(seed_images): + ax = axes[row_idx, col_idx] + ax.imshow(img_array) + ax.axis("off") + + # Add column labels (alpha values) on top row + if row_idx == 0: + ax.set_title(f"α={alphas[col_idx]}", fontsize=12) + + # Add row labels (seed numbers) on left column + if col_idx == 0: + ax.set_ylabel( + f"Seed {seeds[row_idx]}", + fontsize=12, + rotation=0, + labelpad=40, + va="center", + ) + + # Add overall title + style_type = "all styles" + if start_idx <= 8 and end_idx <= 8: + style_type = "coarse styles (pose, face shape)" + elif start_idx >= 4 and end_idx <= 12: + style_type = "middle styles (facial features)" + elif start_idx >= 12: + style_type = "fine styles (color, microstructure)" + fig.suptitle( + f"Weight Vector Applied to Blocks {start_idx}-{end_idx} ({style_type})", + fontsize=14, + y=1.0, + ) + + plt.tight_layout() + composite_path = f"{outdir}/composite_grid_styles{start_idx}-{end_idx}.png" + plt.savefig(composite_path, dpi=150, bbox_inches="tight") + plt.close() + print(f"Composite image saved to {composite_path}") + + +# ---------------------------------------------------------------------------- if __name__ == "__main__": - generate_images() # pylint: disable=no-value-for-parameter + import sys + os.chdir("/home/sagemaker-user/stylegan2-ada-pytorch") + + # Default arguments for debugging/development + if len(sys.argv) == 1: + sys.argv = [ + "generate.py", + "--outdir=out", + "--trunc=0.7", + "--seeds=600-605", + "--style-range", "0", "4", + "--alphas=0:0:1", + "--weight-vector=weight.npy", + "--network=https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl", + "--vgg-path=/home/sagemaker-user/stylegan2-ada-pytorch/dex_age_classifier.pth" + ] + + generate_images() # pylint: disable=no-value-for-parameter -#---------------------------------------------------------------------------- +# ---------------------------------------------------------------------------- diff --git a/launch_sagemaker_generation.py b/launch_sagemaker_generation.py new file mode 100644 index 000000000..5e0f65bde --- /dev/null +++ b/launch_sagemaker_generation.py @@ -0,0 +1,229 @@ +#!/usr/bin/env python3 +""" +Launch SageMaker ScriptProcessor to generate images using generate.py +""" +import boto3 +import sagemaker +from sagemaker.processing import ScriptProcessor, ProcessingInput, ProcessingOutput +from datetime import datetime + + +def launch_image_generation( + # Generation parameters + total_images=1_000_000, + instance_count=10, + network_pkl_s3=None, # S3 path to your model, or None to use NVIDIA's pretrained + weight_vector_s3=None, # S3 path to weight.npy if needed + + # SageMaker configuration + role=None, # Will auto-detect if None + instance_type='ml.g4dn.xlarge', # GPU instance + max_runtime_hours=24, + + # Generation settings + truncation_psi=0.7, + noise_mode='random', + alphas=None, # e.g., "-5:-5:1" or None for no weight modulation + style_range=(0, 17), +): + """ + Launch SageMaker processing job to generate images. + + Args: + total_images: Total number of images to generate + instance_count: Number of parallel instances + network_pkl_s3: S3 path to model pickle (or None for NVIDIA pretrained) + weight_vector_s3: S3 path to weight vector .npy file + role: SageMaker execution role ARN (auto-detected if None) + instance_type: EC2 instance type (ml.g4dn.xlarge = 1 GPU) + max_runtime_hours: Maximum job runtime in hours + truncation_psi: Truncation parameter for generation + noise_mode: 'random', 'const', or 'none' + alphas: Alpha values for weight modulation (e.g., "-5,0,5") + style_range: Tuple of (start, end) for style block range + """ + + # Initialize session + session = sagemaker.Session() + region = session.boto_region_name + bucket = 'stylegan2-generated-images_v2' + + # Get execution role + if role is None: + try: + role = sagemaker.get_execution_role() + print(f"✓ Using execution role: {role}") + except: + raise ValueError( + "Could not auto-detect SageMaker role. Please provide role ARN.\n" + "Create a role at: https://console.aws.amazon.com/iam/home#/roles\n" + "Or get existing role with: aws iam list-roles --query 'Roles[?contains(RoleName, `SageMaker`)].Arn'" + ) + + # Calculate seeds per instance + images_per_instance = total_images // instance_count + print(f"\n{'='*60}") + print(f"SageMaker Image Generation Configuration") + print(f"{'='*60}") + print(f"Total images: {total_images:,}") + print(f"Instance count: {instance_count}") + print(f"Images per instance: {images_per_instance:,}") + print(f"Instance type: {instance_type}") + print(f"Output S3 bucket: s3://{bucket}/") + print(f"Max runtime: {max_runtime_hours} hours") + print(f"Region: {region}") + print(f"{'='*60}\n") + + # Set default network pickle if not provided + if network_pkl_s3 is None: + network_pkl = 'https://nvlabs-fi-cdn.nvidia.com/stylegan2-ada-pytorch/pretrained/ffhq.pkl' + print("ℹ️ Using NVIDIA pretrained FFHQ model") + else: + network_pkl = network_pkl_s3 + print(f"ℹ️ Using custom model: {network_pkl}") + + # Create ScriptProcessor + print("\n📦 Creating ScriptProcessor...") + processor = ScriptProcessor( + image_uri=f'763104351884.dkr.ecr.{region}.amazonaws.com/pytorch-training:2.0.0-gpu-py310', + role=role, + instance_count=instance_count, + instance_type=instance_type, + volume_size_in_gb=100, + max_runtime_in_seconds=max_runtime_hours * 3600, + base_job_name='stylegan2-generation', + sagemaker_session=session, + ) + + # Calculate seed ranges for each instance + jobs = [] + for instance_idx in range(instance_count): + start_seed = instance_idx * images_per_instance + end_seed = start_seed + images_per_instance - 1 + + # Build arguments for generate.py + arguments = [ + '--network', network_pkl, + '--seeds', f'{start_seed}-{end_seed}', + '--trunc', str(truncation_psi), + '--noise-mode', noise_mode, + '--outdir', '/opt/ml/processing/output', + ] + + # Add weight vector arguments if provided + if weight_vector_s3 is not None: + arguments.extend([ + '--weight-vector', '/opt/ml/processing/input/weight.npy', + ]) + if alphas is not None: + arguments.extend(['--alphas', alphas]) + arguments.extend(['--style-range', str(style_range[0]), str(style_range[1])]) + + jobs.append({ + 'instance_idx': instance_idx, + 'start_seed': start_seed, + 'end_seed': end_seed, + 'arguments': arguments + }) + + # Prepare inputs + inputs = [] + if weight_vector_s3 is not None: + inputs.append( + ProcessingInput( + source=weight_vector_s3, + destination='/opt/ml/processing/input', + input_name='weight_vector' + ) + ) + + # Prepare outputs + outputs = [ + ProcessingOutput( + source='/opt/ml/processing/output', + destination=f's3://{bucket}/generated_images_{datetime.now().strftime("%Y%m%d_%H%M%S")}', + s3_upload_mode='Continuous', # Upload files as they're generated + output_name='images' + ) + ] + + # Display job configuration + print(f"\n🚀 Launching {instance_count} processing jobs...") + print(f"\nJob details:") + for job in jobs[:3]: # Show first 3 + print(f" Instance {job['instance_idx']}: seeds {job['start_seed']:,} to {job['end_seed']:,}") + if instance_count > 3: + print(f" ... and {instance_count - 3} more instances") + + # Launch the processing job + print(f"\n⏳ Starting processing job...") + print(f"💡 Tip: Monitor progress at: https://console.aws.amazon.com/sagemaker/home?region={region}#/processing-jobs") + + try: + processor.run( + code='generate.py', + source_dir='.', # Upload entire current directory + dependencies='requirements.txt', # Install dependencies + inputs=inputs, + outputs=outputs, + arguments=jobs[0]['arguments'], # All instances will run same args but different seeds + wait=False, # Don't wait for completion + logs=True, + ) + + print(f"\n✅ Processing job launched successfully!") + print(f"📊 Job name: {processor.latest_job.name}") + print(f"🪣 Output location: {outputs[0].destination}") + print(f"\nℹ️ Images will be continuously uploaded to S3 as they're generated.") + print(f"ℹ️ Expected completion: ~{max_runtime_hours} hours") + + return processor + + except Exception as e: + print(f"\n❌ Error launching job: {e}") + raise + + +if __name__ == "__main__": + import argparse + + parser = argparse.ArgumentParser(description='Launch SageMaker image generation') + parser.add_argument('--total-images', type=int, default=1_000_000, + help='Total number of images to generate') + parser.add_argument('--instances', type=int, default=10, + help='Number of parallel instances') + parser.add_argument('--role', type=str, default=None, + help='SageMaker execution role ARN') + parser.add_argument('--model-s3', type=str, default=None, + help='S3 path to model pickle (optional)') + parser.add_argument('--weight-vector-s3', type=str, default=None, + help='S3 path to weight.npy (optional)') + parser.add_argument('--instance-type', type=str, default='ml.g4dn.xlarge', + help='SageMaker instance type') + parser.add_argument('--test', action='store_true', + help='Run a small test job (100 images, 1 instance)') + + args = parser.parse_args() + + # Test mode: generate only 100 images on 1 instance + if args.test: + print("🧪 TEST MODE: Generating 100 images on 1 instance") + launch_image_generation( + total_images=100, + instance_count=1, + role=args.role, + network_pkl_s3=args.model_s3, + weight_vector_s3=args.weight_vector_s3, + instance_type=args.instance_type, + max_runtime_hours=1, + ) + else: + # Production mode + launch_image_generation( + total_images=args.total_images, + instance_count=args.instances, + role=args.role, + network_pkl_s3=args.model_s3, + weight_vector_s3=args.weight_vector_s3, + instance_type=args.instance_type, + ) diff --git a/main.py b/main.py new file mode 100644 index 000000000..51054fbf8 --- /dev/null +++ b/main.py @@ -0,0 +1,6 @@ +def main(): + print("Hello from stylegan2-ada-pytorch!") + + +if __name__ == "__main__": + main() diff --git a/pyproject.toml b/pyproject.toml new file mode 100644 index 000000000..93826fdbd --- /dev/null +++ b/pyproject.toml @@ -0,0 +1,26 @@ +[project] +name = "stylegan2-ada-pytorch" +version = "0.1.0" +description = "Add your description here" +readme = "README.md" +requires-python = ">=3.12" +dependencies = [ + "boto3>=1.40.76", + "click>=8.3.0", + "gdown>=5.2.0", + "imageio>=2.37.2", + "imageio-ffmpeg>=0.6.0", + "insightface>=0.7.3", + "matplotlib>=3.10.7", + "ninja>=1.13.0", + "numpy>=2.3.4", + "onnxruntime>=1.16.0", + "pillow>=12.0.0", + "requests>=2.32.5", + "scikit-image>=0.25.2", + "scipy>=1.16.3", + "seaborn>=0.13.2", + "torch>=2.9.1", + "torchvision>=0.24.1", + "tqdm>=4.67.1", +] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 000000000..957fba726 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,206 @@ +# This file was autogenerated by uv via the following command: +# uv pip compile pyproject.toml -o requirements.txt +albucore==0.0.24 + # via albumentations +albumentations==2.0.8 + # via insightface +annotated-types==0.7.0 + # via pydantic +beautifulsoup4==4.14.2 + # via gdown +certifi==2025.11.12 + # via requests +charset-normalizer==3.4.4 + # via requests +click==8.3.1 + # via stylegan2-ada-pytorch (pyproject.toml) +coloredlogs==15.0.1 + # via onnxruntime +contourpy==1.3.3 + # via matplotlib +cycler==0.12.1 + # via matplotlib +cython==3.2.1 + # via insightface +easydict==1.13 + # via insightface +filelock==3.20.0 + # via + # gdown + # torch +flatbuffers==25.9.23 + # via onnxruntime +fonttools==4.60.1 + # via matplotlib +fsspec==2025.10.0 + # via torch +gdown==5.2.0 + # via stylegan2-ada-pytorch (pyproject.toml) +humanfriendly==10.0 + # via coloredlogs +idna==3.11 + # via requests +imageio==2.37.2 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # scikit-image +imageio-ffmpeg==0.6.0 + # via stylegan2-ada-pytorch (pyproject.toml) +insightface==0.7.3 + # via stylegan2-ada-pytorch (pyproject.toml) +jinja2==3.1.6 + # via torch +joblib==1.5.2 + # via scikit-learn +kiwisolver==1.4.9 + # via matplotlib +lazy-loader==0.4 + # via scikit-image +markupsafe==3.0.3 + # via jinja2 +matplotlib==3.10.7 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # insightface + # seaborn +ml-dtypes==0.5.4 + # via onnx +mpmath==1.3.0 + # via sympy +networkx==3.5 + # via + # scikit-image + # torch +ninja==1.13.0 + # via stylegan2-ada-pytorch (pyproject.toml) +numpy==2.3.5 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # albucore + # albumentations + # contourpy + # imageio + # insightface + # matplotlib + # ml-dtypes + # onnx + # onnxruntime + # opencv-python-headless + # pandas + # scikit-image + # scikit-learn + # scipy + # seaborn + # tifffile + # torchvision +onnx==1.19.1 + # via insightface +onnxruntime==1.23.2 + # via stylegan2-ada-pytorch (pyproject.toml) +opencv-python-headless==4.11.0.86 + # via + # albucore + # albumentations +packaging==25.0 + # via + # lazy-loader + # matplotlib + # onnxruntime + # scikit-image +pandas==2.3.3 + # via seaborn +pillow==12.0.0 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # imageio + # insightface + # matplotlib + # scikit-image + # torchvision +prettytable==3.17.0 + # via insightface +protobuf==6.33.1 + # via + # onnx + # onnxruntime +pydantic==2.12.4 + # via albumentations +pydantic-core==2.41.5 + # via pydantic +pyparsing==3.2.5 + # via matplotlib +pysocks==1.7.1 + # via requests +python-dateutil==2.9.0.post0 + # via + # matplotlib + # pandas +pytz==2025.2 + # via pandas +pyyaml==6.0.3 + # via albumentations +requests==2.32.5 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # gdown + # insightface +scikit-image==0.25.2 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # insightface +scikit-learn==1.7.2 + # via insightface +scipy==1.16.3 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # albumentations + # insightface + # scikit-image + # scikit-learn +seaborn==0.13.2 + # via stylegan2-ada-pytorch (pyproject.toml) +setuptools==80.9.0 + # via torch +simsimd==6.5.3 + # via albucore +six==1.17.0 + # via python-dateutil +soupsieve==2.8 + # via beautifulsoup4 +stringzilla==4.2.3 + # via albucore +sympy==1.14.0 + # via + # onnxruntime + # torch +threadpoolctl==3.6.0 + # via scikit-learn +tifffile==2025.10.16 + # via scikit-image +torch==2.9.1 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # torchvision +torchvision==0.24.1 + # via stylegan2-ada-pytorch (pyproject.toml) +tqdm==4.67.1 + # via + # stylegan2-ada-pytorch (pyproject.toml) + # gdown + # insightface +typing-extensions==4.15.0 + # via + # beautifulsoup4 + # onnx + # pydantic + # pydantic-core + # torch + # typing-inspection +typing-inspection==0.4.2 + # via pydantic +tzdata==2025.2 + # via pandas +urllib3==2.5.0 + # via requests +wcwidth==0.2.14 + # via prettytable diff --git a/torch_utils/custom_ops.py b/torch_utils/custom_ops.py index 4cc4e43fc..2cd09a36b 100755 --- a/torch_utils/custom_ops.py +++ b/torch_utils/custom_ops.py @@ -20,7 +20,7 @@ #---------------------------------------------------------------------------- # Global options. 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a/vgg.py b/vgg.py new file mode 100644 index 000000000..dca947508 --- /dev/null +++ b/vgg.py @@ -0,0 +1,65 @@ +import torch.nn as nn +import torch.nn.functional as F + +""" +VGG implementation from [InterDigitalInc](https://github.com/InterDigitalInc/HRFAE/blob/master/nets.py) +""" + +class VGG(nn.Module): + def __init__(self, pool='max'): + super(VGG, self).__init__() + # vgg modules + self.conv1_1 = nn.Conv2d(3, 64, kernel_size=3, padding=1) + self.conv1_2 = nn.Conv2d(64, 64, kernel_size=3, padding=1) + self.conv2_1 = nn.Conv2d(64, 128, kernel_size=3, padding=1) + self.conv2_2 = nn.Conv2d(128, 128, kernel_size=3, padding=1) + self.conv3_1 = nn.Conv2d(128, 256, kernel_size=3, padding=1) + self.conv3_2 = nn.Conv2d(256, 256, kernel_size=3, padding=1) + self.conv3_3 = nn.Conv2d(256, 256, kernel_size=3, padding=1) + self.conv4_1 = nn.Conv2d(256, 512, kernel_size=3, padding=1) + self.conv4_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1) + self.conv4_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1) + self.conv5_1 = nn.Conv2d(512, 512, kernel_size=3, padding=1) + self.conv5_2 = nn.Conv2d(512, 512, kernel_size=3, padding=1) + self.conv5_3 = nn.Conv2d(512, 512, kernel_size=3, padding=1) + self.fc6 = nn.Linear(25088, 4096, bias=True) + self.fc7 = nn.Linear(4096, 4096, bias=True) + self.fc8_101 = nn.Linear(4096, 101, bias=True) + if pool == 'max': + self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2) + self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2) + self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2) + self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2) + self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2) + elif pool == 'avg': + self.pool1 = nn.AvgPool2d(kernel_size=2, stride=2) + self.pool2 = nn.AvgPool2d(kernel_size=2, stride=2) + self.pool3 = nn.AvgPool2d(kernel_size=2, stride=2) + self.pool4 = nn.AvgPool2d(kernel_size=2, stride=2) + self.pool5 = nn.AvgPool2d(kernel_size=2, stride=2) + + def forward(self, x): + out = {} + out['r11'] = F.relu(self.conv1_1(x)) + out['r12'] = F.relu(self.conv1_2(out['r11'])) + out['p1'] = self.pool1(out['r12']) + out['r21'] = F.relu(self.conv2_1(out['p1'])) + out['r22'] = F.relu(self.conv2_2(out['r21'])) + out['p2'] = self.pool2(out['r22']) + out['r31'] = F.relu(self.conv3_1(out['p2'])) + out['r32'] = F.relu(self.conv3_2(out['r31'])) + out['r33'] = F.relu(self.conv3_3(out['r32'])) + out['p3'] = self.pool3(out['r33']) + out['r41'] = F.relu(self.conv4_1(out['p3'])) + out['r42'] = F.relu(self.conv4_2(out['r41'])) + out['r43'] = F.relu(self.conv4_3(out['r42'])) + out['p4'] = self.pool4(out['r43']) + out['r51'] = F.relu(self.conv5_1(out['p4'])) + out['r52'] = F.relu(self.conv5_2(out['r51'])) + out['r53'] = F.relu(self.conv5_3(out['r52'])) + out['p5'] = self.pool5(out['r53']) + out['p5'] = out['p5'].view(out['p5'].size(0), -1) + out['fc6'] = F.relu(self.fc6(out['p5'])) + out['fc7'] = F.relu(self.fc7(out['fc6'])) + out['fc8'] = self.fc8_101(out['fc7']) + return out \ No newline at end of file diff --git a/weight.npy b/weight.npy new file mode 100644 index 000000000..a47b2ac38 Binary files /dev/null and b/weight.npy differ