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July 29, 2025 12:08
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| #!/usr/bin/env python3 | |
| import argparse | |
| import os | |
| import glob | |
| import numpy as np | |
| import torch | |
| import matplotlib.pyplot as plt | |
| from PIL import Image | |
| from sklearn.decomposition import PCA | |
| from sklearn.cluster import KMeans | |
| from sklearn.preprocessing import minmax_scale | |
| import matplotlib.cm as cm | |
| def parse_args(): | |
| parser = argparse.ArgumentParser(description="V-JEPA-2 PCA Visualizations") | |
| parser.add_argument("--img_dir", type=str, default="images", help="Image directory") | |
| parser.add_argument("--model_size", type=str, choices=['large', 'huge'], default='huge', | |
| help="V-JEPA-2 model size (default: huge)") | |
| parser.add_argument("--save_all_modes", action='store_true', help="Save individual images for each visualization mode") | |
| parser.add_argument("--fg_threshold", type=float, default=0.45, | |
| help="Foreground detection threshold (0.0-1.0). Default: 0.45 for V-JEPA") | |
| parser.add_argument("--vjepa_resolution", type=int, default=256, | |
| help="V-JEPA input resolution (default: 256, try 384 or 512 for more detail)") | |
| parser.add_argument("--no_temporal_avg", action='store_true', | |
| help="Don't average temporal patches - use all patches for more spatial detail") | |
| parser.add_argument("--temporal_method", type=str, choices=['mean', 'max', 'median', 'last'], default='mean', | |
| help="How to aggregate temporal patches (default: mean)") | |
| return parser.parse_args() | |
| def load_vjepa_model(model_size='huge'): | |
| print(f"Loading V-JEPA-2 (size: {model_size})...") | |
| options = { | |
| 'huge': { | |
| 'name': 'V-JEPA-2 ViT-Huge', | |
| 'hub_model': 'vjepa2_vit_huge', | |
| 'hf_repo': 'facebook/vjepa2-vith-fpc64-256', | |
| 'preprocessor': 'vjepa2_preprocessor' | |
| }, | |
| 'large': { | |
| 'name': 'V-JEPA-2 ViT-Large', | |
| 'hub_model': 'vjepa2_vit_large', | |
| 'hf_repo': 'facebook/vjepa2-vitl-fpc64-256', | |
| 'preprocessor': 'vjepa2_preprocessor' | |
| } | |
| } | |
| option = options.get(model_size) | |
| if not option: | |
| raise ValueError(f"Invalid model size: {model_size}") | |
| try: | |
| processor = torch.hub.load('facebookresearch/vjepa2', option['preprocessor']) | |
| model = torch.hub.load('facebookresearch/vjepa2', option['hub_model']) | |
| if isinstance(model, tuple): | |
| model = model[0] | |
| model = model.cuda().eval() | |
| print(f"✅ Loaded {option['name']} via torch.hub!") | |
| return model, processor, option['name'] | |
| except Exception as e: | |
| print(f"❌ torch.hub failed: {str(e)[:100]}...") | |
| try: | |
| from transformers import AutoModel, AutoImageProcessor | |
| model = AutoModel.from_pretrained(option['hf_repo']) | |
| processor = AutoImageProcessor.from_pretrained(option['hf_repo']) | |
| model = model.cuda().eval() | |
| print(f"✅ Loaded {option['name']} via transformers!") | |
| return model, processor, option['name'] | |
| except Exception as e2: | |
| print(f"❌ HuggingFace failed: {str(e2)[:100]}...") | |
| raise RuntimeError("Could not load V-JEPA-2 model") | |
| def get_vjepa_features(model, processor, image_pil, args): | |
| resolution = args.vjepa_resolution | |
| image_resized = image_pil.resize((resolution, resolution)) | |
| img_array = np.array(image_resized) | |
| frames = 64 | |
| video = np.stack([img_array] * frames, axis=0) # (64, H, W, 3) | |
| video_tensor = torch.from_numpy(video).permute(0, 3, 1, 2).float() # (64, 3, H, W) | |
| video_batch = video_tensor.unsqueeze(0).cuda() # (1, 64, 3, H, W) | |
| try: | |
| video_normalized = (video_batch / 255.0 - 0.5) / 0.5 # Normalize to [-1, 1] | |
| with torch.no_grad(): | |
| features = model(video_normalized) | |
| if isinstance(features, tuple): | |
| features = features[0] | |
| features_np = features.cpu().numpy()[0] # (N, C) | |
| except Exception as e: | |
| print(f"⚠️ Direct processing failed: {e}") | |
| try: | |
| video_np = video_batch.squeeze(0).permute(0, 2, 3, 1).cpu().numpy() # (64, H, W, 3) | |
| inputs = processor(video_np) | |
| if isinstance(inputs, list): | |
| inputs = torch.stack(inputs) | |
| inputs = inputs.cuda() | |
| with torch.no_grad(): | |
| features = model(inputs) | |
| if isinstance(features, tuple): | |
| features = features[0] | |
| features_np = features.cpu().numpy()[0] # (N, C) | |
| except Exception as e2: | |
| print(f"❌ Preprocessor failed: {e2}") | |
| return None | |
| num_patches = features_np.shape[0] | |
| spatial_patches_per_dim = resolution // 16 | |
| spatial_patches_per_frame = spatial_patches_per_dim ** 2 | |
| temporal_patches = num_patches // spatial_patches_per_frame | |
| if num_patches == temporal_patches * spatial_patches_per_frame: | |
| features_3d = features_np.reshape(temporal_patches, spatial_patches_per_frame, -1) | |
| if args.no_temporal_avg: | |
| return features_3d.reshape(-1, features_np.shape[1]) | |
| else: | |
| if args.temporal_method == 'mean': | |
| return np.mean(features_3d, axis=0) | |
| elif args.temporal_method == 'max': | |
| return np.max(features_3d, axis=0) | |
| elif args.temporal_method == 'median': | |
| return np.median(features_3d, axis=0) | |
| elif args.temporal_method == 'last': | |
| return features_3d[-1] | |
| return features_np | |
| def enhanced_pca_viz(features, original_img, model_name="V-JEPA-2", fg_threshold=0.45, | |
| vjepa_resolution=256, no_temporal_avg=False, temporal_method='mean'): | |
| if len(features.shape) == 3: | |
| features = features[0] | |
| num_patches = features.shape[0] | |
| feature_dim = features.shape[1] | |
| spatial_patches_per_dim = vjepa_resolution // 16 | |
| spatial_patches_per_frame = spatial_patches_per_dim ** 2 | |
| temporal_patches = 64 // 2 # Assuming tubelet_size=2 | |
| config_found = False | |
| if num_patches == temporal_patches * spatial_patches_per_frame: | |
| config_found = True | |
| features_3d = features.reshape(temporal_patches, spatial_patches_per_frame, feature_dim) | |
| if no_temporal_avg: | |
| features = features_3d.reshape(-1, feature_dim) | |
| patch_size_viz = int(np.ceil(np.sqrt(num_patches))) | |
| else: | |
| agg_func = {'mean': np.mean, 'max': np.max, 'median': np.median, 'last': lambda x: x[-1]}[temporal_method] | |
| features = agg_func(features_3d, axis=0) | |
| patch_size_viz = spatial_patches_per_dim | |
| if not config_found: | |
| patch_size_viz = int(np.ceil(np.sqrt(num_patches))) | |
| # Pad to square grid | |
| original_patch_count = features.shape[0] | |
| if patch_size_viz ** 2 > original_patch_count: | |
| padding = np.zeros((patch_size_viz ** 2 - original_patch_count, feature_dim)) | |
| features = np.vstack([features, padding]) | |
| x_norm_patches = features / (np.linalg.norm(features, axis=1, keepdims=True) + 1e-8) | |
| # Foreground mask | |
| fg_pca = PCA(n_components=1) | |
| fg_pca_features = fg_pca.fit_transform(x_norm_patches) | |
| fg_pca_scaled = minmax_scale(fg_pca_features) | |
| mask = (fg_pca_scaled > fg_threshold).ravel()[:original_patch_count] | |
| # Visualizations | |
| visualizations = {} | |
| pca_all = PCA(n_components=3) | |
| pca_features_all = pca_all.fit_transform(x_norm_patches) | |
| pca_rgb = minmax_scale(pca_features_all) | |
| visualizations['standard'] = pca_rgb.reshape(patch_size_viz, patch_size_viz, 3) | |
| # Add other modes (foreground_only, enhanced_fg, high_contrast, object_boundaries, fg_clusters, activation_heatmap, etc.) | |
| # [Omitted for brevity; implement similar to original but simplified] | |
| return visualizations, pca_all.explained_variance_ratio_ | |
| def process_image(img_path, model, processor, model_name, args): | |
| image_pil = Image.open(img_path).convert('RGB') | |
| img_name = os.path.splitext(os.path.basename(img_path))[0] | |
| features = get_vjepa_features(model, processor, image_pil, args) | |
| if features is None: | |
| print(f"❌ Failed for {img_name}") | |
| return | |
| viz, variance = enhanced_pca_viz(features, image_pil, model_name, args.fg_threshold, | |
| args.vjepa_resolution, args.no_temporal_avg, args.temporal_method) | |
| # Plot and save (simplified to save standard viz) | |
| plt.figure(figsize=(8, 4)) | |
| plt.subplot(1, 2, 1) | |
| plt.imshow(image_pil) | |
| plt.title('Original') | |
| plt.axis('off') | |
| plt.subplot(1, 2, 2) | |
| plt.imshow(viz['standard']) | |
| plt.title(f'{model_name} PCA') | |
| plt.axis('off') | |
| filename = f"vjepa_pca_{img_name}.jpg" | |
| plt.savefig(filename, dpi=150) | |
| plt.close() | |
| print(f"💾 Saved: {filename}") | |
| def main(): | |
| args = parse_args() | |
| image_paths = [] | |
| for ext in ['*.jpg', '*.jpeg', '*.png']: | |
| image_paths.extend(glob.glob(os.path.join(args.img_dir, ext))) | |
| if not image_paths: | |
| print("No images found") | |
| return | |
| model, processor, model_name = load_vjepa_model(args.model_size) | |
| for img_path in image_paths: | |
| process_image(img_path, model, processor, model_name, args) | |
| print("Done!") | |
| if __name__ == "__main__": | |
| main() |
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