Upload code/model_vis_tools/pca_dinov2.py with huggingface_hub

code/model_vis_tools/pca_dinov2.py

@@ -0,0 +1,100 @@
+import os
+import torch
+import numpy as np
+from PIL import Image
+from sklearn.decomposition import PCA
+from math import sqrt
+from torchvision import transforms
+import matplotlib.pyplot as plt
+from open_clip.transform import ResizeLongest, _convert_to_rgb, ToTensor, Normalize
+from torchvision.transforms import InterpolationMode
+# Ensure reproducibility
+torch.manual_seed(42)
+np.random.seed(42)
+
+# Define constants
+mean = [0.485, 0.456, 0.406]
+std = [0.229, 0.224, 0.225]
+target_size = (560, 560)
+
+# Define transforms
+normalize = Normalize(mean=mean, std=std)
+DINO_transform = transforms.Compose([
+    transforms.Resize((560,560),interpolation=InterpolationMode.BILINEAR),
+    _convert_to_rgb,
+    ToTensor(),
+    normalize
+])
+
+# PCA visualization function
+def plot_pca(f, path, target_size):
+    """
+    Visualize PCA for the given features.
+
+    Args:
+        f (numpy.ndarray): Feature array of shape [N, D].
+        path (str): Path to save the PCA visualization.
+        target_size (tuple): Target size of the output image.
+    """
+    pca = PCA(n_components=3)
+    pca.fit(f)
+    pca_img = pca.transform(f)  # n x 3
+    h = w = int(sqrt(pca_img.shape[0]))
+    pca_img = pca_img.reshape(h, w, 3)
+    pca_img_min = pca_img.min(axis=(0, 1))
+    pca_img_max = pca_img.max(axis=(0, 1))
+    pca_img = (pca_img - pca_img_min) / (pca_img_max - pca_img_min + 1e-8)  # Normalize
+    pca_img = Image.fromarray((pca_img * 255).astype(np.uint8))
+    pca_img = transforms.Resize(target_size, interpolation=transforms.InterpolationMode.NEAREST)(pca_img)
+    pca_img.save(path)
+
+# DINOv2 model loading function
+def build_DINOv2():
+    model_name = 'dinov2_vitb14_reg'
+    hub_path = '/mnt/SSD8T/home/wjj/.cache/torch/hub/facebookresearch_dinov2_main'
+    try:
+        vfm = torch.hub.load(hub_path, model_name, source='local').half()
+    except Exception as e:
+        raise RuntimeError(f"Failed to load DINOv2 model '{model_name}': {e}")
+    return vfm
+
+# Main function for DINOv2 PCA visualization
+def visualize_dinov2_pca(image_path, output_dir):
+    """
+    Visualize PCA for DINOv2 features.
+
+    Args:
+        image_path (str): Path to the input image.
+        output_dir (str): Directory to save the PCA visualization.
+    """
+    # Ensure output directory exists
+    if not os.path.exists(output_dir):
+        os.makedirs(output_dir)
+
+    # Load DINOv2 model
+    device = "cuda" if torch.cuda.is_available() else "cpu"
+    dino = build_DINOv2().to(device)
+
+    # Load and preprocess image
+    image = Image.open(image_path)
+    image_tensor = DINO_transform(image).unsqueeze(0).to(torch.float16).to(device)
+
+    # Extract DINOv2 features
+    with torch.no_grad():
+        dino_feats_raw = dino.get_intermediate_layers(image_tensor, reshape=True)[0]
+        dino_feats_raw = dino_feats_raw.flatten(start_dim=-2).transpose(-2, -1)  # [1, N, D]
+
+    # Prepare features for PCA
+    dino_feats_np = dino_feats_raw[0].cpu().numpy()
+
+    # Save PCA visualization
+    pca_path = os.path.join(output_dir, "dinov2_pca.png")
+    plot_pca(dino_feats_np, pca_path, target_size)
+
+    print(f"PCA visualization saved at: {pca_path}")
+
+# Example usage
+if __name__ == "__main__":
+    image_path = "demo_images/bird4.jpg"  # Replace with your image path
+    output_dir = "dinov2_vis"
+    visualize_dinov2_pca(image_path, output_dir)