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052f26d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 | import tqdm
import torch
import numpy as np
import torch.nn as nn
from PIL import Image
from pathlib import Path
from torchvision import transforms
from typing import List, Union, Tuple, Optional
from torch.utils.data import DataLoader, Dataset
class ImageEmbeddingDataset(Dataset):
"""Dataset for batch image embedding generation"""
def __init__(
self,
image_paths: List[Union[str, Path]],
transform=None
):
self.image_paths = [Path(p) for p in image_paths]
self.transform = transform or self.default_transform()
@staticmethod
def default_transform():
# I-JEPA uses mean=05 and std=0.5 normalization
return transforms.Compose([
transforms.Resize(
224, interpolation=transforms.InterpolationMode.BICUBIC
),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize(
mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]
)
])
def __len__(self):
return len(self.image_paths)
def __getitem__(self, idx):
img_path = self.image_paths[idx]
image = Image.open(img_path).convert('RGB')
image = self.transform(image)
return image, str(img_path)
class EmbeddingGenerator:
"""Generate embeddings for image database using batch inference."""
def __init__(
self,
model: nn.Module,
device: str = "cuda" if torch.cuda.is_available() else "cpu",
batch_size: int = 4,
num_workers: int = 1,
layer_strategy: str = "second_last",
specific_indices: Optional[List[int]] = None,
):
self.model = model.to(device).eval()
self.device = device
self.batch_size = batch_size
self.num_workers = num_workers
self.layer_strategy = layer_strategy
self.specific_indices = specific_indices
# Freeze model
for param in self.model.parameters():
param.requires_grad = False
def _get_features(self, images: torch.Tensor) -> torch.Tensor:
"""Central routing method - all forward calss go through here."""
if self.layer_strategy == "last":
return self.model(images)
return self.model.get_layer_representations(
images,
strategy=self.layer_strategy,
specific_indices=self.specific_indices,
)
@torch.no_grad()
def generate_embeddings(
self,
image_paths: List[Union[str, Path]],
return_paths: bool = True,
show_progress: bool = True,
) -> Union[np.ndarray, Tuple[np.ndarray, List[str]]]:
"""
Generate embeddings for all images.
Returns:
embeddings: (N, D) array of embeddings
paths: (optional) list of image paths
"""
print(" 3.1 Image Embedding Dataset...")
dataset = ImageEmbeddingDataset(image_paths)
print(" 3.2 DataLoader...")
dataloader = DataLoader(
dataset,
batch_size=self.batch_size,
shuffle=False,
num_workers=self.num_workers,
pin_memory=True,
)
all_embeddings = []
all_paths = []
print(" 3.3 tqdm iterator...\n")
iterator = tqdm.tqdm(dataloader, desc="Generating embeddings") if show_progress else dataloader
print(" 3.4 for loop...")
for batch_images, batch_paths in iterator:
batch_images = batch_images.to(self.device, non_blocking=True)
# Get embeddings: average pool patch tokens for global representation
features = self._get_features(batch_images) # (B, N, D)
embeddings = features.mean(dim=1) # (B, D)
# L2 normalization for cosine similarity
embeddings = nn.functional.normalize(embeddings, p=2, dim=1)
all_embeddings.append(embeddings.cpu().numpy())
all_paths.extend(batch_paths)
embeddings = np.vstack(all_embeddings)
if return_paths:
return embeddings, all_paths
return embeddings
def generate_single_embedding(
self, image: Union[str, Path, Image.Image, torch.Tensor]
) -> np.ndarray:
"""Generate embedding for a single image"""
transform = ImageEmbeddingDataset.default_transform()
if isinstance(image, (str, Path)):
image = Image.open(image).convert('RGB')
if isinstance(image, Image.Image):
image = transform(image)
if isinstance(image, torch.Tensor):
image = image.unsqueeze(0) if image.dim() == 3 else image
image = image.to(self.device)
with torch.no_grad():
features = self._get_features(image)
embedding = features.mean(dim=1)
embedding = nn.functional.normalize(embedding, p=2, dim=1)
return embedding.cpu().numpy()
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