mirror of
https://github.com/SikongJueluo/Mini-Nav.git
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SikongJueluo
ab616528b4
- Extract model loading logic from benchmark CLI into task-owned prepare_benchmark - Add RetrievalEncoder class wrapping DINO with optional hash compression - Add accelerate dependency for device management - Switch dataset from CIFAR-10 to CIFAR-100 with fine_label column
124 lines
3.7 KiB
Python
124 lines
3.7 KiB
Python
"""Feature extraction utilities for image models."""
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from typing import Any, List, Union, cast
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import torch
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from aiohttp.web import get
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from PIL import Image
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from rich.progress import track
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from torch import nn
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from torch.utils.data import DataLoader
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from transformers import BitImageProcessor
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from utils import get_device
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def _extract_features_from_output(output: Any) -> torch.Tensor:
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"""Extract features from model output, handling both HuggingFace ModelOutput and raw tensors.
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Args:
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output: Model output (either ModelOutput with .last_hidden_state or raw tensor).
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Returns:
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Feature tensor of shape [B, D].
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"""
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# Handle HuggingFace ModelOutput (has .last_hidden_state)
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if hasattr(output, "last_hidden_state"):
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return output.last_hidden_state[:, 0] # [B, D] - CLS token
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# Handle raw tensor output (like DinoCompressor)
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return cast(torch.Tensor, output)
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@torch.no_grad()
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def infer_vector_dim(
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processor: BitImageProcessor,
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model: nn.Module,
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sample_image: Any,
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) -> int:
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"""Infer model output vector dimension via a single forward pass.
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Args:
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processor: Image preprocessor.
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model: Feature extraction model.
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sample_image: A sample image for dimension inference.
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Returns:
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Vector dimension.
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"""
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inputs = processor(images=sample_image, return_tensors="pt")
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inputs.to(get_device())
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output = model(inputs)
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features = _extract_features_from_output(output)
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return features.shape[-1]
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@torch.no_grad()
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def extract_single_image_feature(
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processor: BitImageProcessor,
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model: nn.Module,
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image: Union[Image.Image, Any],
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) -> List[float]:
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"""Extract feature from a single image.
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Args:
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processor: Image preprocessor.
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model: Feature extraction model.
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image: A single image (PIL Image or other supported format).
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Returns:
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The extracted CLS token feature vector as a list of floats.
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"""
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inputs = processor(images=image, return_tensors="pt")
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inputs.to(get_device(), non_blocking=True)
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outputs = model(inputs)
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features = _extract_features_from_output(outputs) # [1, D]
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return features.cpu().squeeze(0).tolist()
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@torch.no_grad()
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def extract_batch_features(
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processor: BitImageProcessor,
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model: nn.Module,
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images: Union[List[Any], Any],
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batch_size: int = 32,
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) -> torch.Tensor:
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"""Extract features from a batch of images.
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Args:
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processor: Image preprocessor.
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model: Feature extraction model.
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images: List of images, DataLoader, or other iterable.
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batch_size: Batch size for processing.
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Returns:
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Tensor of shape [batch_size, feature_dim].
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"""
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device = get_device()
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# Handle DataLoader input
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if isinstance(images, DataLoader):
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all_features = []
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for batch in track(images, description="Extracting features"):
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imgs = batch["img"] if isinstance(batch, dict) else batch[0]
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inputs = processor(images=imgs, return_tensors="pt")
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inputs.to(device)
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outputs = model(inputs)
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features = _extract_features_from_output(outputs) # [B, D]
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all_features.append(features.cpu())
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return torch.cat(all_features, dim=0)
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# Handle list of images
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all_features = []
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for i in track(
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range(0, len(images), batch_size), description="Extracting features"
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):
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batch_imgs = images[i : i + batch_size]
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inputs = processor(images=batch_imgs, return_tensors="pt")
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inputs.to(device)
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outputs = model(inputs)
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features = _extract_features_from_output(outputs) # [B, D]
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all_features.append(features.cpu())
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return torch.cat(all_features, dim=0)
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