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https://github.com/SikongJueluo/Mini-Nav.git
synced 2026-03-13 04:45:32 +08:00
SikongJueluo
4da08dc3d3
- Remove dino_compressor.py and segament_compressor.py - Rewrite pipeline.py to inline DINO into HashPipeline - Maintain backward compatibility: SAMHashPipeline alias - Update tests and benchmark.py
78 lines
2.3 KiB
Python
78 lines
2.3 KiB
Python
from typing import Any, Optional, cast
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import typer
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from commands import app
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@app.command()
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def benchmark(
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ctx: typer.Context,
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model_path: Optional[str] = typer.Option(
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None, "--model", "-m", help="Path to compressor model weights"
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),
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):
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import torch
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import torch.nn.functional as F
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from benchmarks import run_benchmark
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from configs import cfg_manager
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from transformers import AutoImageProcessor, AutoModel, BitImageProcessorFast
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from utils import get_device
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config = cfg_manager.get()
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benchmark_cfg = config.benchmark
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device = get_device()
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model_cfg = config.model
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processor = cast(
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BitImageProcessorFast,
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AutoImageProcessor.from_pretrained(model_cfg.dino_model, device_map=device),
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)
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# Load DINO model for feature extraction
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dino = AutoModel.from_pretrained(model_cfg.dino_model, device_map=device)
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dino.eval()
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# Optional hash compressor
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compressor = None
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if model_path:
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from compressors import HashCompressor
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compressor = HashCompressor(
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input_dim=model_cfg.compression_dim,
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hash_bits=model_cfg.compression_dim,
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)
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compressor.load_state_dict(torch.load(model_path))
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compressor.to(device)
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compressor.eval()
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# Create wrapper with extract_features method
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class DinoFeatureExtractor:
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def __init__(self, dino, compressor=None):
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self.dino = dino
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self.compressor = compressor
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def extract_features(self, images: list) -> torch.Tensor:
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inputs = processor(images, return_tensors="pt").to(device)
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with torch.no_grad():
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outputs = self.dino(**inputs)
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features = outputs.last_hidden_state.mean(dim=1)
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features = F.normalize(features, dim=-1)
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return features
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def encode(self, images: list) -> torch.Tensor:
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if self.compressor is None:
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return self.extract_features(images)
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tokens = self.dino(**processor(images, return_tensors="pt").to(device)).last_hidden_state
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_, _, bits = self.compressor(tokens)
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return bits
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model = DinoFeatureExtractor(dino, compressor)
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run_benchmark(
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model=model,
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processor=processor,
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config=benchmark_cfg,
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model_name="dinov2",
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)
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