mirror of
https://github.com/SikongJueluo/Mini-Nav.git
synced 2026-03-12 12:25:32 +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
|
|
|
|
import typer
|
|
from commands import app
|
|
|
|
|
|
@app.command()
|
|
def benchmark(
|
|
ctx: typer.Context,
|
|
model_path: Optional[str] = typer.Option(
|
|
None, "--model", "-m", help="Path to compressor model weights"
|
|
),
|
|
):
|
|
import torch
|
|
import torch.nn.functional as F
|
|
from benchmarks import run_benchmark
|
|
from configs import cfg_manager
|
|
from transformers import AutoImageProcessor, AutoModel, BitImageProcessorFast
|
|
from utils import get_device
|
|
|
|
config = cfg_manager.get()
|
|
benchmark_cfg = config.benchmark
|
|
|
|
device = get_device()
|
|
|
|
model_cfg = config.model
|
|
processor = cast(
|
|
BitImageProcessorFast,
|
|
AutoImageProcessor.from_pretrained(model_cfg.dino_model, device_map=device),
|
|
)
|
|
|
|
# Load DINO model for feature extraction
|
|
dino = AutoModel.from_pretrained(model_cfg.dino_model, device_map=device)
|
|
dino.eval()
|
|
|
|
# Optional hash compressor
|
|
compressor = None
|
|
if model_path:
|
|
from compressors import HashCompressor
|
|
|
|
compressor = HashCompressor(
|
|
input_dim=model_cfg.compression_dim,
|
|
hash_bits=model_cfg.compression_dim,
|
|
)
|
|
compressor.load_state_dict(torch.load(model_path))
|
|
compressor.to(device)
|
|
compressor.eval()
|
|
|
|
# Create wrapper with extract_features method
|
|
class DinoFeatureExtractor:
|
|
def __init__(self, dino, compressor=None):
|
|
self.dino = dino
|
|
self.compressor = compressor
|
|
|
|
def extract_features(self, images: list) -> torch.Tensor:
|
|
inputs = processor(images, return_tensors="pt").to(device)
|
|
with torch.no_grad():
|
|
outputs = self.dino(**inputs)
|
|
features = outputs.last_hidden_state.mean(dim=1)
|
|
features = F.normalize(features, dim=-1)
|
|
return features
|
|
|
|
def encode(self, images: list) -> torch.Tensor:
|
|
if self.compressor is None:
|
|
return self.extract_features(images)
|
|
tokens = self.dino(**processor(images, return_tensors="pt").to(device)).last_hidden_state
|
|
_, _, bits = self.compressor(tokens)
|
|
return bits
|
|
|
|
model = DinoFeatureExtractor(dino, compressor)
|
|
|
|
run_benchmark(
|
|
model=model,
|
|
processor=processor,
|
|
config=benchmark_cfg,
|
|
model_name="dinov2",
|
|
)
|