File size: 2,453 Bytes
513d6d1 | 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 | import time
from typing import Dict, Any, Optional
from dataclasses import dataclass, asdict
class VRAMManager:
"""Utility to manage GPU memory during model switching."""
@staticmethod
def purge():
import torch
import gc
if torch.cuda.is_available():
torch.cuda.empty_cache()
gc.collect()
@dataclass
class NerveOutput:
"""Standardized output from a Phase 2 Sensor Model (Nerve)."""
sensor_type: str
timestamp: float
caption: str
metadata: Dict[str, Any]
raw_data: Optional[Any] = None # Pointer to raw pixels or audio tensor
class SensorNerve:
"""Base class for all Phase 2 Nerve models."""
def __init__(self, sensor_type: str):
self.sensor_type = sensor_type
def process(self, raw_data: Any) -> NerveOutput:
raise NotImplementedError
class VisionNerve(SensorNerve):
"""Camera Nerve: Converts video/images to basic visual perception."""
def __init__(self):
super().__init__("camera")
def process(self, caption: str, raw_data: Any = None, objects: list = None) -> NerveOutput:
return NerveOutput(
sensor_type=self.sensor_type,
timestamp=time.time(),
caption=caption,
raw_data=raw_data,
metadata={
"base_objects": objects or [],
"scene_context": "general"
}
)
class AudioNerve(SensorNerve):
"""Microphone Nerve: Converts audio to basic acoustic perception."""
def __init__(self):
super().__init__("microphone")
def process(self, caption: str, raw_data: Any = None) -> NerveOutput:
return NerveOutput(
sensor_type=self.sensor_type,
timestamp=time.time(),
caption=caption,
raw_data=raw_data,
metadata={
"acoustic_summary": caption,
"domain": "ambient"
}
)
# Future placeholders for Radar/WiFi/Lidar
class RadarNerve(SensorNerve):
def __init__(self): super().__init__("radar")
def process(self, raw_signal: Any) -> NerveOutput:
return NerveOutput(self.sensor_type, time.time(), "Radar scan active", {"anomalies": []})
class WiFiNerve(SensorNerve):
def __init__(self): super().__init__("wifi")
def process(self, raw_signal: Any) -> NerveOutput:
return NerveOutput(self.sensor_type, time.time(), "WiFi signal stable", {"snr": 0})
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