phase 4
This commit is contained in:
262
core/detect/inference/client.py
Normal file
262
core/detect/inference/client.py
Normal file
@@ -0,0 +1,262 @@
|
||||
"""
|
||||
HTTP client for the inference server.
|
||||
|
||||
The pipeline stages call this instead of importing ML libraries directly.
|
||||
The inference server runs on the GPU machine (or spot instance).
|
||||
"""
|
||||
|
||||
from __future__ import annotations
|
||||
|
||||
import base64
|
||||
import io
|
||||
import logging
|
||||
import os
|
||||
|
||||
import numpy as np
|
||||
import requests
|
||||
from PIL import Image
|
||||
|
||||
from .types import DetectResult, OCRResult, RegionDebugResult, RegionResult, ServerStatus, VLMResult
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_URL = os.environ.get("INFERENCE_URL", "http://localhost:8000")
|
||||
|
||||
|
||||
def _encode_image(image: np.ndarray) -> str:
|
||||
"""Encode numpy array as base64 JPEG."""
|
||||
img = Image.fromarray(image)
|
||||
buf = io.BytesIO()
|
||||
img.save(buf, format="JPEG", quality=85)
|
||||
return base64.b64encode(buf.getvalue()).decode()
|
||||
|
||||
|
||||
class InferenceClient:
|
||||
"""HTTP client for the GPU inference server."""
|
||||
|
||||
def __init__(self, base_url: str | None = None, timeout: float = 60.0,
|
||||
job_id: str = "", log_level: str = "INFO"):
|
||||
self.base_url = (base_url or DEFAULT_URL).rstrip("/")
|
||||
self.timeout = timeout
|
||||
self.job_id = job_id
|
||||
self.log_level = log_level
|
||||
self.session = requests.Session()
|
||||
if job_id:
|
||||
self.session.headers["X-Job-Id"] = job_id
|
||||
self.session.headers["X-Log-Level"] = log_level
|
||||
|
||||
def health(self) -> ServerStatus:
|
||||
"""Check server health and loaded models."""
|
||||
resp = self.session.get(f"{self.base_url}/health", timeout=self.timeout)
|
||||
resp.raise_for_status()
|
||||
data = resp.json()
|
||||
return ServerStatus(
|
||||
loaded_models=data.get("loaded_models", []),
|
||||
vram_used_mb=data.get("vram_used_mb", 0),
|
||||
vram_budget_mb=data.get("vram_budget_mb", 0),
|
||||
strategy=data.get("strategy", "sequential"),
|
||||
)
|
||||
|
||||
def detect(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
model: str = "yolov8n",
|
||||
confidence: float = 0.3,
|
||||
target_classes: list[str] | None = None,
|
||||
) -> list[DetectResult]:
|
||||
"""Run object detection on an image."""
|
||||
payload = {
|
||||
"image": _encode_image(image),
|
||||
"model": model,
|
||||
"confidence": confidence,
|
||||
}
|
||||
if target_classes:
|
||||
payload["target_classes"] = target_classes
|
||||
|
||||
resp = self.session.post(
|
||||
f"{self.base_url}/detect",
|
||||
json=payload,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
|
||||
results = []
|
||||
for d in resp.json().get("detections", []):
|
||||
result = DetectResult(
|
||||
x=d["x"], y=d["y"], w=d["w"], h=d["h"],
|
||||
confidence=d["confidence"], label=d["label"],
|
||||
)
|
||||
results.append(result)
|
||||
return results
|
||||
|
||||
def ocr(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
languages: list[str] | None = None,
|
||||
) -> list[OCRResult]:
|
||||
"""Run OCR on an image region."""
|
||||
payload = {
|
||||
"image": _encode_image(image),
|
||||
}
|
||||
if languages:
|
||||
payload["languages"] = languages
|
||||
|
||||
resp = self.session.post(
|
||||
f"{self.base_url}/ocr",
|
||||
json=payload,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
|
||||
results = []
|
||||
for d in resp.json().get("results", []):
|
||||
result = OCRResult(
|
||||
text=d["text"],
|
||||
confidence=d["confidence"],
|
||||
bbox=tuple(d["bbox"]),
|
||||
)
|
||||
results.append(result)
|
||||
return results
|
||||
|
||||
def vlm(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
prompt: str,
|
||||
model: str = "moondream2",
|
||||
) -> VLMResult:
|
||||
"""Query a visual language model with an image crop + prompt."""
|
||||
payload = {
|
||||
"image": _encode_image(image),
|
||||
"prompt": prompt,
|
||||
"model": model,
|
||||
}
|
||||
|
||||
resp = self.session.post(
|
||||
f"{self.base_url}/vlm",
|
||||
json=payload,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
|
||||
data = resp.json()
|
||||
return VLMResult(
|
||||
brand=data.get("brand", ""),
|
||||
confidence=data.get("confidence", 0.0),
|
||||
reasoning=data.get("reasoning", ""),
|
||||
)
|
||||
|
||||
def detect_edges(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
edge_canny_low: int = 50,
|
||||
edge_canny_high: int = 150,
|
||||
edge_hough_threshold: int = 80,
|
||||
edge_hough_min_length: int = 100,
|
||||
edge_hough_max_gap: int = 10,
|
||||
edge_pair_max_distance: int = 200,
|
||||
edge_pair_min_distance: int = 15,
|
||||
) -> list[RegionResult]:
|
||||
"""Run edge detection on an image."""
|
||||
payload = {
|
||||
"image": _encode_image(image),
|
||||
"edge_canny_low": edge_canny_low,
|
||||
"edge_canny_high": edge_canny_high,
|
||||
"edge_hough_threshold": edge_hough_threshold,
|
||||
"edge_hough_min_length": edge_hough_min_length,
|
||||
"edge_hough_max_gap": edge_hough_max_gap,
|
||||
"edge_pair_max_distance": edge_pair_max_distance,
|
||||
"edge_pair_min_distance": edge_pair_min_distance,
|
||||
}
|
||||
|
||||
resp = self.session.post(
|
||||
f"{self.base_url}/detect_edges",
|
||||
json=payload,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
|
||||
results = []
|
||||
for r in resp.json().get("regions", []):
|
||||
result = RegionResult(
|
||||
x=r["x"], y=r["y"], w=r["w"], h=r["h"],
|
||||
confidence=r["confidence"], label=r["label"],
|
||||
)
|
||||
results.append(result)
|
||||
return results
|
||||
|
||||
def detect_edges_debug(
|
||||
self,
|
||||
image: np.ndarray,
|
||||
edge_canny_low: int = 50,
|
||||
edge_canny_high: int = 150,
|
||||
edge_hough_threshold: int = 80,
|
||||
edge_hough_min_length: int = 100,
|
||||
edge_hough_max_gap: int = 10,
|
||||
edge_pair_max_distance: int = 200,
|
||||
edge_pair_min_distance: int = 15,
|
||||
) -> RegionDebugResult:
|
||||
"""Run edge detection with debug overlays."""
|
||||
payload = {
|
||||
"image": _encode_image(image),
|
||||
"edge_canny_low": edge_canny_low,
|
||||
"edge_canny_high": edge_canny_high,
|
||||
"edge_hough_threshold": edge_hough_threshold,
|
||||
"edge_hough_min_length": edge_hough_min_length,
|
||||
"edge_hough_max_gap": edge_hough_max_gap,
|
||||
"edge_pair_max_distance": edge_pair_max_distance,
|
||||
"edge_pair_min_distance": edge_pair_min_distance,
|
||||
}
|
||||
|
||||
resp = self.session.post(
|
||||
f"{self.base_url}/detect_edges/debug",
|
||||
json=payload,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
|
||||
data = resp.json()
|
||||
regions = []
|
||||
for r in data.get("regions", []):
|
||||
region = RegionResult(
|
||||
x=r["x"], y=r["y"], w=r["w"], h=r["h"],
|
||||
confidence=r["confidence"], label=r["label"],
|
||||
)
|
||||
regions.append(region)
|
||||
|
||||
return RegionDebugResult(
|
||||
regions=regions,
|
||||
edge_overlay_b64=data.get("edge_overlay_b64", ""),
|
||||
lines_overlay_b64=data.get("lines_overlay_b64", ""),
|
||||
horizontal_count=data.get("horizontal_count", 0),
|
||||
pair_count=data.get("pair_count", 0),
|
||||
)
|
||||
|
||||
def post(self, path: str, payload: dict) -> dict | None:
|
||||
"""Generic POST to the inference server. Returns JSON response or None on error."""
|
||||
try:
|
||||
resp = self.session.post(
|
||||
f"{self.base_url}{path}",
|
||||
json=payload,
|
||||
timeout=self.timeout,
|
||||
)
|
||||
resp.raise_for_status()
|
||||
return resp.json()
|
||||
except Exception as e:
|
||||
logger.warning("Inference POST %s failed: %s", path, e)
|
||||
return None
|
||||
|
||||
def load_model(self, model: str, quantization: str = "fp16") -> None:
|
||||
"""Request the server to load a model into VRAM."""
|
||||
self.session.post(
|
||||
f"{self.base_url}/models/load",
|
||||
json={"model": model, "quantization": quantization},
|
||||
timeout=self.timeout,
|
||||
).raise_for_status()
|
||||
|
||||
def unload_model(self, model: str) -> None:
|
||||
"""Request the server to unload a model from VRAM."""
|
||||
self.session.post(
|
||||
f"{self.base_url}/models/unload",
|
||||
json={"model": model},
|
||||
timeout=self.timeout,
|
||||
).raise_for_status()
|
||||
Reference in New Issue
Block a user