상태 JSON 파일의 워커 수를 10으로 증가시키고, 각 모델에 대한 최소 및 최대 세션 수를 설정하였습니다. API 엔드포인트를 '/api/v1/health'로 변경하고, 세션 풀 관리 로직을 개선하여 VRAM 사용량에 따라 동적으로 세션을 생성 및 제거하도록 하였습니다. 대시보드에서 세션 정보 표시 방식을 개선하였으며, 로그 파일에 서버 프로세스 ID 변경 사항을 반영하였습니다.
This commit is contained in:
AGX
parent
a8e0c166a3
commit
c18667c17d
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@ -103,7 +103,7 @@ def create_response(
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})
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@router.get("/health", response_model=HealthResponse)
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@router.get("/api/v1/health", response_model=HealthResponse, name="health_check")
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async def health_check():
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"""서버 상태 확인"""
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start_time = getattr(settings, 'start_time', time.time())
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@ -3,16 +3,38 @@ Configuration settings for the inpainting server
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"""
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import os
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import platform
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from typing import Dict, Any, Optional
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from typing import Dict, Any, Optional, ClassVar
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from pydantic_settings import BaseSettings
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class Settings(BaseSettings):
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# System detection
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IS_JETSON: bool = platform.machine() == "aarch64" and "tegra" in platform.uname().release.lower()
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IS_X86: bool = platform.machine() in ["x86_64", "amd64"]
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IS_JETSON: ClassVar[bool] = "aarch64" in platform.machine().lower() and "tegra" in platform.release().lower()
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PROJECT_ROOT: ClassVar[str] = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
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# --- 동적 세션 풀 및 메모리 관리 설정 ---
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# 각 모델별 최소/최대 세션 수. 서버 시작 시 min 만큼 생성되고, VRAM 여유 시 max까지 확장됨.
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SIMPLE_LAMA_MIN_SESSIONS: int = 2 if IS_JETSON else 2
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SIMPLE_LAMA_MAX_SESSIONS: int = 4 if IS_JETSON else 4
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# Server settings
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MIGAN_MIN_SESSIONS: int = 2 if IS_JETSON else 1 # x86에서는 Migan을 기본으로 로드하지 않음
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MIGAN_MAX_SESSIONS: int = 4 if IS_JETSON else 4
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REMBG_MIN_SESSIONS: int = 2 if IS_JETSON else 1
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REMBG_MAX_SESSIONS: int = 4 if IS_JETSON else 3
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# 세션 추가 생성 VRAM 임계값 (%). 남은 VRAM이 이 값보다 커야 새 세션 생성.
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# 예: 12GB VRAM에 0.3이면, 3.6GB 이상 여유가 있어야 함.
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SESSION_VRAM_THRESHOLD: float = 0.3 if IS_JETSON else 0.3 # Jetson에서는 통합 메모리이므로 좀 더 여유롭게 설정
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# 유휴 세션 자동 제거 시간 (초). 0이면 비활성화.
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SESSION_IDLE_TIMEOUT: int = 1800 # 30분
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# --- 서버 환경 설정 (클래스 내부로 이동) ---
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APP_VERSION: str = "3.0.0-dynamic-pool"
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APP_NAME: str = "Inpaint & RemoveBG Server"
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API_PREFIX: str = "/api/v1"
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HOST: str = "0.0.0.0"
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PORT: int = 8008
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WORKERS: int = 1 # 개발 모드에서는 1로 고정, 프로덕션에서는 gunicorn으로 관리
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@ -40,13 +62,13 @@ class Settings(BaseSettings):
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REMBG_SESSIONS: int = 3 if IS_JETSON else 2 # 메모리 공유 방식의 이점 활용
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# Worker settings (Jetson은 통합 메모리로 더 효율적)
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MAX_WORKERS: int = 8 if IS_JETSON else 16 # Jetson: 메모리 오버헤드 적음
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MIN_WORKERS: int = 4 if IS_JETSON else 8 # 통합 메모리 활용
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MAX_WORKERS: int = 10 if IS_JETSON else 8
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MIN_WORKERS: int = 10 if IS_JETSON else 4
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WORKER_TIMEOUT: int = 120 # 2 minutes
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# 메모리 관리 (Jetson은 32GB 통합 메모리로 여유로움)
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VRAM_THRESHOLD_HIGH: float = 0.85 if IS_JETSON else 0.75 # Jetson: 32GB 통합 메모리
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VRAM_THRESHOLD_LOW: float = 0.4 if IS_JETSON else 0.3 # 데스크톱: VRAM 제한
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VRAM_THRESHOLD_HIGH: float = 0.85 if IS_JETSON else 0.80 # 워커 추가 생성 중단
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VRAM_THRESHOLD_LOW: float = 0.4 if IS_JETSON else 0.40 # 워커 제거 시작
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VRAM_CHECK_INTERVAL: int = 30 if IS_JETSON else 15 # Jetson은 덜 자주 체크
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# Model paths
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@ -1,21 +1,25 @@
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"""
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세션 풀 관리 시스템
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각 모델(simple-lama, migan, rembg)의 세션을 효율적으로 관리합니다.
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VRAM 사용량을 고려하여 세션을 동적으로 생성하고, 유휴 세션을 자동으로 제거합니다.
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"""
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import asyncio
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import logging
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import time
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from typing import Dict, List, Optional, Any
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from typing import Dict, List, Optional, Any, Tuple
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from enum import Enum
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from dataclasses import dataclass
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from contextlib import asynccontextmanager
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from ..core.config import settings
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from ..utils.gpu_monitor import gpu_monitor
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logger = logging.getLogger(__name__)
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class ModelType(Enum):
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SIMPLE_LAMA = "simple_lama"
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MIGAN = "migan"
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MIGAN = "migan"
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REMBG = "rembg"
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@ -27,86 +31,123 @@ class ModelSession:
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created_at: float
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last_used: float
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in_use: bool = False
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def mark_used(self):
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self.last_used = time.time()
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def is_expired(self, timeout: int = 3600) -> bool:
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return time.time() - self.last_used > timeout
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def is_idle(self, timeout: int) -> bool:
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if timeout <= 0:
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return False
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return not self.in_use and (time.time() - self.last_used > timeout)
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class SessionPool:
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def __init__(self,
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simple_lama_count: int = 2,
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migan_count: int = 2,
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rembg_count: int = 1):
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self.pools: Dict[ModelType, List[ModelSession]] = {
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ModelType.SIMPLE_LAMA: [],
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ModelType.MIGAN: [],
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ModelType.REMBG: []
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}
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self.pool_sizes = {
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ModelType.SIMPLE_LAMA: simple_lama_count,
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ModelType.MIGAN: migan_count,
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ModelType.REMBG: rembg_count
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}
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self.locks: Dict[ModelType, asyncio.Lock] = {
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model_type: asyncio.Lock() for model_type in ModelType
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def __init__(self, model_configs: Dict[ModelType, Tuple[int, int]]):
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self.pools: Dict[ModelType, List[ModelSession]] = {mt: [] for mt in ModelType}
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self.model_configs = model_configs
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self.conditions: Dict[ModelType, asyncio.Condition] = {
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mt: asyncio.Condition() for mt in ModelType
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}
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self._initialized = False
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self._reaper_task: Optional[asyncio.Task] = None
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async def initialize(self):
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"""모든 모델 세션을 초기화합니다."""
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if self._initialized:
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return
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logger.info("Initializing session pools...")
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logger.info("Initializing dynamic session pools...")
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for model_type, count in self.pool_sizes.items():
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await self._initialize_model_pool(model_type, count)
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for model_type, (min_sessions, _) in self.model_configs.items():
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if min_sessions > 0:
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logger.info(f"Pre-loading {min_sessions} sessions for {model_type.value}")
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# Use asyncio.gather for concurrent session creation
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tasks = [
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self._create_session(model_type, f"{model_type.value}_{i}")
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for i in range(min_sessions)
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]
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try:
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results = await asyncio.gather(*tasks, return_exceptions=True)
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for i, res in enumerate(results):
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if isinstance(res, ModelSession):
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self.pools[model_type].append(res)
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else:
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logger.error(f"Failed to create initial session {model_type.value}_{i}: {res}")
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except Exception as e:
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logger.error(f"Error during concurrent session creation for {model_type.value}: {e}", exc_info=True)
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self._initialized = True
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# 유휴 세션 제거 작업 시작
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if settings.SESSION_IDLE_TIMEOUT > 0:
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self._reaper_task = asyncio.create_task(self._reap_idle_sessions())
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logger.info("Session pools initialized successfully")
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async def _initialize_model_pool(self, model_type: ModelType, count: int):
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"""특정 모델의 세션 풀을 초기화합니다."""
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logger.info(f"Initializing {count} sessions for {model_type.value}")
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for i in range(count):
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try:
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session = await self._create_session(model_type, f"{model_type.value}_{i}")
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self.pools[model_type].append(session)
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logger.info(f"Created session {session.session_id}")
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except Exception as e:
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logger.error(f"Failed to create session for {model_type.value}: {e}")
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async def _reap_idle_sessions(self):
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"""유휴 세션을 주기적으로 제거하는 백그라운드 작업."""
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logger.info(f"Idle session reaper started. Timeout: {settings.SESSION_IDLE_TIMEOUT}s, Check Interval: 60s")
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while True:
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await asyncio.sleep(60) # 1분마다 체크
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for model_type, pool in self.pools.items():
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min_sessions, _ = self.model_configs[model_type]
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async with self.conditions[model_type]:
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if len(pool) <= min_sessions:
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continue
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# 사용 중이 아니고, 타임아웃이 지난 세션을 찾음
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idle_sessions = [s for s in pool if s.is_idle(settings.SESSION_IDLE_TIMEOUT)]
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# 제거할 세션 수 결정 (min_sessions 이하로는 줄이지 않음)
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num_to_reap = min(len(idle_sessions), len(pool) - min_sessions)
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if num_to_reap > 0:
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sessions_to_reap = idle_sessions[:num_to_reap]
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logger.info(f"Reaping {len(sessions_to_reap)} idle session(s) for {model_type.value}. Current pool size: {len(pool)}, Min size: {min_sessions}")
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for session in sessions_to_reap:
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pool.remove(session)
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# 명시적으로 모델 객체를 삭제하여 GC가 메모리를 회수하도록 유도
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del session.model
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del session
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# Notify any waiting tasks that pool state has changed
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self.conditions[model_type].notify_all()
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async def _create_session(self, model_type: ModelType, session_id: str) -> ModelSession:
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"""새로운 모델 세션을 생성합니다."""
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model = await self._load_model(model_type)
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return ModelSession(
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session_id=session_id,
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model_type=model_type,
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model=model,
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created_at=time.time(),
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last_used=time.time()
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)
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logger.info(f"Creating new session {session_id} for {model_type.value}...")
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try:
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model = await self._load_model(model_type)
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session = ModelSession(
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session_id=session_id,
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model_type=model_type,
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model=model,
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created_at=time.time(),
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last_used=time.time()
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)
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logger.info(f"Successfully created session {session.session_id}")
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return session
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except Exception as e:
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logger.error(f"Failed to create session {session_id}: {e}", exc_info=True)
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raise
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async def _load_model(self, model_type: ModelType) -> Any:
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"""모델을 로드합니다."""
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# 실제 구현에서는 각 모델을 로드하는 로직이 들어갑니다
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if model_type == ModelType.SIMPLE_LAMA:
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return await self._load_simple_lama_model()
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elif model_type == ModelType.MIGAN:
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return await self._load_migan_model()
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elif model_type == ModelType.REMBG:
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return await self._load_rembg_model()
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return await asyncio.wait_for(self._load_rembg_model(), timeout=180) # 모델 다운로드 시간 고려
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else:
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raise ValueError(f"Unknown model type: {model_type}")
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async def _load_simple_lama_model(self):
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"""Simple LAMA 모델을 로드합니다."""
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from ..models.simple_lama import SimpleLamaInpainter
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from ..core.config import settings
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try:
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model = SimpleLamaInpainter(
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@ -115,19 +156,17 @@ class SessionPool:
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fp16=settings.USE_FP16
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)
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await model.load_model()
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logger.info("Simple LAMA 모델 세션 로드 완료")
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logger.debug("Simple LAMA model instance created and loaded.")
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return model
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except Exception as e:
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logger.error(f"Simple LAMA 모델 로드 실패: {e}")
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logger.error(f"Failed to load Simple LAMA model: {e}", exc_info=True)
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raise
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async def _load_migan_model(self):
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"""MIGAN 모델을 로드합니다."""
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from ..models.migan import MiganInpainter
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from ..core.config import settings
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try:
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# MIGAN 모델 생성 - ONNX Runtime이 자동으로 CUDA 감지
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model = MiganInpainter(
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model_path=getattr(settings, 'MIGAN_ONNX_PATH', settings.MIGAN_MODEL_PATH),
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device="cuda" if settings.USE_CUDA else "cpu",
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@ -135,31 +174,28 @@ class SessionPool:
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use_cuda=settings.USE_CUDA
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)
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await model.load_model()
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logger.info("MIGAN 모델 세션 로드 완료")
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logger.debug("MIGAN model instance created and loaded.")
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return model
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except Exception as e:
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logger.error(f"MIGAN 모델 로드 실패: {e}")
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logger.error(f"Failed to load MIGAN model: {e}", exc_info=True)
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raise
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async def _load_rembg_model(self):
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"""REMBG 모델을 로드합니다."""
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from ..models.rembg_model import RembgProcessor
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from ..core.config import settings
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try:
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# RemBG 모델 생성 - 자동으로 CUDA 감지
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model = RembgProcessor(
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model_name=getattr(settings, 'REMBG_MODEL_NAME', 'birefnet-general-lite'),
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device="cuda" if settings.USE_CUDA else "cpu",
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fp16=settings.USE_FP16,
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local_rembg_model_path=getattr(settings, 'LOCAL_REMBG_MODEL_PATH', None)
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)
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# 프리로드 강제: 실패 시 서버 기동 실패로 처리 (원인 파악을 위함)
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await model.load_model()
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logger.info("REMBG 모델 세션 로드 완료")
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logger.debug("RembgProcessor instance created and model pre-loaded.")
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return model
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except Exception as e:
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logger.error(f"REMBG 모델 로드 실패: {e}")
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logger.error(f"Failed to load REMBG model: {e}", exc_info=True)
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raise
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@asynccontextmanager
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@ -169,122 +205,89 @@ class SessionPool:
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try:
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yield session
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finally:
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await self._release_session(session)
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if session:
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await self._release_session(session)
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async def _acquire_session(self, model_type: ModelType) -> ModelSession:
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"""사용 가능한 세션을 획득합니다."""
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async with self.locks[model_type]:
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# 사용 가능한 세션 찾기
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for session in self.pools[model_type]:
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if not session.in_use:
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session.in_use = True
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session.mark_used()
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logger.debug(f"Acquired session {session.session_id}")
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return session
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# 사용 가능한 세션이 없으면 대기
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logger.warning(f"No available sessions for {model_type.value}, waiting...")
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# 세션이 사용 가능해질 때까지 대기
|
||||
"""사용 가능한 세션을 획득하거나, VRAM 여유 시 새로 생성합니다."""
|
||||
condition = self.conditions[model_type]
|
||||
_, max_sessions = self.model_configs[model_type]
|
||||
|
||||
while True:
|
||||
await asyncio.sleep(0.1)
|
||||
async with self.locks[model_type]:
|
||||
async with condition:
|
||||
# 1. 사용 가능한 세션 찾기
|
||||
for session in self.pools[model_type]:
|
||||
if not session.in_use:
|
||||
session.in_use = True
|
||||
session.mark_used()
|
||||
logger.debug(f"Acquired session {session.session_id} after waiting")
|
||||
logger.debug(f"Acquired existing session {session.session_id}")
|
||||
return session
|
||||
|
||||
# 2. 세션을 새로 생성할 수 있는지 확인
|
||||
if len(self.pools[model_type]) < max_sessions:
|
||||
# 2-1. VRAM 여유 공간 확인
|
||||
gpu_mem_info = gpu_monitor.get_gpu_memory_info()
|
||||
free_vram_ratio = gpu_mem_info.get("free_ratio", 0)
|
||||
|
||||
if free_vram_ratio > settings.SESSION_VRAM_THRESHOLD:
|
||||
current_pool_size = len(self.pools[model_type])
|
||||
session_id = f"{model_type.value}_{current_pool_size}"
|
||||
|
||||
logger.info(f"Attempting to create new session for {model_type.value}. Current size: {current_pool_size}, Max size: {max_sessions}")
|
||||
|
||||
# Condition lock을 잠시 해제하고 세션 생성 (I/O 작업)
|
||||
try:
|
||||
new_session = await self._create_session(model_type, session_id)
|
||||
# 다시 lock을 잡고 풀에 추가
|
||||
new_session.in_use = True
|
||||
new_session.mark_used()
|
||||
self.pools[model_type].append(new_session)
|
||||
logger.info(f"Acquired new session {new_session.session_id} as VRAM is sufficient ({free_vram_ratio:.2f} > {settings.SESSION_VRAM_THRESHOLD:.2f})")
|
||||
return new_session
|
||||
except Exception:
|
||||
# 세션 생성 실패 시 루프를 계속하여 다시 시도하거나 대기
|
||||
logger.error(f"New session creation failed for {model_type.value}. Will wait for an existing session.")
|
||||
pass
|
||||
else:
|
||||
logger.warning(f"Cannot create new session for {model_type.value}. VRAM threshold not met. (Free: {free_vram_ratio:.2f} <= Threshold: {settings.SESSION_VRAM_THRESHOLD:.2f})")
|
||||
|
||||
# 3. 대기
|
||||
logger.debug(f"No available sessions or VRAM for {model_type.value}, waiting...")
|
||||
await condition.wait()
|
||||
|
||||
async def _release_session(self, session: ModelSession):
|
||||
"""세션을 반환합니다."""
|
||||
async with self.locks[session.model_type]:
|
||||
"""세션을 반환하고 대기 중인 다른 요청에 알립니다."""
|
||||
condition = self.conditions[session.model_type]
|
||||
async with condition:
|
||||
session.in_use = False
|
||||
logger.debug(f"Released session {session.session_id}")
|
||||
condition.notify() # 대기 중인 하나의 코루틴을 깨움
|
||||
|
||||
async def get_pool_status(self) -> Dict[str, Any]:
|
||||
"""풀 상태를 반환합니다."""
|
||||
status = {}
|
||||
for model_type in ModelType:
|
||||
pool = self.pools[model_type]
|
||||
total = len(pool)
|
||||
in_use = sum(1 for session in pool if session.in_use)
|
||||
available = total - in_use
|
||||
|
||||
status[model_type.value] = {
|
||||
"total": total,
|
||||
"in_use": in_use,
|
||||
"available": available,
|
||||
"sessions": [
|
||||
{
|
||||
"id": session.session_id,
|
||||
"in_use": session.in_use,
|
||||
"last_used": session.last_used,
|
||||
"created_at": session.created_at
|
||||
}
|
||||
for session in pool
|
||||
]
|
||||
}
|
||||
return status
|
||||
|
||||
async def cleanup_expired_sessions(self, timeout: int = 3600):
|
||||
"""만료된 세션을 정리합니다."""
|
||||
for model_type, pool in self.pools.items():
|
||||
async with self.locks[model_type]:
|
||||
expired_sessions = [s for s in pool if s.is_expired(timeout) and not s.in_use]
|
||||
for session in expired_sessions:
|
||||
pool.remove(session)
|
||||
logger.info(f"Removed expired session {session.session_id}")
|
||||
|
||||
async def scale_pool(self, model_type: ModelType, new_size: int):
|
||||
"""풀 크기를 조정합니다."""
|
||||
async with self.locks[model_type]:
|
||||
current_size = len(self.pools[model_type])
|
||||
|
||||
if new_size > current_size:
|
||||
# 세션 추가
|
||||
for i in range(current_size, new_size):
|
||||
session_id = f"{model_type.value}_{i}"
|
||||
session = await self._create_session(model_type, session_id)
|
||||
self.pools[model_type].append(session)
|
||||
logger.info(f"Added session {session_id}")
|
||||
|
||||
elif new_size < current_size:
|
||||
# 세션 제거 (사용 중이지 않은 것만)
|
||||
sessions_to_remove = []
|
||||
for session in self.pools[model_type]:
|
||||
if not session.in_use and len(sessions_to_remove) < (current_size - new_size):
|
||||
sessions_to_remove.append(session)
|
||||
|
||||
for session in sessions_to_remove:
|
||||
self.pools[model_type].remove(session)
|
||||
logger.info(f"Removed session {session.session_id}")
|
||||
|
||||
self.pool_sizes[model_type] = new_size
|
||||
|
||||
def get_status(self) -> dict:
|
||||
"""세션 풀의 현재 상태를 반환합니다."""
|
||||
status_by_model = {}
|
||||
|
||||
for model_type in ModelType:
|
||||
pool = self.pools[model_type]
|
||||
min_s, max_s = self.model_configs[model_type]
|
||||
total = len(pool)
|
||||
in_use = sum(1 for session in pool if session.in_use)
|
||||
available = total - in_use
|
||||
|
||||
status_by_model[model_type.value] = {
|
||||
"min": min_s,
|
||||
"max": max_s,
|
||||
"total": total,
|
||||
"in_use": in_use,
|
||||
"available": available
|
||||
}
|
||||
|
||||
return status_by_model
|
||||
|
||||
|
||||
# 전역 세션 풀 인스턴스 (설정값으로 초기화)
|
||||
from ..core.config import settings
|
||||
session_pool = SessionPool(
|
||||
simple_lama_count=settings.SIMPLE_LAMA_SESSIONS,
|
||||
migan_count=settings.MIGAN_SESSIONS,
|
||||
rembg_count=settings.REMBG_SESSIONS
|
||||
)
|
||||
model_configs = {
|
||||
ModelType.SIMPLE_LAMA: (settings.SIMPLE_LAMA_MIN_SESSIONS, settings.SIMPLE_LAMA_MAX_SESSIONS),
|
||||
ModelType.MIGAN: (settings.MIGAN_MIN_SESSIONS, settings.MIGAN_MAX_SESSIONS),
|
||||
ModelType.REMBG: (settings.REMBG_MIN_SESSIONS, settings.REMBG_MAX_SESSIONS),
|
||||
}
|
||||
|
||||
session_pool = SessionPool(model_configs=model_configs)
|
||||
|
|
|
|||
|
|
@ -1181,31 +1181,28 @@ HTML_TEMPLATE = """
|
|||
|
||||
// 세션 풀 상세 정보 업데이트
|
||||
if (data.sessions) {
|
||||
// Simple LAMA 세션
|
||||
const lamaSession = data.sessions['simple-lama'] || data.sessions['simple_lama'] || {};
|
||||
const lamaTotalSessions = lamaSession.total || 0;
|
||||
const lamaInUse = lamaSession.in_use || 0;
|
||||
const lamaAvailable = lamaSession.available || (lamaTotalSessions - lamaInUse);
|
||||
document.getElementById('session-lama').textContent = `${lamaInUse}/${lamaTotalSessions} 사용중`;
|
||||
// Helper function to update session info
|
||||
const updateSessionInfo = (modelKey, elementId) => {
|
||||
const session = data.sessions[modelKey] || {};
|
||||
const inUse = session.in_use || 0;
|
||||
const total = session.total || 0;
|
||||
const min = session.min || 0;
|
||||
const max = session.max || 0;
|
||||
document.getElementById(elementId).textContent = `${inUse} / ${total} (min:${min}, max:${max})`;
|
||||
};
|
||||
|
||||
// MIGAN 세션
|
||||
const miganSession = data.sessions['migan'] || {};
|
||||
const miganTotalSessions = miganSession.total || 0;
|
||||
const miganInUse = miganSession.in_use || 0;
|
||||
const miganAvailable = miganSession.available || (miganTotalSessions - miganInUse);
|
||||
document.getElementById('session-migan').textContent = `${miganInUse}/${miganTotalSessions} 사용중`;
|
||||
|
||||
// RemBG 세션
|
||||
const rembgSession = data.sessions['rembg'] || {};
|
||||
const rembgTotalSessions = rembgSession.total || 0;
|
||||
const rembgInUse = rembgSession.in_use || 0;
|
||||
const rembgAvailable = rembgSession.available || (rembgTotalSessions - rembgInUse);
|
||||
document.getElementById('session-rembg').textContent = `${rembgInUse}/${rembgTotalSessions} 사용중`;
|
||||
updateSessionInfo('simple_lama', 'session-lama');
|
||||
updateSessionInfo('migan', 'session-migan');
|
||||
updateSessionInfo('rembg', 'session-rembg');
|
||||
|
||||
// 총 세션 수
|
||||
const totalSessions = lamaTotalSessions + miganTotalSessions + rembgTotalSessions;
|
||||
const totalInUse = lamaInUse + miganInUse + rembgInUse;
|
||||
document.getElementById('session-total').textContent = `${totalInUse}/${totalSessions} 사용중`;
|
||||
let totalInUse = 0;
|
||||
let totalSessions = 0;
|
||||
Object.values(data.sessions).forEach(s => {
|
||||
totalInUse += s.in_use || 0;
|
||||
totalSessions += s.total || 0;
|
||||
});
|
||||
document.getElementById('session-total').textContent = `${totalInUse} / ${totalSessions} 사용중`;
|
||||
|
||||
console.log("세션 풀 상태:", data.sessions);
|
||||
} else {
|
||||
|
|
|
|||
1818
logs/main.log
1818
logs/main.log
File diff suppressed because it is too large
Load Diff
|
|
@ -1,207 +1,71 @@
|
|||
INFO: Started server process [41241]
|
||||
2025-08-29 21:56:12,908 - uvicorn.error - INFO - Started server process [41241]
|
||||
INFO: Started server process [56165]
|
||||
2025-08-29 23:46:34,096 - uvicorn.error - INFO - Started server process [56165]
|
||||
INFO: Waiting for application startup.
|
||||
2025-08-29 21:56:12,909 - uvicorn.error - INFO - Waiting for application startup.
|
||||
2025-08-29 21:56:12,910 - main - INFO - 🚀 인페인팅 서버 시작 중...
|
||||
2025-08-29 21:56:12,911 - main - INFO - ✅ 공유 객체를 app.state에 저장 완료
|
||||
2025-08-29 21:56:12,911 - main - INFO - 🔄 상태 저장 백그라운드 작업 생성 중...
|
||||
2025-08-29 21:56:12,911 - main - INFO - ✅ 상태 저장 백그라운드 작업 생성 완료
|
||||
2025-08-29 21:56:12,912 - main - INFO - 🚀 세션 풀 초기화 (CUDA 자동 감지)
|
||||
2025-08-29 21:56:12,912 - app.core.session_pool - INFO - Initializing session pools...
|
||||
2025-08-29 21:56:12,912 - app.core.session_pool - INFO - Initializing 4 sessions for simple_lama
|
||||
2025-08-29 21:56:16,159 - app.models.simple_lama - INFO - Loading Simple LAMA model...
|
||||
2025-08-29 21:56:20,613 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
|
||||
2025-08-29 21:56:20,614 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
|
||||
2025-08-29 21:56:20,615 - app.core.session_pool - INFO - Simple LAMA 모델 세션 로드 완료
|
||||
2025-08-29 21:56:20,616 - app.core.session_pool - INFO - Created session simple_lama_0
|
||||
2025-08-29 21:56:20,617 - app.models.simple_lama - INFO - Loading Simple LAMA model...
|
||||
2025-08-29 21:56:22,400 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
|
||||
2025-08-29 21:56:22,401 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
|
||||
2025-08-29 21:56:22,401 - app.core.session_pool - INFO - Simple LAMA 모델 세션 로드 완료
|
||||
2025-08-29 21:56:22,402 - app.core.session_pool - INFO - Created session simple_lama_1
|
||||
2025-08-29 21:56:22,402 - app.models.simple_lama - INFO - Loading Simple LAMA model...
|
||||
2025-08-29 21:56:24,088 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
|
||||
2025-08-29 21:56:24,088 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
|
||||
2025-08-29 21:56:24,089 - app.core.session_pool - INFO - Simple LAMA 모델 세션 로드 완료
|
||||
2025-08-29 21:56:24,089 - app.core.session_pool - INFO - Created session simple_lama_2
|
||||
2025-08-29 21:56:24,089 - app.models.simple_lama - INFO - Loading Simple LAMA model...
|
||||
2025-08-29 21:56:25,756 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
|
||||
2025-08-29 21:56:25,756 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
|
||||
2025-08-29 21:56:25,757 - app.core.session_pool - INFO - Simple LAMA 모델 세션 로드 완료
|
||||
2025-08-29 21:56:25,757 - app.core.session_pool - INFO - Created session simple_lama_3
|
||||
2025-08-29 21:56:25,757 - app.core.session_pool - INFO - Initializing 4 sessions for migan
|
||||
2025-08-29 21:56:25,822 - app.models.migan - INFO - Loading MIGAN ONNX model...
|
||||
2025-08-29 21:56:25,822 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
|
||||
2025-08-29 21:56:25,823 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
[0;93m2025-08-29 21:56:27.238315022 [W:onnxruntime:, transformer_memcpy.cc:74 ApplyImpl] 17 Memcpy nodes are added to the graph main_graph for CUDAExecutionProvider. It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.[m
|
||||
2025-08-29 21:56:28,580 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:56:28,581 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
|
||||
2025-08-29 21:56:28,581 - app.core.session_pool - INFO - MIGAN 모델 세션 로드 완료
|
||||
2025-08-29 21:56:28,582 - app.core.session_pool - INFO - Created session migan_0
|
||||
2025-08-29 21:56:28,582 - app.models.migan - INFO - Loading MIGAN ONNX model...
|
||||
2025-08-29 21:56:28,583 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
|
||||
2025-08-29 21:56:28,583 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
[0;93m2025-08-29 21:56:29.684943504 [W:onnxruntime:, transformer_memcpy.cc:74 ApplyImpl] 17 Memcpy nodes are added to the graph main_graph for CUDAExecutionProvider. It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.[m
|
||||
2025-08-29 21:56:29,852 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:56:29,853 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
|
||||
2025-08-29 21:56:29,853 - app.core.session_pool - INFO - MIGAN 모델 세션 로드 완료
|
||||
2025-08-29 21:56:29,854 - app.core.session_pool - INFO - Created session migan_1
|
||||
2025-08-29 21:56:29,854 - app.models.migan - INFO - Loading MIGAN ONNX model...
|
||||
2025-08-29 21:56:29,854 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
|
||||
2025-08-29 21:56:29,855 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
[0;93m2025-08-29 21:56:30.907277353 [W:onnxruntime:, transformer_memcpy.cc:74 ApplyImpl] 17 Memcpy nodes are added to the graph main_graph for CUDAExecutionProvider. It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.[m
|
||||
2025-08-29 21:56:31,072 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:56:31,072 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
|
||||
2025-08-29 21:56:31,073 - app.core.session_pool - INFO - MIGAN 모델 세션 로드 완료
|
||||
2025-08-29 21:56:31,073 - app.core.session_pool - INFO - Created session migan_2
|
||||
2025-08-29 21:56:31,074 - app.models.migan - INFO - Loading MIGAN ONNX model...
|
||||
2025-08-29 21:56:31,074 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
|
||||
2025-08-29 21:56:31,074 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
[0;93m2025-08-29 21:56:32.141164804 [W:onnxruntime:, transformer_memcpy.cc:74 ApplyImpl] 17 Memcpy nodes are added to the graph main_graph for CUDAExecutionProvider. It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.[m
|
||||
2025-08-29 21:56:32,306 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:56:32,307 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
|
||||
2025-08-29 21:56:32,308 - app.core.session_pool - INFO - MIGAN 모델 세션 로드 완료
|
||||
2025-08-29 21:56:32,308 - app.core.session_pool - INFO - Created session migan_3
|
||||
2025-08-29 21:56:32,309 - app.core.session_pool - INFO - Initializing 3 sessions for rembg
|
||||
2025-08-29 21:56:32,310 - app.models.rembg_model - INFO - Loading REMBG model (birefnet-general-lite)...
|
||||
2025-08-29 21:56:34,333 - app.models.rembg_model - INFO - rembg 모듈 임포트 성공 (세션 생성은 지연 로딩)
|
||||
2025-08-29 21:56:34,334 - app.models.rembg_model - INFO - 🔧 rembg 새 세션 생성 필요: birefnet-general-lite_cuda_True
|
||||
2025-08-29 21:56:34,334 - app.models.rembg_model - WARNING - rembg.sessions import 실패, 기본 방식 사용
|
||||
2025-08-29 21:56:34,335 - app.models.rembg_model - INFO - rembg 세션 생성 providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:56:47,648 - app.models.rembg_model - INFO - ✅ rembg 'birefnet-general-lite' GPU 가속로 동작 (providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'])
|
||||
2025-08-29 21:56:47,649 - app.models.rembg_model - INFO - REMBG model (birefnet-general-lite) loaded successfully
|
||||
2025-08-29 21:56:47,650 - app.core.session_pool - INFO - REMBG 모델 세션 로드 완료
|
||||
2025-08-29 21:56:47,650 - app.core.session_pool - INFO - Created session rembg_0
|
||||
2025-08-29 21:56:47,651 - app.models.rembg_model - INFO - Loading REMBG model (birefnet-general-lite)...
|
||||
2025-08-29 21:56:47,651 - app.models.rembg_model - INFO - rembg 모듈 임포트 성공 (세션 생성은 지연 로딩)
|
||||
2025-08-29 21:56:47,651 - app.models.rembg_model - INFO - 🔧 rembg 새 세션 생성 필요: birefnet-general-lite_cuda_True
|
||||
2025-08-29 21:56:47,652 - app.models.rembg_model - WARNING - rembg.sessions import 실패, 기본 방식 사용
|
||||
2025-08-29 21:56:47,652 - app.models.rembg_model - INFO - rembg 세션 생성 providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:57:00,508 - app.models.rembg_model - INFO - ✅ rembg 'birefnet-general-lite' GPU 가속로 동작 (providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'])
|
||||
2025-08-29 21:57:00,509 - app.models.rembg_model - INFO - REMBG model (birefnet-general-lite) loaded successfully
|
||||
2025-08-29 21:57:00,509 - app.core.session_pool - INFO - REMBG 모델 세션 로드 완료
|
||||
2025-08-29 21:57:00,510 - app.core.session_pool - INFO - Created session rembg_1
|
||||
2025-08-29 21:57:00,510 - app.models.rembg_model - INFO - Loading REMBG model (birefnet-general-lite)...
|
||||
2025-08-29 21:57:00,511 - app.models.rembg_model - INFO - rembg 모듈 임포트 성공 (세션 생성은 지연 로딩)
|
||||
2025-08-29 21:57:00,511 - app.models.rembg_model - INFO - 🔧 rembg 새 세션 생성 필요: birefnet-general-lite_cuda_True
|
||||
2025-08-29 21:57:00,512 - app.models.rembg_model - WARNING - rembg.sessions import 실패, 기본 방식 사용
|
||||
2025-08-29 21:57:00,512 - app.models.rembg_model - INFO - rembg 세션 생성 providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 21:57:13,223 - app.models.rembg_model - INFO - ✅ rembg 'birefnet-general-lite' GPU 가속로 동작 (providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'])
|
||||
2025-08-29 21:57:13,223 - app.models.rembg_model - INFO - REMBG model (birefnet-general-lite) loaded successfully
|
||||
2025-08-29 21:57:13,224 - app.core.session_pool - INFO - REMBG 모델 세션 로드 완료
|
||||
2025-08-29 21:57:13,224 - app.core.session_pool - INFO - Created session rembg_2
|
||||
2025-08-29 21:57:13,225 - app.core.session_pool - INFO - Session pools initialized successfully
|
||||
2025-08-29 21:57:13,225 - main - INFO - ✅ 세션 풀 초기화 완료
|
||||
2025-08-29 21:57:13,225 - app.core.worker_manager - INFO - Starting worker manager...
|
||||
2025-08-29 21:57:13,226 - app.core.worker_manager - INFO - Worker manager started with 4 workers
|
||||
2025-08-29 21:57:13,226 - main - INFO - ✅ 워커 매니저 시작 완료
|
||||
2025-08-29 21:57:13,227 - main - INFO - 🎉 인페인팅 서버 시작 완료!
|
||||
2025-08-29 21:57:13,227 - main - INFO - 🔄 상태 저장 백그라운드 작업 시작됨
|
||||
2025-08-29 23:46:34,097 - uvicorn.error - INFO - Waiting for application startup.
|
||||
2025-08-29 23:46:34,098 - main - INFO - 🚀 인페인팅 서버 시작 중...
|
||||
2025-08-29 23:46:34,098 - main - INFO - ✅ 공유 객체를 app.state에 저장 완료
|
||||
2025-08-29 23:46:34,098 - main - INFO - 🔄 상태 저장 백그라운드 작업 생성 중...
|
||||
2025-08-29 23:46:34,099 - main - INFO - ✅ 상태 저장 백그라운드 작업 생성 완료
|
||||
2025-08-29 23:46:34,099 - main - INFO - 🚀 세션 풀 초기화 (CUDA 자동 감지)
|
||||
2025-08-29 23:46:34,100 - app.core.session_pool - INFO - Initializing dynamic session pools...
|
||||
2025-08-29 23:46:34,100 - app.core.session_pool - INFO - Pre-loading 2 sessions for simple_lama
|
||||
2025-08-29 23:46:34,100 - main - INFO - 🔄 상태 저장 백그라운드 작업 시작됨
|
||||
2025-08-29 23:46:34,104 - app.core.session_pool - INFO - Creating new session simple_lama_0 for simple_lama...
|
||||
2025-08-29 23:46:37,259 - app.models.simple_lama - INFO - Loading Simple LAMA model...
|
||||
2025-08-29 23:46:41,562 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
|
||||
2025-08-29 23:46:41,563 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
|
||||
2025-08-29 23:46:41,564 - app.core.session_pool - INFO - Successfully created session simple_lama_0
|
||||
2025-08-29 23:46:41,565 - app.core.session_pool - INFO - Creating new session simple_lama_1 for simple_lama...
|
||||
2025-08-29 23:46:41,566 - app.models.simple_lama - INFO - Loading Simple LAMA model...
|
||||
2025-08-29 23:46:43,429 - app.models.simple_lama - INFO - 실제 SimpleLama 모델 로딩 완료
|
||||
2025-08-29 23:46:43,429 - app.models.simple_lama - INFO - Simple LAMA model loaded successfully
|
||||
2025-08-29 23:46:43,429 - app.core.session_pool - INFO - Successfully created session simple_lama_1
|
||||
2025-08-29 23:46:43,430 - app.core.session_pool - INFO - Pre-loading 2 sessions for migan
|
||||
2025-08-29 23:46:43,432 - app.core.session_pool - INFO - Creating new session migan_0 for migan...
|
||||
2025-08-29 23:46:43,491 - app.models.migan - INFO - Loading MIGAN ONNX model...
|
||||
2025-08-29 23:46:43,491 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
|
||||
2025-08-29 23:46:43,492 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
[0;93m2025-08-29 23:46:44.934790365 [W:onnxruntime:, transformer_memcpy.cc:74 ApplyImpl] 17 Memcpy nodes are added to the graph main_graph for CUDAExecutionProvider. It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.[m
|
||||
2025-08-29 23:46:46,412 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 23:46:46,413 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
|
||||
2025-08-29 23:46:46,413 - app.core.session_pool - INFO - Successfully created session migan_0
|
||||
2025-08-29 23:46:46,414 - app.core.session_pool - INFO - Creating new session migan_1 for migan...
|
||||
2025-08-29 23:46:46,414 - app.models.migan - INFO - Loading MIGAN ONNX model...
|
||||
2025-08-29 23:46:46,415 - app.models.migan - INFO - MIGAN ONNX 런타임 세션 생성 시도...
|
||||
2025-08-29 23:46:46,415 - app.models.migan - INFO - MIGAN ONNX providers 설정: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
[0;93m2025-08-29 23:46:47.468053292 [W:onnxruntime:, transformer_memcpy.cc:74 ApplyImpl] 17 Memcpy nodes are added to the graph main_graph for CUDAExecutionProvider. It might have negative impact on performance (including unable to run CUDA graph). Set session_options.log_severity_level=1 to see the detail logs before this message.[m
|
||||
2025-08-29 23:46:47,630 - app.models.migan - INFO - MIGAN ONNX 세션 생성 완료. Providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 23:46:47,631 - app.models.migan - INFO - MIGAN ONNX model loaded successfully
|
||||
2025-08-29 23:46:47,632 - app.core.session_pool - INFO - Successfully created session migan_1
|
||||
2025-08-29 23:46:47,632 - app.core.session_pool - INFO - Pre-loading 2 sessions for rembg
|
||||
2025-08-29 23:46:47,634 - app.core.session_pool - INFO - Creating new session rembg_0 for rembg...
|
||||
2025-08-29 23:46:47,634 - app.core.session_pool - INFO - Creating new session rembg_1 for rembg...
|
||||
2025-08-29 23:46:47,638 - app.models.rembg_model - INFO - Loading REMBG model (birefnet-general-lite)...
|
||||
2025-08-29 23:46:49,612 - app.models.rembg_model - INFO - rembg 모듈 임포트 성공 (세션 생성은 지연 로딩)
|
||||
2025-08-29 23:46:49,612 - app.models.rembg_model - INFO - 🔧 rembg 새 세션 생성 필요: birefnet-general-lite_cuda_True
|
||||
2025-08-29 23:46:49,613 - app.models.rembg_model - WARNING - rembg.sessions import 실패, 기본 방식 사용
|
||||
2025-08-29 23:46:49,613 - app.models.rembg_model - INFO - rembg 세션 생성 providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 23:47:02,613 - app.models.rembg_model - INFO - ✅ rembg 'birefnet-general-lite' GPU 가속로 동작 (providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'])
|
||||
2025-08-29 23:47:02,614 - app.models.rembg_model - INFO - REMBG model (birefnet-general-lite) loaded successfully
|
||||
2025-08-29 23:47:02,615 - app.models.rembg_model - INFO - Loading REMBG model (birefnet-general-lite)...
|
||||
2025-08-29 23:47:02,615 - app.models.rembg_model - INFO - rembg 모듈 임포트 성공 (세션 생성은 지연 로딩)
|
||||
2025-08-29 23:47:02,616 - app.models.rembg_model - INFO - 🔧 rembg 새 세션 생성 필요: birefnet-general-lite_cuda_True
|
||||
2025-08-29 23:47:02,617 - app.models.rembg_model - WARNING - rembg.sessions import 실패, 기본 방식 사용
|
||||
2025-08-29 23:47:02,617 - app.models.rembg_model - INFO - rembg 세션 생성 providers: ['CUDAExecutionProvider', 'CPUExecutionProvider']
|
||||
2025-08-29 23:47:15,228 - app.models.rembg_model - INFO - ✅ rembg 'birefnet-general-lite' GPU 가속로 동작 (providers: ['CUDAExecutionProvider', 'CPUExecutionProvider'])
|
||||
2025-08-29 23:47:15,229 - app.models.rembg_model - INFO - REMBG model (birefnet-general-lite) loaded successfully
|
||||
2025-08-29 23:47:15,230 - app.core.session_pool - INFO - Successfully created session rembg_0
|
||||
2025-08-29 23:47:15,230 - app.core.session_pool - INFO - Successfully created session rembg_1
|
||||
2025-08-29 23:47:15,232 - app.core.session_pool - INFO - Session pools initialized successfully
|
||||
2025-08-29 23:47:15,232 - main - INFO - ✅ 세션 풀 초기화 완료
|
||||
2025-08-29 23:47:15,233 - app.core.worker_manager - INFO - Starting worker manager...
|
||||
2025-08-29 23:47:15,234 - app.core.worker_manager - INFO - Worker manager started with 10 workers
|
||||
2025-08-29 23:47:15,234 - main - INFO - ✅ 워커 매니저 시작 완료
|
||||
2025-08-29 23:47:15,234 - main - INFO - 🎉 인페인팅 서버 시작 완료!
|
||||
2025-08-29 23:47:15,235 - app.core.session_pool - INFO - Idle session reaper started. Timeout: 1800s, Check Interval: 60s
|
||||
INFO: Application startup complete.
|
||||
2025-08-29 21:57:16,343 - uvicorn.error - INFO - Application startup complete.
|
||||
2025-08-29 23:47:15,235 - uvicorn.error - INFO - Application startup complete.
|
||||
INFO: Uvicorn running on http://0.0.0.0:8008 (Press CTRL+C to quit)
|
||||
2025-08-29 21:57:16,353 - uvicorn.error - INFO - Uvicorn running on http://0.0.0.0:8008 (Press CTRL+C to quit)
|
||||
INFO: 127.0.0.1:54262 - "GET /api/v1/health HTTP/1.1" 404 Not Found
|
||||
INFO: 127.0.0.1:54274 - "GET /api/v1/health HTTP/1.1" 404 Not Found
|
||||
INFO: 192.168.0.119:61112 - "GET /health HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61231 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 21:59:51,134 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 21:59:57,813 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 6.722s
|
||||
INFO: 192.168.0.119:61232 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61299 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:02,737 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:18,011 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 15.314s
|
||||
INFO: 192.168.0.119:61300 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61373 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:22,330 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:23,972 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.694s
|
||||
INFO: 192.168.0.119:61374 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61417 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:28,723 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:30,117 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.445s
|
||||
INFO: 192.168.0.119:61418 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61459 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:35,326 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:36,758 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.471s
|
||||
INFO: 192.168.0.119:61462 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61491 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:41,071 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:42,651 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.618s
|
||||
INFO: 192.168.0.119:61492 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61507 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:47,196 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:48,619 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.450s
|
||||
INFO: 192.168.0.119:61508 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61521 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:52,857 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:54,558 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.736s
|
||||
INFO: 192.168.0.119:61522 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61553 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:00:58,326 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:00:59,657 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.371s
|
||||
INFO: 192.168.0.119:61554 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61568 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:01:04,284 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:01:05,630 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.378s
|
||||
INFO: 192.168.0.119:61569 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61766 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:01:57,794 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:02:00,934 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 3.179s
|
||||
INFO: 192.168.0.119:61767 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61819 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:02:06,382 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:02:07,866 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.514s
|
||||
INFO: 192.168.0.119:61820 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61864 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:02:15,220 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:02:16,792 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.615s
|
||||
INFO: 192.168.0.119:61876 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:61923 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:02:22,461 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:02:23,790 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.377s
|
||||
INFO: 192.168.0.119:61924 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62164 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:03:01,528 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:03:04,020 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 2.532s
|
||||
INFO: 192.168.0.119:62165 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62225 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:03:13,341 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:03:15,572 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 2.260s
|
||||
INFO: 192.168.0.119:62226 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62291 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:03:21,500 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:03:29,405 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 7.941s
|
||||
INFO: 192.168.0.119:62292 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62546 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:04:02,266 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:04:12,367 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 10.139s
|
||||
INFO: 192.168.0.119:62551 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62613 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:04:16,160 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:04:18,086 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.959s
|
||||
INFO: 192.168.0.119:62614 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62653 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:04:23,356 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:04:25,222 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.918s
|
||||
INFO: 192.168.0.119:62654 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62769 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:04:49,806 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:04:51,747 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 1.978s
|
||||
INFO: 192.168.0.119:62770 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: 192.168.0.119:62996 - "GET /api/v1/model HTTP/1.1" 200 OK
|
||||
2025-08-29 22:05:37,916 - app.models.simple_lama - INFO - 실제 SimpleLama 모델로 인페인팅 수행
|
||||
2025-08-29 22:05:41,193 - app.core.worker_manager - INFO - 'simple-lama' inpainting processed in 3.324s
|
||||
INFO: Shutting down
|
||||
2025-08-29 22:05:41,242 - uvicorn.error - INFO - Shutting down
|
||||
INFO: 192.168.0.119:62998 - "POST /api/v1/inpaint HTTP/1.1" 200 OK
|
||||
INFO: Waiting for application shutdown.
|
||||
2025-08-29 22:05:41,344 - uvicorn.error - INFO - Waiting for application shutdown.
|
||||
2025-08-29 22:05:41,345 - main - INFO - 🛑 인페인팅 서버 종료 중...
|
||||
2025-08-29 22:05:41,345 - app.core.worker_manager - INFO - Stopping worker manager...
|
||||
2025-08-29 22:05:41,346 - app.core.worker_manager - INFO - Worker manager stopped
|
||||
2025-08-29 22:05:41,346 - main - INFO - ✅ 워커 매니저 중지 완료
|
||||
2025-08-29 22:05:41,347 - main - INFO - 👋 인페인팅 서버 종료 완료
|
||||
INFO: Application shutdown complete.
|
||||
2025-08-29 22:05:41,347 - uvicorn.error - INFO - Application shutdown complete.
|
||||
INFO: Finished server process [41241]
|
||||
2025-08-29 22:05:41,348 - uvicorn.error - INFO - Finished server process [41241]
|
||||
2025-08-29 23:47:15,236 - uvicorn.error - INFO - Uvicorn running on http://0.0.0.0:8008 (Press CTRL+C to quit)
|
||||
INFO: 127.0.0.1:57044 - "GET /api/v1/health HTTP/1.1" 200 OK
|
||||
INFO: 127.0.0.1:57060 - "GET /api/v1/health HTTP/1.1" 200 OK
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
41241
|
||||
56165
|
||||
|
|
|
|||
|
|
@ -1,10 +1,12 @@
|
|||
INFO: Started server process [41671]
|
||||
INFO: Started server process [56411]
|
||||
INFO: Waiting for application startup.
|
||||
Fan control not available
|
||||
INFO: Application startup complete.
|
||||
INFO: Uvicorn running on http://0.0.0.0:8888 (Press CTRL+C to quit)
|
||||
INFO: 127.0.0.1:50826 - "GET /api/simple HTTP/1.1" 200 OK
|
||||
INFO: Shutting down
|
||||
INFO: Waiting for application shutdown.
|
||||
INFO: Application shutdown complete.
|
||||
INFO: Finished server process [41671]
|
||||
INFO: 127.0.0.1:43630 - "GET /api/simple HTTP/1.1" 200 OK
|
||||
Task exception was never retrieved
|
||||
future: <Task finished name='Task-4' coro=<health_check_and_restart() done, defined at /home/ckh08045/work/inpaintServer/app/monitoring/dashboard.py:2084> exception=AttributeError("module 'asyncio' has no attribute 'to_thread'")>
|
||||
Traceback (most recent call last):
|
||||
File "/home/ckh08045/work/inpaintServer/app/monitoring/dashboard.py", line 2094, in health_check_and_restart
|
||||
response = await asyncio.to_thread(requests.get, health_url, timeout=10)
|
||||
AttributeError: module 'asyncio' has no attribute 'to_thread'
|
||||
|
|
|
|||
|
|
@ -1 +1 @@
|
|||
41671
|
||||
56411
|
||||
|
|
|
|||
|
|
@ -0,0 +1,45 @@
|
|||
import onnxruntime as ort
|
||||
import sys
|
||||
|
||||
def check_onnx_batch_support(model_path):
|
||||
"""
|
||||
ONNX 모델을 로드하여 배치 처리 지원 여부를 확인하고,
|
||||
입력 및 출력 정보를 출력하는 함수
|
||||
"""
|
||||
try:
|
||||
session = ort.InferenceSession(model_path)
|
||||
print(f"✅ 모델 로드 성공: {model_path}")
|
||||
|
||||
print("\n[입력 정보]")
|
||||
for i, input_meta in enumerate(session.get_inputs()):
|
||||
shape = input_meta.shape
|
||||
print(f" - 입력 #{i}")
|
||||
print(f" - 이름: {input_meta.name}")
|
||||
print(f" - 형태: {shape}")
|
||||
# 배치 차원이 문자열(동적)인지 확인
|
||||
if isinstance(shape[0], str) or shape[0] is None:
|
||||
print(f" - ✅ 배치 처리 지원 (동적 배치 크기 '{shape[0]}')")
|
||||
else:
|
||||
print(f" - ⚠️ 배치 처리를 지원하지 않거나, 배치 크기가 '{shape[0]}'으로 고정됨")
|
||||
|
||||
print("\n[출력 정보]")
|
||||
for i, output_meta in enumerate(session.get_outputs()):
|
||||
shape = output_meta.shape
|
||||
print(f" - 출력 #{i}")
|
||||
print(f" - 이름: {output_meta.name}")
|
||||
print(f" - 형태: {shape}")
|
||||
if isinstance(shape[0], str) or shape[0] is None:
|
||||
print(f" - ✅ 배치 처리 지원 (동적 배치 크기 '{shape[0]}')")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 오류 발생: 모델을 로드하거나 분석하는 중 문제가 발생했습니다.")
|
||||
print(f" {model_path}: {e}")
|
||||
|
||||
if __name__ == '__main__':
|
||||
if len(sys.argv) < 2:
|
||||
print("사용법: python check_onnx.py <ONNX_FILE_PATH_1> [ONNX_FILE_PATH_2] ...")
|
||||
else:
|
||||
for model_path in sys.argv[1:]:
|
||||
print("-" * 50)
|
||||
check_onnx_batch_support(model_path)
|
||||
print("-" * 50)
|
||||
|
|
@ -167,7 +167,7 @@ check_environment() {
|
|||
# 가상환경 확인
|
||||
log_info "가상환경 경로 확인: $VENV_PATH"
|
||||
if [ ! -f "$VENV_PATH/bin/activate" ]; then
|
||||
log_error "가상환경을 찾을 수 없습니다: $VENV_PATH"
|
||||
log_error "가상환경을 찾을 수 없습니다: $VENV_PATH/bin/activate 경로가 존재하지 않습니다."
|
||||
exit 1
|
||||
fi
|
||||
|
||||
|
|
|
|||
122
status.json
122
status.json
|
|
@ -1,33 +1,75 @@
|
|||
{
|
||||
"worker_status": {
|
||||
"running": true,
|
||||
"total_workers": 4,
|
||||
"total_workers": 10,
|
||||
"queue_size": 0,
|
||||
"workers_by_status": {
|
||||
"idle": [
|
||||
{
|
||||
"id": "worker_53efd09a",
|
||||
"id": "worker_fcfdab20",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_c1ca06d0",
|
||||
"id": "worker_07900527",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_8f181ed5",
|
||||
"id": "worker_fa21a361",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_0648365c",
|
||||
"id": "worker_aa0517fd",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_f6949bff",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_2dc147eb",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_20ae03ff",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_ed0f495e",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_06d35af9",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
"last_task_at": null
|
||||
},
|
||||
{
|
||||
"id": "worker_325beb67",
|
||||
"status": "idle",
|
||||
"task_count": 0,
|
||||
"error_count": 0,
|
||||
|
|
@ -42,70 +84,52 @@
|
|||
},
|
||||
"session_status": {
|
||||
"simple_lama": {
|
||||
"total": 4,
|
||||
"min": 2,
|
||||
"max": 4,
|
||||
"total": 2,
|
||||
"in_use": 0,
|
||||
"available": 4
|
||||
"available": 2
|
||||
},
|
||||
"migan": {
|
||||
"total": 4,
|
||||
"min": 2,
|
||||
"max": 4,
|
||||
"total": 2,
|
||||
"in_use": 0,
|
||||
"available": 4
|
||||
"available": 2
|
||||
},
|
||||
"rembg": {
|
||||
"total": 3,
|
||||
"min": 2,
|
||||
"max": 4,
|
||||
"total": 2,
|
||||
"in_use": 0,
|
||||
"available": 3
|
||||
"available": 2
|
||||
}
|
||||
},
|
||||
"api_stats": {
|
||||
"total_requests": 47,
|
||||
"successful_requests": 45,
|
||||
"failed_requests": 2,
|
||||
"success_rate": 95.74468085106383,
|
||||
"total_requests": 2,
|
||||
"successful_requests": 2,
|
||||
"failed_requests": 0,
|
||||
"success_rate": 100.0,
|
||||
"endpoint_usage": {
|
||||
"GET /api/v1/health": 2,
|
||||
"GET /health": 1,
|
||||
"GET /api/v1/model": 22,
|
||||
"POST /api/v1/inpaint": 22
|
||||
"GET /api/v1/health": 2
|
||||
},
|
||||
"endpoint_stats": {
|
||||
"GET /api/v1/health": {
|
||||
"count": 2,
|
||||
"avg_time": 0.002225041389465332,
|
||||
"min_time": 0.0013301372528076172,
|
||||
"max_time": 0.003119945526123047,
|
||||
"current_concurrent": 0
|
||||
},
|
||||
"GET /health": {
|
||||
"count": 1,
|
||||
"avg_time": 0.00873565673828125,
|
||||
"min_time": 0.00873565673828125,
|
||||
"max_time": 0.00873565673828125,
|
||||
"current_concurrent": 0
|
||||
},
|
||||
"GET /api/v1/model": {
|
||||
"count": 22,
|
||||
"avg_time": 0.005265561017123136,
|
||||
"min_time": 0.0028429031372070312,
|
||||
"max_time": 0.009748697280883789,
|
||||
"current_concurrent": 0
|
||||
},
|
||||
"POST /api/v1/inpaint": {
|
||||
"count": 22,
|
||||
"avg_time": 3.44641282341697,
|
||||
"min_time": 1.4387421607971191,
|
||||
"max_time": 15.391778945922852,
|
||||
"avg_time": 0.0017114877700805664,
|
||||
"min_time": 0.0015041828155517578,
|
||||
"max_time": 0.001918792724609375,
|
||||
"current_concurrent": 0
|
||||
}
|
||||
},
|
||||
"average_response_time": 1.615959791426963,
|
||||
"min_response_time": 0.0013301372528076172,
|
||||
"max_response_time": 15.391778945922852,
|
||||
"average_response_time": 0.0017114877700805664,
|
||||
"min_response_time": 0.0015041828155517578,
|
||||
"max_response_time": 0.001918792724609375,
|
||||
"current_concurrent": 0,
|
||||
"max_concurrent": 1,
|
||||
"requests_per_second": 0.0826958620348505,
|
||||
"uptime": 568.3476638793945,
|
||||
"requests_per_second": 0.001614421372065027,
|
||||
"uptime": 1238.8339467048645,
|
||||
"recent_errors": []
|
||||
},
|
||||
"timestamp": 1756472741.2439997
|
||||
"timestamp": 1756480032.9159214
|
||||
}
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
from PIL import Image, ImageDraw
|
||||
from pathlib import Path
|
||||
|
||||
# 설정
|
||||
WIDTH, HEIGHT = 800, 1000
|
||||
IMAGE_FILENAME = "test_image.png"
|
||||
MASK_FILENAME = "test_mask.png"
|
||||
SAVE_DIR = Path(__file__).parent
|
||||
|
||||
def generate_images():
|
||||
"""테스트용 이미지와 마스크를 생성합니다."""
|
||||
|
||||
# 1. 테스트 이미지 생성 (간단한 그라데이션)
|
||||
print(f"'{IMAGE_FILENAME}' 생성 중 ({WIDTH}x{HEIGHT})...")
|
||||
image = Image.new("RGB", (WIDTH, HEIGHT))
|
||||
draw = ImageDraw.Draw(image)
|
||||
|
||||
for i in range(HEIGHT):
|
||||
r = int(255 * (i / HEIGHT))
|
||||
g = int(255 * (1 - i / HEIGHT))
|
||||
b = 128
|
||||
draw.line([(0, i), (WIDTH, i)], fill=(r, g, b))
|
||||
|
||||
image_path = SAVE_DIR / IMAGE_FILENAME
|
||||
image.save(image_path)
|
||||
print(f"✅ 이미지를 성공적으로 저장했습니다: {image_path}")
|
||||
|
||||
# 2. 마스크 이미지 생성 (중앙에 20% 크기의 사각형)
|
||||
print(f"'{MASK_FILENAME}' 생성 중 ({WIDTH}x{HEIGHT})...")
|
||||
mask = Image.new("L", (WIDTH, HEIGHT), 0) # 검은색 배경
|
||||
draw = ImageDraw.Draw(mask)
|
||||
|
||||
# 중앙에 약 20% 크기의 흰색 사각형 그리기
|
||||
mask_width = int(WIDTH * 0.45) # 800 * 0.45 = 360
|
||||
mask_height = int(HEIGHT * 0.45) # 1000 * 0.45 = 450
|
||||
# 360 * 450 = 162,000. Total 800,000. 162000/800000 = 0.2025 (약 20%)
|
||||
|
||||
x1 = (WIDTH - mask_width) // 2
|
||||
y1 = (HEIGHT - mask_height) // 2
|
||||
x2 = x1 + mask_width
|
||||
y2 = y1 + mask_height
|
||||
|
||||
draw.rectangle([x1, y1, x2, y2], fill=255) # 흰색
|
||||
|
||||
mask_path = SAVE_DIR / MASK_FILENAME
|
||||
mask.save(mask_path)
|
||||
print(f"✅ 마스크를 성공적으로 저장했습니다: {mask_path}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
generate_images()
|
||||
|
|
@ -0,0 +1,75 @@
|
|||
import asyncio
|
||||
import httpx
|
||||
import base64
|
||||
from pathlib import Path
|
||||
import time
|
||||
|
||||
# 테스트 설정
|
||||
BASE_URL = "http://localhost:8008/api/v1/inpaint"
|
||||
# 테스트에 사용할 이미지와 마스크 경로
|
||||
IMAGE_PATH = Path(__file__).parent / "test_image.png"
|
||||
MASK_PATH = Path(__file__).parent / "test_mask.png"
|
||||
CONCURRENT_REQUESTS = 8 # 동시에 보낼 요청 수 (min_sessions < 요청 수 < max_sessions * 2 정도로 설정)
|
||||
|
||||
def encode_image(image_path: Path) -> str:
|
||||
"""이미지 파일을 base64로 인코딩"""
|
||||
try:
|
||||
with open(image_path, "rb") as f:
|
||||
return base64.b64encode(f.read()).decode('utf-8')
|
||||
except FileNotFoundError as e:
|
||||
print(f"오류: 테스트 이미지를 찾을 수 없습니다 - {e}")
|
||||
print("먼저 tests/generate_test_images.py 를 실행하여 테스트 이미지를 생성해주세요.")
|
||||
raise
|
||||
|
||||
async def send_request(client: httpx.AsyncClient, request_num: int, image_b64: str, mask_b64: str):
|
||||
"""단일 API 요청을 보냄"""
|
||||
payload = {
|
||||
"image": image_b64,
|
||||
"mask": mask_b64,
|
||||
"model_name": "simple-lama",
|
||||
"ldm_steps": 20,
|
||||
"ldm_sampler": "plms",
|
||||
"hd_strategy": "Original",
|
||||
"hd_strategy_crop_margin": 128,
|
||||
"hd_strategy_crop_trigger_size": 800,
|
||||
"hd_strategy_resize_limit": 1280
|
||||
}
|
||||
|
||||
print(f"[{request_num:02d}] 요청 시작...")
|
||||
start_time = time.time()
|
||||
try:
|
||||
response = await client.post(BASE_URL, json=payload, timeout=120)
|
||||
duration = time.time() - start_time
|
||||
|
||||
if response.status_code == 200:
|
||||
print(f"✅ [{request_num:02d}] 요청 성공 ({duration:.2f}초)")
|
||||
# with open(f"result_{request_num}.png", "wb") as f:
|
||||
# f.write(response.content)
|
||||
else:
|
||||
print(f"❌ [{request_num:02d}] 요청 실패 - 상태 코드: {response.status_code} ({duration:.2f}초)")
|
||||
print(f" 응답: {response.text[:200]}")
|
||||
|
||||
except httpx.ReadTimeout:
|
||||
duration = time.time() - start_time
|
||||
print(f"❌ [{request_num:02d}] 요청 시간 초과 ({duration:.2f}초)")
|
||||
except httpx.ConnectError as e:
|
||||
print(f"❌ [{request_num:02d}] 연결 실패: {e}")
|
||||
|
||||
async def main():
|
||||
"""메인 실행 함수"""
|
||||
print("SimpleLama 부하 테스트 시작...")
|
||||
print(f"동시 요청 수: {CONCURRENT_REQUESTS}")
|
||||
|
||||
# 이미지 파일 인코딩
|
||||
image_b64 = encode_image(IMAGE_PATH)
|
||||
mask_b64 = encode_image(MASK_PATH)
|
||||
|
||||
async with httpx.AsyncClient() as client:
|
||||
tasks = [
|
||||
send_request(client, i, image_b64, mask_b64)
|
||||
for i in range(CONCURRENT_REQUESTS)
|
||||
]
|
||||
await asyncio.gather(*tasks)
|
||||
|
||||
if __name__ == "__main__":
|
||||
asyncio.run(main())
|
||||
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Reference in New Issue