'프로그램 사용' 카테고리의 글 목록 (2 Page)

'프로그램 사용'에 해당되는 글 2595건

2026.06.20 openSCAD cheatsheet
2026.06.20 openSCAD 설치
2026.06.19 moviad / stftpm / 1080 ti / webcam feat claude
2026.06.18 moviad stfpm
2026.06.18 llama-swap 구현 (채팅)
2026.06.18 gemma4-e4b mtp..?
2026.06.18 openai api 변경에 따른 llama.cpp / llama-swap 리포트 차이
2026.06.18 llama-swap 버전 업데이트!
2026.06.18 stable diffusion --device-id
2026.06.17 ubuntu 26.04 + 3070 + tensorflow + python 3.14 + docker...

// logo.scad - Basic example of module, top-level variable and $fn usage

Logo(50);

// The $fn parameter will influence all objects inside this module
// It can, optionally, be overridden when instantiating the module
module Logo(size=50, $fn=100) {
    // Temporary variables
    hole = size/2;
    cylinderHeight = size * 1.25;

    // One positive object (sphere) and three negative objects (cylinders)
    difference() {
        sphere(d=size);

        cylinder(d=hole, h=cylinderHeight, center=true);
        // The '#' operator highlights the object
        #rotate([90, 0, 0]) cylinder(d=hole, h=cylinderHeight, center=true);
        rotate([0, 90, 0]) cylinder(d=hole, h=cylinderHeight, center=true);
    }
}

echo(version=version());
// Written by Clifford Wolf <clifford@clifford.at> and Marius
// Kintel <marius@kintel.net>
//
// To the extent possible under law, the author(s) have dedicated all
// copyright and related and neighboring rights to this software to the
// public domain worldwide. This software is distributed without any
// warranty.
//
// You should have received a copy of the CC0 Public Domain
// Dedication along with this software.
// If not, see <http://creativecommons.org/publicdomain/zero/1.0/>.

주석 처리는 모르겠고 딱 봐서 sphere만 있으면 구를 그릴것 같아서 해보니 ok

오호.. 이래서 LLM 연동해서 그리기 쉽다고 하는 거였나?

Logo(50);

module Logo(size=50, $fn=100) {
hole = size/2;
    cylinderHeight = size * 1.25;

difference() {
        sphere(d=size);

    }
}

저작자표시 (새창열림)

'프로그램 사용 > openSCAD' 카테고리의 다른 글

openSCAD MCP (0)	2026.06.20
pythonSCAD (0)	2026.06.20
openSCAD cheatsheet (0)	2026.06.20
openscad (0)	2026.05.27

Posted by 구차니

moviad / stftpm / 1080 ti / webcam feat claude

최초 커밋으로 내려가니 그래도 조금만 손대서 돌아간다. 휴

$git checkout cfef9771
$ python main_stfpm.py --train --eval --model_name mobilenet_v2 --categories bottle --ad_layers 3 4 5 --boot_layer 2 --results_dirpath debug_outputs/metrics --checkpoint_dir debug_outputs/checkpoints --seeds 0 --epochs 1000 --input_size 224 224 --device cuda:1

[링크 : https://github.com/AMCO-UniPD/moviad]

아래 사진을 모니터에 띄우고

예제 클로드(무료)로 작성하라고 했는데

처음에는 모니터 없어서 안된다고(아니.. ssh -X로 해서 떠야 하는데?)

그래서 웹으로 뚝딱 만들어 줌.. 오.. 나보다 100배 낫네

cudnn 비활성화 하고 (빨간색 부분)

웹캠으로 노트북 모니터 비추도록 해서 테스트. 음.. 잘 되는건지 안되는건지 미묘하다

"""
MoViAD STFPM + 웹캠 실시간 이상 감지 (브라우저 스트리밍 버전)
=============================================================

cv2.imshow 없이 HTTP MJPEG 스트리밍으로 브라우저에서 확인합니다.
→  http://localhost:8080  에 접속하면 실시간 영상이 표시됩니다.

[사전 준비]
  cd moviad && pip install -e ./
  pip install opencv-python-headless torch torchvision

[모델 학습]
  python main_scripts/main_stfpm.py \
      --train \
      --model_name mobilenet_v2 \
      --ad_layers 4 7 10 \
      --categories bottle \
      --dataset_path /path/to/mvtec \
      --checkpoint_dir ./checkpoints/stfpm \
      --device cuda:0 \
      --epochs 100

[실행]
  python webcam_stfpm_anomaly.py \
      --checkpoint ./checkpoints/stfpm/bottle/mobilenet_v2_100ep_IMAGENET1K_V2_4_7_10_s0.pth.tar

[REST 제어 API]  (curl 또는 브라우저로 호출)
  GET /threshold/up      임계값 +0.02
  GET /threshold/down    임계값 -0.02
  GET /threshold/auto    자동 보정 (현재 점수 기준)
  GET /heatmap/toggle    히트맵 ON/OFF
  GET /reset             점수 히스토리 초기화
  GET /status            현재 상태 JSON
"""

import argparse
import sys
import time
import threading
import json
from datetime import datetime
from pathlib import Path
from http.server import HTTPServer, BaseHTTPRequestHandler
from io import BytesIO

import cv2
import numpy as np
import torch
from torchvision import transforms

torch.backends.cudnn.enabled = False
torch.backends.cudnn.benchmark = False

try:
    from moviad.models import Stfpm
except ImportError:
    sys.exit(
        "\n[ERROR] moviad 패키지를 찾을 수 없습니다.\n"
        "  moviad 레포 루트에서 'pip install -e ./' 를 실행하세요.\n"
    )

# ──────────────────────────────────────────────────────────────────
# 전역 공유 상태 (스레드 간 공유)
# ──────────────────────────────────────────────────────────────────
class AppState:
    def __init__(self):
        self.lock          = threading.Lock()
        self.jpeg_frame    = b""          # MJPEG 브라우저 전송용 프레임
        self.threshold     = 0.5
        self.show_heat     = True
        self.norm_score    = 0.0
        self.raw_score     = 0.0
        self.is_anomaly    = False
        self.fps           = 0.0
        self.score_hist: list[float] = []
        self.running       = True

STATE = AppState()

# ──────────────────────────────────────────────────────────────────
# 전처리
# ──────────────────────────────────────────────────────────────────
IMG_SIZE = 224
RESIZE   = 256

def make_preprocess(img_size=224):
    resize = int(img_size * 256 / 224)
    return transforms.Compose([
        transforms.ToPILImage(),
        transforms.Resize(resize),
        transforms.CenterCrop(img_size),
        transforms.ToTensor(),
        transforms.Normalize(mean=[0.485, 0.456, 0.406],
                             std =[0.229, 0.224, 0.225]),
    ])

# ──────────────────────────────────────────────────────────────────
# 모델 로드
# ──────────────────────────────────────────────────────────────────
def load_stfpm(checkpoint_path: str, device: torch.device) -> Stfpm:
    print(f"[INFO] 체크포인트 로드: {checkpoint_path}")
    state = torch.load(checkpoint_path, map_location=device)
    model = Stfpm()
    model.load_state_dict(state, strict=False)
    model.to(device)
    model.eval()
    print(f"[INFO] 백본={model.backbone_model_name}  "
          f"AD레이어={model.ad_layers}  입력크기={model.input_size}")
    return model

# ──────────────────────────────────────────────────────────────────
# 시각화 헬퍼
# ──────────────────────────────────────────────────────────────────
C_NORMAL  = (50,  205,  50)
C_ANOMALY = (30,   30, 220)
C_PANEL   = (15,   15,  15)

def score_to_heatmap(score_map: torch.Tensor, wh: tuple) -> np.ndarray:
    arr  = score_map.squeeze().cpu().numpy()
    arr  = cv2.normalize(arr, None, 0, 255, cv2.NORM_MINMAX).astype(np.uint8)
    heat = cv2.applyColorMap(arr, cv2.COLORMAP_JET)
    return cv2.resize(heat, wh)

def draw_ui(frame, norm_score, raw_score, threshold,
            is_anomaly, fps, heatmap, show_heat) -> np.ndarray:
    out = frame.copy()
    h, w = out.shape[:2]

    if show_heat and heatmap is not None:
        out = cv2.addWeighted(out, 0.5, heatmap, 0.5, 0)

    color = C_ANOMALY if is_anomaly else C_NORMAL
    cv2.rectangle(out, (0, 0), (w-1, h-1), color, 6)

    # 반투명 상단 패널
    panel_h = 100
    overlay = out.copy()
    cv2.rectangle(overlay, (0, 0), (w, panel_h), C_PANEL, -1)
    cv2.addWeighted(overlay, 0.70, out, 0.30, 0, out)

    label = "[ ANOMALY ]" if is_anomaly else "[  NORMAL  ]"
    cv2.putText(out, label, (10, 36),
                cv2.FONT_HERSHEY_DUPLEX, 1.1, color, 2, cv2.LINE_AA)

    info = (f"Score(norm): {norm_score:.4f}  Raw: {raw_score:.4f}  "
            f"Thr: {threshold:.4f}  FPS: {fps:.1f}")
    cv2.putText(out, info, (10, 64),
                cv2.FONT_HERSHEY_SIMPLEX, 0.52, (210, 210, 210), 1, cv2.LINE_AA)
    cv2.putText(out, "http://localhost:8080  |  /status /threshold/up /threshold/down /threshold/auto /heatmap/toggle /reset",
                (10, 88), cv2.FONT_HERSHEY_SIMPLEX, 0.38, (160, 160, 160), 1, cv2.LINE_AA)

    # 점수 바
    bx, by0, by1 = 12, 74, 88
    bw = w - 24
    fill = int(bw * min(norm_score, 1.0))
    tx   = bx + int(bw * min(threshold, 1.0))
    cv2.rectangle(out, (bx, by0), (bx+bw, by1), (55,55,55), -1)
    cv2.rectangle(out, (bx, by0), (bx+fill, by1), color, -1)
    cv2.line(out, (tx, by0-3), (tx, by1+3), (0, 255, 255), 2)

    return out

# ──────────────────────────────────────────────────────────────────
# 추론 스레드
# ──────────────────────────────────────────────────────────────────
def inference_loop(model, preprocess, device, camera_id, width, height):
    cap = cv2.VideoCapture(camera_id)
    if not cap.isOpened():
        print(f"[ERROR] 카메라(id={camera_id})를 열 수 없습니다.")
        STATE.running = False
        return

    cap.set(cv2.CAP_PROP_FRAME_WIDTH,  width)
    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, height)

    t_prev = time.time()
    fps    = 0.0
    save_dir = Path("captures")

    print(f"[INFO] 카메라 시작 (id={camera_id}, {width}x{height})")
    print("[INFO] 브라우저에서  http://localhost:8080  접속하세요\n")

    while STATE.running:
        ret, frame = cap.read()
        if not ret:
            time.sleep(0.03)
            continue

        # 추론
        rgb    = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
        tensor = preprocess(rgb).unsqueeze(0).to(device)

        with torch.no_grad():
            score_maps, anomaly_scores = model(tensor)

        raw_score = float(anomaly_scores[0].cpu())

        # 정규화 (min-max, 최근 500프레임)
        with STATE.lock:
            STATE.score_hist.append(raw_score)
            if len(STATE.score_hist) > 500:
                STATE.score_hist.pop(0)
            s_min = min(STATE.score_hist)
            s_max = max(STATE.score_hist)
            norm  = (raw_score - s_min) / (s_max - s_min + 1e-8)
            threshold  = STATE.threshold
            show_heat  = STATE.show_heat

        is_anomaly = norm > threshold

        heatmap = score_to_heatmap(score_maps, (frame.shape[1], frame.shape[0]))

        # FPS
        t_now = time.time()
        fps   = 0.9 * fps + 0.1 / max(t_now - t_prev, 1e-6)
        t_prev = t_now

        # 화면 렌더링
        display = draw_ui(frame, norm, raw_score, threshold,
                          is_anomaly, fps, heatmap, show_heat)

        # JPEG 인코딩 → 공유 버퍼
        ok, buf = cv2.imencode('.jpg', display, [cv2.IMWRITE_JPEG_QUALITY, 85])
        if ok:
            with STATE.lock:
                STATE.jpeg_frame = buf.tobytes()
                STATE.norm_score = norm
                STATE.raw_score  = raw_score
                STATE.is_anomaly = is_anomaly
                STATE.fps        = fps

        # 콘솔 출력
        tag = "ANOMALY ⚠" if is_anomaly else "normal ✓ "
        print(f"\r[{tag}]  norm={norm:.4f}  raw={raw_score:.4f}  "
              f"thr={threshold:.4f}  fps={fps:.1f}   ",
              end="", flush=True)

    cap.release()
    print("\n[INFO] 카메라 종료")

# ──────────────────────────────────────────────────────────────────
# HTTP 서버 (MJPEG + REST API)
# ──────────────────────────────────────────────────────────────────
HTML_PAGE = """\
<!DOCTYPE html>
<html>
<head>
  <meta charset="utf-8">
  <title>MoViAD STFPM - Bottle Anomaly Detection</title>
  <style>
    body {{ background:#111; color:#eee; font-family:monospace;
            display:flex; flex-direction:column; align-items:center; margin:0; padding:16px; }}
    h2   {{ color:#4fc3f7; margin:8px 0; }}
    img  {{ max-width:100%; border:3px solid #333; border-radius:6px; }}
    .controls {{ display:flex; gap:10px; flex-wrap:wrap; justify-content:center; margin:12px 0; }}
    button {{ padding:8px 18px; border:none; border-radius:5px; cursor:pointer;
              background:#1e88e5; color:#fff; font-size:14px; font-weight:bold; }}
    button:hover {{ background:#1565c0; }}
    button.danger {{ background:#e53935; }}
    button.success {{ background:#43a047; }}
    #status {{ background:#1e1e1e; border-radius:6px; padding:12px 20px;
               font-size:15px; min-width:340px; text-align:center; margin-top:6px; }}
    .normal  {{ color:#69f0ae; font-weight:bold; }}
    .anomaly {{ color:#ff5252; font-weight:bold; }}
  </style>
</head>
<body>
  <h2>🔍 MoViAD STFPM — Bottle Anomaly Detection</h2>
  <img src="/stream" alt="webcam stream">
  <div class="controls">
    <button onclick="api('/threshold/up')"  >Threshold ▲ (+0.02)</button>
    <button onclick="api('/threshold/down')">Threshold ▼ (−0.02)</button>
    <button class="success" onclick="api('/threshold/auto')">Auto Calibrate (t)</button>
    <button onclick="api('/heatmap/toggle')">Heatmap Toggle (h)</button>
    <button class="danger"  onclick="api('/reset')"          >Reset History (r)</button>
  </div>
  <div id="status">로딩 중...</div>
  <script>
    function api(path) {{
      fetch(path).then(r=>r.json()).then(updateStatus);
    }}
    function updateStatus(d) {{
      const cls = d.is_anomaly ? 'anomaly' : 'normal';
      const lab = d.is_anomaly ? '⚠ ANOMALY' : '✓ NORMAL';
      document.getElementById('status').innerHTML =
        `<span class="${{cls}}">${{lab}}</span>  |  `+
        `norm: <b>${{d.norm_score.toFixed(4)}}</b>   `+
        `raw: ${{d.raw_score.toFixed(4)}}   `+
        `thr: <b>${{d.threshold.toFixed(4)}}</b>   `+
        `fps: ${{d.fps.toFixed(1)}}   `+
        `heatmap: ${{d.show_heat ? 'ON' : 'OFF'}}`;
    }}
    setInterval(() => fetch('/status').then(r=>r.json()).then(updateStatus), 500);
  </script>
</body>
</html>
"""

class StreamHandler(BaseHTTPRequestHandler):
    def log_message(self, *args):
        pass  # 콘솔 노이즈 억제

    def do_GET(self):
        p = self.path.split('?')[0]

        # ── MJPEG 스트림 ─────────────────────────────
        if p == '/stream':
            self.send_response(200)
            self.send_header('Content-Type',
                             'multipart/x-mixed-replace; boundary=frame')
            self.end_headers()
            try:
                while STATE.running:
                    with STATE.lock:
                        frame = STATE.jpeg_frame
                    if frame:
                        self.wfile.write(
                            b"--frame\r\n"
                            b"Content-Type: image/jpeg\r\n\r\n"
                            + frame + b"\r\n"
                        )
                    time.sleep(0.03)
            except (BrokenPipeError, ConnectionResetError):
                pass
            return

        # ── REST API ──────────────────────────────────
        with STATE.lock:
            if p == '/threshold/up':
                STATE.threshold = min(STATE.threshold + 0.02, 0.99)
            elif p == '/threshold/down':
                STATE.threshold = max(STATE.threshold - 0.02, 0.01)
            elif p == '/threshold/auto':
                STATE.threshold = float(
                    np.clip(STATE.norm_score + 0.05, 0.01, 0.99)
                )
            elif p == '/heatmap/toggle':
                STATE.show_heat = not STATE.show_heat
            elif p == '/reset':
                STATE.score_hist.clear()

            resp = {
                "threshold" : STATE.threshold,
                "norm_score": STATE.norm_score,
                "raw_score" : STATE.raw_score,
                "is_anomaly": STATE.is_anomaly,
                "fps"       : STATE.fps,
                "show_heat" : STATE.show_heat,
            }

        # ── HTML 홈 ───────────────────────────────────
        if p == '/':
            body = HTML_PAGE.encode()
            self.send_response(200)
            self.send_header('Content-Type', 'text/html; charset=utf-8')
            self.send_header('Content-Length', str(len(body)))
            self.end_headers()
            self.wfile.write(body)
            return

        body = json.dumps(resp).encode()
        self.send_response(200)
        self.send_header('Content-Type', 'application/json')
        self.send_header('Content-Length', str(len(body)))
        self.end_headers()
        self.wfile.write(body)

# ──────────────────────────────────────────────────────────────────
# 메인
# ──────────────────────────────────────────────────────────────────
def run(args):
    # 디바이스
    dev_str = args.device
    if "cuda" in dev_str and not torch.cuda.is_available():
        print("[WARN] CUDA 불가 — CPU로 전환합니다.")
        dev_str = "cpu"
    device = torch.device(dev_str)

    # 모델 로드
    model = load_stfpm(args.checkpoint, device)

    # 입력 크기 동기화
    img_size = model.input_size[0] if model.input_size else 224
    preprocess = make_preprocess(img_size)

    STATE.threshold = args.threshold

    # 추론 스레드 시작
    t = threading.Thread(
        target=inference_loop,
        args=(model, preprocess, device,
              args.camera_id, args.width, args.height),
        daemon=True,
    )
    t.start()

    # HTTP 서버 시작
    server = HTTPServer(("0.0.0.0", args.port), StreamHandler)
    print(f"[INFO] HTTP 서버 시작: http://localhost:{args.port}")
    print("[INFO] Ctrl+C 로 종료\n")
    try:
        server.serve_forever()
    except KeyboardInterrupt:
        print("\n[INFO] 서버 종료")
    finally:
        STATE.running = False
        server.server_close()

# ──────────────────────────────────────────────────────────────────
# CLI
# ──────────────────────────────────────────────────────────────────
def parse_args():
    p = argparse.ArgumentParser(
        description="MoViAD STFPM bottle — 웹캠 이상 감지 (브라우저 스트리밍)"
    )
    p.add_argument("--checkpoint", type=str, required=True,
                   help=".pth.tar 체크포인트 경로")
    p.add_argument("--camera_id",  type=int,   default=0)
    p.add_argument("--device",     type=str,   default="cuda:0")
    p.add_argument("--threshold",  type=float, default=0.5)
    p.add_argument("--width",      type=int,   default=640)
    p.add_argument("--height",     type=int,   default=480)
    p.add_argument("--port",       type=int,   default=8080)
    return p.parse_args()

if __name__ == "__main__":
    run(parse_args())

저작자표시 (새창열림)

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Posted by 구차니

moviad stfpm

하.. 갈길이 멀다 ㅠㅠ

patchcore는 cudnn만 꺼주면 어떻게 되는데

stfpm은 소스가 문제라 .. -_-

~/src/moviad/main_scripts$ python3 main_stfpm.py --train --eval     --model_name mobilenet_v2     --categories bottle     --ad_layers 3 4 5     --boot_layer 2     --results_dirpath debug_outputs/metrics     --checkpoint_dir debug_outputs/checkpoints     --seeds 0 --epochs 3 --input_size 224 224     --device cuda:0
ERROR:root:micromind not found in current environment
Traceback (most recent call last):
  File "/home/falinux/src/moviad/main_scripts/main_stfpm.py", line 12, in <module>
    from moviad.trainers.trainer_stfpm import train_param_grid_search
ImportError: cannot import name 'train_param_grid_search' from 'moviad.trainers.trainer_stfpm' (/home/falinux/src/moviad/moviad/trainers/trainer_stfpm.py)

$ git log
commit 5d547292f3e1e4402b91d2950b884be74a37900f (HEAD -> main, origin/main, origin/HEAD)
Author: FrancescoBorsatti <francesco.borsatti.1@phd.unipd.it>
Date: Mon Apr 13 18:16:40 2026 +0200

update readme

괜히 verified가 붙은게 아닌듯. 저 버전으로는 내려가야 한다. merge 하다가 꼬인듯.

[링크 : https://github.com/AMCO-UniPD/moviad/blob/5bf4b63a89a860ab3d76679fffe35ee50225901d/moviad/trainers/trainer_stfpm.py] 없음

[링크 : https://github.com/AMCO-UniPD/moviad/blob/ec89d3e145615c9c1fbae69480f7da2eb4f9c606/moviad/trainers/trainer_stfpm.py] 있음(verified)

[링크 : https://github.com/AMCO-UniPD/moviad/blob/a209ec364aba6f5f33be010af948a578af4971ce/moviad/trainers/trainer_stfpm.py] 있음

+

아래 커밋으로 이동해버리고(!)

git checkout ec89d3e145615c9c1fbae69480f7da2eb4f9c606

실행하면 안된다!

정해진 경로에 넣으란거지? -_-+

~/src/moviad/main_scripts$ python3 main_stfpm.py --train --eval     --model_name mobilenet_v2     --categories bottle     --ad_layers 3 4 5     --boot_layer 2     --results_dirpath debug_outputs/metrics     --checkpoint_dir debug_outputs/checkpoints     --seeds 0 --epochs 3 --input_size 224 224     --device cuda:0
ERROR:root:micromind not found in current environment
Training with params: {'dataset_path': '../../datasets/mvtec/', 'categories': ['bottle'], 'ad_layers': [[3, 4, 5]], 'epochs': [3], 'seeds': [0], 'batch_size': 64, 'backbone_model_name': 'mobilenet_v2', 'device': device(type='cuda', index=0), 'img_input_size': [224, 224], 'img_output_size': (224, 224), 'early_stopping': None, 'student_bootstrap_layer': [2], 'checkpoint_dir': 'debug_outputs/checkpoints', 'normalize_dataset': True, 'log_dirpath': None, 'contamination_ratio': None, 'test_dataset': False}
TRAIN | cat: bottle, ad_layers: [3, 4, 5], epochs: 3, seed: 0, early_stopping: None, bootstrap: 2
Traceback (most recent call last):
  File "/home/minimonk/src/moviad/main_scripts/main_stfpm.py", line 405, in <module>
    raise e
  File "/home/minimonk/src/moviad/main_scripts/main_stfpm.py", line 387, in <module>
    main(args)
  File "/home/minimonk/src/moviad/main_scripts/main_stfpm.py", line 86, in main
    trained_models_filepaths = train_param_grid_search(params)
  File "/home/minimonk/src/moviad/moviad/trainers/trainer_stfpm.py", line 321, in train_param_grid_search
    log, snapshot_path = train_param_grid_step(
  File "/home/minimonk/src/moviad/moviad/trainers/trainer_stfpm.py", line 209, in train_param_grid_step
    train_dataset.load_dataset()
  File "/home/minimonk/src/moviad/moviad/datasets/mvtec/mvtec_dataset.py", line 175, in load_dataset
    raise RuntimeError(msg)
RuntimeError: Found 0 images in ../../datasets/mvtec/bottle

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Posted by 구차니

llama-swap 구현 (채팅)

/v1/chat/completions 통해서 문맥을 유지할때 어떻게 구현되나 했더니

llama-swap 에서 대화내용을 보니 이해된다.

assistant에 ai 대답을 넣는다고만 해서 복수개면 어떻게 하나 했는데

UI 상으로는 이렇게 나오고

로그 상으로는 아래와 같이 나온다

1번 째 질문 "하이하이"

2번 쩨 질문 "엉 왜 refused"

그리고 이전 대화를 messages의 배열에 순서대로 넣으면

가장 마지막 대화를 기준으로 답을 주게 되는걸려나?

당연(?) 하지만 reasoning은 빼고 순수 응답 내용만 assistant에 넣어서 보낸다.

저작자표시 (새창열림)

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Posted by 구차니

gemma4-e4b mtp..?

변환해서 내꺼에서 돌려보니 성능 차이가 없...다?

내꺼 그래픽 카드가 구려서 그런가.. 그게 아니라면.. 변환을 잘못했다거나

llama.cpp 에서 지원은 안한다거나 그런건가?

	MTP x	MTP 8	MTP 4	MTP 3	MTP 2	MTP 1
직접	61.1	18.6	40.9	58.4	55.6	61.7
unsloth	61.1		45.0	58.6	62.4	68.4

-------

비교군

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli 6435MiB
> 안녕?
[ Prompt: 105.7 t/s | Generation: 61.1 t/s ]

> 빨라?
[ Prompt: 51.1 t/s | Generation: 60.6 t/s ]

직접 변환(양자화 안함)

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/gemma-4-E4B-it-assistant.gguf --spec-type draft-mtp --spec-draft-n-max 8 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6735MiB
> 안녕?
[ Prompt: 101.1 t/s | Generation: 18.6 t/s ]

> 빨라?
[ Prompt: 351.2 t/s | Generation: 16.9 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/gemma-4-E4B-it-assistant.gguf --spec-type draft-mtp --spec-draft-n-max 4 -fit off -ngl 999 -fa on -sm none #--reasoning  off
...wnloads/llama-b9553/llama-cli       6735MiB
> 안녕?
[ Prompt: 292.5 t/s | Generation: 40.9 t/s ]

> 빨라?
[ Prompt: 207.7 t/s | Generation: 46.6 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/gemma-4-E4B-it-assistant.gguf --spec-type draft-mtp --spec-draft-n-max 3 -fit off -ngl 999 -fa on -sm none #--reasoning  off
...wnloads/llama-b9553/llama-cli       6735MiB
> 안녕?
[ Prompt: 398.8 t/s | Generation: 58.4 t/s ]

> 빨라?
[ Prompt: 236.3 t/s | Generation: 60.9 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/gemma-4-E4B-it-assistant.gguf --spec-type draft-mtp --spec-draft-n-max 2 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6735MiB
> 안녕?
[ Prompt: 360.7 t/s | Generation: 55.6 t/s ]

> 빨라?
[ Prompt: 284.9 t/s | Generation: 62.7 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/gemma-4-E4B-it-assistant.gguf --spec-type draft-mtp --spec-draft-n-max 1 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6735MiB
> 안녕?
[ Prompt: 314.1 t/s | Generation: 61.7 t/s ]

> 빨라?
[ Prompt: 441.2 t/s | Generation: 63.7 t/s ]

unsloth 모델

[링크 : https://huggingface.co/unsloth/gemma-4-E4B-it-GGUF]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/mtp-gemma-4-E4B-it.gguf --spec-type draft-mtp --spec-draft-n-max 4 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6666MiB
> 안녕?
[ Prompt: 42.4 t/s | Generation: 45.0 t/s ]

> 빨라?
[ Prompt: 302.6 t/s | Generation: 47.4 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/mtp-gemma-4-E4B-it.gguf --spec-type draft-mtp --spec-draft-n-max 3 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6666MiB
> 안녕?
[ Prompt: 174.0 t/s | Generation: 58.6 t/s ]

> 빨라?
[ Prompt: 327.7 t/s | Generation: 60.2 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/mtp-gemma-4-E4B-it.gguf --spec-type draft-mtp --spec-draft-n-max 2 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6666MiB
> 안녕?
[ Prompt: 98.5 t/s | Generation: 62.4 t/s ]

> 빨라?
[ Prompt: 331.4 t/s | Generation: 64.7 t/s ]

$ /mnt/Downloads/llama-b9553/llama-cli --model /mnt/Downloads/model/gemma4-e4b/gemma-4-E4B-it-Q4_K_M.gguf -mm ./model/gemma4-e4b/mmproj-F16.gguf --model-draft ./gemma-4-E4B-it-assistant/mtp-gemma-4-E4B-it.gguf --spec-type draft-mtp --spec-draft-n-max 1 -fit off -ngl 999 -fa on -sm none #--reasoning off
...wnloads/llama-b9553/llama-cli       6666MiB
> 안녕?
[ Prompt: 168.7 t/s | Generation: 68.4 t/s ]

> 빨라?
[ Prompt: 343.2 t/s | Generation: 67.2 t/s ]

[링크 : https://huggingface.co/google/gemma-4-E4B-it-assistant]

저작자표시 (새창열림)

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Posted by 구차니

openai api 변경에 따른 llama.cpp / llama-swap 리포트 차이

그런데 208 이던 228 이던

client.chat.completions.create 함수를

client.responses.create 로 바꾸었더니 prompt speed / gen speed가 출력되지 않는다.

reasoning off 하기 위해서는 함수를 바꾸어야 하고. 바꾸면 리포트가 안되고 흐음..

걍 서버에서 끄고 해야하나? (llama-cli --reasoning off)

저작자표시 (새창열림)

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stable diffusion --device-id (0)	2026.06.18
stable diffusion illustruousXL LoRA (0)	2026.06.15

Posted by 구차니

llama-swap 버전 업데이트!

208 에서 228로 올렸더니

1. config.yaml 의 명시적 사용

기존에는 config.yaml을 바로 가져가더니(llama-swap 과 동일 경로에서) 이제는 명시적으로 지정해주어야 한다.1

$ ./llama-swap
2026/06/18 12:43:40 ERROR -config is required

$ ./llama-swap --help
Usage of ./llama-swap:
  -config string
     path to config file (required)
  -listen string
     listen address (default :8080 or :8443 for TLS)
  -tls-cert-file string
     TLS certificate file
  -tls-key-file string
     TLS key file
  -version
     show version and exit
  -watch-config
     reload config on file change

2. 모니터링 추가

performance 탭에서 그래프가 생긴것 같다. 오오 이쁜데?

저작자표시 (새창열림)

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Posted by 구차니

stable diffusion --device-id

버전에 따라 안 먹는게 있는지 라고 하기에는

저번에 잘 한거 같기도 한데 잘 모르겠네

아무튼 아래처럼 바꾸어 주면 자로딘다.

./webui.sh --server-name=0.0.0.0 --device-id=1 --api --medvram

CUDA_VISIBLE_DEVICES=1 ./webui.sh --server-name=0.0.0.0 --api --medvram

일단 현재 버전(?)은 아래와 같은 상태.

저번에 확장 깔다가 꼬여서 1.7 버전이라고 받아둔걸로 했더니 안되는건가..

$ git history
commit 1937682a20f7f0442311a1ede68f9f0cb480163b (HEAD -> dev, origin/dev)
Merge: 76759a18 fd0f475a
Author: w-e-w <40751091+w-e-w@users.noreply.github.com>
Date:   Mon Mar 2 16:00:53 2026 +0900

    Merge pull request #17313 from WhizZest/fix-setuptools-version

    Fix the issue of `pip install 'setuptools<70'` failing in cmd

$ git branch
* dev
  master

$ git remote -v
origin  https://github.com/AUTOMATIC1111/stable-diffusion-webui.git (fetch)
origin  https://github.com/AUTOMATIC1111/stable-diffusion-webui.git (push)

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Posted by 구차니

ubuntu 26.04 + 3070 + tensorflow + python 3.14 + docker...

개발환경 구축하려고 이것저것 보고 있는데

구형 모델을 쓰려고 하다보니(ssd mobilenet v3) 영 환경 잡는것 자체가 쉽지 않아 보인다.

[링크 : https://www.tensorflow.org/install/docker?hl=ko] 텐서플로우 docker

[링크 : https://github.com/NVIDIA/nvidia-docker] 요건 deprecated

[링크 : https://github.com/NVIDIA/nvidia-container-toolkit] 요걸 쓰라고

tensorflow 2.21.0

2026.03.07 release

지원 파이썬 버전 3.13 최대.

[링크 : https://pypi.org/project/tensorflow/]

tensorflow-gpu 2.12.0

2023.01.25 release

[링크 : https://pypi.org/project/tensorflow-gpu/]

그 와중에 우분투 26.04 의 파이썬 버전이 3.14 대이고

$ python3 --version
Python 3.14.4

텐서플로우는 지원버전이 없다고 배째고

$ pip3 install tensorflow
ERROR: Could not find a version that satisfies the requirement tensorflow (from versions: none)
ERROR: No matching distribution found for tensorflow

난 어짜피 gpu 가속받을거니까~ 하고 실행하는데 에러

$ pip3 install tensorflow-gpu
Collecting tensorflow-gpu
  Using cached tensorflow-gpu-2.12.0.tar.gz (2.6 kB)
  Installing build dependencies ... done
  Getting requirements to build wheel ... error
  error: subprocess-exited-with-error

  × Getting requirements to build wheel did not run successfully.
  │ exit code: 1
  ╰─> [77 lines of output]
      Traceback (most recent call last):
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/requirements.py", line 36, in __init__
          parsed = _parse_requirement(requirement_string)
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/_parser.py", line 71, in parse_requirement
          return _parse_requirement(Tokenizer(source, rules=DEFAULT_RULES))
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/_parser.py", line 89, in _parse_requirement
          url, specifier, marker = _parse_requirement_details(tokenizer)
                                   ~~~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/_parser.py", line 135, in _parse_requirement_details
          marker = _parse_requirement_marker(
              tokenizer,
          ...<5 lines>...
              ),
          )
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/_parser.py", line 156, in _parse_requirement_marker
          tokenizer.raise_syntax_error(
          ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
              f"Expected {expected} or end",
              ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
              span_start=span_start,
              ^^^^^^^^^^^^^^^^^^^^^^
              span_end=None,
              ^^^^^^^^^^^^^^
          )
          ^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/_tokenizer.py", line 166, in raise_syntax_error
          raise ParserSyntaxError(
          ...<3 lines>...
          )
      packaging._tokenizer.ParserSyntaxError: Expected semicolon (after name with no version specifier) or end
          python_version>"3.7"
                        ^

      The above exception was the direct cause of the following exception:

      Traceback (most recent call last):
        File "/home/minimonk/venv/lib/python3.14/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 389, in <module>
          main()
          ~~~~^^
        File "/home/minimonk/venv/lib/python3.14/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 373, in main
          json_out["return_val"] = hook(**hook_input["kwargs"])
                                   ~~~~^^^^^^^^^^^^^^^^^^^^^^^^
        File "/home/minimonk/venv/lib/python3.14/site-packages/pip/_vendor/pyproject_hooks/_in_process/_in_process.py", line 143, in get_requires_for_build_wheel
          return hook(config_settings)
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/build_meta.py", line 333, in get_requires_for_build_wheel
          return self._get_build_requires(config_settings, requirements=[])
                 ~~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/build_meta.py", line 301, in _get_build_requires
          self.run_setup()
          ~~~~~~~~~~~~~~^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/build_meta.py", line 520, in run_setup
          super().run_setup(setup_script=setup_script)
          ~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/build_meta.py", line 317, in run_setup
          exec(code, locals())
          ~~~~^^^^^^^^^^^^^^^^
        File "<string>", line 40, in <module>
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/__init__.py", line 114, in setup
          _install_setup_requires(attrs)
          ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/__init__.py", line 85, in _install_setup_requires
          dist.parse_config_files(ignore_option_errors=True)
          ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/dist.py", line 764, in parse_config_files
          self._finalize_requires()
          ~~~~~~~~~~~~~~~~~~~~~~~^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/dist.py", line 382, in _finalize_requires
          self._normalize_requires()
          ~~~~~~~~~~~~~~~~~~~~~~~~^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/dist.py", line 400, in _normalize_requires
          self.install_requires = list_(map(str, _reqs.parse(install_requires)))
                                  ~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
        File "/tmp/pip-build-env-kb1b92jj/overlay/lib/python3.14/site-packages/setuptools/_vendor/packaging/requirements.py", line 38, in __init__
          raise InvalidRequirement(str(e)) from e
      packaging.requirements.InvalidRequirement: Expected semicolon (after name with no version specifier) or end
          python_version>"3.7"
                        ^
      [end of output]

  note: This error originates from a subprocess, and is likely not a problem with pip.
error: subprocess-exited-with-error

× Getting requirements to build wheel did not run successfully.
│ exit code: 1
╰─> See above for output.

note: This error originates from a subprocess, and is likely not a problem with pip.

tensorflow 도 이제 인기가 없어졌다고 해야하나.. 지는 해가 되어버렸나 보다.

[링크 : https://github.com/tensorflow/tensorflow/issues/102890]

짧고 명확하다... deprecation. 후..

TensorFlow Object Detection API
TensorFlow 2.2 TensorFlow 1.15 Python 3.6

Deprecation

[링크 : https://github.com/tensorflow/models/blob/master/research/object_detection/README.md]

keras도 희망이 없고, pytorch도 좋은소리 안나오고 어떻게 해야하나..

by GPT

by claude

저작자표시 (새창열림)

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