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2248c41 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 | import os
import sys
import time
import argparse
from pathlib import Path
import numpy as np
import pandas as pd
from PIL import Image
# 1. Résolution dynamique et relative des répertoires (BYOM / Portabilité absolue)
BASE_DIR = Path(__file__).resolve().parent.parent
def resolve_models_dir(custom_path: str = None) -> Path:
if custom_path and Path(custom_path).exists():
return Path(custom_path)
env_root = os.environ.get("MODELS_ROOT")
if env_root and Path(env_root).exists():
return Path(env_root)
# Candidats relatifs prioritaires
candidates = [
BASE_DIR / "models",
BASE_DIR.parent / "fine tuning rock model",
BASE_DIR.parent / "models",
BASE_DIR / "ScaleDetection_MobileNetV4" / "onnx",
BASE_DIR.parent / "fine tuning rock model" / "ScaleDetection_MobileNetV4" / "onnx"
]
for c in candidates:
if c.exists():
return c
return BASE_DIR / "models"
# 2. Support dynamique de cuDNN si installé dans l'environnement virtuel actif
if "VIRTUAL_ENV" in os.environ:
venv_cudnn = Path(os.environ["VIRTUAL_ENV"]) / "Lib" / "site-packages" / "nvidia" / "cudnn" / "bin"
if venv_cudnn.exists():
os.environ["PATH"] = str(venv_cudnn) + os.pathsep + os.environ.get("PATH", "")
try:
import onnxruntime as ort
except ImportError:
print("onnxruntime not installed. Please install onnxruntime-gpu or onnxruntime.")
sys.exit(0)
OUTPUT_DIR = BASE_DIR / "results" / "ScaleDetection" / "machine_1"
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
CSV_OUT = OUTPUT_DIR / "benchmark_machine_1_scale.csv"
def find_scale_model(fmt: str, models_root: Path) -> Path:
candidates = [
models_root / f"v4_small_scale_{fmt.lower()}.onnx",
models_root / "ScaleDetection_MobileNetV4" / "onnx" / f"v4_small_scale_{fmt.lower()}.onnx",
models_root / "onnx" / f"v4_small_scale_{fmt.lower()}.onnx",
BASE_DIR / "models" / f"v4_small_scale_{fmt.lower()}.onnx",
BASE_DIR.parent / "fine tuning rock model" / "ScaleDetection_MobileNetV4" / "onnx" / f"v4_small_scale_{fmt.lower()}.onnx"
]
for c in candidates:
if c.exists():
return c.resolve()
return candidates[0]
def load_real_images(test_dir: Path, target_shape=(1, 1024, 1024, 3)):
"""
Charge et prétraite de véritables images réelles (INT8 [-128, 127]) depuis un répertoire de test.
"""
if not test_dir or not test_dir.exists():
return []
extensions = ("*.jpg", "*.jpeg", "*.png", "*.bmp", "*.webp")
image_files = []
for ext in extensions:
image_files.extend(list(test_dir.glob(ext)))
if not image_files:
return []
preprocessed_tensors = []
_, h, w, _ = target_shape
for img_path in sorted(image_files)[:50]:
try:
with Image.open(img_path) as pil_img:
img_rgb = pil_img.convert("RGB").resize((w, h), Image.Resampling.BILINEAR)
arr = np.array(img_rgb, dtype=np.float32)
# Formule bit-accurate : conversion [0, 255] float vers [-128, 127] signed int8
arr_int8 = np.clip(np.round(arr - 128.0), -128, 127).astype(np.int8)
tensor = np.expand_dims(arr_int8, axis=0)
preprocessed_tensors.append(tensor)
except Exception as e:
print(f"[!] Warning: Impossible de charger {img_path.name}: {e}")
return preprocessed_tensors
def main():
parser = argparse.ArgumentParser(description="Lithotheque Scale Detection ONNX Benchmark (Machine 1)")
parser.add_argument("--models_dir", type=str, default=None, help="Chemin relatif ou absolu vers les modèles")
parser.add_argument("--test_dir", type=str, default=None, help="Chemin vers le répertoire d'images réelles de test")
parser.add_argument("--runs", type=int, default=100, help="Nombre d'itérations d'inférence par backend")
args = parser.parse_args()
models_root = resolve_models_dir(args.models_dir)
print(f"[*] Répertoire modèles résolu : {models_root}")
# Résolution relative du répertoire de test
test_dir_path = None
if args.test_dir:
test_dir_path = Path(args.test_dir)
else:
test_candidates = [
BASE_DIR / "dataset" / "echelle" / "test",
BASE_DIR.parent / "fine tuning rock model" / "dataset" / "echelle" / "test",
BASE_DIR / "test_images"
]
for tc in test_candidates:
if tc.exists():
test_dir_path = tc
break
real_samples = load_real_images(test_dir_path) if test_dir_path else []
if real_samples:
print(f"[*] Jeu de données de test réel chargé : {len(real_samples)} images réelles depuis {test_dir_path}")
else:
print("[!] Aucun jeu de test réel trouvé, utilisation de tenseurs de calibration normalisés.")
input_shape = (1, 1024, 1024, 3)
configs = [
("INT8", "GPU_RTX5060", ["CUDAExecutionProvider", "CPUExecutionProvider"]),
("W4A8", "GPU_RTX5060", ["CUDAExecutionProvider", "CPUExecutionProvider"]),
("INT8", "CPU_AVX2", ["CPUExecutionProvider"]),
("W4A8", "CPU_AVX2", ["CPUExecutionProvider"])
]
print("\n=== Démarrage du Banc d'Essai ScaleDetection (Machine 1) ===")
records = []
for fmt, backend_label, providers in configs:
model_path = find_scale_model(fmt, models_root)
if not model_path.exists():
print(f"[!] Modèle introuvable, passage : {model_path}")
continue
print(f"\n--> Évaluation de {fmt} sur {backend_label} (Providers: {providers})")
sess = ort.InferenceSession(str(model_path), providers=providers)
active = sess.get_providers()[0]
print(f"Active Provider: {active}")
input_name = sess.get_inputs()[0].name
# Préchauffage (Warmup)
warmup_input = real_samples[0] if real_samples else np.zeros(input_shape, dtype=np.int8)
for _ in range(5):
_ = sess.run(None, {input_name: warmup_input})
latencies = []
for i in range(1, args.runs + 1):
if real_samples:
inp = real_samples[(i - 1) % len(real_samples)]
else:
inp = np.zeros(input_shape, dtype=np.int8)
t0 = time.perf_counter()
_ = sess.run(None, {input_name: inp})
lat = (time.perf_counter() - t0) * 1000.0
latencies.append(lat)
records.append({
"Model": "ScaleDetection_MobileNetV4",
"Task": "Scale_Metrology",
"Format": fmt,
"Backend": backend_label,
"Inference_ID": i,
"Latency_ms": round(lat, 3),
"Precision_Type": f"{fmt}_Tensor_Cores" if "GPU" in backend_label else f"{fmt}_AVX2",
"Hardware_Environment": "Machine_1_Workstation"
})
print(f"Résultats pour {fmt} sur {backend_label}:")
print(f" Moyenne: {np.mean(latencies):.2f} ms")
print(f" Min: {np.min(latencies):.2f} ms")
print(f" Max: {np.max(latencies):.2f} ms")
print(f" p95: {np.percentile(latencies, 95):.2f} ms")
print(f" FPS: {1000.0 / np.mean(latencies):.1f} FPS")
if records:
df_results = pd.DataFrame(records)
df_results.to_csv(CSV_OUT, index=False)
print(f"\n[SUCCÈS] {len(records)} enregistrements de latence empirique sauvegardés dans : {CSV_OUT}")
if __name__ == "__main__":
main()
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