onnx_agent/mads.ini at main · MADS-NET/onnx_agent · GitHub

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#   ___  _   _ _   ___  __     _                    _
#  / _ \| \ | | \ | \ \/ /    / \   __ _  ___ _ __ | |_
# | | | |  \| |  \| |\  /    / _ \ / _` |/ _ \ '_ \| __|
# | |_| | |\  | |\  |/  \   / ___ \ (_| |  __/ | | | |_
#  \___/|_| \_|_| \_/_/\_\ /_/   \_\__, |\___|_| |_|\__|
#                                   |___/


# ─── Broker / shared settings ────────────────────────────────────────────────

[agents]
# Settings shared by all agents.
frontend_address  = "tcp://localhost:9090"
backend_address   = "tcp://localhost:9091"
settings_address  = "tcp://localhost:9092"
timecode_fps      = 25

# ── ONNX inference engine settings (apply to all agents) ────────────────────
# Execution provider.  Supported values:
#   cpu      – CPU only (always available, default).
#   coreml   – Apple CoreML (macOS/iOS with ANE or GPU; graceful CPU fallback).
#   cuda     – NVIDIA CUDA (Linux/Windows GPU build only; graceful CPU fallback).
#   directml – DirectX ML (Windows GPU build only; graceful CPU fallback).
#   webgpu   – WebGPU EP (experimental; graceful CPU fallback).
#   auto     – pick the best available EP on the current platform.
# If the requested EP is not compiled into this ONNX Runtime build a yellow
# warning is printed at startup and inference falls back to CPU automatically.
execution_provider = "cpu"

# Intra-op thread count (parallelism inside a single operator).
# 0 = let ORT decide (recommended; typically uses all available cores).
# Positive values cap the thread pool.  The old default of 1 was a pessimisation.
intra_op_threads = 0

# Inter-op thread count (parallelism across independent graph nodes).
# 0 = let ORT decide.  Rarely needs tuning.
inter_op_threads = 0

# GPU device index (used only when execution_provider = "cuda" or "directml").
gpu_device_id = 0


# ─── Broker ──────────────────────────────────────────────────────────────────

[broker]
frontend_address = "tcp://*:9090"
backend_address  = "tcp://*:9091"
settings_address = "tcp://*:9092"
# auth_verbose   = false
# ip_whitelist   = ["127.0.0.1"]


# ─── ONNX source agent ───────────────────────────────────────────────────────
# Section name: executable minus "mads-" prefix → [onnx-source].

[onnx-source]
# Path to the ONNX classification model (e.g. MobileNetV2).
model_path = "examples/imagenet/mobilenetv2-12.onnx"

# Path to the ImageNet class labels file (1000 lines, one label per line).
labels_path = "examples/imagenet/imagenet_classes.txt"

# Publication topic.
pub_topic = "onnx-source"

# Loop period in milliseconds (100 ms → ~10 fps max).
period = 100

# Number of top predictions to publish.
top_k = 5

# Camera device index (0 = default system camera).
camera = 0

# Requested capture resolution (hint; camera may deliver a different size).
capture_width  = 640
capture_height = 480
capture_fps    = 30.0

# ── Image pre-processing constants ──────────────────────────────────────────
# Target model input size.
input_width  = 224
input_height = 224

# Layout: true → NCHW (MobileNetV2 / most ONNX zoo models).
nchw = true

# Channel order: true → convert BGR (OpenCV default) to RGB.
rgb = true

# Pixel scale: multiply raw [0,255] values by this factor before mean/std.
scale = 0.00392156862745098   # 1/255

# Per-channel mean and std (ImageNet normalisation defaults).
mean = [0.485, 0.456, 0.406]
std  = [0.229, 0.224, 0.225]

# Model input tensor name (from --inspect output).
input_tensor_name = "input"


# ─── ONNX filter agent ───────────────────────────────────────────────────────
# Section name follows MADS convention: executable name minus "mads-" prefix,
# with dots replaced by dashes → [onnx-filter].

[onnx-filter]
# Path to the ONNX model file.  Can be overridden by --model-path on the CLI.
model_path = "/path/to/your/model.onnx"

# Topic to publish inference results on.
pub_topic = "onnx-filter"

# Topics to subscribe to.  Must be an array; use [""] to subscribe to all.
sub_topic = ["onnx-filter-input"]