File size: 5,330 Bytes
da6986a | 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 | HEG BRep Component Identification — Distribution Bundle
=========================================================
This folder is a self-contained Windows distribution. It runs without any
pre-installed Python or conda. Drop the whole folder next to your viewer's
installer; the viewer launches `heg_brep_service.bat` as a child process and
talks to it over localhost HTTP.
Contents
--------
heg_brep_service.bat Launches the classification service. Prints
`READY port=<N>` to stdout once models are loaded.
Use `--device cpu` (default) or `--device cuda`.
heg_brep_batch.bat Optional batch CLI: STEP folder -> Excel report.
Useful for offline regression testing.
python\ Conda-packed Python 3.10 environment (~2 GB).
Includes pythonocc-core, occwl, torch (CPU),
torch_geometric, fastapi, uvicorn, numpy, MKL.
heg_brep\ The classification package (model, inference,
extraction, server, batch CLI).
BRepExtractor\ STEP -> NPZ feature extraction pipeline.
models\ Three checkpoints used by the two-pass classifier:
pass1.pt (parent), elbow.pt, tee.pt.
csharp_sample\ Reference C# integration: HegBrepClient.cs is a
drop-in async client; Program.cs is a smoke test
(build with `dotnet build`).
Quick smoke test
----------------
From this folder, in a regular cmd.exe:
heg_brep_batch.bat C:\path\to\step_folder C:\path\to\out.xlsx
Or run the service standalone and poke it with curl:
heg_brep_service.bat
(waits ~10-15s; prints `READY port=51571`)
curl http://127.0.0.1:51571/health
curl -X POST http://127.0.0.1:51571/classify ^
-H "Content-Type: application/json" ^
-d "{\"step_path\":\"C:\\path\\to\\part.step\"}"
Service HTTP API
----------------
GET /health
-> {"status":"ok","models_loaded":true,"device":"cpu","uptime_sec":...}
POST /classify Single STEP file -> classification result.
Body: {"step_path": "C:\\full\\path\\to\\part.step",
"npz_keep_dir": "optional\\persist\\dir"}
Resp: {"status":"ok",
"final_label": "4_tee_wf",
"final_conf": 0.9997,
"route": "tee",
"pass1_argmax": "tee", "pass1_conf": 1.0,
"pass2_argmax": "4_tee_wf", "pass2_predicted": "4_tee_wf",
"pass2_conf": 0.9997,
"npz_path": "...", "step_path": "..."}
POST /classify_batch Multiple STEPs in one call. Same JSON schema,
but body is `{"step_paths": ["...", "..."]}`.
POST /shutdown Graceful exit. The host (your viewer) should call
this on exit so the python.exe child doesn't linger.
Performance budget (RTX 3050 laptop, --device cpu)
--------------------------------------------------
Service startup: ~10-15s (OCC + torch + 3 models cold-load)
First /classify call: ~1.0s (single STEP extract + inference)
Warm /classify calls: ~0.8s (warm OCC stack, varies with STEP size)
Switch to `--device cuda` for ~5-10x faster inference if the target machine
has CUDA 12.x drivers + a supported GPU. With CPU torch (default), no NVIDIA
runtime is required.
Output label space
------------------
Pass-1 (parent classifier): elbow | tee | pipe | miscellaneous
Pass-2 elbow specialist: 1_elbow_wf | 2_elbow_pef | 3_elbow_sf | 8_elbow_misc
Pass-2 tee specialist: 4_tee_wf | 5_tee_pef | 6_tee_sf | 9_tee_misc
For pipe / miscellaneous routes the response has `final_label="random"` with
the pass-1 confidence (no specialist exists for those families).
C# integration
--------------
See csharp_sample/HegBrepClient.cs for a drop-in async client. Typical usage:
var client = new HegBrepClient();
await client.StartAsync(@"C:\Program Files\YourViewer\heg_brep\heg_brep_service.bat");
// Later, when user clicks "Identify":
var r = await client.ClassifyAsync(@"C:\tmp\selection.step");
label.Text = $"{r.FinalLabel} ({r.FinalConf:P1})";
// On viewer exit:
await client.StopAsync();
The client spawns the .bat as a child process, reads the `READY port=<N>`
line from its stdout, then POSTs to http://127.0.0.1:<N>/classify. Stderr is
forwarded to Debug.WriteLine for your logger to capture.
Troubleshooting
---------------
* "DLL load failed" on first run.
The bat sets PATH to include python\Library\bin where MKL / OCC DLLs
live. If you launch python.exe directly without the bat, you'll hit this.
* Cold service start takes 30+ seconds.
The first run after extracting the zip is slow because Windows loads each
DLL from disk for the first time. Subsequent starts are ~5-10s once the
OS file cache is warm.
* "Bodies which are not closed are not supported".
Upstream extractor limitation. Some STEP files (typically Inventor
exports without explicit BREP closure) cannot be processed. The HTTP
response is {"status": "extraction_failed", "error": "..."}.
* `KMP_DUPLICATE_LIB_OK=TRUE` warning.
Set automatically. Coexists fine with both OpenMP runtimes; the warning
is overly conservative for inference.
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