wip inference
This commit is contained in:
Jan Kowalczyk
@@ -638,57 +638,185 @@ def main(
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cfg.save_config(export_json=xp_path + "/config.json")
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elif action == "infer":
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# Inference uses a deterministic, non-shuffled loader to preserve temporal order
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dataset = load_dataset(
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dataset_name,
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cfg.settings["dataset_name"],
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data_path,
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normal_class,
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known_outlier_class,
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n_known_outlier_classes,
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ratio_known_normal,
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ratio_known_outlier,
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ratio_pollution,
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cfg.settings["normal_class"],
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cfg.settings["known_outlier_class"],
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cfg.settings["n_known_outlier_classes"],
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cfg.settings["ratio_known_normal"],
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cfg.settings["ratio_known_outlier"],
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cfg.settings["ratio_pollution"],
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random_state=np.random.RandomState(cfg.settings["seed"]),
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k_fold_num=False,
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inference=True,
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)
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# Log random sample of known anomaly classes if more than 1 class
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if n_known_outlier_classes > 1:
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logger.info("Known anomaly classes: %s" % (dataset.known_outlier_classes,))
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# Initialize DeepSAD model and set neural network phi
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deepSAD = DeepSAD(latent_space_dim, cfg.settings["eta"])
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deepSAD.set_network(net_name)
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# If specified, load Deep SAD model (center c, network weights, and possibly autoencoder weights)
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if not load_model:
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# --- Expect a model DIRECTORY (aligned with 'retest') ---
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if (
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(not load_model)
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or (not Path(load_model).exists())
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or (not Path(load_model).is_dir())
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):
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logger.error(
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"For inference mode a model has to be loaded! Pass the --load_model option with the model path!"
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"For inference mode a model directory has to be loaded! "
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"Pass the --load_model option with the model directory path!"
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)
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return
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load_model = Path(load_model)
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# Resolve expected model artifacts (single-model / no k-fold suffixes)
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deepsad_model_path = load_model / "model_deepsad.tar"
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ae_model_path = load_model / "model_ae.tar"
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ocsvm_model_path = load_model / "model_ocsvm.pkl"
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isoforest_model_path = load_model / "model_isoforest.pkl"
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# Sanity check model files exist
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model_paths = [
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deepsad_model_path,
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ae_model_path,
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ocsvm_model_path,
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isoforest_model_path,
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]
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missing = [p.name for p in model_paths if not p.exists() or not p.is_file()]
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if missing:
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logger.error(
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"The following model files do not exist in the provided model directory: "
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+ ", ".join(missing)
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)
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return
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deepSAD.load_model(model_path=load_model, load_ae=True, map_location=device)
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logger.info("Loading model from %s." % load_model)
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# Prepare output paths
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inf_dir = Path(xp_path) / "inference"
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inf_dir.mkdir(parents=True, exist_ok=True)
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base_stem = Path(Path(dataset.root).stem) # keep your previous naming
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# DeepSAD outputs (keep legacy filenames for backward compatibility)
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deepsad_scores_path = inf_dir / Path(
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base_stem.stem + "_deepsad_scores"
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).with_suffix(".npy")
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deepsad_outputs_path = inf_dir / Path(base_stem.stem + "_outputs").with_suffix(
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".npy"
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)
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# Baselines
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ocsvm_scores_path = inf_dir / Path(
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base_stem.stem + "_ocsvm_scores"
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).with_suffix(".npy")
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isoforest_scores_path = inf_dir / Path(
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base_stem.stem + "_isoforest_scores"
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).with_suffix(".npy")
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inference_results, all_outputs = deepSAD.inference(
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dataset, device=device, n_jobs_dataloader=n_jobs_dataloader
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)
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inference_results_path = (
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Path(xp_path)
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/ "inference"
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/ Path(Path(dataset.root).stem).with_suffix(".npy")
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)
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inference_outputs_path = (
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Path(xp_path)
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/ "inference"
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/ Path(Path(dataset.root).stem + "_outputs").with_suffix(".npy")
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# Common loader settings
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_n_jobs = (
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n_jobs_dataloader
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if "n_jobs_dataloader" in locals()
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else cfg.settings.get("n_jobs_dataloader", 0)
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)
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inference_results_path.parent.mkdir(parents=True, exist_ok=True)
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np.save(inference_results_path, inference_results, fix_imports=False)
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np.save(inference_outputs_path, all_outputs, fix_imports=False)
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# ----------------- DeepSAD -----------------
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deepSAD = DeepSAD(cfg.settings["latent_space_dim"], cfg.settings["eta"])
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deepSAD.set_network(cfg.settings["net_name"])
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deepSAD.load_model(
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model_path=deepsad_model_path, load_ae=True, map_location=device
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)
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logger.info("Loaded DeepSAD model from %s.", deepsad_model_path)
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deepsad_scores, deepsad_all_outputs = deepSAD.inference(
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dataset, device=device, n_jobs_dataloader=_n_jobs
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)
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np.save(deepsad_scores_path, deepsad_scores)
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# np.save(deepsad_outputs_path, deepsad_all_outputs)
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logger.info(
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f"Inference: median={np.median(inference_results)} mean={np.mean(inference_results)} min={inference_results.min()} max={inference_results.max()}"
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"DeepSAD inference: median=%.6f mean=%.6f min=%.6f max=%.6f",
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float(np.median(deepsad_scores)),
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float(np.mean(deepsad_scores)),
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float(np.min(deepsad_scores)),
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float(np.max(deepsad_scores)),
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)
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# ----------------- OCSVM (hybrid) -----------------
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ocsvm_scores = None
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ocsvm = OCSVM(
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kernel=cfg.settings["ocsvm_kernel"],
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nu=cfg.settings["ocsvm_nu"],
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hybrid=True,
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latent_space_dim=cfg.settings["latent_space_dim"],
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)
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# load AE to build the feature extractor for hybrid OCSVM
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ocsvm.load_ae(
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net_name=cfg.settings["net_name"],
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model_path=ae_model_path,
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device=device,
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)
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ocsvm.load_model(import_path=ocsvm_model_path)
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ocsvm_scores = ocsvm.inference(
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dataset, device=device, n_jobs_dataloader=_n_jobs, batch_size=32
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)
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if ocsvm_scores is not None:
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np.save(ocsvm_scores_path, ocsvm_scores)
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logger.info(
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"OCSVM inference: median=%.6f mean=%.6f min=%.6f max=%.6f",
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float(np.median(ocsvm_scores)),
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float(np.mean(ocsvm_scores)),
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float(np.min(ocsvm_scores)),
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float(np.max(ocsvm_scores)),
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)
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else:
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logger.warning("OCSVM scores could not be determined; no array saved.")
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# ----------------- Isolation Forest -----------------
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isoforest_scores = None
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Isoforest = IsoForest(
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hybrid=False,
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n_estimators=cfg.settings["isoforest_n_estimators"],
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max_samples=cfg.settings["isoforest_max_samples"],
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contamination=cfg.settings["isoforest_contamination"],
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n_jobs=cfg.settings["isoforest_n_jobs_model"],
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seed=cfg.settings["seed"],
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)
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Isoforest.load_model(import_path=isoforest_model_path, device=device)
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isoforest_scores = Isoforest.inference(
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dataset, device=device, n_jobs_dataloader=_n_jobs
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)
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if isoforest_scores is not None:
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np.save(isoforest_scores_path, isoforest_scores)
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logger.info(
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"IsolationForest inference: median=%.6f mean=%.6f min=%.6f max=%.6f",
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float(np.median(isoforest_scores)),
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float(np.mean(isoforest_scores)),
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float(np.min(isoforest_scores)),
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float(np.max(isoforest_scores)),
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)
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else:
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logger.warning(
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"Isolation Forest scores could not be determined; no array saved."
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)
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# Final summary (DeepSAD always runs; baselines are best-effort)
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logger.info(
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"Inference complete. Saved arrays to %s:\n"
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" DeepSAD scores: %s\n"
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" DeepSAD outputs: %s\n"
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" OCSVM scores: %s\n"
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" IsoForest scores: %s",
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inf_dir,
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deepsad_scores_path.name,
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deepsad_outputs_path.name,
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ocsvm_scores_path.name if ocsvm_scores is not None else "(not saved)",
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isoforest_scores_path.name
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if isoforest_scores is not None
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else "(not saved)",
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)
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elif action == "ae_elbow_test":
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# Load data once
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dataset = load_dataset(
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