| """Evaluate a frozen pilot checkpoint on a separate feature corpus, no refitting.""" |
| import argparse |
| import json |
| from pathlib import Path |
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| import torch |
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| from .models import SpeakerPredictor,load_state |
| from .train import Windows,predictions,evaluate,grouped_metrics,accumulation_metrics,teacher_oracle,stable_context_metrics |
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| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--checkpoint",required=True) |
| parser.add_argument("--data",required=True) |
| parser.add_argument("--out",required=True) |
| args = parser.parse_args() |
| torch.set_num_threads(6) |
| state = torch.load(args.checkpoint,map_location="cpu",weights_only=False) |
| cfg = state["config"] |
| model = SpeakerPredictor(cfg["architecture"],cfg["width"],centered=cfg.get("centered_target",False)) |
| load_state(model,state["model"]) |
| model.eval() |
| data = Windows(args.data,"test",cfg["context"]) |
| validation = Windows(args.data,"val",cfg["context"]) |
| pred = predictions(model,data,"cpu") |
| threshold = state.get("verification_threshold",state["validation"]["far05_threshold"]) |
| payload = {"checkpoint":args.checkpoint,"data":args.data,"config":cfg, |
| "threshold_source":"Frozen original checkpoint validation threshold; no adaptation to new corpus", |
| "test":evaluate(pred,data.teachers,threshold), |
| "validation":evaluate(predictions(model,validation,"cpu"),validation.teachers,threshold), |
| "test_by_scenario":grouped_metrics(pred,data,threshold), |
| "test_stable_contexts_oracle":stable_context_metrics(pred,data,threshold), |
| "accumulation_test_oracle_resets":accumulation_metrics(pred,data,threshold), |
| "long_clean_teacher_oracle":teacher_oracle(args.data,Windows(args.data,"val",12),data)} |
| Path(args.out).write_text(json.dumps(payload,indent=2)) |
| print(json.dumps({"test":payload["test"]})) |
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| if __name__ == "__main__":main() |
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