| import json |
|
|
| import h5py |
| import numpy as np |
| from omegaconf import OmegaConf |
|
|
| |
| |
|
|
|
|
| def load_eval(dir): |
| summaries, results = {}, {} |
| with h5py.File(str(dir / "results.h5"), "r") as hfile: |
| for k in hfile.keys(): |
| r = np.array(hfile[k]) |
| if len(r.shape) < 3: |
| results[k] = r |
| for k, v in hfile.attrs.items(): |
| summaries[k] = v |
| with open(dir / "summaries.json", "r") as f: |
| s = json.load(f) |
| summaries = {k: v if v is not None else np.nan for k, v in s.items()} |
| return summaries, results |
|
|
|
|
| def save_eval(dir, summaries, figures, results): |
| with h5py.File(str(dir / "results.h5"), "w") as hfile: |
| for k, v in results.items(): |
| arr = np.array(v) |
| if not np.issubdtype(arr.dtype, np.number): |
| arr = arr.astype("object") |
| hfile.create_dataset(k, data=arr) |
| |
| for k, v in summaries.items(): |
| hfile.attrs[k] = v |
| s = { |
| k: float(v) if np.isfinite(v) else None |
| for k, v in summaries.items() |
| if not isinstance(v, list) |
| } |
| s = {**s, **{k: v for k, v in summaries.items() if isinstance(v, list)}} |
| with open(dir / "summaries.json", "w") as f: |
| json.dump(s, f, indent=4) |
|
|
| for fig_name, fig in figures.items(): |
| fig.savefig(dir / f"{fig_name}.png") |
|
|
|
|
| def exists_eval(dir): |
| return (dir / "results.h5").exists() and (dir / "summaries.json").exists() |
|
|
|
|
| class EvalPipeline: |
| default_conf = {} |
|
|
| export_keys = [] |
| optional_export_keys = [] |
|
|
| def __init__(self, conf): |
| """Assumes""" |
| self.default_conf = OmegaConf.create(self.default_conf) |
| self.conf = OmegaConf.merge(self.default_conf, conf) |
| self._init(self.conf) |
|
|
| def _init(self, conf): |
| pass |
|
|
| @classmethod |
| def get_dataloader(cls, data_conf=None): |
| """Returns a data loader with samples for each eval datapoint""" |
| raise NotImplementedError |
|
|
| def get_predictions(self, experiment_dir, model=None, overwrite=False): |
| """Export a prediction file for each eval datapoint""" |
| raise NotImplementedError |
|
|
| def run_eval(self, loader, pred_file): |
| """Run the eval on cached predictions""" |
| raise NotImplementedError |
|
|
| def run(self, experiment_dir, model=None, overwrite=False, overwrite_eval=False): |
| """Run export+eval loop""" |
| self.save_conf(experiment_dir, overwrite=overwrite, overwrite_eval=overwrite_eval) |
| pred_file = self.get_predictions(experiment_dir, model=model, overwrite=overwrite) |
|
|
| f = {} |
| if not exists_eval(experiment_dir) or overwrite_eval or overwrite: |
| s, f, r = self.run_eval(self.get_dataloader(self.conf.data, 1), pred_file) |
| save_eval(experiment_dir, s, f, r) |
| s, r = load_eval(experiment_dir) |
| return s, f, r |
|
|
| def save_conf(self, experiment_dir, overwrite=False, overwrite_eval=False): |
| |
| conf_output_path = experiment_dir / "conf.yaml" |
| if conf_output_path.exists(): |
| saved_conf = OmegaConf.load(conf_output_path) |
| if (saved_conf.data != self.conf.data) or (saved_conf.model != self.conf.model): |
| assert ( |
| overwrite |
| ), "configs changed, add --overwrite to rerun experiment with new conf" |
| if saved_conf.eval != self.conf.eval: |
| assert ( |
| overwrite or overwrite_eval |
| ), "eval configs changed, add --overwrite_eval to rerun evaluation" |
| OmegaConf.save(self.conf, experiment_dir / "conf.yaml") |
|
|
