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