mirror of https://github.com/JDAI-CV/fast-reid.git
53 lines
1.8 KiB
Python
53 lines
1.8 KiB
Python
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# encoding: utf-8
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"""
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@author: xingyu liao
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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from ray import tune
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from fastreid.engine.hooks import EvalHook, flatten_results_dict
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from fastreid.utils.checkpoint import Checkpointer
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class TuneReportHook(EvalHook):
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def __init__(self, eval_period, eval_function):
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super().__init__(eval_period, eval_function)
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self.step = 0
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def _do_eval(self):
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results = self._func()
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if results:
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assert isinstance(
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results, dict
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), "Eval function must return a dict. Got {} instead.".format(results)
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flattened_results = flatten_results_dict(results)
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for k, v in flattened_results.items():
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try:
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v = float(v)
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except Exception:
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raise ValueError(
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"[EvalHook] eval_function should return a nested dict of float. "
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"Got '{}: {}' instead.".format(k, v)
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)
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# Remove extra memory cache of main process due to evaluation
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torch.cuda.empty_cache()
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self.step += 1
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# Here we save a checkpoint. It is automatically registered with
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# RayTune and will potentially be passed as the `checkpoint_dir`
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# parameter in future iterations.
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with tune.checkpoint_dir(step=self.step) as checkpoint_dir:
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additional_state = {"epoch": int(self.trainer.epoch)}
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# Change path of save dir where tune can find
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self.trainer.checkpointer.save_dir = checkpoint_dir
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self.trainer.checkpointer.save(name="checkpoint", **additional_state)
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metrics = dict(r1=results["Rank-1"], map=results["mAP"], score=(results["Rank-1"] + results["mAP"]) / 2)
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tune.report(**metrics)
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