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22 results

admin.py

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  • Forked from KIF / AKPlanning
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    utils.py 995 B
    import torch
    import tensorflow as tf
    import os
    import sys
    import logging
    
    
    def restore_checkpoint(ckpt_dir, state, device):
      if not tf.io.gfile.exists(ckpt_dir):
        tf.io.gfile.makedirs(os.path.dirname(ckpt_dir))
        logging.warning(f"No checkpoint found at {ckpt_dir}. "
                        f"Returned the same state as input")
        return state
      else:
        loaded_state = torch.load(ckpt_dir, map_location=device)
        state['optimizer'].load_state_dict(loaded_state['optimizer'])
        state['model'].load_state_dict(loaded_state['model'], strict=False)
        state['ema'].load_state_dict(loaded_state['ema'])
        state['step'] = loaded_state['step']
        return state
    
    
    def save_checkpoint(ckpt_dir, state):
      saved_state = {
        'optimizer': state['optimizer'].state_dict(),
        'model': state['model'].state_dict(),
        'ema': state['ema'].state_dict(),
        'step': state['step']
      }
      torch.save(saved_state, ckpt_dir)
      
    def eprint(*args, **kwargs):
      print(*args, file=sys.stderr, **kwargs)