o #iA @s*ddlZddlmZGdddejZdS)N)nncsFeZdZdfdd ZddZddZd d Zd d Zd dZZ S)LitEmaH.?Tcst|dks |dkrtdi|_|dtj|tjd|d|r-tjdtjdntjdtjd| D] \}}|j rZ| d d }|j ||i||| jq:g|_dS) Ng?zDecay must be between 0 and 1decaydtype num_updatesr.)super__init__ ValueError m_name2s_nameregister_buffertorchtensorfloat32intnamed_parameters requires_gradreplaceupdateclonedetachdatacollected_params)selfmodelrZuse_num_upatesnamepZs_name __class__1/data/cameron/vidgen/VidTok/vidtok/modules/ema.pyrs "  zLitEma.__init__cCs |`|dtjdtjddS)Nr rr)r rrrr)rr$r$r%reset_num_updatesszLitEma.reset_num_updatescCs|j}|jdkr|jd7_t|jd|jd|j}d|}tFt|}t|}|D].}||jr[|j |}|| ||||<|| |||||q4||j vsbJq4WddS1snwYdS)Nr r) rr minrno_graddictr named_buffersrrtype_assub_)rrrZone_minus_decaym_param shadow_paramskeysnamer$r$r%forwards        "zLitEma.forwardcCs\t|}t|}|D]}||jr$||j||j|jq||jvs+JqdS)N)r+rr,rrcopy_r)rrr/r0r1r$r$r%copy_to4s   zLitEma.copy_tocCsdd|D|_dS)z Save the current parameters for restoring later. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be temporarily stored. cSsg|]}|qSr$)r).0paramr$r$r% Dsz LitEma.store..N)r)r parametersr$r$r%store=sz LitEma.storecCs(t|j|D] \}}|j|jqdS)a Restore the parameters stored with the `store` method. Useful to validate the model with EMA parameters without affecting the original optimization process. Store the parameters before the `copy_to` method. After validation (or model saving), use this to restore the former parameters. Args: parameters: Iterable of `torch.nn.Parameter`; the parameters to be updated with the stored parameters. N)ziprrr4)rr9Zc_paramr7r$r$r%restoreFs zLitEma.restore)rT) __name__ __module__ __qualname__rr&r3r5r:r< __classcell__r$r$r"r%rs  r)rrModulerr$r$r$r%s