
    f                        d dl Z d dlZd dlZd dlZd dlZd dlZd dlmZ d dl	m	Z	m
Z
 d dlmZmZ d dlmZ d dlZd dlZd dlZd dlZd dlmZ d dlZd Zd dlZ ej4                  d	      Zej9                  d
deddd       ej9                  ddeddd       ej9                  dddd       ej9                  dded       ej9                  ddedd       ej9                  d d!ed"d#       ej9                  d$ed%d&       ej9                  d'ed(d)       ej9                  d*ed+d,       ej9                  d-e ed.      d,       ej9                  d/ddd01       ej9                  d2ddd31       ej9                  d4ed5d6       ej9                  d7ed8d9       ej9                  d:ddd;1       ej9                  d<ddd=1       ej9                  d>ed?d@       ej9                  dAedBdC       ej9                  dDdddE1       ej9                  dFe ed.      dG       ej9                  dHdIddJ1       dK Z dOdLZ!dM Z"dPdNZ#y)Q    N)partial)tqdmtrange)	rearrangerepeat)
functional)SUNRGBDc                     t        | t              r-| j                         D ci c]  \  }}|t        |       c}}S | j	                         S c c}}w N)
isinstancedictitemsto_gpucuda)obkvs      9/home/cameronsmith/repos/inference_as_optim/args_setup.pyr   r      s?    
2t@T<A6!9<cZ\ZaZaZcc<s   Azsimple training job)descriptionz-nz--name Fzwandb training name)typedefaultrequiredhelpz-cz--init_ckptzIFile for checkpoint loading. If folder specific, will use latest .pt filez-oz--online
store_true)r   actionz-dz	--datasethydrant)r   r   z-bz--batch_size   z,number of videos/sequences per training step)r   r   r   z-vz	--vid_len   z*video length or number of images per batchz--n_workers
   z number of workers per dataloaderz--until_savei  z number of steps until model savez--lrg-C6?zlearning ratez--n_train_stepsg    חAz	--overfitz$Whether to overfit on a single scene)r   r   r   z--no_shufflezWhether to shuffle datasetz--until_img2   z%Number of steps until image summary. z--overfit_sizei z Number of scenes to overfit on. z--load_savezVWhether to load the previously saved data if overfitting (to avoid running flow again)z--scratch_netzIWhether to turn off the midas weight prior and use a network from scratchz--fdimi   zlatent code dimensionz--spatial_dimsd   zImax number of spatial grids to use (usually ~4 and set to 0 for just cnn)z--evalz"whether to train or run evaluationz--n_evalzNumber of eval samples to runz--save_imgsTz-whether to save out the all-trajectory imagesc                     t        | d      S )Nz... c x y -> ... (x y) c)r   xs    r   <lambda>r&   1   s    9Q9:     c           	      f    t        | d|"t        | j                  d      dz              S |      S )Nz... (x y) c -> ... c x yg      ?r$   )r   intsize)srcr%   s     r   r&   r&   2   s2    9S)Cabajc#((SU,Y[J\F]r pqr r'   c                    i }d }t        |d         d d d | d   d d f   | d   z
  j                         j                         dz  |d<    ||d   j                  dd      d d d | d   f          || d         z
  |d   j                  dd      d d d | d   f   z  j	                         j                         |d<   t        d	 |j                  D              d
z  |d<   |S )Nc                     dd| z   dz   z  S )Nr   gMbP?g:0yE> r$   s    r   r&   zloss_fn.<locals>.<lambda>7   s    Qa& r'   rgb
render_pixg      I@depthr      
depth_maskc              3   P   K   | ]  }|j                   j                            y wr   )weightnorm).0latents     r   	<genexpr>zloss_fn.<locals>.<genexpr>:   s     Pv}}113Ps   $&ga2U0*3?gauss_latent)ch_secsquaremeanflattenabssumlatents)	model_outgtmodel_inputmodelsteplossesinvs          r   loss_fnrJ   4   sP   F
&C+e,-aY|5LQ.NOPYZ_P``hhjooqruuF5M+g.66q;Ad9\CZ<Z[\]`ajkras]tt  wB  CO  wP  wX  wX  YZ  [\  w]  ^_  `d  en  o{  e|  ^|  w}  }  B  B  D  I  I  KF7OP%--PPQUUF>Mr'   c                     t         j                          t        j                         }t	        j
                         }t        d|         j                  rd _        t        j                  dd j                  rdnd j                  d      }t        j                  j                  d	       t        j                   j#                  d
|j                        |_        t        |j$                         t        j&                  |j$                  d       t)         j*                        |_         fd|_        t1        j2                         j5                         |_         j8                  t        j                   j;                  t        j                   j=                   j8                              r j8                  n_t?        tA        j@                  t        j                   j#                   j8                  d            t        j                   jB                        }|j6                  jE                  tG        jH                  |      d   d        |_%        ||_	        |S )Nzuser=r   cosmithinference_as_optimonlinedisabledz
/tmp/wandb)entityprojectmodenamedir.z/home/cameronsmith/logsT)exist_ok)overfit_sizec           
         t        t        j                  j                  j	                  | j
                  t        j                  j                         z  t        j                  j
                        j                   d            S )NF)
batch_sizenum_workersshuffle
pin_memory)itertorchutilsdata
DataLoaderrY   r   device_countmin	n_workers
no_shuffle)datasetargss    r   r&   zmake_run.<locals>.<lambda>O   s    $u{{/?/?/J/J7_c_n_notoyoy  pG  pG  pI  `I@CDNNSWSbSb@cptppl  LQ 0K 0R +S r'   z*.pt)keymodel_state_dictF)strict)&parser
parse_argsargparse	Namespacegetpassgetuserprintoverfitrd   wandbinitrN   rS   runlog_codeospathjoinsave_dirmakedirsr	   rW   rf   get_dataloadermodelsSceneLearnerr   rF   	init_ckptisfile
expandusermaxglobgetctimeload_state_dictr^   loadrg   )rg   valselfuserru   	ckpt_files   `     r   make_runr   >   s   T"DD??D	E$.||AT^ **I.BTXT_T_eouyu~u~  FP  RC	IIsGGLL!:SXXFDM	$--KKt, (9(9:DLSD $$T*//1DJ~~!&(ggnnRWW5G5G5W&XDNN^abfbkbklnlslslxlxy}  zH  zH  IO  mP  cQ  WY  W^  W^  Wg  Wg  _h	

""5::i#89K#LTY"ZDIDJKr'   r   )NF)$rw   randomtimer   sysnumpynp	functoolsr   r   r   einopsr   r   torch.nnr   Frs   r^   r}   vis_scriptsdata.sun_rgbdr	   ro   r   rm   ArgumentParserrk   add_argumentstrr*   floatr<   ch_fstrJ   r   r/   r'   r   <module>r      s         # $     !  c 	 	 	 -B	C   DBK`  a   DSu  S^   _   DU<  H   D3y  A   Dc!Ao  p   D3q>j  k   Ms2;]  ^   ND>`  a   Fd  H   %3s3x_  U   K|Io  p   NE,Lh  i   MB<c  d   $3xEg  h   M5  Ld   e   OU<  NY   Z   H#c7N  O   $#c  @K   L   HeLFj  k   JSS?^  _   M4Jy  z	:	rr'   