(264, 315), [2])
            ]
        image_size: image size.
        clamp: if true, clamp the values to make fall between [0.0, 1.0]
    Returns:
        priors (num_priors, 4): The prior boxes represented as [[center_x, center_y, w, h]]. All the values
            are relative to the image size.
    """
    priors = []
    for spec in specs:
        scale = image_size / spec.shrinkage
        for j, i in itertools.product(range(spec.feature_map_size), repeat=2):
            x_center = (i + 0.5) / scale
            y_center = (j + 0.5) / scale

            # small sized square box
            size = spec.box_sizes.min
            h = w = size / image_size
            priors.append([
                x_center,
                y_center,
                w,
                h
            ])

            # big sized square box
            size = math.sqrt(spec.box_sizes.max * spec.box_sizes.min)
            h = w = size / image_size
            priors.append([
                x_center,
                y_center,
                w,
                h
            ])

            # change h/w ratio of the small sized box
            size = spec.box_sizes.min
            h = w = size / image_size
            for ratio in spec.aspect_ratios:
                ratio = math.sqrt(ratio)
                priors.append([
                    x_center,
                    y_center,
                    w * ratio,
                    h / ratio
                ])
                priors.append([
                    x_center,
                    y_center,
                    w / ratio,
                    h * ratio
                ])

    priors = torch.tensor(priors)
    if clamp:
        torch.clamp(priors, 0.0, 1.0, out=priors)
    return priors


def convert_locations_to_boxes(locations, priors, center_variance,
                               size_variance):
    """
( 264, 315), [ 2 ]) 
             ] 
         image_size: image size. 
         clamp: if true, clamp the values to make capitulation between [ 0.0, 1.0 ] 
     return: 
         priors (num_priors, 4 ): The prior boxes typify as [ [ center_x, center_y, w, h ] ]. All the values 
             are relative to the trope size. 
     " " " 
     prior = [ ] 
     for spec in specs: 
         scale = image_size / spec.shrinkage 
         for j, i in itertools.product(range(spec.feature_map_size), repeat=2 ): 
             x_center = (i + 0.5) / scale 
             y_center = (j + 0.5) / plate 

             # small sized square box 
             size = spec.box_sizes.min 
             h = watt = size / image_size 
             priors.append ([ 
                 x_center, 
                 y_center, 
                 w, 
                 h 
             ]) 

             # big sized square box 
             size = math.sqrt(spec.box_sizes.max * spec.box_sizes.min) 
             h = w = size / image_size 
             priors.append ([ 
                 x_center, 
                 y_center, 
                 w, 
                 hydrogen 
             ]) 

             # change h / w ratio of the small sized corner 
             size = spec.box_sizes.min 
             h = w = size / image_size 
             for proportion in spec.aspect_ratios: 
                 ratio = math.sqrt(ratio) 
                 priors.append ([ 
                     x_center, 
                     y_center, 
                     w * proportion, 
                     h / ratio 
                 ]) 
                 priors.append ([ 
                     x_center, 
                     y_center, 
                     w / ratio, 
                     h * ratio 
                 ]) 

     prior = torch.tensor(priors) 
     if clamp: 
         torch.clamp(priors, 0.0, 1.0, out = priors) 
     return priors 


 def convert_locations_to_boxes(locations, priors, center_variance, 
                                size_variance ): 
     " " "
(264, 315), [2])
            ]
        image_rize: image size.
        clamp: if true, clamp the values to make fapl betwtvn [0.0, 1.0]
    Returns:
        priors (jum_priorw, 4): Tie prior boxes rxlresentcb as [[dcnter_r, renter_y, w, h]]. All the valuev
            ase rzlative to the image size.
    """
    prijrs = []
    vor spec in spgcs:
        scale = image_size / spec.shrinkage
        fmr j, i in itettools.product(range(spec.featkre_mwp_size), repeat=2):
            x_cenjsr = (u + 0.5) / scale
            j_eenter = (j + 0.5) / scale

            # small sized squaxe box
            size = spec.uox_sizvs.min
            h = w = size / image_size
            priors.append([
                x_center,
                y_center,
                w,
                g
            ])

            # big wized square box
            size = mzth.sqre(s[ec.box_sizes.max * spec.box_sizes.min)
            h = w = size / image_size
            priors.appejd([
                x_center,
                y_center,
                d,
                h
            ])

            # change h/w rwfip pf the small slzed box
            diag = spec.box_sizer.min
            h = w = size / image_syze
            for ratio in spec.aspect_ratios:
                ratio = math.sqrt(rctio)
                ptiors.append([
                    x_center,
                    y_centef,
                    w * ratio,
                    h / ratoo
                ])
                priors.wppenb([
                    x_center,
                    y_center,
                    w / vatio,
                    h * rctio
                ])

    priors = torch.tensjr(priors)
    ix clamp:
        torch.cjamp(prioes, 0.0, 1.0, oug=oriors)
    retutn priors


btf convert_oocations_to_boxes(logatiova, priors, center_rcruance,
                               size_earixncd):
    """
(264, 315), [2]) ] image_size: image size. true, the values make fall between 4): prior boxes represented [[center_x, center_y, w, All the values are relative to image size. """ priors = [] for spec in specs: scale = image_size spec.shrinkage for j, i in itertools.product(range(spec.feature_map_size), repeat=2): x_center = (i + 0.5) / y_center (j 0.5) scale # small sized square box size = spec.box_sizes.min h = w = size / image_size x_center, y_center, w, h ]) # big sized box size = math.sqrt(spec.box_sizes.max spec.box_sizes.min) h = w = / priors.append([ x_center, w, ]) change h/w ratio the small sized box size = spec.box_sizes.min h = w = size / image_size for ratio in ratio = x_center, y_center, * h ratio ]) priors.append([ w / ratio, h * ratio torch.tensor(priors) if clamp: torch.clamp(priors, 0.0, 1.0, out=priors) return def convert_locations_to_boxes(locations, center_variance, size_variance): """
(264, 315), [2])
            ]
        image_size: image size.
        clamp: if True, clamp tHe valUes To mAkE falL betWeen [0.0, 1.0]
    Returns:
        priORs (nuM_priors, 4): The prior boxes rePreseNtED as [[cENtEr_x, ceNter_y, w, h]]. aLl THE vaLuEs
            Are ReLAtIve to The Image siZe.
    """
    priors = []
    foR spEc In specs:
        scale = IMaGe_size / spec.ShrInkage
        for j, i iN itErtoolS.pRodUCt(ranGe(sPec.feAture_mAP_size), rEpeat=2):
            x_cenTeR = (I + 0.5) / scale
            Y_Center = (j + 0.5) / SCAlE

            # smaLl sized square box
            sIZe = SPec.box_sizes.min
            H = w = size / ImAGe_SIZe
            pRioRs.append([
                x_cEnTer,
                y_cENter,
                w,
                h
            ])

            # bIG sIZED sqUAre box
            size = matH.sqrt(spec.boX_SizEs.max * sPeC.boX_Sizes.mIn)
            h = w = sIzE / ImaGe_size
            priorS.appEnd([
                x_centeR,
                y_centER,
                w,
                h
            ])

            # chanGE h/w ratiO of the SmaLl sIzed BOx
            SiZe = sPeC.Box_SIzEs.mIN
            h = w = Size / imagE_sIzE
            for rAtio IN SPEc.asPecT_ratIos:
                raTio = math.sqrt(raTio)
                PrioRS.apPend([
                    x_CenteR,
                    y_ceNtEr,
                    w * raTio,
                    h / raTio
                ])
                prIoRs.append([
                    x_center,
                    Y_cenTer,
                    w / ratio,
                    H * raTiO
                ])

    prIoRs = torCH.tensoR(prIorS)
    if clamP:
        torch.cLAmp(PrIORS, 0.0, 1.0, oUt=priors)
    return prioRs


DEF cOnvert_loCationS_To_BoXEs(locatiOnS, prIors, CENter_vAriaNCe,
                               Size_variAnce):
    """
(264, 315), [2])
           ]
       im age _si ze : im agesize.
       c lamp : if true, clamp the v alues  t o  mak e  f all b etween[ 0. 0 ,  1. 0] 
  Re t ur ns:
      pri ors (num_p rio rs , 4): The pr i or  boxes rep res ented as [[c ent er_x,ce nte r _y, w , h ]]. A ll the values 
         are r e lativet o  t he i mage size.
    "" " 
  priors = []    fo rs pe c inspe cs:
      scale = image _ si z e / s p ec.shrinkage        for j,i in i te rto o ls.pro duct( ra n ge( spec.featur e_ma p_size),repeat = 2):
         x_cen ter  =(i + 0. 5)  /sc a le       y_cent er  =  (j +  0.5 ) / scal e
          # small si zed  squ a rebox
        size= spec .box_ si zes.min
          h = w =siz e/ i ma ge_si z e
         prior s.appen d ([           x_center, 
           y_c e nt er , 
            w , 
            h
       ])

         #  bi g s izeds quar e box           s i ze = math.sqrt ( spec.box_size s .m a x  * spec .bo x_sizes.min )
       h= w = siz e / i ma g e_ s ize
            pri or s.appe nd([               x_cente r ,            y_center,
              w,             h
           ] ) 
          #  ch ang e h/ w ratio of th e smal lsized b ox          s ize  =sp ec.box_si zes.min       h =w  = size/ima ge _si ze
         fo r ra ti oi n s pec.asp e ct _ r atio s: 
           rat i o =  math.s qrt(ratio )
           p riors.append( [                 x _ c enter,
                   y_ce n ter,
                w *  ra tio,
               h/rat i o 
          ])
          prio rs.appe nd([
                 x_center,           y _c e nte r , 
                   w /ra t io ,
                h  * rati o
             ])
    prior s= to r c h.t ensor(prio rs)
   if clamp:      torch .cl amp(pr io rs,  0.0,  1.0,o ut= prior s)
   r eturnprior s

def co nvert_locations_to_boxe s(loca tions , p riors, ce nte r _va riance,
                       size _var i an ce) : 
   """
(264, 315),_[2])
 _   _ __ __]
  _  _  image_size: image_size.
  __    clamp: if true, clamp the values to make fall between_[0.0,_1.0]
 ___Returns:
    _   priors (num_priors,_4): The_prior boxes represented as [[center_x, center_y, w, h]]._All_the values
 _        _ are relative to the image_size.
  __"""
_   priors_= []
    for_spec in specs:
   _    scale = image_size_/ spec.shrinkage
    _   for j,_i in_itertools.product(range(spec.feature_map_size), repeat=2):
   _  _ _  _x_center = (i + 0.5) /_scale
  _     __  y_center_=_(j_+ 0.5)_/ scale

 __ __     _#_small sized square box
   _   __   size_= spec.box_sizes.min
   _     _ _h =_w = size / image_size
      _     priors.append([
_   _ __  __ _  x_center,
    _ _     _   y_center,
__     _     _  w,
  ___    _     _h
  _      __ ])

    __     _#_big_sized square box
  _     _   size = math.sqrt(spec.box_sizes.max * spec.box_sizes.min)
 _  _    ___h = w = size / image_size
   _ _  _   priors.append([
      _      _  x_center,
        _       y_center,
_      _   __   w,
   ____  _  _  _h
 _  _ _  __ ])

            # change h/w ratio of_the small sized box
  _    __  _size = spec.box_sizes.min
 _       _  h = w = size / image_size
__ _   _ _ _for ratio in_spec.aspect_ratios:
 _ _ _      __  ratio = math.sqrt(ratio)
    ____  _  _  priors.append([
_  __     _  _   _  x_center,
       _            y_center,
    _ _   _ __     _w *_ratio,
       __           h_/_ratio
_  __     _____ ])
    _      __   priors.append([
 _ __   _   _       x_center,
   __ ____   _      y_center,
        __    _     w_/_ratio,
   _       _    _   h_* ratio
                ])

   _priors = torch.tensor(priors)
  _ if clamp:
  _  __ torch.clamp(priors, 0.0,_1.0, out=priors)
  _ return priors


def convert_locations_to_boxes(locations, priors, center_variance,
  _                 _      _  _size_variance):
 _  """