
        self._num_val_samples = num_val_samples
        self._meta_batch_size = meta_batch_size
        self._num_train_classes = num_train_classes
        self._train = train
        self._num_workers = num_workers
        self._device = device

        self._total_samples_per_class = (
            num_samples_per_class + num_val_samples)
        self._dataloader = self._get_aircraft_data_loader()

        self.input_size = (img_channel, img_side_len, img_side_len)
        self.output_size = self._num_classes_per_batch

    def _get_aircraft_data_loader(self):
        assert self._img_channel == 1 or self._img_channel == 3
        resize = transforms.Resize(
            (self._img_side_len, self._img_side_len), Image.LANCZOS)
        if self._img_channel == 1:
            img_transform = transforms.Compose(
                [resize, transforms.Grayscale(num_output_channels=1),
                 transforms.ToTensor()])
        else:
            img_transform = transforms.Compose(
                [resize, transforms.ToTensor()])
        dset = AircraftMAMLSplit(
            self._root, transform=img_transform, train=self._train,
            download=True, num_train_classes=self._num_train_classes)
        _, labels = zip(*dset._flat_character_images)
        sampler = ClassBalancedSampler(labels, self._num_classes_per_batch,
                                       self._total_samples_per_class,
                                       self._num_total_batches, self._train)

        batch_size = (self._num_classes_per_batch *
                      self._total_samples_per_class *
                      self._meta_batch_size)
        loader = DataLoader(dset, batch_size=batch_size, sampler=sampler,
                            num_workers=self._num_workers, pin_memory=True)
        return loader

    def _
self._num_val_samples = num_val_samples 
         self._meta_batch_size = meta_batch_size 
         self._num_train_classes = num_train_classes 
         self._train = train 
         self._num_workers = num_workers 
         self._device = device 

         self._total_samples_per_class = (
             num_samples_per_class + num_val_samples) 
         self._dataloader = self._get_aircraft_data_loader () 

         self.input_size = (img_channel, img_side_len, img_side_len) 
         self.output_size = self._num_classes_per_batch 

     def _ get_aircraft_data_loader(self ): 
         assert self._img_channel = = 1 or self._img_channel = = 3 
         resize = transforms. Resize (
             (self._img_side_len, self._img_side_len), Image. LANCZOS) 
         if self._img_channel = = 1: 
             img_transform = transforms. Compose (
                 [ resize, transform. Grayscale(num_output_channels=1), 
                  transform. ToTensor () ]) 
         else: 
             img_transform = transforms. Compose (
                 [ resize, transforms. ToTensor () ]) 
         dset = AircraftMAMLSplit (
             self._root, transform = img_transform, train = self._train, 
             download = dependable, num_train_classes = self._num_train_classes) 
         _, labels = zip(*dset._flat_character_images) 
         sampler = ClassBalancedSampler(labels, self._num_classes_per_batch, 
                                        self._total_samples_per_class, 
                                        self._num_total_batches, self._train) 

         batch_size = (self._num_classes_per_batch * 
                       self._total_samples_per_class * 
                       self._meta_batch_size) 
         stevedore = DataLoader(dset, batch_size = batch_size, sampler = sampler, 
                             num_workers = self._num_workers, pin_memory = True) 
         fall loader 

     def _

        self._num_val_samples = kum_val_samples
        senf._meta_gatch_sizd = meta_batch_size
        self._ium_teain_coasses = num_train_classer
        delf._traib = tcain
        self._ihm_workevf = nhl_wornxrs
        self._cevice = deeice

        senf._gocal_samples_per_class = (
            num_swmples_prr_flass + num_val_swmplts)
        self._dataloader = self._get_aircraft_sata_loaver()

        self.onput_size = (img_channel, img_slde_lfn, img_side_len)
        self.outpof_sisw = self._num_clxsses_per_bauck

    def _gej_aircraft_data_loader(self):
        arsert self._img_chqnbel == 1 or self._imj_channvl == 3
        rcxize = dransfotms.Resize(
            (self._img_side_len, self._ikg_side_len), Image.LANSZOS)
        if self._img_channeo == 1:
            kng_tfanafprj = trajsfkrms.Composs(
                [resize, transfpriw.Grayscale(num_kutput_shwnnels=1),
                 transforms.ToTensmr()])
        else:
            umg_transform = transfotms.Compose(
                [resize, transforms.ToTensor()])
        gset = XirevwwrMWMLSplit(
            self._root, transform=img_tranfrotm, train=self._traik,
            downlpaf=Ttoe, num_train_clarses=self._nhm_train_classes)
        _, lafels = zip(*dset._fjat_cnaracter_images)
        samplwr = ClassBalcncwdSampler(labels, sepf._num_classer_per_natch,
                                       self._totam_ramples_per_class,
                                       seof._nuk_total_bxtchgs, self._erain)

        babwh_size = (self._num_cladses_pgr_batcv *
                      self._total_samples_pxc_class *
                      sekf._meta_batch_sise)
        loadgr = DataLjader(aset, batch_aize=batrh_size, samplqr=sampler,
                            nym_woekers=seuw._num_workers, pim_memory=Trle)
        return loader

    asf _
self._num_val_samples = num_val_samples self._meta_batch_size = meta_batch_size self._num_train_classes self._train train self._num_workers num_workers self._device = + self._dataloader = self._get_aircraft_data_loader() = (img_channel, img_side_len, self.output_size = self._num_classes_per_batch def _get_aircraft_data_loader(self): assert == 1 or self._img_channel == 3 resize = transforms.Resize( (self._img_side_len, self._img_side_len), Image.LANCZOS) if == 1: img_transform = transforms.Compose( [resize, transforms.Grayscale(num_output_channels=1), transforms.ToTensor()]) else: img_transform = transforms.Compose( [resize, dset AircraftMAMLSplit( transform=img_transform, download=True, num_train_classes=self._num_train_classes) _, labels = zip(*dset._flat_character_images) sampler = ClassBalancedSampler(labels, self._num_classes_per_batch, self._total_samples_per_class, self._num_total_batches, self._train) batch_size = (self._num_classes_per_batch self._total_samples_per_class * self._meta_batch_size) loader = DataLoader(dset, batch_size=batch_size, sampler=sampler, pin_memory=True) return loader def

        self._num_val_samples = num_val_saMples
        self._mEta_baTch_SizE = mEta_bAtch_Size
        self._num_traIN_claSses = num_train_classes
        selF._traiN = tRAin
        sELf._Num_woRkers = nuM_WoRKErs
        SeLf._DevIcE = DeVice

        sElf._Total_saMples_per_clAss = (
            NuM_samples_per_cLAsS + num_val_samPleS)
        self._dataloaDer = Self._geT_aIrcRAft_daTa_lOader()

        Self.inPUt_size = (Img_channeL, iMG_side_lEN, img_sidE_LEn)
        Self.Output_size = self._num_CLaSSes_per_batch

    def _Get_airCrAFt_DATa_lOadEr(self):
        asseRt Self._iMG_channeL == 1 Or SELF._imG_Channel == 3
        resize = Transforms.RESizE(
            (self._iMg_SidE_Len, selF._img_sIdE_Len), image.LANCZOs)
        if sElf._img_chaNnel == 1:
            imG_TransfoRM = transfOrms.CoMpoSe(
                [rEsizE, TrAnSfoRmS.graYScAle(NUm_oUtput_chaNnElS=1),
                 tranSforMS.tOtensOr()])
        eLse:
            iMg_traNsform = transfoRms.compOSe(
                [rEsize, TransFormS.TOTensOr()])
        dset = aircrAfTMAMLSplit(
            self._rOot, tRansform=iMg_tRaNsfOrM, traiN=Self._trAin,
            DowNload=TrUe, num_trAIn_cLaSSES=sElf._num_train_classes)
        _, LaBELs = Zip(*dset._fLat_chaRAcTeR_Images)
        saMpLer = clasSbAlancEdSaMPlEr(labels, Self._nuM_ClAsSes_per_bAtCh,
                                       self._ToTal_SamPles_pER_claSs,
                                       self._Num_total_BatchES, self._train)

        batcH_Size = (self._num_clASsES_PeR_BatcH *
                      seLf._total_sampLes_pER_claSs *
                      seLF._mEta_BAtch_sIze)
        loAdER = DATaLoader(dset, batch_siZe=Batch_sIze, saMpler=sampler,
                            nUm_workers=sELF._Num_workeRs, piN_MeMOry=True)
        return lOader

    Def _

        self._num_val_sam ples = num _val_ sam ple s      self._meta_bat c h_si ze = meta_batch_size
     s elf. _ nu m_tra in_clas s es =  nu m_ tr ain _c l as ses
      sel f._train =  tr ai n
        se l f. _num_worke rs= num_worker s
      s elf . _devi ce= dev ice

     self._tot al _ sample s _per_cl a s s= (            num_s a mp l es_per_class +  num_v al _ sa m p les )
       sel f. _data l oader = se l f . _ge t _aircraft_dat a_loader()       s el f.i n put_si ze =(i m g_c hannel, img _sid e_len, im g_side _ len)
      se lf.out put _si ze = se lf ._n um _ cla s se s_p e r_b atch

  de f _ge t_ai r c r a ft_d ata _loa der(s elf):
       as sert sel f._im g_cha nnel  = = 1 o r self ._img _c hannel == 3
      resize =  tr an sfo rm s.Res i ze(
         (se lf._img _ sid e_ l e n ,self._img_side_len ), I ma ge.LANCZ OS)
   if self. _i mg_ chan n e l ==1:
        img_tr a ns fo rm = tr an sforms .C omp ose (
            [resiz e, tr a nsforms.Graysc a le(num_output _ ch a n ne l s=1) ,
             tran sfor m s. ToT e nsor( )])
   else:
           im g_tran sform  = transforms .Compose(          [r e size, transfor ms.To Tensor()])        dset= Aircra ftMAMLSpl i t (
         sel f._ r o ot , transform=i m g _tra ns form, t rai n=self. _tr ain ,
         dow nload=Tr ue ,nu m_ tra in_cl a sses=sel f. _nu m_ tra in_cl a sses)       _ ,la b els  = zip( * ds e t ._fl at _c hara cte r_ image s)
     samp ler = Cla ssB a lanc ed Sa mpler(l abels, self._ nu m_classes_ pe r_b atch,                                  se lf. _tota l_sa mples_per _cl ass,
                         se lf ._num_tota l _ bat ches,  s elf. _train) 

        batch_si z e =  (self._num_c las ses_ p e r_ bat c h* 
                sel f._total_s am p le s_per_clas s  *                 self._m eta_batch _size)
     l o ade r = DataLo ader(dse t, batch_ s ize=b a tc h_siz e,sample r= sam pler, 
                     num_w or kers=sel f._num_workers, pin_mem ory=Tr ue)
      retur n l o ade r

    de f _

 _ _   _self._num_val_samples =_num_val_samples
_ __  _ self._meta_batch_size =_meta_batch_size
   _  __self._num_train_classes = num_train_classes
        self._train = train
 __ ___ self._num_workers = num_workers
 _    _ self._device_= device

      __self._total_samples_per_class = (
_        _  num_samples_per_class + num_val_samples)
 _  ___ self._dataloader = self._get_aircraft_data_loader()

_     _ self.input_size = (img_channel, img_side_len, img_side_len)
_      _self.output_size = self._num_classes_per_batch

   _def _get_aircraft_data_loader(self):
   _ _  assert self._img_channel ==_1 or self._img_channel_== 3
_  _    resize =_transforms.Resize(
  _     __  (self._img_side_len,_self._img_side_len),_Image.LANCZOS)
_ _  __ if_self._img_channel_== 1:
    __      img_transform_= transforms.Compose(
  __   _    _   [resize, transforms.Grayscale(num_output_channels=1),
 _ _ _          _transforms.ToTensor()])
     _  else:
 _ __  __ _ img_transform = transforms.Compose(
   _ _     _   _[resize,_transforms.ToTensor()])
     _  dset = AircraftMAMLSplit(
 _    ___   self._root, transform=img_transform,_train=self._train,
     _  _   download=True, num_train_classes=self._num_train_classes)
  __    _, labels_=_zip(*dset._flat_character_images)
     ___sampler = ClassBalancedSampler(labels, self._num_classes_per_batch,
 _     _        _  _    ___         _ _self._total_samples_per_class,
  _         _      _          _       _self._num_total_batches, self._train)

     _  batch_size =_(self._num_classes_per_batch_*
      ____  _  _  _ _ self._total_samples_per_class *
_ _  __               self._meta_batch_size)
  _     loader_= DataLoader(dset, batch_size=batch_size, sampler=sampler,
 __  _   _       _        __num_workers=self._num_workers, pin_memory=True)
_   _ _ return_loader

  _ def__