h@n ddlZddlmZddlmZddlZddlmZddlmZ m Z gdZ ded e d e d e d eee f d ZGddeZGddej"j$ZGddej"j$ZGddej"j$ZGddej"j$Zy)N)Enum)Optional)Tensor) functionalInterpolationMode)AutoAugmentPolicy AutoAugment RandAugmentTrivialAugmentWideAugMiximgop_name magnitude interpolationfillc |dk(rKtj|dddgdtjtj|dg||ddg}|S|dk(rKtj|dddgddtjtj|g||ddg}|S|dk(r+tj|dt |dgd|ddg|}|S|d k(r+tj|ddt |gd|ddg|}|S|d k(rtj |||| }|S|d k(rtj|d|z}|S|d k(rtj|d|z}|S|dk(rtj|d|z}|S|dk(rtj|d|z}|S|dk(r!tj|t |}|S|dk(rtj||}|S|dk(rtj|}|S|dk(rtj|}|S|dk(rtj|}|S|dk(r |St!d|d)NShearXr?)angle translatescaleshearrrcenterShearY TranslateX)rrrrrr TranslateYRotaterr BrightnessColorContrast Sharpness PosterizeSolarize AutoContrastEqualizeInvertIdentityzThe provided operator  is not recognized.)Faffinemathdegreesatanintrotateadjust_brightnessadjust_saturationadjust_contrastadjust_sharpness posterizesolarize autocontrastequalizeinvert ValueError)rrrrrs `/var/www/html/eduruby.in/venv/lib/python3.12/site-packages/torchvision/transforms/autoaugment.py _apply_opr> s(hh !f<< ) 45s;'q6  F Js H hh !f TYYy%9:;'q6  l JY L hh 9~q)'* V JE L hh #i.)'* B J1 H hhsI]N. J- L !!#sY7* J) G !!#sY7& J% J S9_5" J! K   cIo6 J K kk#s9~. J J jji( J N "nnS! J J jjo J H hhsm J J   J1':MNOOceZdZdZdZdZdZy)r zoAutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN. imagenetcifar10svhnN)__name__ __module__ __qualname____doc__IMAGENETCIFAR10SVHNr?r=r r ]sHG Dr?r c (eZdZdZej ejdfdededee e ddffd Z dede e e e e eefe e e eefffdZd ed e eefdee e eefffd Zed ede eeeffd ZdedefdZde fdZxZS)r a?AutoAugment data augmentation method based on `"AutoAugment: Learning Augmentation Strategies from Data" `_. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". Args: policy (AutoAugmentPolicy): Desired policy enum defined by :class:`torchvision.transforms.autoaugment.AutoAugmentPolicy`. Default is ``AutoAugmentPolicy.IMAGENET``. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. Npolicyrrreturncxt|||_||_||_|j ||_yN)super__init__rMrr _get_policiespolicies)selfrMrr __class__s r=rRzAutoAugment.__init__ys8  * **62 r?c|tjk(rgdS|tjk(rgdS|tjk(rgdSt d|d)N)))r%皙?)r333333? )r&rZr'rZNr(皙?Nr(rZN))r%rZ)r%rZr(rXN)r&皙?)rfrrarY))r&rZrb))r%rar]r(rN))rrgrj)r&rZrY)rb)r%rXrd)rir"rXr))rrXr[rb))r(rNr`r)rZNrk)r"rZrh)r#rrY)ri)r"r))r"rarY)r&rarc))r$rXrcrn))rrZr]rk)rlrbrer\rmror_))r)皙?N)r#rgrd))rffffff?rp)r333333?r[))r$rar)r$?rj))r?rYrrsr[))r'rvNr(ruN))rrgrc)r%rtrc))r"rXrj)r!rZrc))r$rtr[)r!rsr[)rb)r(rvN))r#rZrc)r$rZr]))r"rsrc)rrvrY))r(rtN)r'rXN))rrXrj)r$rgrd))r!rurd)r"rgrY))r&rvrp)r)rN)r(rgNr^)ryrb))r"rur[rb)r'raN)r&rgrY))r!rrrj)r"rsr))r&rXr]r'ruN))rrur[rw)r{)r&rarj)r`rq)rwr{))rrurh)r)rgN)rrurYr)rsN)rb)r&rZrdr)ruNrbrb)rrurj)r|rz)r})r)rXN))rrur])r&rgrd)rrzr)r|)r&rtrj))rrarYr~)rx)rrZrdr))r#rtrjrrarh)r)raN)rrrp))rrsrd)r&rXrY)rnr))rrtrc)rrurj))rrrrdrn))r&rsrp)rrZrc))rrarhr))rrsr[)rrarj))rrar])r'rsN))rrsrprqzThe provided policy r+)r rHrIrJr<)rUrMs r=rSzAutoAugment._get_policiessk &// / 6(00 0 6(-- - 83F8;NOP Pr?num_bins image_sizectjdd|dftjdd|dftjdd|dz|dftjdd|dz|dftjdd|dftjdd|dftjdd|dftjdd|dftjdd|dfd tj||dz d z z jj z d ftjd d|d ftj dd ftj dd ftj dd fd S)NrrtTt?rr>@rurYrhFo@)rrrrrr!r"r#r$r%r&r'r(r))torchlinspacearangeroundr1tensorrUrrs r=_augmentation_spacezAutoAugment._augmentation_spaces^~~c394@~~c394@ >>#}z!}/LhWY]^ >>#}z!}/LhWY]^~~c4:DA >>#sH=tDnnS#x8$?S(;TB..c8r)rUrrchannelsheightwidthf transform_idrrop_metairp magnitude_id magnitudessignedrs r=forwardzAutoAugment.forwards>yy"#"2"23"7&% c6 "$e -d }x/!*./Qa//%)__S5G%H" eU**2?-6t}}\7R-S f )A)LQx1}%,W%5" FFRF^E*\":"?"?"ABdg eAh!m%IWitGYGY`de  f 0s"D2ch|jjd|jd|jdS)Nz(policy=, fill=))rVrDrMr)rUs r=__repr__zAutoAugment.__repr__s/..))*(4;;-wtyykQRSSr?)rDrErFrGr rHrNEARESTrlistrrRtuplestrr1rSdictrboolr staticmethodrrr __classcell__rVs@r=r r hs)$%6$>$>+<+D+D&* 3! 3) 3tE{# 3  3XQ'XQ eE#uhsm34eCQT `_. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". Args: num_ops (int): Number of augmentation transformations to apply sequentially. magnitude (int): Magnitude for all the transformations. num_magnitude_bins (int): The number of different magnitude values. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. rpr[Nnum_opsrnum_magnitude_binsrrrNcht|||_||_||_||_||_yrP)rQrRrrrrr)rUrrrrrrVs r=rRzRandAugment.__init__2s5  ""4* r?rrctjddftjdd|dftjdd|dftjdd|dz|dftjdd|dz|dftjdd|dftjdd |dftjdd |dftjdd |dftjdd |dfd tj||dz d z z j j z dftjd d|dftjddftjddfd S)NrFrtTrrrrrurYrhrr*rrrrrr!r"r#r$r%r&r'r(rrrrrr1rs r=rzRandAugment._augmentation_spaceAs^c*E2~~c394@~~c394@ >>#}z!}/LhWY]^ >>#}z!}/LhWY]^~~c4:DA >>#sH=tDnnS#x8$?S(;TB..c8r)rUrrrrrrr_op_indexrrrrs r=rzRandAugment.forwardTsOyy"#"2"23"7&% c6 "$e -d }x/!*./Qa//**4+B+BVUOTt||$ bA5==Wt<AACDH7<<>*84G!(!1 JDNOOVWDWj8==?@]`I%--40T! C)4CUCU\`aC b 0s"E8c |jjd|jd|jd|jd|j d|j d }|S)Nz (num_ops=z , magnitude=z, num_magnitude_bins=, interpolation=rr)rVrDrrrrrrUss r=rzRandAugment.__repr__osg~~&&'(||n4>>*#D$;$;#<t112dii[   r?)rDrErFrGrrr1rrrrRrrrrrrrrrrs@r=r r s("$+<+D+D&*     )  tE{#    C U38_ QUVY[`agimam[nVnQo &6f6 # r?r c eZdZdZdej dfdededeee ddffd Z d ede e e eefffd Zd edefd Zde fd ZxZS)r aDataset-independent data-augmentation with TrivialAugment Wide, as described in `"TrivialAugment: Tuning-free Yet State-of-the-Art Data Augmentation" `_. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". Args: num_magnitude_bins (int): The number of different magnitude values. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. rNrrrrNcLt|||_||_||_yrP)rQrRrrr)rUrrrrVs r=rRzTrivialAugmentWide.__init__s' "4* r?rctjddftjdd|dftjdd|dftjdd|dftjdd|dftjdd|dftjdd|dftjdd|dftjdd|dftjdd|dfdtj||dz d z z j j z dftjd d|dftjddftjddfd S) NrFgGz?Tg@@g`@rYrrdrrr)rUrs r=rz&TrivialAugmentWide._augmentation_spacesJc*E2~~c4:DA~~c4:DA >>#tX>E >>#tX>E~~c5(;TB >>#tX>EnnS$94@T8r) rUrrrrrrrrrrrrs r=rzTrivialAugmentWide.forwardsCyy"#"2"23"7&% c6 "$e -d }x/!*./Qa//**4+B+BCu}}S\48==?@w||~&x0$W- F" *U]]3z?D STYY[ \  emmAt,  Igy@R@RY]^^0s"E<c|jjd|jd|jd|jd}|S)Nz(num_magnitude_bins=rrr)rVrDrrrrs r=rzTrivialAugmentWide.__repr__sP~~&&'(""&"9"9!:t112dii[   r?)rDrErFrGrrr1rrrrRrrrrrrrrrrs@r=r r |s "#%+<+D+D&*  ) tE{#    C DeFDL>Q9Q4R &_6_f_:#r?r cNeZdZdZdddddej dfdeded ed ed ed ed e e eddffd Z dede eefde ee eefffdZej$j&defdZej$j&defdZdedefdZdedefdZdefdZxZS)r aAugMix data augmentation method based on `"AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty" `_. If the image is torch Tensor, it should be of type torch.uint8, and it is expected to have [..., 1 or 3, H, W] shape, where ... means an arbitrary number of leading dimensions. If img is PIL Image, it is expected to be in mode "L" or "RGB". Args: severity (int): The severity of base augmentation operators. Default is ``3``. mixture_width (int): The number of augmentation chains. Default is ``3``. chain_depth (int): The depth of augmentation chains. A negative value denotes stochastic depth sampled from the interval [1, 3]. Default is ``-1``. alpha (float): The hyperparameter for the probability distributions. Default is ``1.0``. all_ops (bool): Use all operations (including brightness, contrast, color and sharpness). Default is ``True``. interpolation (InterpolationMode): Desired interpolation enum defined by :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.NEAREST``. If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported. fill (sequence or number, optional): Pixel fill value for the area outside the transformed image. If given a number, the value is used for all bands respectively. rjrTNseverity mixture_width chain_depthalphaall_opsrrrNct|d|_d|cxkr|jksntd|jd|d||_||_||_||_||_||_ ||_ y)Nrrz!The severity must be between [1, z]. Got z instead.) rQrR_PARAMETER_MAXr<rrrrrrr) rUrrrrrrrrVs r=rRzAugMix.__init__s  X4!4!44@ATAT@UU\]e\ffopq q  *&  * r?rrc tjdd|dftjdd|dftjd|ddz |dftjd|ddz |dftjdd|dfdtj||dz dz z jj z d ftjd d|d ftj dd ftj dd fd }|j rr|jtjdd |dftjdd |dftjdd |dftjdd |dfd |S)NrrtTrg@rrrhFr) rrrrrr%r&r'r(ru)r!r"r#r$)rrrrr1rrupdate)rUrrrs r=rzAugMix._augmentation_spacesi~~c394@~~c394@ >>#z!}s/BHMtT >>#z!}s/BHMtT~~c4:DAu||H5(Q,!9KLSSUYY[[]bcsH=uE"\\#.6c*E2  << HH#(>>#sH#Et"L#nnS#x@$G!&S(!CT J"'..c8"Dd!K  r?c,tj|SrP)r, pil_to_tensorrUrs r=_pil_to_tensorzAugMix._pil_to_tensorss##r?rc,tj|SrP)r, to_pil_imagers r=_tensor_to_pilzAugMix._tensor_to_pils~~c""r?paramsc,tj|SrP)r_sample_dirichlet)rUrs r=rzAugMix._sample_dirichlets&&v..r?orig_imgc |j}tj|\}}}t|trC|}t|t t frt |g|z}n,|*|Dcgc] }t |}}n|j|}|j|j||f}t|j} |jdgtd|jz dz| z} | jdgdg| jdz zz} |j!t#j$|j&|j&g| j(j+| dd} |j!t#j$|j&g|j,z| j(j+| dd| dddfj| ddgz} | dddfj| | z}t/|j,D]u}| }|j0dkDr |j0n.t t#j2dddj5}t/|D]}t t#j2t7|dj5}t|j9|}||\}}|jdkDrJt |t#j2|j:dt"j< j5nd }|rt#j2d dr|d z}t?||||j@| }|jC| dd|fj| |zx|j| jE|jF }t|ts|jI|S|Scc}w)rNrrhr)devicerr)lowhighsizerrrprr )%rr,rrrr1rrrrrshapeviewmaxrrrrrrrexpandrrrrrrrrrr>radd_torr)rUrrrrrrrr orig_dimsbatch batch_dimsmcombined_weightsmixraugdepthrrrrrrs r=rzAugMix.forward!s]yy"#"2"28"<&% h 'C$e -d }x/!*./Qa//%%h/C**4+>+>PO !s1sxx<33i?@jjm_sejj1n'==   " " LL$**djj1%,, G N NzZ[}^` a   11 LL$**(:(::5<< P W WXbcdXegi j adGLL*Q-, -.1gll:&.t))* DAC(,(8(81(r nnModuler r r r rKr?r=r s  0 ]M MM*/M@QMYabfglbmYnM`tT%((//tTnZ%((//ZzSSlUUXX__Ur?