o ,h@n @sddlZddlmZddlmZddlZddlmZddlmZ m Z gdZ ded e d e d e d eee f d dZGdddeZGdddejjZGdddejjZGdddejjZGdddejjZdS)N)Enum)Optional)Tensor) functionalInterpolationMode)AutoAugmentPolicy AutoAugment RandAugmentTrivialAugmentWideAugMiximgop_name magnitude interpolationfillc Cs|dkrtj|dddgdtt|dg||ddgd}|S|dkr>tj|dddgddtt|g||ddgd}|S|dkrVtj|dt|dgd|ddg|d}|S|d krntj|ddt|gd|ddg|d}|S|d kr}tj||||d }|S|d krt|d|}|S|d krt|d|}|S|dkrt |d|}|S|dkrt |d|}|S|dkrt |t|}|S|dkrt ||}|S|dkrt |}|S|dkrt|}|S|dkrt|}|S|dkr |Std|d)NShearXr?)angle translatescaleshearrrcenterShearY TranslateX)rrrrrr TranslateYRotaterr BrightnessColorContrast Sharpness PosterizeSolarize AutoContrastEqualizeInvertIdentityzThe provided operator  is not recognized.)Faffinemathdegreesatanintrotateadjust_brightnessadjust_saturationadjust_contrastadjust_sharpness posterizesolarize autocontrastequalizeinvert ValueError)r rrrrr;]/var/www/html/scripts/venv/lib/python3.10/site-packages/torchvision/transforms/autoaugment.py _apply_op sC6 + !       r=c@seZdZdZdZdZdZdS)rzoAutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN. imagenetcifar10svhnN)__name__ __module__ __qualname____doc__IMAGENETCIFAR10SVHNr;r;r;r<r]s rc seZdZdZejejdfdededee e ddffdd Z dede e e e e eefe e e eefffd d Zd ed e eefdee e eefffd dZedede eeeffddZdedefddZde fddZZS)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. Npolicyrrreturncs,t||_||_||_|||_dSN)super__init__rHrr _get_policiespolicies)selfrHrr __class__r;r<rLys zAutoAugment.__init__cCsF|tjkr gdS|tjkrgdS|tjkrgdStd|d)N)))r#皙?)r333333? )r$rTr%rTNr&皙?Nr&rTN))r#rT)r#rTr&rRN)r$皙?)r`rr[rS))r$rTr\))r#r[rWr&rN))rrard)r$rTrS)r\)r#rRr^)rcr rRr))rrRrUr\))r&rNrZr'rTNre)r rTrb)r!rrS)rc)r r))r r[rS)r$r[r]))r"rRr]rh))rrTrWre)rfr\r_rVrgrirY))r'皙?N)r!rar^))rffffff?rj)r333333?rU))r"r[r)r"?rd))r?rSrrmrU))r%rpNr&roN))rrar])r#rnr]))r rRrd)rrTr]))r"rnrU)rrmrU)r\)r&rpN))r!rTr])r"rTrW))r rmr])rrprS))r&rnN)r%rRN))rrRrd)r"rar^))rror^)r rarS))r$rprj)r'rN)r&raNrX)rsr\))r rorUr\)r%r[N)r$rarS))rrlrd)r rmr))r$rRrWr%roN))rrorUrq)ru)r$r[rd)rZrk)rqru))rrorb)r'raN)rrorSr'rmN)r\)r$rTr^r'roNr\r\)rrord)rvrt)rw)r'rRN))rrorW)r$rar^)rzrtr{)rv)r$rnrd))rr[rSrx)rr)rrTr^ry))r!rnrdrr[rb)r'r[N)rrrj))rrmr^)r$rRrS)rhr|))rrnr])rrord))rrlr^rh))r$rmrj)rrTr]))rr[rbr}))rrmrU)rr[rd))rr[rW)r%rmN))rrmrjrkzThe provided policy r))rrErFrGr:)rOrHr;r;r<rMs   zAutoAugment._get_policiesnum_bins image_sizecCstdd|dftdd|dftdd|d|dftdd|d|dftdd|dftdd|dftdd|dftdd|dftdd|dfd t||dd d ftd d|d ftdd ftdd ftdd fd S)NrrnTt?rr>@rorSrbFo@)rrrrrrr r!r"r#r$r%r&r')torchlinspacearangeroundr/tensorrOr~rr;r;r<_augmentation_spaces$   zAutoAugment._augmentation_space transform_numcCs4tt|d}td}tdd}|||fS)zGet parameters for autoaugment transformation Returns: params required by the autoaugment transformation r)rjrj)r/rrandintitemrand)r policy_idprobssignsr;r;r< get_paramss   zAutoAugment.get_paramsr cCs|j}t|\}}}t|tr*t|ttfrt|g|}n |dur*dd|D}|t|j \}}}| d||f} t |j |D]7\} \} } } || | kr{| | \}}| durct||  nd}|rq|| dkrq|d9}t || ||j|d}qD|S) z img (PIL Image or Tensor): Image to be transformed. Returns: PIL Image or Tensor: AutoAugmented image. NcSg|]}t|qSr;float.0fr;r;r< z'AutoAugment.forward.. rrr)rr*get_dimensions isinstancerr/rrlenrNr enumeraterr=r)rOr rchannelsheightwidth transform_idrrop_metairp magnitude_id magnitudessignedrr;r;r<forwards$   zAutoAugment.forwardcCs|jjd|jd|jdS)Nz(policy=, fill=))rQrArHr)rOr;r;r<__repr__szAutoAugment.__repr__)rArBrCrDrrErNEARESTrlistrrLtuplestrr/rMdictrboolr staticmethodrrr __classcell__r;r;rPr<r hs0  * .Z r c seZdZdZdddejdfdededed ed eee d df fd d Z dede eefd e e e eefffddZded efddZd e fddZZS)r a~RandAugment data augmentation method based on `"RandAugment: Practical automated data augmentation with a reduced search space" `_. 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. rjrUNnum_opsrnum_magnitude_binsrrrIcs,t||_||_||_||_||_dSrJ)rKrLrrrrr)rOrrrrrrPr;r<rL2s  zRandAugment.__init__r~rcCstddftdd|dftdd|dftdd|d|dftdd|d|dftdd|dftdd |dftdd |dftdd |dftdd |dfd t||dd dftd d|dftddftddfd S)NrFrnTrrrrrorSrbrr(rrrrrrr r!r"r#r$r%r&rrrrrr/rr;r;r<rAs $  zRandAugment._augmentation_spacer c Cs|j}t|\}}}t|tr*t|ttfrt|g|}n |dur*dd|D}||j||f}t |j D]B}tt t |d}t||} || \} } | jdkrbt| |jnd} | rpt ddrp| d9} t|| | |j|d }q8|S) img (PIL Image or Tensor): Image to be transformed. Returns: PIL Image or Tensor: Transformed image. NcSrr;rrr;r;r<rarz'RandAugment.forward..rrrrjrr)rr*rrrr/rrrrangerrrrrrkeysndimrr=r) rOr rrrrr_op_indexrrrrr;r;r<rTs"   zRandAugment.forwardc Cs:|jjd|jd|jd|jd|jd|jd }|S)Nz (num_ops=z , magnitude=z, num_magnitude_bins=, interpolation=rr)rQrArrrrrrOsr;r;r<ros  zRandAugment.__repr__)rArBrCrDrrr/rrrrLrrrrrrrrrr;r;rPr<r s. .r c seZdZdZdejdfdededeee ddffdd Z d ede e e eefffd d Zd edefddZde fddZZS)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. rNrrrrIcs t||_||_||_dSrJ)rKrLrrr)rOrrrrPr;r<rLs  zTrivialAugmentWide.__init__r~cCstddftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dfdt||dd dftd d|dftddftddfd S) NrFgGz?Tg@@g`@rSrr^rrr)rOr~r;r;r<rs $  z&TrivialAugmentWide._augmentation_spacer c Cs|j}t|\}}}t|tr*t|ttfrt|g|}n |dur*dd|D}||j}tt t |d }t ||}||\} } | jdkr`t| t j t | dt jd nd} | rnt ddrn| d 9} t||| |j|d S) rNcSrr;rrr;r;r<rrz.TrivialAugmentWide.forward..rrdtyperrjrr)rr*rrrr/rrrrrrrrrrlongr=r) rOr rrrrrrrrrrr;r;r<rs$    "zTrivialAugmentWide.forwardcCs*|jjd|jd|jd|jd}|S)Nz(num_magnitude_bins=rrr)rQrArrrrr;r;r<rs zTrivialAugmentWide.__repr__)rArBrCrDrrr/rrrrLrrrrrrrrrr;r;rPr<r |s" " r cseZdZdZdddddejdfdeded ed ed ed ed e e eddffdd Z dede eefde ee eefffddZejjdefddZejjdefddZdedefddZdedefddZdefd d!ZZS)"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. rdrTNseverity mixture_width chain_depthalphaall_opsrrrIcsntd|_d|kr|jks ntd|jd|d||_||_||_||_||_||_ ||_ dS)Nrrz!The severity must be between [1, z]. Got z instead.) rKrL_PARAMETER_MAXr:rrrrrrr)rOrrrrrrrrPr;r<rLs  zAugMix.__init__r~rc Cstdd|dftdd|dftd|dd|dftd|dd|dftdd|dfdt||ddd ftd d|d ftdd ftdd fd }|jr|tdd |dftdd |dftdd |dftdd |dfd |S)NrrnTrg@rrrbFr) rrrrrr#r$r%r&ro)rr r!r")rrrrr/rrupdate)rOr~rrr;r;r<rs&$   zAugMix._augmentation_spacecC t|SrJ)r* pil_to_tensorrOr r;r;r<_pil_to_tensor zAugMix._pil_to_tensorr cCrrJ)r* to_pil_imagerr;r;r<_tensor_to_pilrzAugMix._tensor_to_pilparamscCrrJ)r_sample_dirichlet)rOrr;r;r<rrzAugMix._sample_dirichletorig_imgc Cst|j}t|\}}}t|tr-|}t|ttfr!t|g|}n|dur,dd|D}n||}||j ||f}t |j }| dgt d|jd|} | dgdg| jd} |tj|j|jg| jd| dd} |tj|jg|j| jd| dd| dddf | ddg} | dddf | | } t|jD]x}| }|jdkr|jn ttjddd d }t|D]K}ttt|d }t ||}||\}}|jdkrt|tj|jd tjd nd }|rtd d r|d9}t||||j |d}q| !| dd|f | |q| |j"|j#d } t|ts8|$| S| S)rNcSrr;rrr;r;r<r/rz"AugMix.forward..rrbr)devicerr)lowhighsizerrrjrr)%rr*rrrr/rrrrrshapeviewmaxrrrrrrrexpandrrrrrrrrrr=radd_torr)rOrrrrrr r orig_dimsbatch batch_dimsmcombined_weightsmixraugdepthrrrrrrr;r;r<r!sT    "$(    "  zAugMix.forwardcCsJ|jjd|jd|jd|jd|jd|jd|jd|jd}|S) Nz (severity=z, mixture_width=z, chain_depth=z, alpha=z , all_ops=rrr) rQrArrrrrrrrr;r;r<r[s"  zAugMix.__repr__)rArBrCrDrBILINEARr/rrrrrLrrrrrrjitunusedrrrrrrr;r;rPr<r sD  .:r )r,enumrtypingrrrrr*r__all__rrrr=rnnModuler r r r r;r;r;r<s0     P 8]V