U (dSn@sddlZddlmZddlmZmZmZmZddlZddlm Z ddl m Z m Z ddd d d gZe eee eeed d dZGdddeZGdddejjZGdd d ejjZGdd d ejjZGdd d ejjZdS)N)Enum)ListTupleOptionalDict)Tensor) functionalInterpolationModeAutoAugmentPolicy AutoAugment RandAugmentTrivialAugmentWideAugMiximgop_name magnitude interpolationfillc Cs|dkr>tj|dddgdtt|dg||ddgd}n|dkr|tj|dddgddtt|g||ddgd}n|dkrtj|dt|dgd|ddg|d}nP|d krtj|ddt|gd|ddg|d}n |d krtj||||d }n|d krt|d|}n|d kr2t|d|}n|dkrNt |d|}n|dkrjt |d|}n|dkrt |t|}nv|dkrt ||}n^|dkrt |}nH|dkrt|}n2|dkrt|}n|dkrntd|d|S)NShearXr?)angle translatescaleshearrrcenterShearY TranslateX)rrrrrr TranslateYRotaterr BrightnessColorContrast Sharpness PosterizeSolarize AutoContrastEqualizeInvertIdentityzThe provided operator  is not recognized.)FZaffinemathdegreesatanintrotateZadjust_brightnessZadjust_saturationZadjust_contrastZadjust_sharpnessZ posterizeZsolarizeZ autocontrastZequalizeinvert ValueErrorrr6F/tmp/pip-unpacked-wheel-vx7f76es/torchvision/transforms/autoaugment.py _apply_op s                   r8c@seZdZdZdZdZdZdS)r zoAutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN. ZimagenetZcifar10ZsvhnN)__name__ __module__ __qualname____doc__IMAGENETCIFAR10SVHNr6r6r6r7r ]sc seZdZdZejejdfeeee e ddfdd Z ee e e e e eefe e e eeffdddZee eefee e eeffd d d Zeee eeefd d dZeedddZe dddZZS)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. N)policyrrreturncs,t||_||_||_|||_dSN)super__init__r@rr _get_policiespolicies)selfr@rr __class__r6r7rDys  zAutoAugment.__init__)r@rAcCs|tjkr@ddddddddd d d d d ddddddddddddgS|tjkrdddddddddddd d!d"d#d$d%d&d'd(d)d*d+d,d-gS|tjkrd.d/d0d1d2d3d4d5d6d2d7d8d9d1d:d;dd?d@dAdBdCdDgStdE|dFdS)GN))r'皙?)r!333333? ))r(rLr)rLN)r*皙?Nr*rLN))r'rL)r'rL)r*rJN)r(皙?)rUr!rQrK))r(rLrR))r'rQrNr*rN))r!rVrY)r(rLrK)rR)r'rJrT)rXr$rJr))r!rJrMrR))r*rNrP)r+rLNrZ))r$rLrW)r%rrK)rX)r$r))r$rQrK)r(rQrS))r&rJrSr\))rrLrNrZ)r[rR)r+皙?N)r%rVrT))r!ffffff?r])r333333?rM))r&rQr)r&?rY))r?rKr r`rM))r)rcNr*rbN))rrVrS)r'rarS))r$rJrY)r#rLrS))r&rarM)r#r`rM)rR)r*rcN))r%rLrS)r&rLrN))r$r`rS)rrcrK))r*raN)r)rJN))r rJrY)r&rVrT))r#rbrT)r$rVrK))r(rcr])r+rN)r*rVNrO)rfrR))r$rbrMrR)r)rQN)r(rVrK))r#r_rY)r$r`r))r(rJrNr)rbN))r rbrMrd)rh)r(rQrY)rPr^)rdrh)rrbrW)r+rVN)rrbrKr+r`N)rR)r(rLrT)r+rbNrR)rR)r!rbrY)rirg)rj)r+rJN))rrbrN)r(rVrT)rlrg)ri)r(rarY))rrQrKrk)re)r rLrT))r%rarYr!rQrW)r+rQN)r rr]))rr`rT)r(rJrK)r\rm))rrarS)rrbrY))rr_rTr\))r(r`r])r rLrS))rrQrWrn))rr`rM)r rQrY))rrQrN)r)r`N))rr`r]r^zThe provided policy r-)r r=r>r?r5)rGr@r6r6r7rEs   zAutoAugment._get_policiesnum_bins image_sizerAcCstdd|dftdd|dftdd|d|dftdd|d|dftdd|dftdd|dftdd|dftdd|dftdd|dfd t||dd d ftd d|d ftdd ftdd ftdd fd S)NrraTt?rr>@rbrKrWFo@)rrrr r!r#r$r%r&r'r(r)r*r+)torchlinspacearangeroundr2tensorrGrprqr6r6r7_augmentation_spaces$   zAutoAugment._augmentation_space) transform_numrAcCs4tt|d}td}tdd}|||fS)zGet parameters for autoaugment transformation Returns: params required by the autoaugment transformation r)r]r])r2rurandintitemZrand)r|Z policy_idprobssignsr6r6r7 get_paramss  zAutoAugment.get_paramsrrAcCs|j}t|\}}}t|trTt|ttfr>t|g|}n|dk rTdd|D}|t|j \}}}| d||f} t |j |D]n\} \} } } || | kr| | \}}| dk rt||  nd}|r|| dkr|d9}t || ||j|d}q|S) z img (PIL Image or Tensor): Image to be transformed. Returns: PIL Image or Tensor: AutoAugmented image. NcSsg|] }t|qSr6float.0fr6r6r7 sz'AutoAugment.forward.. rrr")rr.get_dimensions isinstancerr2rrlenrFr{ enumeraterr8r)rGrrchannelsheightwidthZ transform_idrrop_metairpZ magnitude_id magnitudessignedrr6r6r7forwards"   zAutoAugment.forwardrAcCs|jjd|jd|jdS)Nz(policy=, fill=))rIr9r@r)rGr6r6r7__repr__szAutoAugment.__repr__)r9r:r;r<r r=r NEARESTrrrrDrstrr2rErrboolr{ staticmethodrrr __classcell__r6r6rHr7r hs$  * Z* cseZdZdZdddejdfeeeeeee ddfdd Z ee eefe e e eeffd d d Zeed d dZe dddZZS)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. r]rMN)num_opsrnum_magnitude_binsrrrAcs,t||_||_||_||_||_dSrB)rCrDrrrrr)rGrrrrrrHr6r7rD2s  zRandAugment.__init__rocCstddftdd|dftdd|dftdd|d|dftdd|d|dftdd|dftdd |dftdd |dftdd |dftdd |dfd t||dd dftd d|dftddftddfd S)NrFraTrrrrrsrbrKrWrtr,rrrr r!r#r$r%r&r'r(r)r*ruryrvrwrxr2rzr6r6r7r{As $  zRandAugment._augmentation_spacerc Cs|j}t|\}}}t|trTt|ttfr>t|g|}n|dk rTdd|D}||j||f}t |j D]}tt t |d}t||} || \} } | jdkrt| |jnd} | rt ddr| d9} t|| | |j|d }qp|S) img (PIL Image or Tensor): Image to be transformed. Returns: PIL Image or Tensor: Transformed image. NcSsg|] }t|qSr6rrr6r6r7rasz'RandAugment.forward..r}rrr]rr")rr.rrrr2rr{rrangerrur~rrlistkeysndimrr8r) rGrrrrrr_op_indexrrrrr6r6r7rTs"   zRandAugment.forwardrc Cs:|jjd|jd|jd|jd|jd|jd }|S)Nz (num_ops=z , magnitude=z, num_magnitude_bins=, interpolation=rr)rIr9rrrrrrGsr6r6r7ros4 zRandAugment.__repr__)r9r:r;r<r rr2rrrrDrrrrrr{rrrr6r6rHr7r s" *cs|eZdZdZdejdfeeeee ddfdd Z ee e e eeffddd Zeed d d Ze d ddZZS)raDataset-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. rN)rrrrAcs t||_||_||_dSrB)rCrDrrr)rGrrrrHr6r7rDs zTrivialAugmentWide.__init__)rprAcCstddftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dfdt||dd dftd d|dftddftddfd S) NrFgGz?Tg@@g`@rKrrTrtrr)rGrpr6r6r7r{s $  z&TrivialAugmentWide._augmentation_spacerc Cs|j}t|\}}}t|trTt|ttfr>t|g|}n|dk rTdd|D}||j}tt t |d }t ||}||\} } | jdkrt| t j t | dt jd nd} | rt ddr| d 9} t||| |j|d S) rNcSsg|] }t|qSr6rrr6r6r7rsz.TrivialAugmentWide.forward..r}rdtyperr]rr")rr.rrrr2rr{rrur~rrrrrlongr8r) rGrrrrrrrrrrrr6r6r7rs$   $zTrivialAugmentWide.forwardrcCs*|jjd|jd|jd|jd}|S)Nz(num_magnitude_bins=rrr)rIr9rrrrr6r6r7rs$zTrivialAugmentWide.__repr__)r9r:r;r<r rr2rrrrDrrrrrr{rrrr6r6rHr7r|s  c seZdZdZdddddejdfeeeeeee e eddfdd Z ee eefe ee eeffd d d Zejjed ddZejjedddZeedddZeedddZed ddZZS)raAugMix 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. rYrTN)severity mixture_width chain_depthalphaall_opsrrrAcsntd|_d|kr&|jks@ntd|jd|d||_||_||_||_||_||_ ||_ dS)Nrrz!The severity must be between [1, z]. Got z instead.) rCrD_PARAMETER_MAXr5rrrrrrr)rGrrrrrrrrHr6r7rDs zAugMix.__init__roc Cs tdd|dftdd|dftd|dd|dftd|dd|dftdd|dfdt||ddd ftd d|d ftdd ftdd fd }|jr|tdd |dftdd |dftdd |dftdd |dfd |S)NrraTrg@rrsrWFrt) rrrr r!r'r(r)r*rb)r#r$r%r&)rurvrwrxr2ryrupdate)rGrprqrr6r6r7r{s&$   zAugMix._augmentation_spacercCs t|SrB)r.Z pil_to_tensorrGrr6r6r7_pil_to_tensorszAugMix._pil_to_tensor)rcCs t|SrB)r.Z to_pil_imagerr6r6r7_tensor_to_pilszAugMix._tensor_to_pil)paramsrAcCs t|SrB)ru_sample_dirichlet)rGrr6r6r7rszAugMix._sample_dirichlet)orig_imgrAc Cs||j}t|\}}}t|trZ|}t|ttfrBt|g|}qd|dk rddd|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]}| }|jdkrn|jnttjddd d }t|D]}ttt|d }t ||}||\}}|jdkrt|tj|jd tjd nd }|rtd d r|d9}t||||j |d}q| !| dd|f | |qT| |j"|j#d } t|tsx|$| S| S)rNcSsg|] }t|qSr6rrr6r6r7r/sz"AugMix.forward..rrWr)devicerr})lowhighsizerrr]rr")%rr.rrrr2rrr{rrshapeviewmaxrrrruryrrexpandrrrr~rrrrrr8rZadd_torr)rGrrrrrrrZ orig_dimsbatchZ batch_dimsmZcombined_weightsZmixraugdepthrrrrrrr6r6r7r!sR    "$*  $$  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) rIr9rrrrrrrrr6r6r7r[sD zAugMix.__repr__)r9r:r;r<r ZBILINEARr2rrrrrDrrrrr{ruZjitZunusedrrrrrrr6r6rHr7rs4 *:)r/enumrtypingrrrrrurr r.r __all__rrr8r nnModuler r rrr6r6r6r7s$   P 8]V