o V hSn @sddlZddlmZddlmZmZmZmZddlZddlm Z ddl m Z m Z gdZde d ed ed e d eeef d dZGdddeZGdddejjZGdddejjZGdddejjZGdddejjZdS)N)Enum)DictListOptionalTuple)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)rrrrrr>V/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/transforms/autoaugment.py _apply_op sC6 + !       r@c@seZdZdZdZdZdZdS)r zoAutoAugment policies learned on different datasets. Available policies are IMAGENET, CIFAR10 and SVHN. imagenetcifar10svhnN)__name__ __module__ __qualname____doc__IMAGENETCIFAR10SVHNr>r>r>r?r ]s r c 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__rKrr _get_policiespolicies)selfrKrr __class__r>r?rOys zAutoAugment.__init__cCsF|tjkr gdS|tjkrgdS|tjkrgdStd|d)N)))r&皙?)r 333333? )r'rWr(rWNr)皙?Nr)rWN))r&rW)r&rWr)rUN)r'皙?)rcr r^rV))r'rWr_))r&r^rZr)rN))r rdrg)r'rWrV)r_)r&rUra)rfr#rUr))r rUrXr_))r)rNr]r*rWNrh)r#rWre)r$rrV)rf)r#r))r#r^rV)r'r^r`))r%rUr`rk))rrWrZrh)rir_rbrYrjrlr\))r*皙?N)r$rdra))r ffffff?rm)r333333?rX))r%r^r)r%?rg))r?rVrrprX))r(rsNr)rrN))rrdr`)r&rqr`))r#rUrg)r"rWr`))r%rqrX)r"rprX)r_)r)rsN))r$rWr`)r%rWrZ))r#rpr`)rrsrV))r)rqN)r(rUN))rrUrg)r%rdra))r"rrra)r#rdrV))r'rsrm)r*rN)r)rdNr[)rvr_))r#rrrXr_)r(r^N)r'rdrV))r"rorg)r#rpr))r'rUrZr(rrN))rrrrXrt)rx)r'r^rg)r]rn)rtrx))rrrre)r*rdN)rrrrVr*rpN)r_)r'rWrar*rrNr_r_)r rrrg)ryrw)rz)r*rUN))rrrrZ)r'rdra)r}rwr~)ry)r'rqrg))rr^rVr{)ru)rrWrar|))r$rqrgr r^re)r*r^N)rrrm))rrpra)r'rUrV)rkr))rrqr`)rrrrg))rrorark))r'rprm)rrWr`))rr^rer))rrprX)rr^rg))rr^rZ)r(rpN))rrprmrnzThe provided policy r,)r rHrIrJr=)rRrKr>r>r?rPs   zAutoAugment._get_policiesnum_bins image_sizecCstdd|dftdd|dftdd|d|dftdd|d|dftdd|dftdd|dftdd|dftdd|dftdd|dfd t||dd d ftd d|d ftdd ftdd ftdd fd S)NrrqTt?rr>@rrrVreFo@)rrrrr r"r#r$r%r&r'r(r)r*)torchlinspacearangeroundr2tensorrRrrr>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)rmrm)r2rrandintitemrand)r policy_idprobssignsr>r>r? get_paramss   zAutoAugment.get_paramsrcCs|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 isinstancerr2rrlenrQr enumeraterr@r)rRrrchannelsheightwidth transform_idrrop_metairp magnitude_id magnitudessignedrr>r>r?forwards$   zAutoAugment.forwardcCs|jjd|jd|jdS)Nz(policy=, fill=))rTrDrKr)rRr>r>r?__repr__szAutoAugment.__repr__)rDrErFrGr rHr NEARESTrrrrOrstrr2rPrrboolr staticmethodrrr __classcell__r>r>rSr?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. rmrXNnum_opsrnum_magnitude_binsrrrLcs,t||_||_||_||_||_dSrM)rNrOrrrrr)rRrrrrrrSr>r?rO2s  zRandAugment.__init__rrcCstddftdd|dftdd|dftdd|d|dftdd|d|dftdd|dftdd |dftdd |dftdd |dftdd |dfd t||dd dftd d|dftddftddfd S)NrFrqTrrrrrrrVrerr+rrrrr r"r#r$r%r&r'r(r)rrrrrr2rr>r>r?rAs $  zRandAugment._augmentation_spacerc 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..rrrrmrr!)rr-rrrr2rrrrangerrrrrlistkeysndimrr@r) rRrrrrrr_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)rTrDrrrrrrRsr>r>r?ros  zRandAugment.__repr__)rDrErFrGr rr2rrrrOrrrrrrrrrr>r>rSr?r s. .r c seZdZdZdejdfdededeee ddffdd Z d ede e e eefffd d Zd edefddZde fddZZS)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. rNrrrrLcs t||_||_||_dSrM)rNrOrrr)rRrrrrSr>r?rOs  zTrivialAugmentWide.__init__rcCstddftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dftdd|dfdt||dd dftd d|dftddftddfd S) NrFgGz?Tg@@g`@rVrrarrr)rRrr>r>r?rs $  z&TrivialAugmentWide._augmentation_spacerc 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..rrdtyperrmrr!)rr-rrrr2rrrrrrrrrrlongr@r) rRrrrrrrrrrrrr>r>r?rs$    "zTrivialAugmentWide.forwardcCs*|jjd|jd|jd|jd}|S)Nz(num_magnitude_bins=rrr)rTrDrrrrr>r>r?rs zTrivialAugmentWide.__repr__)rDrErFrGr rr2rrrrOrrrrrrrrrr>r>rSr?r|s" " rcseZdZdZdddddejdfdeded ed ed ed ed e e eddffdd Z dede eefde ee eefffddZejjdefddZejjdefddZdedefddZdedefddZdefd d!ZZS)"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. rgrTNseverity mixture_width chain_depthalphaall_opsrrrLcsntd|_d|kr|jks ntd|jd|d||_||_||_||_||_||_ ||_ dS)Nrrz!The severity must be between [1, z]. Got z instead.) rNrO_PARAMETER_MAXr=rrrrrrr)rRrrrrrrrrSr>r?rOs  zAugMix.__init__rrc Cstdd|dftdd|dftd|dd|dftd|dd|dftdd|dfdt||ddd ftd d|d ftdd ftdd fd }|jr|tdd |dftdd |dftdd |dftdd |dfd |S)NrrqTrg@rrreFr) rrrrr r&r'r(r)rr)r"r#r$r%)rrrrr2rrupdate)rRrrrr>r>r?rs&$   zAugMix._augmentation_spacecC t|SrM)r- pil_to_tensorrRrr>r>r?_pil_to_tensor zAugMix._pil_to_tensorrcCrrM)r- to_pil_imagerr>r>r?_tensor_to_pilrzAugMix._tensor_to_pilparamscCrrM)r_sample_dirichlet)rRrr>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|$| S| S)rNcSrr>rrr>r>r?r/rz"AugMix.forward..rrer)devicerr)lowhighsizerrrmrr!)%rr-rrrr2rrrrrshapeviewmaxrrrrrrrexpandrrrrrrrrrr@radd_torr)rRrrrrrrr orig_dimsbatch batch_dimsmcombined_weightsmixraugdepthrrrrrrr>r>r?r!sT    "$(    "  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) rTrDrrrrrrrrr>r>r?r[s"  zAugMix.__repr__)rDrErFrGr BILINEARr2rrrrrOrrrrrrjitunusedrrrrrrr>r>rSr?rsD  .:r)r/enumrtypingrrrrrrr r-r __all__rrr@r nnModuler r rrr>r>r>r?s0    P 8]V