U hJ@s$ddlZddlmZmZmZmZmZmZmZm Z ddl Z ddl Z ddl mZmZddlmZmZddlmZmZddlmZmZmZmZmZmZGdd d eZGd d d eZ Gd d d eZ!GdddeZ"GdddeZ#GdddeZ$GdddeZ%GdddeZ&GdddeZ'dS)N)AnyCallableDictListOptionalSequenceTypeUnion) tree_flattentree_unflatten) transforms tv_tensors) functional Transform)_parse_labels_getter_setup_number_or_seq _setup_sizeget_bounding_boxeshas_anyis_pure_tensorc@s&eZdZeeeefedddZdS)IdentityinptparamsreturncCs|SNselfrrrrQ/var/www/html/venv/lib/python3.8/site-packages/torchvision/transforms/v2/_misc.py _transformszIdentity._transformN)__name__ __module__ __qualname__rrstrr!rrrr rsrcs`eZdZdZefZeegefedfdd Z ee e efedddZ e dd d Z ZS) LambdazApply a user-defined function as a transform. This transform does not support torchscript. Args: lambd (function): Lambda/function to be used for transform. )lambdtypescs t||_|p|j|_dSr)super__init__r'_transformed_typesr()rr'r( __class__rr r* s zLambda.__init__rcCst||jr||S|SdSr) isinstancer(r'rrrr r!%s  zLambda._transform)rcCsFg}t|jdd}|r |||ddd|jDd|S)Nr"ztypes=cSsg|] }|jqSr)r").0typerrr 0sz%Lambda.extra_repr..z, )getattrr'appendr(join)rextrasnamerrr extra_repr+s  zLambda.extra_repr)r"r#r$__doc__objectr+rrrr*rr%r!r7 __classcell__rrr,r r&s r&csjeZdZdZejZeej ej fZ e j e j dfdd ZeedddZeeeefedd d ZZS) LinearTransformationa,Transform a tensor image or video with a square transformation matrix and a mean_vector computed offline. This transform does not support PIL Image. Given transformation_matrix and mean_vector, will flatten the torch.*Tensor and subtract mean_vector from it which is then followed by computing the dot product with the transformation matrix and then reshaping the tensor to its original shape. Applications: whitening transformation: Suppose X is a column vector zero-centered data. Then compute the data covariance matrix [D x D] with torch.mm(X.t(), X), perform SVD on this matrix and pass it as transformation_matrix. Args: transformation_matrix (Tensor): tensor [D x D], D = C x H x W mean_vector (Tensor): tensor [D], D = C x H x W )transformation_matrix mean_vectorcst|d|dkr6tdt|d|d|dkrntd|ddt|d|j|jkrtd|jd |j|j|jkrtd |jd |j||_||_dS) Nrrz,transformation_matrix should be square. Got z rectangular matrix.z(mean_vector should have the same length z< as any one of the dimensions of the transformation_matrix []z0Input tensors should be on the same device. Got z and z.Input tensors should have the same dtype. Got ) r)r*size ValueErrortupledevicedtyper<r=)rr<r=r,rr r*Ks&   zLinearTransformation.__init__samplercCs&t|tjjr"tt|jddSNz() does not support PIL images.rPILImage TypeErrorr0r"rrErrr _check_inputsfsz"LinearTransformation._check_inputsrc Cs|j}|d|d|d}||jjdkrhtdd|dd|dd|dd|jjd|jj|jjjkrtd |jd |jj|d||j}|j|j}t ||}||}t |t j t jfrt j||d }|S) Nrz?Input tensor and transformation matrix have incompatible shape.[z x z] != zXInput tensor should be on the same device as transformation matrix and mean vector. Got z vs like)shaper<r@rBr0r=ZreshapetorCtorchmmr.r rIVideowrap)rrrrSnZ flat_inptr<outputrrr r!js*"   zLinearTransformation._transform)r"r#r$r8 _transformsr;_v1_transform_clsrr rIrWr+rUTensorr*rrLrr%r!r:rrr,r r;4s r;csdeZdZdZejZd eeeee dfdd Z e e dddZ e e ee fe d d d ZZS) NormalizeaNormalize a tensor image or video with mean and standard deviation. This transform does not support PIL Image. Given mean: ``(mean[1],...,mean[n])`` and std: ``(std[1],..,std[n])`` for ``n`` channels, this transform will normalize each channel of the input ``torch.*Tensor`` i.e., ``output[channel] = (input[channel] - mean[channel]) / std[channel]`` .. note:: This transform acts out of place, i.e., it does not mutate the input tensor. Args: mean (sequence): Sequence of means for each channel. std (sequence): Sequence of standard deviations for each channel. inplace(bool,optional): Bool to make this operation in-place. Fmeanstdinplacecs(tt||_t||_||_dSr)r)r*listr`rarb)rr`rarbr,rr r*s   zNormalize.__init__rDcCs&t|tjjr"tt|jddSrFrGrKrrr rLszNormalize._check_inputsrcCs|jtj||j|j|jdS)Nr_) _call_kernelF normalizer`rarbrrrr r!szNormalize._transform)F)r"r#r$r8r[r^r\rfloatboolr*rrLrr%r!r:rrr,r r^s  r^cseZdZdZejZd eee efeee e e fddfdd Z e e eee fddd Ze eee fe d d d ZZS) GaussianBluraBlurs image with randomly chosen Gaussian blur kernel. The convolution will be using reflection padding corresponding to the kernel size, to maintain the input shape. If the input is a Tensor, it is expected to have [..., C, H, W] shape, where ... means an arbitrary number of leading dimensions. Args: kernel_size (int or sequence): Size of the Gaussian kernel. sigma (float or tuple of float (min, max)): Standard deviation to be used for creating kernel to perform blurring. If float, sigma is fixed. If it is tuple of float (min, max), sigma is chosen uniformly at random to lie in the given range. 皙?g@N) kernel_sizesigmarcstt|d|_|jD] }|dks4|ddkrtdqt|d|_d|jdkrj|jdks|ntd|jdS) Nz2Kernel size should be a tuple/list of two integersrz7Kernel size value should be an odd and positive number.rmrz@sigma values should be positive and of the form (min, max). Got )r)r*rrlr@rrm)rrlrmksr,rr r*s     "zGaussianBlur.__init__) flat_inputsrcCs0td|jd|jd}t||gdS)Nrr)rm)rUemptyZuniform_rmitemdict)rrqrmrrr _get_paramss"zGaussianBlur._get_paramsrcCs|jtj||jf|Sr)rdreZ gaussian_blurrlrrrr r!szGaussianBlur._transform)rj)r"r#r$r8r[rir\r intrrgr*rrrr%rur!r:rrr,r risricsFeZdZdZd eeddfdd Zeeeefed d d Z Z S) GaussianNoiseaAdd gaussian noise to images or videos. The input tensor is expected to be in [..., 1 or 3, H, W] format, where ... means it can have an arbitrary number of leading dimensions. Each image or frame in a batch will be transformed independently i.e. the noise added to each image will be different. The input tensor is also expected to be of float dtype in ``[0, 1]``. This transform does not support PIL images. Args: mean (float): Mean of the sampled normal distribution. Default is 0. sigma (float): Standard deviation of the sampled normal distribution. Default is 0.1. clip (bool, optional): Whether to clip the values in ``[0, 1]`` after adding noise. Default is True. rorkTN)r`rmrcs t||_||_||_dSr)r)r*r`rmclip)rr`rmrxr,rr r*s zGaussianNoise.__init__rcCs|jtj||j|j|jdS)N)r`rmrx)rdreZgaussian_noiser`rmrxrrrr r!szGaussianNoise._transform)rorkT) r"r#r$r8rgr*rrr%r!r:rrr,r rwsrwcsneZdZdZejfZd eeje ee e fe ejffe ddfdd Zee e efeddd ZZS) ToDtypeaConverts the input to a specific dtype, optionally scaling the values for images or videos. .. note:: ``ToDtype(dtype, scale=True)`` is the recommended replacement for ``ConvertImageDtype(dtype)``. Args: dtype (``torch.dtype`` or dict of ``TVTensor`` -> ``torch.dtype``): The dtype to convert to. If a ``torch.dtype`` is passed, e.g. ``torch.float32``, only images and videos will be converted to that dtype: this is for compatibility with :class:`~torchvision.transforms.v2.ConvertImageDtype`. A dict can be passed to specify per-tv_tensor conversions, e.g. ``dtype={tv_tensors.Image: torch.float32, tv_tensors.Mask: torch.int64, "others":None}``. The "others" key can be used as a catch-all for any other tv_tensor type, and ``None`` means no conversion. scale (bool, optional): Whether to scale the values for images or videos. See :ref:`range_and_dtype`. Default: ``False``. FN)rCscalercszttttjfs.tdtdttrjtjkrjt fddt j t j fDrjt d|_||_dS)Nz+dtype must be a dict or a torch.dtype, got z insteadc3s|]}|kVqdSrr)r/clsrCrr sz#ToDtype.__init__..aGot `dtype` values for `torch.Tensor` and either `tv_tensors.Image` or `tv_tensors.Video`. Note that a plain `torch.Tensor` will *not* be transformed by this (or any other transformation) in case a `tv_tensors.Image` or `tv_tensors.Video` is present in the input.)r)r*r.rtrUrCr@r0r]anyr rIrWwarningswarnrz)rrCrzr,r|r r*s zToDtype.__init__rcCst|jtjr4t|s,t|tjtjfs,|S|j}nHt||jkrR|jt|}n*d|jkrh|jd}ntdt|dt|pt|tjtjf}|dkr|j r|rt d|S|j t j|||j dS)NZothersz No dtype was specified for type a. If you only need to convert the dtype of images or videos, you can just pass e.g. dtype=torch.float32. If you're passing a dict as dtype, you can use "others" as a catch-all key e.g. dtype={tv_tensors.Mask: torch.int64, "others": None} to pass-through the rest of the inputs.z_scale was set to True but no dtype was specified for images or videos: no scaling will be done.rCrz)r.rCrUrr rIrWr0r@rzrrrdreto_dtype)rrrrCZsupports_scalingrrr r!s&   zToDtype._transform)F)r"r#r$r8rUr]r+r rCrrr%rrhr*rr!r:rrr,r rys"rycsPeZdZdZejZejfej ddfdd Z e e e e fe dddZZS) ConvertImageDtypea[DEPRECATED] Use ``v2.ToDtype(dtype, scale=True)`` instead. Convert input image to the given ``dtype`` and scale the values accordingly. .. warning:: Consider using ``ToDtype(dtype, scale=True)`` instead. See :class:`~torchvision.transforms.v2.ToDtype`. This function does not support PIL Image. Args: dtype (torch.dtype): Desired data type of the output .. note:: When converting from a smaller to a larger integer ``dtype`` the maximum values are **not** mapped exactly. If converted back and forth, this mismatch has no effect. Raises: RuntimeError: When trying to cast :class:`torch.float32` to :class:`torch.int32` or :class:`torch.int64` as well as for trying to cast :class:`torch.float64` to :class:`torch.int64`. These conversions might lead to overflow errors since the floating point ``dtype`` cannot store consecutive integers over the whole range of the integer ``dtype``. N)rCrcst||_dSr)r)r*rC)rrCr,rr r*Ns zConvertImageDtype.__init__rcCs|jtj||jddS)NTr)rdrerrCrrrr r!RszConvertImageDtype._transform)r"r#r$r8r[rr\rUZfloat32rCr*rrr%r!r:rrr,r r3srcsleZdZdZdeeeeegefedfddfdd Z eedd d Z ee eefed d d Z Z S)SanitizeBoundingBoxesauRemove degenerate/invalid bounding boxes and their corresponding labels and masks. This transform removes bounding boxes and their associated labels/masks that: - are below a given ``min_size`` or ``min_area``: by default this also removes degenerate boxes that have e.g. X2 <= X1. - have any coordinate outside of their corresponding image. You may want to call :class:`~torchvision.transforms.v2.ClampBoundingBoxes` first to avoid undesired removals. It can also sanitize other tensors like the "iscrowd" or "area" properties from COCO (see ``labels_getter`` parameter). It is recommended to call it at the end of a pipeline, before passing the input to the models. It is critical to call this transform if :class:`~torchvision.transforms.v2.RandomIoUCrop` was called. If you want to be extra careful, you may call it after all transforms that may modify bounding boxes but once at the end should be enough in most cases. Args: min_size (float, optional): The size below which bounding boxes are removed. Default is 1. min_area (float, optional): The area below which bounding boxes are removed. Default is 1. labels_getter (callable or str or None, optional): indicates how to identify the labels in the input (or anything else that needs to be sanitized along with the bounding boxes). By default, this will try to find a "labels" key in the input (case-insensitive), if the input is a dict or it is a tuple whose second element is a dict. This heuristic should work well with a lot of datasets, including the built-in torchvision datasets. It can also be a callable that takes the same input as the transform, and returns either: - A single tensor (the labels) - A tuple/list of tensors, each of which will be subject to the same sanitization as the bounding boxes. This is useful to sanitize multiple tensors like the labels, and the "iscrowd" or "area" properties from COCO. If ``labels_getter`` is None then only bounding boxes are sanitized. ?defaultN)min_sizemin_area labels_getterrcsZt|dkr"td|d||_|dkr@td|d||_||_t||_dS)Nrzmin_size must be >= 1, got .zmin_area must be >= 1, got )r)r*r@rrrr_labels_getter)rrrrr,rr r*|s zSanitizeBoundingBoxes.__init__)inputsrc s,t|dkr|n|d}|}|dk rd}t|tjrB|f}nNt|ttfr||D]$}t|tjsTt|jt |dqTnt|jt |dt |\}}t |}|dk r|D]2}|j d|j dkrtd|j d|j dqt jj||j|jjjd} t| |d fd d |D} t| |S) NrrzRThe labels in the input to forward() must be a tensor or None, got {type} instead.)r0zNumber of boxes (shape=z>) and must match the number of labels.Found labels with shape=z).)format canvas_sizerr)validlabelscsg|]}|qSr)r!)r/rrrrr r1sz1SanitizeBoundingBoxes.forward..)lenrr.rUr]rArcr@rr0r rrSreZ_miscZ!_get_sanitize_bounding_boxes_maskrrrrtr ) rrrmsgentryrqspecZboxeslabelrZ flat_outputsrrr forwards:     zSanitizeBoundingBoxes.forwardrcsj|ddk o$tfdd|dD}ttjtjf}|sD|sDS|d}|rX|Stj|dSdS)Nrc3s|]}|kVqdSrr)r/rrrr r}sz3SanitizeBoundingBoxes._transform..rrQ)r~r.r Z BoundingBoxesMaskrX)rrrZis_labelZis_bounding_boxes_or_maskrZrrr r!s& z SanitizeBoundingBoxes._transform)rrr)r"r#r$r8rgr rrr%r*rrr!r:rrr,r rVs'(r)(rtypingrrrrrrrr Z PIL.ImagerHrUZtorch.utils._pytreer r Z torchvisionr r[r Ztorchvision.transforms.v2rrerZ_utilsrrrrrrrr&r;r^rirwryrrrrrr s ( Q#(H#