o oi:@s6dZddlmZddlmZmZmZmZmZddl Z ddl m Z m Z m Z ddl mZddlmZgdZdLdMddZdNdOddZdPddZededZ dNdQd"d#ZdNdRd%d&ZdLdSd)d*ZdLdTd,d-ZdNdUd0d1ZdLdTd2d3ZdNdVd4d5ZdNdVd6d7ZdNdVd8d9ZdWdXd=d>Z dLdYdBdCZ!dLdZdEdFZ"d[dJdKZ#dS)\z8The testing package contains testing-specific utilities.) annotations)AnyOptionalSequenceTypeVarcastN)float16float32float64) TypeGuard)Tensor) KORNIA_CHECKKORNIA_CHECK_DM_DESCKORNIA_CHECK_IS_COLORKORNIA_CHECK_IS_GRAYKORNIA_CHECK_IS_IMAGEKORNIA_CHECK_IS_LIST_OF_TENSORKORNIA_CHECK_IS_TENSORKORNIA_CHECK_LAFKORNIA_CHECK_SAME_DEVICEKORNIA_CHECK_SAME_DEVICESKORNIA_CHECK_SHAPEKORNIA_CHECK_TYPE KORNIA_UNWRAPTxr shape list[str]raisesboolreturncCsd|dkr|dd}|jt| dd}nd|dkr1|dd}|jdt|d}n|}|j}t|t|krO|rMt|d|d|jdStt|D]'}||}|s`qUt|}|||kr||ryt|d|d|jdSqUd S) aCheck whether a tensor has a specified shape. The shape can be specified with a implicit or explicit list of strings. The guard also check whether the variable is a type `Tensor`. Args: x: the tensor to evaluate. shape: a list with strings with the expected shape. raises: bool indicating whether an exception should be raised upon failure. Raises: Exception: if the input tensor is has not the expected shape and raises is True. Example: >>> x = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_SHAPE(x, ["B", "C", "H", "W"]) # implicit True >>> x = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_SHAPE(x, ["2", "3", "H", "W"]) # explicit True *rNz shape must be [z]. Got FT)rlen TypeErrorrange isnumericint)rrrshape_to_checkx_shape_to_checkidim_dimr-E/home/ubuntu/.local/lib/python3.10/site-packages/kornia/core/check.pyr2s.     r conditionmsg Optional[str]cCs"|s|r t|d|dSdS)aCheck any arbitrary boolean condition. Args: condition: the condition to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: Exception: if the condition is met and raises is True. Example: >>> x = torch.rand(2, 3, 3) >>> KORNIA_CHECK(x.shape[-2:] == (3, 3), "Invalid homography") True z not true. FT) Exception)r/r0rr-r-r.r is r maybe_objobjecttyprcCs t||S)zUnwraps an optional contained value that may or not be present. Args: maybe_obj: the object to unwrap. typ: expected type after unwrap. )r)r3r5r-r-r.rs rT)boundT | tuple[T, ...] TypeGuard[T]cCs.t||s|rtdt|d|dSdS)aCheck the type of an aribratry variable. Args: x: any input variable. typ: the expected type of the variable. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the input variable does not match with the expected and raises is True. Example: >>> KORNIA_CHECK_TYPE("foo", str, "Invalid string") True zInvalid type: . FT) isinstancer$type)rr5r0rr-r-r.rs rTypeGuard[Tensor]cCs.t|ts|rtdt|d|dSdS)aCheck the input variable is a Tensor. Args: x: any input variable. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the input variable does not match with the expected and raises is True. Example: >>> x = torch.rand(2, 3, 3) >>> KORNIA_CHECK_IS_TENSOR(x, "Invalid tensor") True zNot a Tensor type. Got: r:FT)r;r r$r<rr0rr-r-r.rs rOptional[Sequence[object]]TypeGuard[list[Tensor]]cCs@t|to tdd|D}|s|rtdt|ddSdS)a3Check the input variable is a List of Tensors. Args: x: Any sequence of objects raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the input variable does not match with the expected and raises is True. Return: True if the input is a list of Tensors, otherwise return False. Example: >>> x = torch.rand(2, 3, 3) >>> KORNIA_CHECK_IS_LIST_OF_TENSOR(x, raises=False) False >>> KORNIA_CHECK_IS_LIST_OF_TENSOR([x]) True css|]}t|tVqdS)N)r;r ).0dr-r-r. sz1KORNIA_CHECK_IS_LIST_OF_TENSOR..zProvided container of type z is not a list of tensorsFT)r;listallr$r<)rr are_tensorsr-r-r.rs rycC0|j|jkr|rtd|jd|jdSdS)aCheck whether two tensor in the same device. Args: x: first tensor to evaluate. y: sencod tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the two tensors are not in the same device and raises is True. Example: >>> x1 = torch.rand(2, 3, 3) >>> x2 = torch.rand(1, 3, 1) >>> KORNIA_CHECK_SAME_DEVICE(x1, x2) True "Not same device for tensors. Got:  and FT)devicer$rrGrr-r-r.r rtensors list[Tensor]cs^ttto tdkd|tfddDs-|r+tdddDd|d Sd S) aCheck whether a list provided tensors live in the same device. Args: tensors: a list of tensors. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: Exception: if all the tensors are not in the same device and raises is True. Example: >>> x1 = torch.rand(2, 3, 3) >>> x2 = torch.rand(1, 3, 1) >>> KORNIA_CHECK_SAME_DEVICES([x1, x2], "Tensors not in the same device") True r!z)Expected a list with at least one elementc3s |] }dj|jkVqdS)rNrKrArrNr-r.rCsz,KORNIA_CHECK_SAME_DEVICES..rIcSsg|]}|jqSr-rPrQr-r-r. sz-KORNIA_CHECK_SAME_DEVICES..r:FT)r r;rDr#rEr2)rNr0rr-rRr.rs rcCrH)aCheck whether two tensor have the same shape. Args: x: first tensor to evaluate. y: sencod tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the two tensors have not the same shape and raises is True. Example: >>> x1 = torch.rand(2, 3, 3) >>> x2 = torch.rand(2, 3, 3) >>> KORNIA_CHECK_SAME_SHAPE(x1, x2) True z!Not same shape for tensors. Got: rJFT)rr$rLr-r-r.KORNIA_CHECK_SAME_SHAPErMrTcCs@t|jdks|jddkr|rtdt|d|dSdS)aCheck whether an image tensor is a color images. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if all the input tensor has not a shape :math:`(3,H,W)` and raises is True. Example: >>> img = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_IS_COLOR(img, "Image is not color") True zNot a color tensor. Got: r:FTr#rr$r<r>r-r-r.r0 rcCsNt|jdkst|jdkr%|jddkr%|r#tdt|d|dSdS) aCheck whether an image tensor is grayscale. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the tensor has not a shape :math:`(1,H,W)` or :math:`(H,W)` and raises is True. Example: >>> img = torch.rand(2, 1, 4, 4) >>> KORNIA_CHECK_IS_GRAY(img, "Image is not grayscale") True rUrVr!zNot a gray tensor. Got: r:FTrWr>r-r-r.rHs *rcCs@t|jdks|jddvr|rtdt|d|dSdS)aCheck whether an image tensor is grayscale or color. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the tensor has not a shape :math:`(1,H,W)` or :math:`(3,H,W)` and raises is True. Example: >>> img = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_IS_COLOR_OR_GRAY(img, "Image is not color or grayscale") True rUrV)r!rUz!Not a color or gray tensor. Got: r:FTrWr>r-r-r.KORNIA_CHECK_IS_COLOR_OR_GRAY`rXrZbitsr'cCsz|s t|||s dSt|\}}|jtttfvr"|dk|dkB}nd|>d}|dk||kB}|r;t||||SdS)aCheck whether an image tensor is ranged properly [0, 1] for float or [0, 2 ** bits] for int. Args: x: image tensor to evaluate. msg: message to show in the exception. raises: bool indicating whether an exception should be raised upon failure. bits: the image bits. The default checks if given integer input image is an 8-bit image (0-255) or not. Raises: TypeException: if all the input tensor has not 1) a shape :math:`(3,H,W)`, 2) [0, 1] for float or [0, 255] for int, 3) and raises is True. Example: >>> img = torch.rand(2, 3, 4, 4) >>> KORNIA_CHECK_IS_IMAGE(img, "It is not an image") True Frr!T) rZtorchaminmaxdtyperr r item_handle_invalid_range)rr0rr\aminamaxinvalid max_int_valuer-r-r.rxs rdesc1desc2dmcCsT|d|dkr|d|dks(|r&td|jd|jd|jdSdS)amCheck whether the provided descriptors match with a distance matrix. Args: desc1: first descriptor tensor to evaluate. desc2: second descriptor tensor to evaluate. dm: distance matrix tensor to evaluate. raises: bool indicating whether an exception should be raised upon failure. Raises: TypeException: if the descriptors shape do not match with the distance matrix and raises is True. Example: >>> desc1 = torch.rand(4) >>> desc2 = torch.rand(8) >>> dm = torch.rand(4, 8) >>> KORNIA_CHECK_DM_DESC(desc1, desc2, dm) True rr!zdistance matrix shape z0 is not onsistent with descriptors shape: desc1 z desc2 FT)sizer$r)rfrgrhrr-r-r.rs(rlafcCst|gd|S)aCheck whether a Local Affine Frame (laf) has a valid shape. Args: laf: local affine frame tensor to evaluate. raises: bool indicating whether an exception should be raised upon failure. Raises: Exception: if the input laf does not have a shape :math:`(B,N,2,3)` and raises is True. Example: >>> lafs = torch.rand(2, 10, 2, 3) >>> KORNIA_CHECK_LAF(lafs) True )BN23)r)rjrr-r-r.rsrmin_valfloat | Tensormax_valcCs8d|d|d}|dur|d|7}|rt|dS)z.Helper function to handle invalid range cases.z2Invalid image value range. Expect [0, 1] but got [z, z].N F) ValueError)r0rrorqerr_msgr-r-r.ras ra)T)rr rrrrrr)NT)r/rr0r1rrrr)r3r4r5rrr) rr4r5r8r0r1rrrr9)rr4r0r1rrrr=)rr?rrrr@)rr rGr rrrr)rNrOr0r1rrrr)rr r0r1rrrr)NTr[) rr r0r1rrr\r'rr) rfr rgr rhr rrrr)rjr rrrr) r0r1rrrorprqrprr)$__doc__ __future__rtypingrrrrrr]rr r typing_extensionsr kornia.corer __all__rr rr<r6rrrrrrTrrrZrrrrar-r-r-r.s4     7            &