o oi+ @sdZddlmZmZmZddlZddlmZddlm Z gdZ GdddeZ d e d e d e d e fd dZ GdddeZ d e d ee efd ee efd e fddZdde dededed e f ddZdS)z:Module containing functionals for intensity normalisation.)ListTupleUnionN) ImageModule)Tensor) Denormalize Normalize denormalize normalizenormalize_min_maxcsveZdZdZdeeeeeeefdeeeeeeefddffdd Z dedefd d Z de fd d Z Z S) radNormalize a tensor image with mean and standard deviation. .. math:: \text{input[channel] = (input[channel] - mean[channel]) / std[channel]} Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels, Args: mean: Mean for each channel. std: Standard deviations for each channel. Shape: - Input: Image tensor of size :math:`(*, C, ...)`. - Output: Normalised tensor with same size as input :math:`(*, C, ...)`. Examples: >>> x = torch.rand(1, 4, 3, 3) >>> out = Normalize(0.0, 255.)(x) >>> out.shape torch.Size([1, 4, 3, 3]) >>> x = torch.rand(1, 4, 3, 3) >>> mean = torch.zeros(4) >>> std = 255. * torch.ones(4) >>> out = Normalize(mean, std)(x) >>> out.shape torch.Size([1, 4, 3, 3]) meanstdreturnNcstt|ttfrt|g}t|ttfrt|g}t|ttfr-t|d}t|ttfr;t|d}||_ ||_ dSN) super__init__ isinstanceintfloattorchtensortuplelistr r selfr r  __class__L/home/ubuntu/.local/lib/python3.10/site-packages/kornia/enhance/normalize.pyr=s    zNormalize.__init__inputcCt||j|jSr)r r r rrrrrforwardSzNormalize.forwardcC"d|jd|jd}|jj|SNz(mean=z, std=)r r r__name__rreprrrr__repr__V zNormalize.__repr__)r( __module__ __qualname____doc__rrrrrrr"strr+ __classcell__rrrrrsrdatar r rcCs|j}tjr2t|trt|tstd|jddks$|jddkr1td|jd|jdnt|trFtj|g|d|j |j d}t|trZtj|g|d|j |j d}|jr|jddkr|jd|jdkr|jdd |jdd krtd |jd |jd |jr|jddkr|jd|jdkr|jdd |jdd krtd |jd |jd tj ||j |j d}tj ||j |j d}|d}|d}| |d|dd||}| |S)a{Normalize an image/video tensor with mean and standard deviation. .. math:: \text{input[channel] = (input[channel] - mean[channel]) / std[channel]} Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels, Args: data: Image tensor of size :math:`(B, C, *)`. mean: Mean for each channel. std: Standard deviations for each channel. Return: Normalised tensor with same size as input :math:`(B, C, *)`. Examples: >>> x = torch.rand(1, 4, 3, 3) >>> out = normalize(x, torch.tensor([0.0]), torch.tensor([255.])) >>> out.shape torch.Size([1, 4, 3, 3]) >>> x = torch.rand(1, 4, 3, 3) >>> mean = torch.zeros(4) >>> std = 255. * torch.ones(4) >>> out = normalize(x, mean, std) >>> out.shape torch.Size([1, 4, 3, 3]) 0Only tensor is accepted when converting to ONNX.rzqBatch dimension must be one for broadcasting when converting to ONNX.Try changing mean shape and std shape from (z, z) to (1, C) or (1, C, 1, 1).devicedtypeN5mean length and number of channels do not match. Got  and .4std length and number of channels do not match. Got ).N) shaperonnxis_in_onnx_exportrr ValueErrorrrr6r7 as_tensorview)r2r r r>outrrrr [s:   00 r cs^eZdZdZdeeefdeeefddffdd Zdedefd d Zde fd d Z Z S) raxDenormalize a tensor image with mean and standard deviation. .. math:: \text{input[channel] = (input[channel] * std[channel]) + mean[channel]} Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels, Args: mean: Mean for each channel. std: Standard deviations for each channel. Shape: - Input: Image tensor of size :math:`(*, C, ...)`. - Output: Denormalised tensor with same size as input :math:`(*, C, ...)`. Examples: >>> x = torch.rand(1, 4, 3, 3) >>> out = Denormalize(0.0, 255.)(x) >>> out.shape torch.Size([1, 4, 3, 3]) >>> x = torch.rand(1, 4, 3, 3, 3) >>> mean = torch.zeros(1, 4) >>> std = 255. * torch.ones(1, 4) >>> out = Denormalize(mean, std)(x) >>> out.shape torch.Size([1, 4, 3, 3, 3]) r r rNcst||_||_dSr)rrr r rrrrrs  zDenormalize.__init__rcCr r)r r r r!rrrr"r#zDenormalize.forwardcCr$r%r'r)rrrr+r,zDenormalize.__repr__) r(r-r.r/rrrrr"r0r+r1rrrrrs *rcCsV|j}tjr)t|trt|tstd|jddks$|jddkr(tdnt|tr=tj|g|d|j |j d}t|trQtj|g|d|j |j d}|jr|jddkr|jd|jdkr|jdd|jddkrtd |jd |jd |jr|jddkr|jd|jdkr|jdd|jddkrtd |jd |jd tj ||j |j d}tj ||j |j d}| dkr|j dd gdg| dR}nt|j| kr|d }t|j| ks| dkr |j dd gdg| dR}nt|j| kr$|d }t|j| kst|||S)aoDenormalize an image/video tensor with mean and standard deviation. .. math:: \text{input[channel] = (input[channel] * std[channel]) + mean[channel]} Where `mean` is :math:`(M_1, ..., M_n)` and `std` :math:`(S_1, ..., S_n)` for `n` channels, Args: data: Image tensor of size :math:`(B, C, *)`. mean: Mean for each channel. std: Standard deviations for each channel. Return: Denormalised tensor with same size as input :math:`(B, C, *)`. Examples: >>> x = torch.rand(1, 4, 3, 3) >>> out = denormalize(x, 0.0, 255.) >>> out.shape torch.Size([1, 4, 3, 3]) >>> x = torch.rand(1, 4, 3, 3, 3) >>> mean = torch.zeros(1, 4) >>> std = 255. * torch.ones(1, 4) >>> out = denormalize(x, mean, std) >>> out.shape torch.Size([1, 4, 3, 3, 3]) r3rr4zEBatch dimension must be one for broadcasting when converting to ONNX.r5Nr8r9r:r;r<r=)r>rr?r@rrrArrr6r7rBdimrClen unsqueezeaddcmul)r2r r r>rrrr s<   00 $ $ r ?ư>xmin_valmax_valepsc Cst|tstdt|dt|tstdt|dt|ts-tdt|dt|jdkr=td|jd|j}|d|d}}|||d }|j d d d d}|j d d d d} ||||| |||} | |S) ayNormalise an image/video tensor by MinMax and re-scales the value between a range. The data is normalised using the following formulation: .. math:: y_i = (b - a) * \frac{x_i - \text{min}(x)}{\text{max}(x) - \text{min}(x)} + a where :math:`a` is :math:`\text{min_val}` and :math:`b` is :math:`\text{max_val}`. Args: x: The image tensor to be normalised with shape :math:`(B, C, *)`. min_val: The minimum value for the new range. max_val: The maximum value for the new range. eps: Float number to avoid zero division. Returns: The normalised image tensor with same shape as input :math:`(B, C, *)`. Example: >>> x = torch.rand(1, 5, 3, 3) >>> x_norm = normalize_min_max(x, min_val=-1., max_val=1.) >>> x_norm.min() tensor(-1.) >>> x_norm.max() tensor(1.0000) zdata should be a tensor. Got: r;z"'min_val' should be a float. Got: z'b' should be a float. Got: z/Input shape must be at least a 3d tensor. Got: rr4r=T)keepdim) rr TypeErrortyperrGr>rArCminmax) rMrNrOrPr>BC x_reshapedx_minx_maxx_outrrrr s     r )rJrKrL)r/typingrrrr kornia.corerModuler__all__rr rrr r rrrrs  =D*-$I