o yi// @sddlmZmZmZmZmZddlZddlmZddlm Z ddl m Z ddl m Z ddlmZddlmZdd lmZd d giZd ed ededefddZd!dedededeededef ddZ d!dedededeededef ddZGdd d e ZdS)")AnyListOptionalTupleUnionN)Tensor)Module)NoTrainInceptionV3)Metric)rank_zero_warn) dim_zero_cat)_TORCH_FIDELITY_AVAILABLE)KernelInceptionDistanceKIDtorch_fidelityk_xxk_xyk_yyreturnc Cs|jd}t|}t|}|jdd|}|jdd|}|jdd}|} |} |} | | ||d} | d| |d8} | S)Adapted from `KID Score`_r)dim)shapetorchdiagsum) rrrmdiag_xdiag_y kt_xx_sums kt_yy_sums k_xy_sums kt_xx_sum kt_yy_sumk_xy_sumvaluer(J/home/ubuntu/.local/lib/python3.10/site-packages/torchmetrics/image/kid.pymaximum_mean_discrepancys    r*?f1f2degreegammacoefcCs0|dur d|jd}||j|||}|S)rNr,r)rT)r-r.r/r0r1kernelr(r(r) poly_kernel1sr4f_realf_fakecCs<t|||||}t|||||}t|||||}t|||S)r)r4r*)r5r6r/r0r1k_11k_22k_12r(r(r)poly_mmd9s r:cseZdZUdZdZeed<dZeed<dZeed<e e ed<e e ed<   d#de e e efde de de deedededededd ffdd Zde dedd fddZdee e ffdd Zd$fd!d" ZZS)%raCalculates Kernel Inception Distance (KID) which is used to access the quality of generated images. Given by. .. math:: KID = MMD(f_{real}, f_{fake})^2 where :math:`MMD` is the maximum mean discrepancy and :math:`I_{real}, I_{fake}` are extracted features from real and fake images, see `kid ref1`_ for more details. In particular, calculating the MMD requires the evaluation of a polynomial kernel function :math:`k` .. math:: k(x,y) = (\gamma * x^T y + coef)^{degree} which controls the distance between two features. In practise the MMD is calculated over a number of subsets to be able to both get the mean and standard deviation of KID. Using the default feature extraction (Inception v3 using the original weights from `kid ref2`_), the input is expected to be mini-batches of 3-channel RGB images of shape ``(3 x H x W)``. If argument ``normalize`` is ``True`` images are expected to be dtype ``float`` and have values in the ``[0, 1]`` range, else if ``normalize`` is set to ``False`` images are expected to have dtype ``uint8`` and take values in the ``[0, 255]`` range. All images will be resized to 299 x 299 which is the size of the original training data. The boolian flag ``real`` determines if the images should update the statistics of the real distribution or the fake distribution. .. note:: using this metric with the default feature extractor requires that ``torch-fidelity`` is installed. Either install as ``pip install torchmetrics[image]`` or ``pip install torch-fidelity`` As input to ``forward`` and ``update`` the metric accepts the following input - ``imgs`` (:class:`~torch.Tensor`): tensor with images feed to the feature extractor of shape ``(N,C,H,W)`` - ``real`` (`bool`): bool indicating if ``imgs`` belong to the real or the fake distribution As output of `forward` and `compute` the metric returns the following output - ``kid_mean`` (:class:`~torch.Tensor`): float scalar tensor with mean value over subsets - ``kid_std`` (:class:`~torch.Tensor`): float scalar tensor with mean value over subsets Args: feature: Either an str, integer or ``nn.Module``: - an str or integer will indicate the inceptionv3 feature layer to choose. Can be one of the following: 'logits_unbiased', 64, 192, 768, 2048 - an ``nn.Module`` for using a custom feature extractor. Expects that its forward method returns an ``(N,d)`` matrix where ``N`` is the batch size and ``d`` is the feature size. subsets: Number of subsets to calculate the mean and standard deviation scores over subset_size: Number of randomly picked samples in each subset degree: Degree of the polynomial kernel function gamma: Scale-length of polynomial kernel. If set to ``None`` will be automatically set to the feature size coef: Bias term in the polynomial kernel. reset_real_features: Whether to also reset the real features. Since in many cases the real dataset does not change, the features can cached them to avoid recomputing them which is costly. Set this to ``False`` if your dataset does not change. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If ``feature`` is set to an ``int`` (default settings) and ``torch-fidelity`` is not installed ValueError: If ``feature`` is set to an ``int`` not in ``(64, 192, 768, 2048)`` ValueError: If ``subsets`` is not an integer larger than 0 ValueError: If ``subset_size`` is not an integer larger than 0 ValueError: If ``degree`` is not an integer larger than 0 ValueError: If ``gamma`` is niether ``None`` or a float larger than 0 ValueError: If ``coef`` is not an float larger than 0 ValueError: If ``reset_real_features`` is not an ``bool`` Example: >>> import torch >>> _ = torch.manual_seed(123) >>> from torchmetrics.image.kid import KernelInceptionDistance >>> kid = KernelInceptionDistance(subset_size=50) >>> # generate two slightly overlapping image intensity distributions >>> imgs_dist1 = torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8) >>> imgs_dist2 = torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> kid.update(imgs_dist1, real=True) >>> kid.update(imgs_dist2, real=False) >>> kid_mean, kid_std = kid.compute() >>> print((kid_mean, kid_std)) (tensor(0.0337), tensor(0.0023)) Fhigher_is_betteris_differentiablefull_state_update real_features fake_featuresdr+Nr,Tfeaturesubsets subset_sizer/r0r1reset_real_features normalizekwargsrc  stjdi| tdtt|ttfr7tstdd} || vr,t d| d|dt dt|gd|_ n t|t r@||_ nt d t|trM|d ksQt d ||_t|tr]|d ksat d ||_t|trm|d ksqt d ||_|durt|tr|d kst d||_t|tr|d kst d||_t|tst d||_t|tst d||_|jdgdd|jdgdddS)NzMetric `Kernel Inception Distance` will save all extracted features in buffer. For large datasets this may lead to large memory footprint.zKernel Inception Distance metric requires that `Torch-fidelity` is installed. Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`.)logits_unbiased@ir@z3Integer input to argument `feature` must be one of z , but got .zinception-v3-compat)name features_listz'Got unknown input to argument `feature`rz7Argument `subsets` expected to be integer larger than 0z;Argument `subset_size` expected to be integer larger than 0z6Argument `degree` expected to be integer larger than 0z=Argument `gamma` expected to be `None` or float larger than 0z2Argument `coef` expected to be float larger than 0z4Arugment `reset_real_features` expected to be a boolz*Argument `normalize` expected to be a boolr>)dist_reduce_fxr?r()super__init__r UserWarning isinstancestrintr ModuleNotFoundError ValueErrorr inceptionr TypeErrorrDrEr/floatr0r1boolrFrG add_state) selfrCrDrEr/r0r1rFrGrHvalid_int_input __class__r(r)rQsT    z KernelInceptionDistance.__init__imgsrealcCsD|jr |dn|}||}|r|j|dS|j|dS)z)Update the state with extracted features.N)rGbyterXr>appendr?)r]rarbfeaturesr(r(r)updates  zKernelInceptionDistance.updatec Cst|j}t|j}|jd}||jkrtd|jd}||jkr&tdg}t|jD].}t |}||d|j}t |}||d|j} t || |j |j |j } || q-t|} | | jddfS)zCalculate KID score based on accumulated extracted features from the two distributions. Implementation inspired by `Fid Score`_ rzCArgument `subset_size` should be smaller than the number of samplesNF)unbiased)r r>r?rrErWrangerDrrandpermr:r/r0r1restackmeanstd) r]r>r?n_samples_realn_samples_fake kid_scores__permr5r6o kid_scoresr(r(r)computes$          zKernelInceptionDistance.computecs8|js|jd}t||jd<dStdS)Nr>)rF _defaultspoprPreset)r]r'r_r(r)rxs   zKernelInceptionDistance.reset)r@rArBr+Nr,TF)rN)__name__ __module__ __qualname____doc__r;r[__annotations__r<r=rrrrTrUrrrZrrQrgrrurx __classcell__r(r(r_r)rCsN  X        F r)r+Nr,)typingrrrrrrrtorch.nnrtorchmetrics.image.fidr torchmetrics.metricr torchmetrics.utilitiesr torchmetrics.utilities.datar torchmetrics.utilities.importsr __doctest_requires__r*rUrZr4r:rr(r(r(r)s6         (