:jW *ddlmZddlmZddlmZmZmZddlZddlm Z ddl m Z ddl m Z ddlmZdd lmZmZdd lmZmZesd gZerdd lmZdd lmZddlmZnGdde ZdZdZdd gZGddeZde de de de de f dZGddeZ y))Sequence)deepcopy)AnyOptionalUnionN)Tensor)Module)adaptive_avg_pool2d)Metric)_MATPLOTLIB_AVAILABLE_TORCH_FIDELITY_AVAILABLE)_AX_TYPE_PLOT_OUT_TYPEzFrechetInceptionDistance.plot)FeatureExtractorInceptionV3)vassert))interpolate_bilinear_2d_like_tensorflow1xc eZdZy)_FeatureExtractorInceptionV3N)__name__ __module__ __qualname__k/home/mcse/projects/srt_converter/srt-converter-venv/lib/python3.12/site-packages/torchmetrics/image/fid.pyrr$s rrFrechetInceptionDistancec eZdZUdZeed< ddedeedeede ddf fd Z d e ddffd Z d e de e d ffdZd e de fdZxZS)NoTrainInceptionV3z)Module that never leaves evaluation mode.INPUT_IMAGE_SIZENname features_listfeature_extractor_weights_path antialiasreturncxts tdt| |||||_|j y)NzNoTrainInceptionV3 module requires that `Torch-fidelity` is installed. Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`.)r ModuleNotFoundErrorsuper__init__ use_antialiaseval)selfrr r!r" __class__s rr'zNoTrainInceptionV3.__init__2s@)%h  }.LM& rmodec"t|dS)z.Force network to always be in evaluation mode.F)r&train)r*r,r+s rr.zNoTrainInceptionV3.trainDsw}U##rx.c ttj|xr|jtjk(di|j j }t|dr|j|jn|jtj}|jrEtjjj||j|jfddd}n$t!||j|jfd}|dz dz }|j#|}|j%|}|j'|}|j)|}d |vrkt+|d j-d j-d d <|j/d t1|d k(rt3fd|j DS|j5|}|j7|}|j9|}d|vrkt+|d j-d j-d d<|j/dt1|d k(rt3fd|j DS|j;|}|j=|}|j?|}|jA|}|jC|}|jE|}|jG|}|jI|}d|vrkt+|d j-d j-d d<|j/dt1|d k(rt3fd|j DS|jK|}|jM|}|jO|}|jQ|}tjR|d}d|vrB|d<|j/dt1|d k(rt3fd|j DSd|vr|jU|jVjXjZ}|d<|j/dt1|d k(rt3fd|j DS||jVj\j_d z}n|jW|}|d<t3fd|j DS)aForward method of inception net. Copy of the forward method from this file: https://github.com/toshas/torch-fidelity/blob/master/torch_fidelity/feature_extractor_inceptionv3.py with a single line change regarding the casting of `x` in the beginning. Corresponding license file (Apache License, Version 2.0): https://github.com/toshas/torch-fidelity/blob/master/LICENSE.md z6Expecting image as torch.Tensor with dtype=torch.uint8_dtypebilinearFT)sizer, align_cornersr")r3r464)r7) output_sizerc3(K|] }| ywNr.0afeaturess r z=NoTrainInceptionV3._torch_fidelity_forward..rEQXa[E192c3(K|] }| ywr;rr<s rr@z=NoTrainInceptionV3._torch_fidelity_forward..|rArB768c3(K|] }| ywr;rr<s rr@z=NoTrainInceptionV3._torch_fidelity_forward..rArBr72048c3(K|] }| ywr;rr<s rr@z=NoTrainInceptionV3._torch_fidelity_forward..rArBlogits_unbiasedc3(K|] }| ywr;rr<s rr@z=NoTrainInceptionV3._torch_fidelity_forward..rArBlogitsc3(K|] }| ywr;rr<s rr@z=NoTrainInceptionV3._torch_fidelity_forward..s=QXa[=rB)0rtorch is_tensordtypeuint8r copyhasattrtor1floatr(nn functional interpolaterr Conv2d_1a_3x3 Conv2d_2a_3x3 Conv2d_2b_3x3 MaxPool_1r squeezeremovelentuple Conv2d_3b_1x1 Conv2d_4a_3x3 MaxPool_2Mixed_5bMixed_5cMixed_5dMixed_6aMixed_6bMixed_6cMixed_6dMixed_6eMixed_7aMixed_7bMixed_7cAvgPoolflattenmmfcweightTbias unsqueeze)r*r/remaining_featuresr?s @r_torch_fidelity_forwardz*NoTrainInceptionV3._torch_fidelity_forwardHs0 "=qww%++'=?wx!//446!(x!8ADD add5;;>O   ##//++T-B-BC# 0A:++T-B-BC#A WO   q !   q !   q ! NN1  % %0GOOPRS[[\^_HTN  % %d +%&!+E$2D2DEEE   q !   q ! NN1  & &1!HPPQST\\]_`HUO  % %e ,%&!+E$2D2DEEE MM!  MM!  MM!  MM!  MM!  MM!  MM!  MM!  & &1!HPPQST\\]_`HUO  % %e ,%&!+E$2D2DEEE MM!  MM!  MM!  LLO MM!Q  ' ' HV   % %f -%&!+E$2D2DEEE  2 2TWW^^%%&A*+H& '  % %&7 8%&!+E$2D2DEEEDGGLL**1--A A=$*<*<===rch|j|}|dj|jddS)z8Forward pass of neural network with reshaping of output.rr9)rwreshapeshape)r*r/outs rforwardzNoTrainInceptionV3.forwards/**1-1v~~aggaj"--r)NT)rrr__doc__int__annotations__strlistrboolr'r.rr_rwr| __classcell__r+s@rrr-s3 9= Cy)1     $$$$#7$^>^>E&#+4F^>@..F.rrmu1sigma1mu2sigma2r#c4||z jjd}|j|jz}tjj ||zj jjd}||zd|zz S)arCompute adjusted version of `Fid Score`_. The Frechet Inception Distance between two multivariate Gaussians X_x ~ N(mu_1, sigm_1) and X_y ~ N(mu_2, sigm_2) is d^2 = ||mu_1 - mu_2||^2 + Tr(sigm_1 + sigm_2 - 2*sqrt(sigm_1*sigm_2)). Args: mu1: mean of activations calculated on predicted (x) samples sigma1: covariance matrix over activations calculated on predicted (x) samples mu2: mean of activations calculated on target (y) samples sigma2: covariance matrix over activations calculated on target (y) samples Returns: Scalar value of the distance between sets. r9dim)squaresumtracerMlinalgeigvalssqrtreal)rrrrr>bcs r _compute_fidrs s  R (A 'A Vf_-22499=="=EA q51q5=rceZdZUdZdZeed<dZeed<dZeed<dZ e ed<e ed<e ed <e ed <e ed <e ed <e ed <e ed<dZ eed< d%deee fdededeeeefdeedededdffd Zde deddfdZde fdZd&fd Zdeeej2fdd ffd! Z d'd"eee ee fd#eedefd$ZxZS)(ruCalculate Fréchet inception distance (FID_) which is used to assess the quality of generated images. .. math:: FID = \|\mu - \mu_w\|^2 + tr(\Sigma + \Sigma_w - 2(\Sigma \Sigma_w)^{\frac{1}{2}}) where :math:`\mathcal{N}(\mu, \Sigma)` is the multivariate normal distribution estimated from Inception v3 (`fid ref1`_) features calculated on real life images and :math:`\mathcal{N}(\mu_w, \Sigma_w)` is the multivariate normal distribution estimated from Inception v3 features calculated on generated (fake) images. The metric was originally proposed in `fid ref1`_. Using the default feature extraction (Inception v3 using the original weights from `fid ref2`_), the input is expected to be mini-batches of 3-channel RGB images of shape ``(3xHxW)``. 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. Using custom feature extractor is also possible. One can give a torch.nn.Module as `feature` argument. This custom feature extractor is expected to have output shape of ``(1, num_features)``. This would change the used feature extractor from default (Inception v3) to the given network. In case network doesn't have ``num_features`` attribute, a random tensor will be given to the network to infer feature dimensionality. Size of this tensor can be controlled by ``input_img_size`` argument and type of the tensor can be controlled with ``normalize`` argument (``True`` uses float32 tensors and ``False`` uses int8 tensors). In this case, update method expects to have the tensor given to `imgs` argument to be in the correct shape and type that is compatible to the custom feature extractor. This metric is known to be unstable in its calculatations, and we recommend for the best results using this metric that you calculate using `torch.float64` (default is `torch.float32`) which can be set using the `.set_dtype` method of the metric. .. hint:: 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 with - ``real`` (:class:`~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 - ``fid`` (:class:`~torch.Tensor`): float scalar tensor with mean FID value over samples Args: feature: Either an integer or ``nn.Module``: - an integer will indicate the inceptionv3 feature layer to choose. Can be one of the following: 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. reset_real_features: Whether to also reset the real features. Since in many cases the real dataset does not change, the features can be cached them to avoid recomputing them which is costly. Set this to ``False`` if your dataset does not change. normalize: Argument for controlling the input image dtype normalization: - If default feature extractor is used, controls whether input imgs have values in range [0, 1] or not: - True: if input imgs have values ranged in [0, 1]. They are cast to int8/byte tensors. - False: if input imgs have values ranged in [0, 255]. No casting is done. - If custom feature extractor module is used, controls type of the input img tensors: - True: if input imgs are expected to be in the data type of torch.float32. - False: if input imgs are expected to be in the data type of torch.int8. input_img_size: tuple of integers. Indicates input img size to the custom feature extractor network if provided. use_antialias: boolian flag to indicate whether to use antialiasing when resizing images. This will change the resize function to use bilinear interpolation with antialiasing, which is different from the original Inception v3 implementation. Does not apply to custom feature extractor networks. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. Raises: ValueError: If torch version is lower than 1.9 ModuleNotFoundError: 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] TypeError: If ``feature`` is not an ``str``, ``int`` or ``torch.nn.Module`` ValueError: If ``reset_real_features`` is not an ``bool`` Example: >>> from torch import rand >>> from torchmetrics.image.fid import FrechetInceptionDistance >>> fid = FrechetInceptionDistance(feature=64) >>> # 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) >>> fid.update(imgs_dist1, real=True) >>> fid.update(imgs_dist2, real=False) >>> fid.compute() tensor(12.6388) Fhigher_is_betteris_differentiablefull_state_updategplot_lower_boundreal_features_sumreal_features_cov_sumreal_features_num_samplesfake_features_sumfake_features_cov_sumfake_features_num_samples inceptionfeature_networkNfeaturereset_real_features normalizeinput_img_sizer!r"kwargsr#c t |di|t|ts t d||_d|_|}t|trK|}ts tdd} || vrt d| d|dtdt|g|| |_ nVt|tr:||_ d |_t|jd rt|jjtr|jj}nt|jjt r.t|jjj#}nt%d |j r(t'j(d g|dt&j*i} n+t'j,ddd g|t&j.} |j| j0d}n t%dt|ts t d||_||f} |j5dt'j6|j9d|j5dt'j6| j9d|j5dt'j:dj=d|j5dt'j6|j9d|j5dt'j6| j9d|j5dt'j:dj=dy)Nz*Argument `normalize` expected to be a boolFzFrechetInceptionDistance metric requires that `Torch-fidelity` is installed. Either install as `pip install torchmetrics[image]` or `pip install torch-fidelity`.)@iz3Integer input to argument `feature` must be one of z , but got .zinception-v3-compat)rr r!r"T num_featureszCExpected `self.inception.num_features` to be of type int or Tensor.r7rOr)rOr9z'Got unknown input to argument `feature`z4Argument `reset_real_features` expected to be a boolrr)dist_reduce_fxrrrrrr)r&r' isinstancer ValueErrorrused_custom_modelr~r r%rrrr rRrritem TypeErrorrMrandfloat32randintrPrzr add_statezerosdoubletensorlong) r*rrrrr!r"rrvalid_int_input dummy_image mx_num_featsr+s rr'z!FrechetInceptionDistance.__init__:s "6")T*IJ J"!& gs #"L,)l3Oo- I/IZZdeldmmno0*"7|n/M# DN ($DN%)D "t~~~6dnn993?#'>>#>#>L ; ;VD#&t~~'B'B'G'G'I#JL#$ijj>>"'**Q"U"Uu}}"UK"'--38L^8LTYT_T_"`K#~~k:@@D EF F-t4ST T#6 $l3  *EKK ,E,L,L,N_de . L0I0P0P0Rchi 2ELLO4H4H4J[`a *EKK ,E,L,L,N_de . L0I0P0P0Rchi 2ELLO4H4H4J[`arimgsrc|jr|js|dzjn|}|j|}|j|_|j }|jdk(r|jd}|rz|xj|jdz c_ |xj|jj|z c_ |xj|jdz c_y|xj |jdz c_|xj"|jj|z c_|xj$|jdz c_y)aUpdate the state with extracted features. Args: imgs: Input img tensors to evaluate. If used custom feature extractor please make sure dtype and size is correct for the model. real: Whether given image is real or fake. rr7rrN)rrbyterrO orig_dtyperrrurrrtrprrzrrr)r*rrr?s rupdatezFrechetInceptionDistance.updates'+nnd>T>Ts   "[_>>$'"..??$ <<>Q ))!,H   " "hllql&9 9 "  & &(**,//(*C C &  * *djjm ; *  " "hllql&9 9 "  & &(**,//(*C C &  * *djjm ; *rc|jdks|jdkr td|j|jz j d}|j |jz j d}|j |j|jj|zz }||jdz z }|j|j|jj|zz }||jdz z }t|jd||jd|j|jS)zWCalculate FID score based on accumulated extracted features from the two distributions.rzVMore than one sample is required for both the real and fake distributed to compute FIDrr7)rr RuntimeErrorrrurrrrprrr\rSr)r* mean_real mean_fake cov_real_numcov_real cov_fake_numcov_fakes rcomputez FrechetInceptionDistance.computes4  ) )A -1O1ORS1Swx x++d.L.LLWWXYZ ++d.L.LLWWXYZ 11D4R4RU^U`U`UbUeUefoUp4pp 4#A#AA#EF11D4R4RU^U`U`UbUeUefoUp4pp 4#A#AA#EFI--a0(ID &-FD * GMOrdst_typer c|t||}t|jtr||j_|S)zTransfer all metric state to specific dtype. Special version of standard `type` method. Arguments: dst_type: the desired type as ``torch.dtype`` or string )r& set_dtyperrrr1)r*rr{r+s rrz"FrechetInceptionDistance.set_dtypes4g) cmm%7 8#+CMM  rvalaxc&|j||S)ajPlot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.image.fid import FrechetInceptionDistance >>> 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) >>> metric = FrechetInceptionDistance(feature=64) >>> metric.update(imgs_dist1, real=True) >>> metric.update(imgs_dist2, real=False) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.image.fid import FrechetInceptionDistance >>> imgs_dist1 = lambda: torch.randint(0, 200, (100, 3, 299, 299), dtype=torch.uint8) >>> imgs_dist2 = lambda: torch.randint(100, 255, (100, 3, 299, 299), dtype=torch.uint8) >>> metric = FrechetInceptionDistance(feature=64) >>> values = [ ] >>> for _ in range(3): ... metric.update(imgs_dist1(), real=True) ... metric.update(imgs_dist2(), real=False) ... values.append(metric.compute()) ... metric.reset() >>> fig_, ax_ = metric.plot(values) )_plot)r*rrs rplotzFrechetInceptionDistance.plots`zz#r""r)rTF)+rNT)r#N)NN) rrrr}rrrrrrrTrr rrrr~r_rrr'rrrrMrOrrrrrrrs@rrrsbH#d"#t##t#!e!!!%%!!%%&OS&'+$(/<8<DbsF{#Db"Db Db c3m, Db )1 DbDbDb DbL<6<<$<2 p p  %U[[(8"9 h _c0#E&(6*:":;<0#IQRZI[0# 0#r)!collections.abcrrQrtypingrrrrMrtorch.nnr torch.nn.functionalr torchmetrics.metricr torchmetrics.utilities.importsr r torchmetrics.utilities.plotrr__doctest_skip__,torch_fidelity.feature_extractor_inceptionv3rrtorch_fidelity.helpersr,torch_fidelity.interpolate_compat_tensorflowrrrrrrrrs%'' 3&[@78x.f v G04-24ST~.5~.Bff66f.j#vj#r