Package: paddle-lpips
Perceptual Similarity Metric on PaddlePaddleThis repository is converted from its PyTorch versionrichzhang/PerceptualSimilarity.Follow the command lines to install it use pip:pipinstallpaddle-lpipsor install it from source:gitclonehttps://github.com/AgentMaker/Paddle-PerceptualSimilarity.gitcdPaddle-PerceptualSimilarity
pipinstall.Use it like the original PyTorch package but replace import method like this:importpaddle_lpipsaslpipsThe following is the originalREADME.mdof the original repository.Perceptual Similarity Metric and Dataset[Project Page]The Unreasonable Effectiveness of Deep Features as a Perceptual MetricRichard Zhang,Phillip Isola,Alexei A. Efros,Eli Shechtman,Oliver Wang. InCVPR, 2018.Quick startRunpip install lpips. The following Python code is all you need.importlpipsloss_fn_alex=lpips.LPIPS(net='alex')# best forward scoresloss_fn_vgg=lpips.LPIPS(net='vgg')# closer to "traditional" perceptual loss, when used for optimizationimporttorchimg0=torch.zeros(1,3,64,64)# image should be RGB, IMPORTANT: normalized to [-1,1]img1=torch.zeros(1,3,64,64)d=loss_fn_alex(img0,img1)More thorough information about variants is below. This repository contains ourperceptual metric (LPIPS)anddataset (BAPPS). It can also be used as a "perceptual loss". This uses PyTorch; a Tensorflow alternative ishere.Table of ContentsLearned Perceptual Image Patch Similarity (LPIPS) metrica.Basic UsageIf you just want to run the metric through command line, this is all you need.b."Perceptual Loss" usagec.About the metricBerkeley-Adobe Perceptual Patch Similarity (BAPPS) dataseta.Downloadb.Evaluationc.About the datasetd.Train the metric using the dataset(0) Dependencies/SetupInstallationInstall PyTorch 1.0+ and torchvision fomhttp://pytorch.orgpipinstall-rrequirements.txtClone this repo:gitclonehttps://github.com/richzhang/PerceptualSimilaritycdPerceptualSimilarity(1) Learned Perceptual Image Patch Similarity (LPIPS) metricEvaluate the distance between image patches.Higher means further/more different. Lower means more similar.(A) Basic Usage(A.I) Line commandsExample scripts to take the distance between 2 specific images, all corresponding pairs of images in 2 directories, or all pairs of images within a directory:python lpips_2imgs.py -p0 imgs/ex_ref.png -p1 imgs/ex_p0.png
python lpips_2dirs.py -d0 imgs/ex_dir0 -d1 imgs/ex_dir1 -o imgs/example_dists.txt
python lpips_1dir_allpairs.py -d imgs/ex_dir_pair -o imgs/example_dists_pair.txt(A.II) Python codeFiletest_network.pyshows example usage. This snippet is all you really need.importlpipsloss_fn=lpips.LPIPS(net='alex')d=loss_fn.forward(im0,im1)Variablesim0, im1is a PyTorch Tensor/Variable with shapeNx3xHxW(Npatches of sizeHxW, RGB images scaled in[-1,+1]). This returnsd, a lengthNTensor/Variable.Runpython test_network.pyto take the distance between example reference imageex_ref.pngto distorted imagesex_p0.pngandex_p1.png. Before running it - which do you thinkshouldbe closer?Some OptionsBy default inmodel.initialize:By default,net='alex'. Networkalexis fastest, performs the best (as a forward metric), and is the default. For backpropping,net='vgg'loss is closer to the traditional "perceptual loss".By default,lpips=True. This adds a linear calibration on top of intermediate features in the net. Set this tolpips=Falseto equally weight all the features.(B) Backpropping through the metricFilelpips_loss.pyshows how to iteratively optimize using the metric. Runpython lpips_loss.pyfor a demo. The code can also be used to implement vanilla VGG loss, without our learned weights.(C) About the metricHigher means further/more different. Lower means more similar.We found that deep network activations work surprisingly well as a perceptual similarity metric. This was true across network architectures (SqueezeNet [2.8 MB], AlexNet [9.1 MB], and VGG [58.9 MB] provided similar scores) and supervisory signals (unsupervised, self-supervised, and supervised all perform strongly). We slightly improved scores by linearly "calibrating" networks - adding a linear layer on top of off-the-shelf classification networks. We provide 3 variants, using linear layers on top of the SqueezeNet, AlexNet (default), and VGG networks.If you use LPIPS in your publication, please specify which version you are using. The current version is 0.1. You can setversion='0.0'for the initial release.(2) Berkeley Adobe Perceptual Patch Similarity (BAPPS) dataset(A) Downloading the datasetRunbash ./scripts/download_dataset.shto download and unzip the dataset into directory./dataset. It takes [6.6 GB] total. Alternatively, runbash ./scripts/get_dataset_valonly.shto only download the validation set [1.3 GB].2AFC train [5.3 GB]2AFC val [1.1 GB]JND val [0.2 GB](B) Evaluating a perceptual similarity metric on a datasetScripttest_dataset_model.pyevaluates a perceptual model on a subset of the dataset.Dataset flags--dataset_mode:2afcorjnd, which type of perceptual judgment to evaluate--datasets: list the datasets to evaluateif--dataset_mode 2afc: choices are [train/traditional,train/cnn,val/traditional,val/cnn,val/superres,val/deblur,val/color,val/frameinterp]if--dataset_mode jnd: choices are [val/traditional,val/cnn]Perceptual similarity model flags--model: perceptual similarity model to uselpipsfor our LPIPS learned similarity model (linear network on top of internal activations of pretrained network)baselinefor a classification network (uncalibrated with all layers averaged)l2for Euclidean distancessimfor Structured Similarity Image Metric--net: [squeeze,alex,vgg] for thenet-linandnetmodels; ignored forl2andssimmodels--colorspace: choices are [Lab,RGB], used for thel2andssimmodels; ignored fornet-linandnetmodelsMisc flags--batch_size: evaluation batch size (will default to 1)An example usage is as follows:python ./test_dataset_model.py --dataset_mode 2afc --datasets val/traditional val/cnn --model lpips --net alex --batch_size 50. This would evaluate our model on the "traditional" and "cnn" validation datasets.(C) About the datasetThe dataset contains two types of perceptual judgements:Two Alternative Forced Choice (2AFC)andJust Noticeable Differences (JND).(1) 2AFCEvaluators were given a patch triplet (1 reference + 2 distorted). They were asked to select which of the distorted was "closer" to the reference.Training sets contain 2 judgments/triplet.train/traditional[56.6k triplets]train/cnn[38.1k triplets]train/mix[56.6k triplets]Validation sets contain 5 judgments/triplet.val/traditional[4.7k triplets]val/cnn[4.7k triplets]val/superres[10.9k triplets]val/deblur[9.4k triplets]val/color[4.7k triplets]val/frameinterp[1.9k triplets]Each 2AFC subdirectory contains the following folders:ref: original reference patchesp0,p1: two distorted patchesjudge: human judgments - 0 if all preferred p0, 1 if all humans preferred p1(2) JNDEvaluators were presented with two patches - a reference and a distorted - for a limited time. They were asked if the patches were the same (identically) or different.Each set contains 3 human evaluations/example.val/traditional[4.8k pairs]val/cnn[4.8k pairs]Each JND subdirectory contains the following folders:p0,p1: two patchessame: human judgments: 0 if all humans thought patches were different, 1 if all humans thought patches were same(D) Using the dataset to train the metricSee scripttrain_test_metric.shfor an example of training and testing the metric. The script will train a model on the full training set for 10 epochs, and then test the learned metric on all of the validation sets. The numbers should roughly match theAlex - linrow in Table 5 in thepaper. The code supports training a linear layer on top of an existing representation. Training will add a subdirectory in thecheckpointsdirectory.You can also train "scratch" and "tune" versions by runningtrain_test_metric_scratch.shandtrain_test_metric_tune.sh, respectively.CitationIf you find this repository useful for your research, please use the following.@inproceedings{zhang2018perceptual,
  title={The Unreasonable Effectiveness of Deep Features as a Perceptual Metric},
  author={Zhang, Richard and Isola, Phillip and Efros, Alexei A and Shechtman, Eli and Wang, Oliver},
  booktitle={CVPR},
  year={2018}
}AcknowledgementsThis repository borrows partially from thepytorch-CycleGAN-and-pix2pixrepository. The average precision (AP) code is borrowed from thepy-faster-rcnnrepository.Angjoo Kanazawa,Connelly Barnes,Gaurav Mittal,wilhelmhb,Filippo Mameli,SuperShinyEyes,Minyoung Huhhelped to improve the codebase.