From ff6c653b112c9cb9d70b3baeb11abf8a45a27713 Mon Sep 17 00:00:00 2001 From: Rahul Dey Date: Thu, 27 Dec 2018 19:34:42 -0500 Subject: [PATCH] First commit --- README.md | 38 + checkpoints.py | 47 + completion.py | 55 ++ config.py | 140 +++ dataloader.py | 349 ++++++++ datasets/__init__.py | 8 + datasets/casia.py | 16 + datasets/celeba.py | 51 ++ datasets/filelist.py | 121 +++ datasets/folderlist.py | 140 +++ datasets/gmm.py | 78 ++ datasets/gogan.py | 39 + datasets/loaders.py | 14 + datasets/reconstruction.py | 68 ++ datasets/transforms.py | 338 ++++++++ evaluate/__init__.py | 3 + evaluate/classification.py | 23 + evaluation.py | 472 ++++++++++ generation.py | 223 +++++ images/flowchart_red.png | Bin 0 -> 10991740 bytes images/generation_results.png | Bin 0 -> 1695855 bytes images/idea.png | Bin 0 -> 130261 bytes images/image_completion.png | Bin 0 -> 1476660 bytes images/rankgan_idea.png | Bin 0 -> 342381 bytes losses/__init__.py | 5 + losses/classification.py | 14 + losses/gogan_loss.py | 181 ++++ losses/regression.py | 15 + losses/uncertainty.py | 25 + main.py | 105 +++ model.py | 69 ++ models/SpatialCrossMapLRN_temp.py | 114 +++ models/__init__.py | 10 + models/dropout.py | 64 ++ models/gogan.py | 895 +++++++++++++++++++ models/hourglass.py | 229 +++++ models/layer/depthconcat.py | 26 + models/layer/lambdas.py | 28 + models/layer/normalize.py | 31 + models/layer/spatialcrossmaplrn.py | 60 ++ models/layer/spatiallppooling.py | 17 + models/lightcnn.py | 182 ++++ models/net.py | 24 + models/openface.py | 324 +++++++ models/openface2.py | 218 +++++ models/resnet.py | 117 +++ plugins/__init__.py | 7 + plugins/image.py | 28 + plugins/logger.py | 32 + plugins/lr_scheduler.py | 55 ++ plugins/monitor.py | 41 + plugins/visualizer.py | 386 +++++++++ train.py | 1282 ++++++++++++++++++++++++++++ train_no_vae.py | 1069 +++++++++++++++++++++++ train_vae_enc.py | 772 +++++++++++++++++ utils.py | 114 +++ 56 files changed, 8762 insertions(+) create mode 100644 README.md create mode 100644 checkpoints.py create mode 100644 completion.py create mode 100755 config.py create mode 100644 dataloader.py create mode 100644 datasets/__init__.py create mode 100644 datasets/casia.py create mode 100644 datasets/celeba.py create mode 100644 datasets/filelist.py create mode 100644 datasets/folderlist.py create mode 100644 datasets/gmm.py create mode 100644 datasets/gogan.py create mode 100644 datasets/loaders.py create mode 100644 datasets/reconstruction.py create mode 100644 datasets/transforms.py create mode 100644 evaluate/__init__.py create mode 100644 evaluate/classification.py create mode 100644 evaluation.py create mode 100644 generation.py create mode 100644 images/flowchart_red.png create mode 100644 images/generation_results.png create mode 100644 images/idea.png create mode 100644 images/image_completion.png create mode 100644 images/rankgan_idea.png create mode 100644 losses/__init__.py create mode 100644 losses/classification.py create mode 100644 losses/gogan_loss.py create mode 100644 losses/regression.py create mode 100644 losses/uncertainty.py create mode 100644 main.py create mode 100644 model.py create mode 100644 models/SpatialCrossMapLRN_temp.py create mode 100644 models/__init__.py create mode 100644 models/dropout.py create mode 100644 models/gogan.py create mode 100644 models/hourglass.py create mode 100644 models/layer/depthconcat.py create mode 100644 models/layer/lambdas.py create mode 100644 models/layer/normalize.py create mode 100644 models/layer/spatialcrossmaplrn.py create mode 100644 models/layer/spatiallppooling.py create mode 100644 models/lightcnn.py create mode 100644 models/net.py create mode 100644 models/openface.py create mode 100644 models/openface2.py create mode 100644 models/resnet.py create mode 100644 plugins/__init__.py create mode 100644 plugins/image.py create mode 100644 plugins/logger.py create mode 100644 plugins/lr_scheduler.py create mode 100644 plugins/monitor.py create mode 100644 plugins/visualizer.py create mode 100755 train.py create mode 100755 train_no_vae.py create mode 100755 train_vae_enc.py create mode 100644 utils.py diff --git a/README.md b/README.md new file mode 100644 index 0000000..baa6464 --- /dev/null +++ b/README.md @@ -0,0 +1,38 @@ +# RankGAN + +### Abstract +We present a new stage-wise learning paradigm for training generative adversarial networks (GANs). The goal of our work is to progressively strengthen the discriminator and thus, the generators, with each subsequent stage without changing the network architecture. We call this proposed method the RankGAN. We first propose a margin-based loss for the GAN discriminator. We then extend it to a margin-based ranking loss to train the multiple stages of RankGAN. We focus on face images from the CelebA dataset in our work and show visual as well as quantitative improvements in face generation and completion tasks over other GAN approaches, including WGAN and LSGAN. + +*** + +### Overview +![alt text](images/rankgan_idea.png "Stagewise Training using Ranking Mechanism") + +Ranking based Progressive Training in RankGAN. + +![alt text](images/flowchart_red.png "RankGAN Training Flowchart") + +RankGAN Training Flowchart and Architecture. + +*** + +### Contributions +* A progressive training framework where GANs at later stages improve upon their earlier versions. +* A margin-based Ranking Loss function to train GANs. +* Evaluation of GANs based on image completion tasks. + +*** + +### Results +![alt text](images/generation_results.png "Image Generation Results") + +Visual and quantitative results on face generation with RankGAN, WGAN and LSGAN. + +![alt text](images/image_completion.png "Image Completion Results") + +Image Completion Results. + +### References + +1. Rahul Dey, Felix Juefei-Xu, Vishnu Naresh Boddeti and Marios Savvides. [**RankGAN: A Maximum Margin Ranking GAN for Generating Faces.**](https://arxiv.org/abs/1812.08196) Asian Conference on Computer Vision (ACCV 2018. + diff --git a/checkpoints.py b/checkpoints.py new file mode 100644 index 0000000..58540a8 --- /dev/null +++ b/checkpoints.py @@ -0,0 +1,47 @@ +# checkpoints.py + +import os +import torch + +class Checkpoints: + + def __init__(self,args): + self.dir_save = args.save + self.dir_load = args.resume + # self.prevmodel = args.prevmodel + self.prevmodel = None + + if os.path.isdir(self.dir_save) == False: + os.makedirs(self.dir_save) + + def latest(self, name): + output = {} + if self.dir_load == None: + output['resume'] = None + else: + output['resume'] = self.dir_load + + if (self.prevmodel != None): + output['prevmodel'] = self.prevmodel + else: + output['prevmodel'] = None + + return output[name] + + def save(self, epoch, model, best): + if best == True: + output = {} + num = len(model) + for key, value in model[0].items(): + output[key] = value.state_dict() + torch.save(output, '%s/model_%d_epoch_%d.pth' % + (self.dir_save, num, epoch)) + + def load(self, filename): + if os.path.isfile(filename): + print("=> loading checkpoint '{}'".format(filename)) + model = torch.load(filename) + else: + print("=> no checkpoint found at '{}'".format(filename)) + + return model diff --git a/completion.py b/completion.py new file mode 100644 index 0000000..2f8a62f --- /dev/null +++ b/completion.py @@ -0,0 +1,55 @@ +#completion.py + +import torch +import random +import torchvision +from model import Model +from config import parser +from dataloader import Dataloader +from checkpoints import Checkpoints +from evaluation import Evaluate +from generation import Generator +import os +import datetime +import utils +import copy + +# parse the arguments +args = parser.parse_args() +random.seed(args.manual_seed) +torch.manual_seed(args.manual_seed) +args.save = os.path.join(args.result_path, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S/'), 'results') +args.logs = os.path.join(args.result_path, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S/'), 'logs') +utils.saveargs(args) + +# initialize the checkpoint class +checkpoints = Checkpoints(args) + +# Create Model +models = Model(args) +gogan_model, criterion = models.setup(checkpoints) +netD = gogan_model[0] +netG = gogan_model[1] +netE = gogan_model[2] + +if args.netD is not '': + checkpointD = checkpoints.load(args.netD) + netD.load_state_dict(checkpointD) +if args.netG is not '': + checkpointG = checkpoints.load(args.netG) + netG.load_state_dict(checkpointG) +if args.netE is not '': + checkpointE = checkpoints.load(args.netE) + netE.load_state_dict(checkpointE) + +# Data Loading +dataloader = Dataloader(args) +test_loader = dataloader.create("Test", shuffle=False) + +# The trainer handles the training loop and evaluation on validation set +evaluate = Evaluate(args, netD, netG, netE) +# generator = Generator(args, netD, netG, netE) + +# test for a single epoch +test_loss = evaluate.complete(test_loader) +# loss = generator.generate_one(test_loader) diff --git a/config.py b/config.py new file mode 100755 index 0000000..2c9bb45 --- /dev/null +++ b/config.py @@ -0,0 +1,140 @@ +# config.py +import os +import datetime +import argparse + +result_path = "results/" +result_path = os.path.join(result_path, datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S/')) + +parser = argparse.ArgumentParser(description='Your project title goes here') + +# ======================== Data Setings ============================================ +parser.add_argument('--dataset-test', type=str, default='CELEBA', metavar='', help='name of training dataset') +parser.add_argument('--dataset-train', type=str, default='CELEBA', metavar='', help='name of training dataset') +parser.add_argument('--split_test', type=float, default=None, metavar='', help='test split') +parser.add_argument('--split_train', type=float, default=None, metavar='', help='train split') +parser.add_argument('--dataroot', type=str, default=None, metavar='', help='path to the data') +parser.add_argument('--result-path', type=str, default=result_path, help='path where to save results') +parser.add_argument('--resume', type=str, default=None, metavar='', help='full path of models to resume training') +parser.add_argument('--nclasses', type=int, default=None, metavar='', help='number of classes for classification') +parser.add_argument('--input-filename-test', type=str, default=None, metavar='', help='input test filename for filelist and folderlist') +parser.add_argument('--label-filename-test', type=str, default=None, metavar='', help='label test filename for filelist and folderlist') +parser.add_argument('--input-filename-train', type=str, default=None, metavar='', help='input train filename for filelist and folderlist') +parser.add_argument('--label-filename-train', type=str, default=None, metavar='', help='label train filename for filelist and folderlist') +parser.add_argument('--loader-input', type=str, default=None, metavar='', help='input loader') +parser.add_argument('--loader-label', type=str, default=None, metavar='', help='label loader') + +# ======================== Network Model Setings =================================== +parser.add_argument('--nchannels', type=int, default=3, metavar='', help='number of input channels') +parser.add_argument('--resolution-high', type=int, default=64, metavar='', help='image resolution height') +parser.add_argument('--resolution-wide', type=int, default=64, metavar='', help='image resolution width') +parser.add_argument('--ndim', type=int, default=None, metavar='', help='number of feature dimensions') +parser.add_argument('--nunits', type=int, default=None, metavar='', help='number of units in hidden layers') +parser.add_argument('--dropout', type=float, default=None, metavar='', help='dropout parameter') +parser.add_argument('--net-type', type=str, default='dcgan', metavar='', help='type of network') +parser.add_argument('--length-scale', type=float, default=None, metavar='', help='length scale') +parser.add_argument('--tau', type=float, default=None, metavar='', help='Tau') +parser.add_argument('--mini-batch-disc', action='store_true', default=False, help='enable minibatch discrimination in discriminator') + +# ======================== Training Settings ======================================= +parser.add_argument('--cuda', action='store_true', default=False, help='run on gpu') +parser.add_argument('--ngpu', type=int, default=1, metavar='', help='number of gpus to use') +parser.add_argument('--batch-size', type=int, default=32, metavar='', help='batch size for training') +parser.add_argument('--nepochs', type=int, default=None, metavar='', help='number of epochs to train') +parser.add_argument('--niters', type=int, default=None, metavar='', help='number of iterations at test time') +parser.add_argument('--epoch-number', type=int, default=None, metavar='', help='epoch number') +parser.add_argument('--nthreads', type=int, default=2, metavar='', help='number of threads for data loading') +parser.add_argument('--manual-seed', type=int, default=101, metavar='', help='manual seed for randomness') +parser.add_argument('--port', type=int, default=8097, metavar='', help='port for visualizing training at http://localhost:port') +parser.add_argument('--env', type=str, default='main', help='visdom environment name') +parser.add_argument('--dataset-fraction', type=float, default=1, help='fraction of dataset to train (between 0-1)') +parser.add_argument('--plot-update-interval', type=int, default=30, help='number of iterations per plot update') + +# ======================== Hyperparameter Setings ================================== +parser.add_argument('--optim-method', type=str, default='Adam', metavar='', help='the optimization routine ') +parser.add_argument('--learning-rate-vae', type=float, default=1e-4, metavar='', help='learning rate for vae') +parser.add_argument('--learning-rate-dis', type=float, default=5e-5, metavar='', help='learning rate for discriminator') +parser.add_argument('--learning-rate-gen', type=float, default=1e-7, metavar='', help='learning rate for generator') +parser.add_argument('--learning-rate-decay', type=float, default=0.8, metavar='', help='learning rate decay') +parser.add_argument('--momentum', type=float, default=0, metavar='', help='momentum') +parser.add_argument('--weight-decay', type=float, default=0, metavar='', help='weight decay') +parser.add_argument('--stage1-weight-decay', type=float, default=0.5, metavar='', help='stage 1 weight decay for hinge loss') +parser.add_argument('--adam-beta1', type=float, default=0.5, metavar='', help='Beta 1 parameter for Adam') +parser.add_argument('--adam-beta2', type=float, default=0.999, metavar='', help='Beta 2 parameter for Adam') +parser.add_argument('--gp', action='store_true', default=False, help='use gradient penalty') +parser.add_argument('--gp-lambda', type=float, default=10, help="gradient penalty lambda") +parser.add_argument('--scheduler-patience', type=int, default=500, help='patience value for lr scheduler') +parser.add_argument('--scheduler-maxlen', type=int, default=1000, help='patience value for lr scheduler') + +# ======================== GoGAN Setings ================================== +parser.add_argument('--stage-epochs', type=int, default=10, help='number of epochs per gogan stage') +parser.add_argument('--num-stages', type=int, default=10, help='number of gogan stages') +parser.add_argument('--margin', type=float, default=2.0, help='initial margin of gogan loss') +parser.add_argument('--weight-gan-final', type=float, default=1.0, help='weight of discriminator loss') +parser.add_argument('--weight-vae-init', type=float, default=1.0, help='weight of mse loss') +parser.add_argument('--ngf', type=int, default=32) +parser.add_argument('--ndf', type=int, default=32) +parser.add_argument('--nz', type=int, default=100, help='size of the latent z vector') +parser.add_argument('--clamp-lower', type=float, default=-0.01, help='WGAN lower weight clip') +parser.add_argument('--clamp-upper', type=float, default=0.01, help='WGAN upper weight clip') +parser.add_argument('--d-iter', type=int, default=5, help='number of discriminator iterations per generation iteration') +parser.add_argument('--g-iter', type=int, default=1, help='number of generator iterations per discriminator iteration') +parser.add_argument('--netG', default='', help="path to netG (to continue training)") +parser.add_argument('--netD', default='', help="path to netD (to continue training)") +parser.add_argument('--netE', default='', help="path to Encoder (to continue training)") +parser.add_argument('--prevD', default='', help="path to prevD (to continue training)") +parser.add_argument('--prevG', default='', help="path to prevG (to continue training)") +parser.add_argument('--vae-loss-type', default='l2', help='type of vae loss l1 or l2') +parser.add_argument('--disc-diff-weight', type=float, default=1.0, help="weightage of discriminator difference loss") +parser.add_argument('--weight-kld', type=float, default=10.0, help='weightage of kl-divergence loss') +parser.add_argument('--start-stage', type=int, default=0, help='starging stage (0/1/2/...)') +parser.add_argument('--normalize', action='store_true', default=False, help='whether to have batch-norm') +parser.add_argument('--gogan-type', type=str, default="vae", help="no_vae/vae_no_gen") +parser.add_argument('--norm-type', type=str, default='batch', help="type of normalization to use in models") +parser.add_argument('--wgan', action='store_true', default=False, help='whether to use wgan loss in first stage of GAN') +parser.add_argument('--extra-D-cap', action='store_true', default=False, help='whether to add extra capacity to the discriminator') +parser.add_argument('--extra-G-cap', action='store_true', default=False, help='whether to add extra capacity to the generator') +parser.add_argument('--correlation-sigma', type=float, default=10.0, help='variance of impulse in correlation loss for VAE') +parser.add_argument('--add-capacity', action='store_true', default=False, help='whether to add extra layer to the discriminator to increase capacity') +parser.add_argument('--add-clamp', action='store_true', default=False, help='whether to change clamping in the discriminator to increase capacity') +parser.add_argument('--disc-optimize', action='store_true', default=False, help='optimize discriminator before training gogan') +parser.add_argument('--gen-gamma', type=float, default=0, help='curriculum learning gamma') +parser.add_argument('--add-noise', action='store_true', default=False, help='adds noise to the discriminator input') +parser.add_argument('--noise-var', type=float, default=0.1, help='std of noise to be added to GAN training') +parser.add_argument('--gp-norm', action='store_true', default=False, help='penalizes sum of gradient squares') +parser.add_argument('--rank-weight', type=float, default=1, help='weight of discriminator ranking loss') +parser.add_argument('--adaptive-iter', action='store_false', default=True, help='enables adaptive iterations for discriminator and generator') +parser.add_argument('--use-upsampling', action='store_true', default=False, help='use upsampling in dcgan') +parser.add_argument('--optimize-mse', action='store_true', default=False, help='wheter to optimize mse during gogan training') +parser.add_argument('--weight-mse', type=float, default=1, help='weight for mse loss during gogan training') +parser.add_argument('--n-extra-layers', type=int, default=0, help='number of extra layers in DCGAN architecture') + +# ======================== Image Completion Setings ================================== +parser.add_argument('--disc-loss-weight', type=float, default=0.1, help='weight for discriminator loss in image completion') +parser.add_argument('--ssim-weight', type=float, default=1000, help='weight of ssim loss') +parser.add_argument('--citers', type=int, default=100, help='number of iterations for image completion') +parser.add_argument('--scale', type=float, default=0.2, help='mask scale for image completion') +parser.add_argument('--use-encoder', action='store_true', default=False, help='whether to use encoder for image completion or not') +parser.add_argument('--blend', action='store_true', default=False, help='enable poisson blending') +parser.add_argument('--mask-type', type=str, default='central', help='mask type (central/periocular)') +parser.add_argument('--netG1', default='', help="path to netG1 (to continue training)") +parser.add_argument('--netG2', default='', help="path to netG2 (to continue training)") +parser.add_argument('--netG3', default='', help="path to netG3 (to continue training)") +parser.add_argument('--netG4', default='', help="path to netG4 (to continue training)") +parser.add_argument('--netG5', default='', help="path to netG5 (to continue training)") +parser.add_argument('--start-index', type=int, default=0, help="start index of images") +parser.add_argument('--disc-type', type=str, default='wgan', help='discriminator loss type for image completion') + +# ======================== OpenFace Setings ================================== +parser.add_argument('--model', type=str, default='', help="model path") +parser.add_argument('--splits', type=int, default=1, help="number of splits for computing inception score") + +# ======================== GMM Setings ================================== +parser.add_argument('--num-gaus', type=int, default=2, help='number of Gaussians in GMM') +parser.add_argument('--gmm-dim', type=int, default=1, help='dimensionality of GMM') +parser.add_argument('--num-samples', type=int, default=10000, help='number of GMM data samples to generate') +parser.add_argument('--gmm-range', type=float, default=3.0, help='range of GMM') +parser.add_argument('--gmm-hidden', type=int, default=8, help='dimensionality of hidden layers in GMM model') +parser.add_argument('--gmm-nlayers', type=int, default=3, help='number of layers in GMM model') + +args = parser.parse_args() diff --git a/dataloader.py b/dataloader.py new file mode 100644 index 0000000..026b900 --- /dev/null +++ b/dataloader.py @@ -0,0 +1,349 @@ +# dataloader.py + +import math + +import torch +import datasets +import torchvision +import torch.utils.data +import torchvision.transforms as transforms + +import utils as utils + +class Dataloader: + + def __init__(self, args): + self.args = args + + self.loader_input = args.loader_input + self.loader_label = args.loader_label + + self.split_test = args.split_test + self.split_train = args.split_train + self.dataset_test_name = args.dataset_test + self.dataset_train_name = args.dataset_train + self.resolution = (args.resolution_wide, args.resolution_high) + + self.input_filename_test = args.input_filename_test + self.label_filename_test = args.label_filename_test + self.input_filename_train = args.input_filename_train + self.label_filename_train = args.label_filename_train + + if self.dataset_train_name == 'LSUN': + self.dataset_train = getattr(datasets, self.dataset_train_name)(db_path=args.dataroot, classes=['bedroom_train'], + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'CASIA': + self.dataset_train = datasets.ImageFolder(root=self.args.dataroot, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'RECONSTRUCTION': + self.dataset_train = datasets.RECONSTRUCTION(root=self.args.dataroot, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]), nc=args.nchannels + ) + + elif self.dataset_train_name == 'CELEBA': + self.dataset_train = datasets.ImageFolder(root=self.args.dataroot + "/train", + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.RandomHorizontalFlip(), + # transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.01), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'SSFF': + self.dataset_train = datasets.ImageFolder(root=self.args.dataroot + "/train", + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'CIFAR10' or self.dataset_train_name == 'CIFAR100': + self.dataset_train = getattr(datasets, self.dataset_train_name)(root=self.args.dataroot, train=True, download=True, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.RandomHorizontalFlip(), + transforms.ColorJitter(brightness=0.3, contrast=0.3, saturation=0.2, hue=0.01), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + ]) + ) + + elif self.dataset_train_name == 'GMM': + self.dataset_train = datasets.GMM(args) + + elif self.dataset_train_name == 'GMMRing': + self.dataset_train = datasets.GMM_Ring(args) + + elif self.dataset_train_name == 'CocoCaption' or self.dataset_train_name == 'CocoDetection': + self.dataset_train = getattr(datasets, self.dataset_train_name)(root=self.args.dataroot, train=True, download=True, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'STL10' or self.dataset_train_name == 'SVHN': + self.dataset_train = getattr(datasets, self.dataset_train_name)(root=self.args.dataroot, split='train', download=True, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'MNIST': + self.dataset_train = getattr(datasets, self.dataset_train_name)(root=self.args.dataroot, train=True, download=True, + transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + ) + + elif self.dataset_train_name == 'ImageNet': + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.dataset_train = datasets.ImageFolder(root=self.args.dataroot+self.args.input_filename_train, + transform=transforms.Compose([ + transforms.RandomSizedCrop(224), + transforms.RandomHorizontalFlip(), + transforms.ToTensor(), + normalize, + ]) + ) + + elif self.dataset_train_name == 'FRGC': + self.dataset_train = datasets.ImageFolder(root=self.args.dataroot+self.args.input_filename_train, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'Folder': + self.dataset_train = datasets.ImageFolder(root=self.args.dataroot+self.args.input_filename_train, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_train_name == 'FileList': + self.dataset_train = datasets.FileList(self.input_filename_train, self.label_filename_train, self.split_train, + self.split_test, train=True, + transform_train=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]), + transform_test=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]), + loader_input=self.loader_input, + loader_label=self.loader_label, + ) + + elif self.dataset_train_name == 'FolderList': + self.dataset_train = datasets.FileList(self.input_filename_train, self.label_filename_train, self.split_train, + self.split_test, train=True, + transform_train=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]), + transform_test=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]), + loader_input=self.loader_input, + loader_label=self.loader_label, + ) + + else: + raise(Exception("Unknown Dataset")) + + if self.dataset_test_name == 'LSUN': + self.dataset_test = getattr(datasets, self.dataset_test_name)(db_path=args.dataroot, classes=['bedroom_val'], + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'CIFAR10' or self.dataset_test_name == 'CIFAR100': + self.dataset_test = getattr(datasets, self.dataset_test_name)(root=self.args.dataroot, train=False, download=True, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'CELEBA': + self.dataset_test = datasets.ImageFolder(root=self.args.dataroot + "/test", + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'RECONSTRUCTION': + pass + + elif self.dataset_test_name == 'SSFF': + self.dataset_test = datasets.ImageFolder(root=self.args.dataroot + "/test", + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'CocoCaption' or self.dataset_test_name == 'CocoDetection': + self.dataset_test = getattr(datasets, self.dataset_test_name)(root=self.args.dataroot, train=False, download=True, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'STL10' or self.dataset_test_name == 'SVHN': + self.dataset_test = getattr(datasets, self.dataset_test_name)(root=self.args.dataroot, split='test', download=True, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'MNIST': + self.dataset_test = getattr(datasets, self.dataset_test_name)(root=self.args.dataroot, train=False, download=True, + transform=transforms.Compose([ + transforms.ToTensor(), + transforms.Normalize((0.1307,), (0.3081,)) + ]) + ) + + elif self.dataset_test_name == 'ImageNet': + normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], + std=[0.229, 0.224, 0.225]) + self.dataset_test = datasets.ImageFolder(root=self.args.dataroot+self.args.input_filename_test, + transform=transforms.Compose([ + transforms.Scale(256), + transforms.CenterCrop(224), + transforms.ToTensor(), + normalize, + ]) + ) + + elif self.dataset_test_name == 'FRGC': + self.dataset_test = datasets.ImageFolder(root=self.args.dataroot+self.args.input_filename_test, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'Folder': + self.dataset_test = datasets.ImageFolder(root=self.args.dataroot+self.args.input_filename_test, + transform=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]) + ) + + elif self.dataset_test_name == 'FileList': + self.dataset_test = datasets.FileList(self.input_filename_test, self.label_filename_test, self.split_train, + self.split_test, train=True, + transform_train=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]), + loader_input=self.loader_input, + loader_label=self.loader_label, + ) + + elif self.dataset_test_name == 'FolderList': + self.dataset_test = datasets.FileList(self.input_filename_test, self.label_filename_test, self.split_train, + self.split_test, train=True, + transform_train=transforms.Compose([ + transforms.Scale(self.resolution), + transforms.CenterCrop(self.resolution), + transforms.ToTensor(), + transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)), + ]), + loader_input=self.loader_input, + loader_label=self.loader_label, + ) + elif self.dataset_test_name == 'GMM': + self.dataset_test = datasets.GMM(args) + + elif self.dataset_test_name == 'GMMRing': + self.dataset_test = datasets.GMM_Ring(args) + + elif self.dataset_test_name is None: + pass + + else: + raise(Exception("Unknown Dataset")) + + def create(self, flag=None, shuffle=True): + if flag == "Train": + dataloader_train = torch.utils.data.DataLoader(self.dataset_train, batch_size=self.args.batch_size, + shuffle=shuffle, num_workers=int(self.args.nthreads), pin_memory=True) + return dataloader_train + + if flag == "Test": + dataloader_test = torch.utils.data.DataLoader(self.dataset_test, batch_size=self.args.batch_size, + shuffle=shuffle, num_workers=int(self.args.nthreads), pin_memory=True) + return dataloader_test + + if flag == None: + dataloader_train = torch.utils.data.DataLoader(self.dataset_train, batch_size=self.args.batch_size, + shuffle=shuffle, num_workers=int(self.args.nthreads), pin_memory=True) + + dataloader_test = torch.utils.data.DataLoader(self.dataset_test, batch_size=self.args.batch_size, + shuffle=shuffle, num_workers=int(self.args.nthreads), pin_memory=True) + return dataloader_train, dataloader_test diff --git a/datasets/__init__.py b/datasets/__init__.py new file mode 100644 index 0000000..0cd39ca --- /dev/null +++ b/datasets/__init__.py @@ -0,0 +1,8 @@ +# __init__.py + +from torchvision.datasets import * +from .filelist import FileList +from .folderlist import FolderList +from .gmm import * +from .celeba import CELEBA +from .reconstruction import RECONSTRUCTION diff --git a/datasets/casia.py b/datasets/casia.py new file mode 100644 index 0000000..319ecfd --- /dev/null +++ b/datasets/casia.py @@ -0,0 +1,16 @@ +import torch.utils.data as data +from PIL import Image +import os + +class CASIA(data.Dataset): + def __init__(self, dataroot, transforms=None): + self.dataroot = dataroot + self.transforms = transforms + self.objects = [] + + for _, folder, _ in os.walk(dataroot): + for _, _, files in os.walk("{}/{}".format(dataroot, folder[0])): + for file in files: + image = Image.open("{}/{}/{}".format(dataroot, folder[0], file)) + break + break diff --git a/datasets/celeba.py b/datasets/celeba.py new file mode 100644 index 0000000..a6823d7 --- /dev/null +++ b/datasets/celeba.py @@ -0,0 +1,51 @@ +import numpy as np +import os +import torch +import json +from PIL import Image +import torch.utils.data as data +import pickle +import torchvision.transforms as transforms + + +class CELEBA(data.Dataset): + def __init__(self, root, transform=None): + self.root = root + self.transform = transform + self.objects = [] + self.normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + i = 0 + + for _, folders, _ in os.walk(self.root): + folder = folders[0] + for _, _, files in os.walk("{}/{}".format(self.root, folder)): + for file in files: + with Image.open("{}/{}/{}".format(self.root, folder, file)) as image: + image_trans = transform(image) + image_trans = np.array(image_trans) + im_tensor = torch.from_numpy(image_trans.transpose((2, 0, 1))) + self.objects.append(im_tensor) + i += 1 + print("Image {} added".format(i)) + break + break + + self.length = len(self.objects) + print("Total = {}".format(self.length)) + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the index of the target category. + target contains the cropped objects, camera and vehicle parameters + """ + image = self.objects[index] + image = image.float() / 255 + image = self.normalize(image) + return image, 1 + + def __len__(self): + return self.length diff --git a/datasets/filelist.py b/datasets/filelist.py new file mode 100644 index 0000000..6d3094e --- /dev/null +++ b/datasets/filelist.py @@ -0,0 +1,121 @@ +# filelist.py + +import torch +import numpy as np +import utils as utils +import torch.utils.data as data +from sklearn.utils import shuffle +import datasets.loaders as loaders +import torchvision.transforms as transforms + +class FileList(data.Dataset): + def __init__(self, ifile, lfile=None, split_train=1.0, split_test=0.0, train=True, + transform_train=None, transform_test=None, loader_input=loaders.loader_image, loader_label=loaders.loader_torch): + + self.ifile = ifile + self.lfile = lfile + self.train = train + self.split_test = split_test + self.split_train = split_train + self.transform_test = transform_test + self.transform_train = transform_train + + self.loader_input = loader_input + self.loader_label = loader_label + + if loader_input == 'image': + self.loader_input = loaders.loader_image + if loader_input == 'torch': + self.loader_input = loaders.loader_torch + if loader_input == 'numpy': + self.loader_input = loaders.loader_numpy + + if loader_label == 'image': + self.loader_label = loaders.loader_image + if loader_label == 'torch': + self.loader_label = loaders.loader_torch + if loader_label == 'numpy': + self.loader_label = loaders.loader_numpy + + if ifile != None: + imagelist = utils.readtextfile(ifile) + imagelist = [x.rstrip('\n') for x in imagelist] + else: + imagelist = [] + + if lfile != None: + labellist = utils.readtextfile(lfile) + labellist = [x.rstrip('\n') for x in labellist] + else: + labellist = [] + + if len(imagelist) == len(labellist): + shuffle(imagelist, labellist) + + if len(imagelist) > 0 and len(labellist) == 0: + shuffle(imagelist) + + if len(labellist) > 0 and len(imagelist) == 0: + shuffle(labellist) + + if (self.split_train < 1.0) & (self.split_train > 0.0): + if len(imagelist) > 0: + num = math.floor(self.split*len(imagelist)) + self.images_train = imagelist[0:num] + self.images_test = images[num+1:len(imagelist)] + if len(labellist) > 0: + num = math.floor(self.split*len(labellist)) + self.labels_train = labellist[0:num] + self.labels_test = labellist[num+1:len(labellist)] + + elif self.split_train == 1.0: + if len(imagelist) > 0: + self.images_train = imagelist + if len(labellist) > 0: + self.labels_train = labellist + + elif self.split_test == 1.0: + if len(imagelist) > 0: + self.images_test = imagelist + if len(labellist) > 0: + self.labels_test = labellist + + def __len__(self): + if self.train == True: + return len(self.images_train) + if self.train == False: + return len(self.images_test) + + def __getitem__(self, index): + input = {} + if self.train == True: + if len(self.images_train) > 0: + path = self.images_train[index] + input['inp'] = self.loader_input(path) + + if len(self.labels_train) > 0: + path = self.labels_train[index] + input['tgt'] = self.loader_label(path) + + if self.transform_train is not None: + input = self.transform_train(input) + + image = input['inp'] + label = input['tgt'] + + if self.train == False: + if len(self.images_test) > 0: + path = self.images_test[index] + input['inp'] = self.loader_input(path) + + if len(self.labels_test) > 0: + path = self.labels_test[index] + input['tgt'] = self.loader_label(path) + + if self.transform_test is not None: + input = self.transform_test(input) + + image = input['inp'] + label = input['tgt'] + + return image, label \ No newline at end of file diff --git a/datasets/folderlist.py b/datasets/folderlist.py new file mode 100644 index 0000000..ae90429 --- /dev/null +++ b/datasets/folderlist.py @@ -0,0 +1,140 @@ +# folderlist.py + +import os +import os.path +import utils as utils +import torch.utils.data as data +import datasets.loaders as loaders + +IMG_EXTENSIONS = ['.jpg', '.JPG', '.jpeg', '.JPEG','.png', '.PNG', '.ppm', '.PPM', '.bmp', '.BMP'] + +def is_image_file(filename): + return any(filename.endswith(extension) for extension in IMG_EXTENSIONS) + +def make_dataset(classlist, labellist=None): + images = [] + labels = [] + classes = utils.readtextfile(ifile) + classes = [x.rstrip('\n') for x in classes] + classes.sort() + + for i in len(classes): + for fname in os.listdir(classes[i]): + if is_image_file(fname): + label = {} + label['class'] = os.path.split(classes[i]) + images.append(fname) + labels.append(label) + + if labellist != None: + labels = utils.readtextfile(ifile) + labels = [x.rstrip('\n') for x in labels] + labels.sort() + for i in len(labels): + for fname in os.listdir(labels[i]): + if is_image_file(fname): + labels.append(os.path.split(classes[i])) + + return images, labels + +class FolderList(data.Dataset): + def __init__(self, ifile, lfile=None, split_train=1.0, split_test=0.0, train=True, + transform_train=None, transform_test=None, loader_input=loaders.loader_image, loader_label=loaders.loader_torch): + + imagelist, labellist = make_dataset(ifile, lfile) + if len(imagelist) == 0: + raise(RuntimeError("No images found")) + if len(labellist) == 0: + raise(RuntimeError("No labels found")) + + self.loader_input = loader_input + self.loader_label = loader_label + + if loader_input == 'image': + self.loader_input = loaders.loader_image + if loader_input == 'torch': + self.loader_input = loaders.loader_torch + if loader_input == 'numpy': + self.loader_input = loaders.loader_numpy + + if loader_label == 'image': + self.loader_label = loaders.loader_image + if loader_label == 'torch': + self.loader_label = loaders.loader_torch + if loader_label == 'numpy': + self.loader_label = loaders.loader_numpy + + self.imagelist = imagelist + self.labellist = labellist + self.transform_test = transform_test + self.transform_train = transform_train + + if len(imagelist) == len(labellist): + shuffle(imagelist, labellist) + + if len(imagelist) > 0 and len(labellist) == 0: + shuffle(imagelist) + + if len(labellist) > 0 and len(imagelist) == 0: + shuffle(labellist) + + if (args.split_train < 1.0) & (args.split_train > 0.0): + if len(imagelist) > 0: + num = math.floor(args.split*len(imagelist)) + self.images_train = imagelist[0:num] + self.images_test = images[num+1:len(imagelist)] + if len(labellist) > 0: + num = math.floor(args.split*len(labellist)) + self.labels_train = labellist[0:num] + self.labels_test = labellist[num+1:len(labellist)] + + elif args.split_train == 1.0: + if len(imagelist) > 0: + self.images_train = imagelist + if len(labellist) > 0: + self.labels_train = labellist + + elif args.split_test == 1.0: + if len(imagelist) > 0: + self.images_test = imagelist + if len(labellist) > 0: + self.labels_test = labellist + + def __len__(self): + if self.train == True: + return len(self.images_train) + if self.train == False: + return len(self.images_test) + + def __getitem__(self, index): + if self.train == True: + if len(self.images_train) > 0: + path = self.images_train[index] + input['inp'] = self.loader_input(path) + + if len(self.labels_train) > 0: + path = self.labels_train[index] + input['tgt'] = self.loader_label(path) + + if self.transform_train is not None: + input = self.transform_train(input) + + image = input['inp'] + label = input['tgt'] + + if self.train == False: + if len(self.images_test) > 0: + path = self.images_test[index] + input['inp'] = self.loader_input(path) + + if len(self.labels_test) > 0: + path = self.labels_test[index] + input['tgt'] = self.loader_label(path) + + if self.transform_test is not None: + input = self.transform_test(input) + + image = input['inp'] + label = input['tgt'] + + return image, label \ No newline at end of file diff --git a/datasets/gmm.py b/datasets/gmm.py new file mode 100644 index 0000000..2d3fa4d --- /dev/null +++ b/datasets/gmm.py @@ -0,0 +1,78 @@ +import torch +import numpy as np +import torch.utils.data as data + +class GMM(data.Dataset): + def __init__(self, args): + self.num_gaus = args.num_gaus + self.dim = args.gmm_dim + self.range = args.gmm_range + self.num_samples = args.num_samples + + self.mu = (2*np.random.rand(self.num_gaus, self.dim)-1)*self.range + self.sigma = np.random.rand(self.num_gaus, self.dim)*self.range*0.1 + self.mix = np.random.rand(self.num_gaus) + self.mix = self.mix/np.sum(self.mix) + self.mix = self.mix.tolist() + self.data = self.sample(self.num_samples) + + def sample(self, N): + ind = np.random.choice(self.num_gaus, N, self.mix) + samples = np.zeros((N,self.dim)) + for i in np.arange(N): + samples[i] = np.random.multivariate_normal(self.mu[ind[i]], np.diag(self.sigma[ind[i]]), 1) + return samples + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the index of the target category. + target contains the cropped objects, camera and vehicle parameters + """ + sample = self.data[index] + return sample, 1 + + def __len__(self): + return self.num_samples + +class GMM_Ring(data.Dataset): + def __init__(self, args): + self.dim = args.gmm_dim + self.num_samples = args.num_samples + self.range = args.gmm_range + self.rad = 1/np.sqrt(2) + + self.mu = np.array([[0,-1],[-self.rad,-self.rad],[-1,0],[-self.rad,self.rad], + [0,1],[self.rad,self.rad],[1,0],[self.rad,-self.rad]])*self.range + self.num_gaus = self.mu.shape[0] + self.sigma = np.ones((self.num_gaus, self.dim))*0.1 + self.mix = np.ones(self.num_gaus) + self.mix = self.mix/np.sum(self.mix) + self.mix = self.mix.tolist() + # self.data = self.sample(self.num_samples) + + def sample(self, N): + ind = np.random.choice(self.num_gaus, N, self.mix) + samples = np.zeros((N,self.dim)) + for i in np.arange(N): + samples[i] = np.random.multivariate_normal(self.mu[ind[i]], np.diag(self.sigma[ind[i]]), 1) + return samples + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the index of the target category. + target contains the cropped objects, camera and vehicle parameters + """ + # sample = self.data[index] + sample = self.sample(1)[0] + return sample, 1 + + def __len__(self): + return self.num_samples diff --git a/datasets/gogan.py b/datasets/gogan.py new file mode 100644 index 0000000..7a783fe --- /dev/null +++ b/datasets/gogan.py @@ -0,0 +1,39 @@ +import numpy as np +import os +import torch +import json +from PIL import Image +import torch.utils.data as data +import pickle +import torchvision.transforms as transforms + + +class CELEBA(data.Dataset): + def __init__(self, root, transform=None): + self.root = root + self.transform = transform + self.objects = [] + self.normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)) + + with open("{}/celeba.pkl".format(self.root), 'rb') as f_stream: + self.objects = pickle.load(f_stream) + + self.length = len(self.objects) + print("Total = {}".format(self.length)) + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the index of the target category. + target contains the cropped objects, camera and vehicle parameters + """ + image = self.objects[index] + image = image.float() / 255 + image = self.normalize(image) + return image, 1 + + def __len__(self): + return self.length diff --git a/datasets/loaders.py b/datasets/loaders.py new file mode 100644 index 0000000..61afecb --- /dev/null +++ b/datasets/loaders.py @@ -0,0 +1,14 @@ +# loaders.py + +import torch +import numpy as np +from PIL import Image + +def loader_image(path): + return Image.open(path).convert('RGB') + +def loader_torch(path): + return torch.load(path) + +def loader_numpy(path): + return np.load(path) \ No newline at end of file diff --git a/datasets/reconstruction.py b/datasets/reconstruction.py new file mode 100644 index 0000000..7342c6a --- /dev/null +++ b/datasets/reconstruction.py @@ -0,0 +1,68 @@ +import numpy as np +import os +import torch +import json +from PIL import Image +import torch.utils.data as data +import pickle +import torchvision.transforms as transforms + + +class RECONSTRUCTION(data.Dataset): + def __init__(self, root, transform=None, nc=3): + self.root = root + self.transform = transform + self.objects = [] + + path, dirs, files = os.walk(root).__next__() + # stages = ['stage1', 'stage2', 'stage3', 'wgan'] + stages = ['stage1', 'stage2', 'stage3'] + file_count = int(len(files)/(len(stages)+1)) # 8 for reconstruction + + for file_i in range(file_count): + prefix = files[(len(stages)+1)*file_i].split("_")[0] + im_real = Image.open("{}/{}_Original.png".format(path, prefix)) + im_real_trans = transform(im_real) + im_real.close() + im_stages = [Image.open("{}/{}_Reconstructed_Stage_{}.png".format(path, prefix, k+1)) for k in range(len(stages))] # Reconstructed for reconstruciton + im_stages_trans = [transform(im_stages[k]) for k in range(len(stages))] + im_closed = [im_stages[k].close() for k in range(len(stages))] + if nc == 1: + im_real_trans = togray(im_real_trans) + im_stages_trans = [togray(im_stages_trans[j]) for j in range(len(stages))] + # self.objects.append((im_real_trans, im_stages_trans[0], im_stages_trans[1], im_stages_trans[2], im_stages_trans[3])) + self.objects.append((im_real_trans, im_stages_trans[0], im_stages_trans[1], im_stages_trans[2])) + + + # for i in range(128, file_count+128): + # im_real = Image.open("{}/{}_Original.png".format(path, i)) + # im_real_trans = transform(im_real) + # im_real.close() + # im_stages = [Image.open("{}/{}_Reconstructed_Stage_{}.png".format(path, i, k)) for k in range(1, 5)] # Reconstructed for reconstruciton + # im_stages_trans = [transform(im_stages[k]) for k in range(4)] + # im_closed = [im_stages[k].close() for k in range(4)] + # if nc == 1: + # im_real_trans = togray(im_real_trans) + # im_stages_trans = [togray(im_stages_trans[j]) for j in range(4)] + # self.objects.append((im_real_trans, im_stages_trans[0], im_stages_trans[1], im_stages_trans[2], im_stages_trans[3])) + + self.length = len(self.objects) + print("Total = {}".format(self.length)) + + def __getitem__(self, index): + """ + Args: + index (int): Index + + Returns: + tuple: Tuple (image, target) where target is the index of the target category. + target contains the cropped objects, camera and vehicle parameters + """ + + return self.objects[index] + + def __len__(self): + return min(self.length, 50000) + +def togray(image): + return 0.2989*image[0:1] + 0.5870*image[1:2] + 0.1140*image[2:3] diff --git a/datasets/transforms.py b/datasets/transforms.py new file mode 100644 index 0000000..cc59a22 --- /dev/null +++ b/datasets/transforms.py @@ -0,0 +1,338 @@ +# transforms.py + +from __future__ import division + +import math +import types +import torch +import utils +import random +import numbers +import numpy as np +import scipy as sp +from scipy import misc +from PIL import Image, ImageOps, ImageDraw + +class Compose(object): + """Composes several transforms together. + + Args: + transforms (List[Transform]): list of transforms to compose. + + Example: + >>> transforms.Compose([ + >>> transforms.CenterCrop(10), + >>> transforms.ToTensor(), + >>> ]) + """ + def __init__(self, transforms): + self.transforms = transforms + + def __call__(self, input): + for t in self.transforms: + input = t(input) + + return input + + + +class ToTensor(object): + """Converts a PIL.Image (RGB) or numpy.ndarray (H x W x C) in the range + [0, 255] to a torch.FloatTensor of shape (C x H x W) in the range [0.0, 1.0].""" + + def __call__(self, input): + for key in input.keys(): + value = input[key] + if isinstance(value, np.ndarray): + # handle numpy array + input[key] = torch.from_numpy(value) + else: + # handle PIL Image + tmp = torch.ByteTensor(torch.ByteStorage.from_buffer(value.tobytes())) + value = tmp.view(value.size[1], value.size[0], len(value.mode)) + # put it from HWC to CHW format + # yikes, this transpose takes 80% of the loading time/CPU + value = value.transpose(0, 1).transpose(0, 2).contiguous() + input[key] = value.float().div(255) + return input + + + +class ToPILImage(object): + """Converts a torch.*Tensor of range [0, 1] and shape C x H x W + or numpy ndarray of dtype=uint8, range[0, 255] and shape H x W x C + to a PIL.Image of range [0, 255] + """ + def __call__(self, input): + if isinstance(input['img'], np.ndarray): + # handle numpy array + input['img'] = Image.fromarray(input['img']) + else: + npimg = input['img'].mul(255).byte().numpy() + npimg = np.transpose(npimg, (1,2,0)) + input['img'] = Image.fromarray(npimg) + return input + +class Normalize(object): + """Given mean: (R, G, B) and std: (R, G, B), + will normalize each channel of the torch.*Tensor, i.e. + channel = (channel - mean) / std + """ + def __init__(self, mean, std): + self.mean = mean + self.std = std + + def __call__(self, input): + # TODO: make efficient + for t, m, s in zip(input['img'], self.mean, self.std): + t.sub_(m).div_(s) + return input + +class Scale(object): + """Rescales the input PIL.Image to the given 'size'. + 'size' will be the size of the smaller edge. + For example, if height > width, then image will be + rescaled to (size * height / width, size) + size: size of the smaller edge + interpolation: Default: PIL.Image.BILINEAR + """ + def __init__(self, size, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + + def __call__(self, input): + w, h = input['img'].size + if (w <= h and w == self.size) or (h <= w and h == self.size): + return input + if w < h: + ow = self.size + oh = int(self.size * h / w) + input['img'] = input['img'].resize((ow, oh), self.interpolation) + return input + else: + oh = self.size + ow = int(self.size * w / h) + input['img'] = input['img'].resize((ow, oh), self.interpolation) + return input + + +class CenterCrop(object): + """Crops the given PIL.Image at the center to have a region of + the given size. size can be a tuple (target_height, target_width) + or an integer, in which case the target will be of a square shape (size, size) + """ + def __init__(self, size): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + + def __call__(self, input): + w, h = input['img'].size + th, tw = self.size + x1 = int(round((w - tw) / 2.)) + y1 = int(round((h - th) / 2.)) + input['img'] = input['img'].crop((x1, y1, x1 + tw, y1 + th)) + return input + +class Pad(object): + """Pads the given PIL.Image on all sides with the given "pad" value""" + def __init__(self, padding, fill=0): + assert isinstance(padding, numbers.Number) + assert isinstance(fill, numbers.Number) + self.padding = padding + self.fill = fill + + def __call__(self, input): + input['img'] = ImageOps.expand(input['img'], border=self.padding, fill=self.fill) + return input + +class Lambda(object): + """Applies a lambda as a transform.""" + def __init__(self, lambd): + assert type(lambd) is types.LambdaType + self.lambd = lambd + + def __call__(self, input): + input['img'] = self.lambd(img) + return input + + +class RandomCrop(object): + """Crops the given PIL.Image at a random location to have a region of + the given size. size can be a tuple (target_height, target_width) + or an integer, in which case the target will be of a square shape (size, size) + """ + def __init__(self, size, padding=0): + if isinstance(size, numbers.Number): + self.size = (int(size), int(size)) + else: + self.size = size + self.padding = padding + + def __call__(self, input): + if self.padding > 0: + input['img'] = ImageOps.expand(img, border=self.padding, fill=0) + + w, h = input['img'].size + th, tw = self.size + if w == tw and h == th: + return input + + x1 = random.randint(0, w - tw) + y1 = random.randint(0, h - th) + input['img'] = input['img'].crop((x1, y1, x1 + tw, y1 + th)) + return input + + +class RandomHorizontalFlip(object): + """Randomly horizontally flips the given PIL.Image with a probability of 0.5 + """ + def __call__(self, input): + if random.random() < 0.5: + input['img'] = input['img'].transpose(Image.FLIP_LEFT_RIGHT) + input['tgt'] = input['tgt'].transpose(Image.FLIP_LEFT_RIGHT) + input['loc'][0] = input['loc'][0] - math.ceil(input['img'].size[0]/2) + return input + +class RandomSizedCrop(object): + """Random crop the given PIL.Image to a random size of (0.08 to 1.0) of the original size + and and a random aspect ratio of 3/4 to 4/3 of the original aspect ratio + This is popularly used to train the Inception networks + size: size of the smaller edge + interpolation: Default: PIL.Image.BILINEAR + """ + def __init__(self, size, interpolation=Image.BILINEAR): + self.size = size + self.interpolation = interpolation + + def __call__(self, input): + for attempt in range(10): + area = input['img'].size[0] * input['img'].size[1] + target_area = random.uniform(0.08, 1.0) * area + aspect_ratio = random.uniform(3. / 4, 4. / 3) + + w = int(round(math.sqrt(target_area * aspect_ratio))) + h = int(round(math.sqrt(target_area / aspect_ratio))) + + if random.random() < 0.5: + w, h = h, w + + if w <= input['img'].size[0] and h <= input['img'].size[1]: + x1 = random.randint(0, input['img'].size[0] - w) + y1 = random.randint(0, input['img'].size[1] - h) + + input['img'] = input['img'].crop((x1, y1, x1 + w, y1 + h)) + assert(input['img'].size == (w, h)) + input['img'] = input['img'].resize((self.size, self.size), self.interpolation) + return input + + # Fallback + scale = Scale(self.size, interpolation=self.interpolation) + crop = CenterCrop(self.size) + return crop(scale(input)) + +class NormalizeLandmarks(object): + """ max-min normalization of landmarks to range [-1,1]""" + def __init__(self,xsize,ysize): + self.xsize = xsize + self.ysize = ysize + + def __call__(self, input): + valid_points = [v for v in input['loc'] if v[0] != 0 and v[1] != 0] + mean = np.mean(valid_points,axis = 0) + for i in range(input['loc'].shape[0]): + input['loc'][i][0] = -1 + (input['loc'][i][0] * 2. )/(inputx_res) + input['loc'][i][1] = -1 + (input['loc'][i][1] * 2. )/(inputy_res) + + return input + +class AffineCrop(object): + def __init__(self,nlandmark,ix,iy,ox,oy,rangle=0,rscale=0,rtrans=0,gauss=1): + self.rangle=rangle + self.rscale=rscale + self.rtrans=rtrans + self.nlandmark=nlandmark + self.ix = ix + self.iy = iy + self.ox = ox + self.oy = oy + self.utils = utils + self.gauss = gauss + + def __call__(self, input): + + angle = self.rangle*(2*torch.rand(1)[0] - 1) + grad_angle = angle * math.pi / 180 + scale = 1+self.rscale*(2*torch.rand(1)[0] - 1) + transx = self.rtrans*(2*torch.rand(1)[0] - 1) + transy = self.rtrans*(2*torch.rand(1)[0] - 1) + + img = input['img'] + size = img.size + h, w = size[0], size[1] + centerX, centerY = int(w/2), int(h/2) + + # perform rotation + img = img.rotate(angle, Image.BICUBIC) + # perform translation + img = img.transform(img.size, Image.AFFINE, (1, 0, transx, 0, 1, transy)) + # perform scaling + img = img.resize((int(math.ceil(scale*h)) , int(math.ceil(scale*w))) , Image.ANTIALIAS) + + w, h = img.size + x1 = int(round((w - self.ix) / 2.)) + y1 = int(round((h - self.ix) / 2.)) + input['img'] = img.crop((x1, y1, x1 + self.ix, y1 + self.iy)) + + if (np.sum(input['loc']) != 0): + + occ = input['occ'] + loc = input['loc'] + newloc = np.ones((3,loc.shape[1]+1)) + newloc[0:2,0:loc.shape[1]] = loc + newloc[0,loc.shape[1]] = centerY + newloc[1,loc.shape[1]] = centerX + + trans_matrix = np.array([[1,0,-1*transx],[0,1,-1*transy],[0,0,1]]) + scale_matrix = np.array([[scale,0,0],[0,scale,0],[0,0,1]]) + angle_matrix = np.array([[math.cos(grad_angle),math.sin(grad_angle),0],[-math.sin(grad_angle),math.cos(grad_angle),0],[0,0,1]]) + + # perform rotation + newloc[0,:] = newloc[0,:] - centerY + newloc[1,:] = newloc[1,:] - centerX + newloc = np.dot(angle_matrix, newloc) + newloc[0,:] = newloc[0,:] + centerY + newloc[1,:] = newloc[1,:] + centerX + # perform translation + newloc = np.dot(trans_matrix, newloc) + # perform scaling + newloc = np.dot(scale_matrix, newloc) + + newloc[0,:] = newloc[0,:] - y1 + newloc[1,:] = newloc[1,:] - x1 + input['loc'] = newloc[0:2,:] + + for i in range(input['loc'].shape[1]): + if ~((input['loc'][0,i] == np.nan) & (input['loc'][1,i] == np.nan)): + if ((input['loc'][0,i] < 0) | (input['loc'][0,i] > self.iy) | (input['loc'][1,i] < 0) | (input['loc'][1,i] > self.ix)): + input['loc'][:,i] = np.nan + input['occ'][i] = 0 + + # generate heatmaps + input['tgt'] = np.zeros((self.nlandmark+1, self.ox, self.oy)) + for i in range(self.nlandmark): + if (not np.isnan(input['loc'][:,i][0]) and not np.isnan(input['loc'][:,i][1])): + tmp = self.utils.gaussian(np.array([self.ix,self.iy]),input['loc'][:,i],self.gauss) + scaled_tmp = sp.misc.imresize(tmp, [self.ox, self.oy]) + scaled_tmp = (scaled_tmp - min(scaled_tmp.flatten()) ) / ( max(scaled_tmp.flatten()) - min(scaled_tmp.flatten())) + else: + scaled_tmp = np.zeros([self.ox,self.oy]) + input['tgt'][i] = scaled_tmp + + tmp = self.utils.gaussian(np.array([self.iy,self.ix]),input['loc'][:,-1],4*self.gauss) + scaled_tmp = sp.misc.imresize(tmp, [self.ox, self.oy]) + scaled_tmp = (scaled_tmp - min(scaled_tmp.flatten()) ) / ( max(scaled_tmp.flatten()) - min(scaled_tmp.flatten())) + input['tgt'][self.nlandmark] = scaled_tmp + + return input diff --git a/evaluate/__init__.py b/evaluate/__init__.py new file mode 100644 index 0000000..2e008e8 --- /dev/null +++ b/evaluate/__init__.py @@ -0,0 +1,3 @@ +# __init__.py + +from .classification import Classification \ No newline at end of file diff --git a/evaluate/classification.py b/evaluate/classification.py new file mode 100644 index 0000000..7043cde --- /dev/null +++ b/evaluate/classification.py @@ -0,0 +1,23 @@ +# classification.py + +import torch + +class Classification(): + + def __init__(self, topk=(1,)): + self.topk = topk + + def forward(self, output, target): + """Computes the precision@k for the specified values of k""" + maxk = max(self.topk) + batch_size = target.size(0) + + _, pred = output.topk(maxk, 1, True, True) + pred = pred.t() + correct = pred.eq(target.view(1, -1).expand_as(pred)) + + res = [] + for k in self.topk: + correct_k = correct[:k].view(-1).float().sum(0) + res.append(correct_k.mul_(100.0 / batch_size)) + return res \ No newline at end of file diff --git a/evaluation.py b/evaluation.py new file mode 100644 index 0000000..61efd84 --- /dev/null +++ b/evaluation.py @@ -0,0 +1,472 @@ +from torch.autograd import Variable +# evaluate.py + +import torch +import plugins +import torchvision +import numpy as np +import scipy +import torch.nn as nn +import torch.optim as optim +from torch.autograd import Variable +from skimage.measure import compare_ssim as ssim +from skimage.measure import compare_psnr as psnr +import pyamg +import PIL.Image +from torchvision.utils import save_image +import pytorch_ssim +import warnings +warnings.filterwarnings("ignore") +import time + + +class Evaluate(): + + def __init__(self, args, netD, netG, netE): + self.netD = netD + self.netG = netG + self.netE = netE + for p in self.netD.parameters(): + p.requires_grad = False + for p in self.netG.parameters(): + p.requires_grad = False + + self.args = args + self.nchannels = args.nchannels + self.resolution_high = args.resolution_high + self.resolution_wide = args.resolution_wide + self.nz = args.nz + self.wcom = args.disc_loss_weight + self.ssim_weight = args.ssim_weight + self.cuda = args.cuda + self.citers = args.citers + self.lr = args.learning_rate_vae + self.momentum = args.momentum + self.batch_size = args.batch_size + self.use_encoder = args.use_encoder + self.start_index = args.start_index + + self.input = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide), requires_grad=False).cuda() + self.input_occluded = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide), requires_grad=False).cuda() + self.fake_out = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide), requires_grad=True).cuda() + # self.recon_out = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide)).cuda() + self.noise = torch.FloatTensor(self.batch_size, self.nz, 1, 1).normal_(0, 1).cuda() + self.epsilon = Variable(torch.randn(self.batch_size, self.nz), requires_grad=False).cuda() + self.noise = Variable(self.noise, requires_grad=True) + + self.optimizerC = optim.RMSprop([self.noise], lr=self.lr) + self.scheduler = plugins.AutomaticLRScheduler(self.optimizerC, maxlen=500, factor=0.1, patience=self.args.scheduler_patience) + self.log_eval_loss = plugins.Logger(args.logs, 'CompletionLog.txt') + self.params_eval_loss = ['Image', 'Input_SSIM', 'Input_PSNR', 'SSIM', 'PSNR', 'C_Loss', 'P_Loss'] + self.log_eval_loss.register(self.params_eval_loss) + + # Create the mask + # self.pmask = torch.ones(self.batch_size, self.nchannels, self.resolution_high, self.args.resolution_wide) + # if args.mask_type == 'central': + # self.l = int(self.resolution_high*self.args.scale) + # self.u = int(self.resolution_wide*(1-self.args.scale)) + # if self.l != self.u: + # self.pmask[:, :, 5+self.l:5+self.u, self.l:self.u] = 0.0 + # elif args.mask_type == 'periocular': + # self.pmask[:,:,int(0.4*self.resolution_high):,:] = 0.0 + # self.pmask[:,:,:,:8] = 0.0 + # self.pmask[:,:,:,56:] = 0.0 + # self.nmask = torch.add(-self.pmask, 1) + self.pmask = torch.ones(self.resolution_high, self.args.resolution_wide) + if args.mask_type == 'central': + self.l = int(self.resolution_high*self.args.scale) + self.u = int(self.resolution_wide*(1-self.args.scale)) + if self.l != self.u: + self.pmask[5+self.l:5+self.u, self.l:self.u] = 0.0 + elif args.mask_type == 'periocular': + self.pmask[int(0.4*self.resolution_high):,:] = 0.0 + # self.pmask[int(0.4*self.resolution_high):int(0.9*self.resolution_high),8:56] = 0.0 + if self.args.scale == 0.3: + self.pmask[:,:8] = 0.0 + self.pmask[:,56:] = 0.0 + self.nmask = torch.add(-self.pmask, 1) + self.non_mask_pixels = (self.nmask.view(-1) == 0).nonzero().squeeze() + + # create coefficient matrix + self.num_pixels = self.resolution_high * self.resolution_wide + A = scipy.sparse.identity(self.num_pixels, format='lil') + for y in range(self.resolution_high): + for x in range(self.resolution_wide): + if self.nmask[y,x]: + index = x+y*self.resolution_wide + A[index, index] = 4 + if index+1 < self.num_pixels: + A[index, index+1] = -1 + if index-1 >= 0: + A[index, index-1] = -1 + if index+self.resolution_wide < self.num_pixels: + A[index, index+self.resolution_wide] = -1 + if index-self.resolution_wide >= 0: + A[index, index-self.resolution_wide] = -1 + A = torch.Tensor(A.toarray()) + # self.A = Variable(A) + self.Ainv = Variable(torch.inverse(A)) + + # Construct Poisson Matrix for Blending + P = 4*torch.eye(self.num_pixels) + diag = np.arange(0, self.num_pixels-1) + P[diag, diag+1] = -1 + P[diag+1, diag] = -1 + diag = np.arange(self.resolution_high, self.num_pixels) + P[diag - self.resolution_high, diag] = -1 + P[diag, diag - self.resolution_high] = -1 + self.P = Variable(P) + + self.criterion = nn.L1Loss() + self.ssim_loss = pytorch_ssim.SSIM() + if self.args.cuda == True: + self.pmask = self.pmask.cuda() + self.nmask = self.nmask.cuda() + self.non_mask_pixels = self.non_mask_pixels.cuda() + self.criterion = self.criterion.cuda() + # self.A = self.A.cuda() + self.Ainv = self.Ainv.cuda() + self.P = self.P.cuda() + + self.pmask = Variable(self.pmask) + self.nmask = Variable(self.nmask) + self.blend = args.blend + + self.losses = {} + + self.log_eval_monitor = plugins.Monitor() + self.params_eval_monitor = ['Image', 'Input_SSIM', 'Input_PSNR', 'SSIM', 'PSNR', 'C_Loss', 'P_Loss'] + self.log_eval_monitor.register(self.params_eval_monitor) + + self.print = '[%d/%d] [%d/%d]' + for item in self.params_eval_loss: + if item == 'Image': + self.print = self.print + item + " %d " + else: + self.print = self.print + item + " %.4f " + + # self.visualizer = plugins.Visualizer(port=self.args.port, env=self.args.env, title='Image Completion') + # self.visualizer_dict = { + # 'Recon_SSIM': {'dtype':'scalar', 'vtype': 'plot', 'win': 'ssim'}, + # 'Recon_PSNR': {'dtype':'scalar', 'vtype': 'plot', 'win': 'psnr'}, + # 'Z_Norm': {'dtype':'scalar', 'vtype': 'plot', 'win': 'z_norm'}, + # 'Contextual_Loss': {'dtype':'scalar', 'vtype': 'plot', 'win': 'Contextual_Loss'}, + # 'Perceptual_Loss': {'dtype':'scalar', 'vtype': 'plot', 'win': 'Perceptual_Loss'}, + # # 'SSIM_Loss': {'dtype':'scalar', 'vtype': 'plot', 'win': 'SSIM_Loss'}, + # 'LR': {'dtype':'scalar', 'vtype': 'plot', 'win': 'lr'}, + # 'Original_Image': {'dtype':'images', 'vtype': 'images', 'win': 'input'}, + # 'Occluded_Image': {'dtype':'images', 'vtype': 'images', 'win': 'input_real'}, + # 'Fake_Image': {'dtype':'images', 'vtype': 'images', 'win': 'fake'}, + # 'Completed_Image': {'dtype':'images', 'vtype': 'images', 'win': 'completed'}, + # } + # self.visualizer.register(self.visualizer_dict) + + self.c_loss = 0 + self.d_loss = 0 + self.disc_type = args.disc_type + + def ssim(self, data1, data2): + num = data1.size(0) + nchannels = data1.size(1) + + data1 = data1.transpose(1, 2).transpose(2, 3) + data2 = data2.transpose(1, 2).transpose(2, 3) + + score = 0 + + if nchannels > 1: + for i in range(num): + img1 = data1[i].numpy() + img2 = data2[i].numpy() + + range1 = img1.max() - img1.min() + range2 = img2.max() - img2.min() + range3 = max(range1, range2) + score += ssim(img1, img2, dynamic_range=range3, + multichannel=True) + else: + for i in range(num): + img1 = data1[i].numpy() + img2 = data2[i].numpy() + score += ssim(img1, img2, dynamic_range=self.range) + return score/num + + def psnr(self, data1, data2): + num = data1.size(0) + nchannels = data1.size(1) + + data1 = data1.transpose(1, 2).transpose(2, 3) + data2 = data2.transpose(1, 2).transpose(2, 3) + + score = 0 + + for i in range(num): + img1 = data1[i].numpy() + img2 = data2[i].numpy() + + range1 = img1.max() - img1.min() + range2 = img2.max() - img2.min() + range3 = max(range1, range2) + score += psnr(img1, img2, dynamic_range=range3) + + return score/num + + def loss(self, real, fake, mask): + tmp1 = torch.mul(real, mask) + tmp2 = torch.mul(fake, mask) + loss = self.criterion(tmp2, tmp1) + diff = tmp1 - tmp2 + # L2 + # loss = (0.5 * torch.mul(diff, diff)).mean(0).sum() + # L1 + loss = torch.abs(diff).mean(0).sum() + loss -= self.ssim_weight * self.ssim_loss(tmp1, tmp2) + return loss + + def complete(self, dataloader): + data_iter = iter(dataloader) + + data_i = 0 + while data_i < len(dataloader): + self.optimizerC = optim.RMSprop([self.noise], lr=self.lr) + self.scheduler = plugins.AutomaticLRScheduler(self.optimizerC, maxlen=500, factor=0.1, patience=self.args.scheduler_patience) + + data_real = data_iter.next()[0] + data_i += 1 + if data_i < self.start_index: + continue + + batch_size = data_real.size(0) + self.input.data.resize_(data_real.size()).copy_(data_real) + + self.input_norm = 2*(torch.mul(0.5*(self.input+1), self.pmask))-1 + self.input_occluded = torch.mul(self.input, self.pmask) + + if self.use_encoder: + self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1) + noise_mu, noise_logvar = self.netE(self.input_occluded) + noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5)) + latents = noise_mu + torch.mul(noise_sigma, self.epsilon) + latents = latents.unsqueeze(-1).unsqueeze(-1) + self.noise.data.copy_(latents.data) + else: + self.noise.data.normal_(0, 1) + + # Save original images first + for b in range(batch_size): + save_image(normalize(self.input.data[b].cpu()), "{}/{}_Original.png".format(self.args.save, data_i*batch_size+b), padding=0, normalize=True) + save_image(normalize(self.input_norm.data[b].cpu()), "{}/{}_Occluded.png".format(self.args.save, data_i*batch_size+b), padding=0, normalize=True) + + for j in range(self.citers): + + fake = self.netG.forward(self.noise) + fake_out = fake.clone() + # score = self.netD.forward(self.fake_out) + score = self.netD.forward(fake) + + disc_loss = get_disc_loss(score, type=self.disc_type) + Contextual_Loss = self.loss(self.input_occluded, fake, self.pmask) + + if not self.blend: + recon_out = torch.mul(self.input_norm, self.pmask) + torch.mul(self.fake_out, self.nmask) + else: + recon_out = self.poisson_blending(self.input_norm, fake, self.nmask) + disc_loss += get_disc_loss(self.netD.forward(recon_out), type=self.disc_type) + # ssim_loss = - self.ssim_loss(self.input, fake) + # Contextual_Loss = self.criterion(recon_out, self.input) + # loss_vals = Parallel(n_jobs=3)(delayed(process_stage)(i) for i in zip(self.netG, self.netD, self.fake_out, [self.input_occluded]*3, [self.pmask]*3, self.noise, [self.loss]*3, [self.wcom]*3, self.optimizerC, self.scheduler)) + + if j % 10 == 0 or j == self.citers-1: + + # disc_loss -= self.netD.forward(recon_out).mean(0).view(1) + # ssims = self.ssim(self.input.data.cpu(), recon_out.data.cpu()) + ssims = pytorch_ssim.ssim(self.input, recon_out).data[0] + psnrs = self.psnr(self.input.data.cpu(), recon_out.data.cpu()) + # cl = self.loss(self.input, recon_out, self.pmask[0:batch_size]).data + cl = Contextual_Loss.data + dl = disc_loss.data + # cl = self.c_loss + # dl = self.d_loss + losses = {} + losses['Recon_SSIM'] = ssims + losses['Recon_PSNR'] = psnrs + losses['Z_Norm'] = self.noise.data.norm() + losses['Contextual_Loss'] = cl + losses['Perceptual_Loss'] = dl + # losses['SSIM_Loss'] = ssim_loss.data[0] + losses['LR'] = self.optimizerC.param_groups[0]['lr'] + losses['Fake_Image'] = fake_out.data.cpu() + losses['Completed_Image'] = recon_out.data.cpu() + losses['Original_Image'] = self.input.data.cpu() + losses['Occluded_Image'] = self.input_norm.data.cpu() + # self.visualizer.update(losses) + + self.losses['Image'] = float(data_i) + self.losses['Input_SSIM'] = self.ssim(self.input.data.cpu(), self.input_norm.data.cpu()) + self.losses['Input_PSNR'] = self.psnr(self.input.data.cpu(), self.input_norm.data.cpu()) + self.losses['SSIM'] = ssims + self.losses['PSNR'] = psnrs + self.losses['C_Loss'] = cl + self.losses['P_Loss'] = dl + self.log_eval_monitor.update(self.losses, batch_size, keepsame=True) + print(self.print % tuple([data_i, len(dataloader), j, self.citers] + [self.losses[key] + for key in self.params_eval_monitor])) + loss = self.log_eval_monitor.getvalues() + self.log_eval_loss.update(loss) + + loss = Contextual_Loss + self.wcom*disc_loss + loss.backward() + self.optimizerC.step() + self.scheduler.step(disc_loss.data[0]) + self.optimizerC.zero_grad() + + if j % 250 == 0: + # Save intermediate results + for b in range(batch_size): + save_image(normalize(fake_out.data[b].cpu()), "{}/{}_Fake_Stage_{}.png".format(self.args.save, data_i*batch_size+b, j), padding=0, normalize=True) + save_image(normalize(recon_out.data[b].cpu()), "{}/{}_Reconstructed_Stage_{}.png".format(self.args.save, data_i*batch_size+b, j), padding=0, normalize=True) + + # self.log_eval_image.update([data_real.clone(), self.input_occluded.data, self.recon_out.clone()]) + for b in range(batch_size): + save_image(normalize(fake_out.data[b].cpu()), "{}/{}_Fake_Stage.png".format(self.args.save, data_i*batch_size+b), padding=0, normalize=True) + save_image(normalize(recon_out.data[b].cpu()), "{}/{}_Reconstructed_Stage.png".format(self.args.save, data_i*batch_size+b), padding=0, normalize=True) + + def poisson_blending(self, target, source, mask, offset=(0,0)): + # compute regions to be blended + region_source = ( + max(-offset[0], 0), + max(-offset[1], 0), + min(target.shape[2]-offset[0], source.shape[2]), + min(target.shape[3]-offset[1], source.shape[3])) + region_target = ( + max(offset[0], 0), + max(offset[1], 0), + min(target.shape[2], source.shape[2]+offset[0]), + min(target.shape[3], source.shape[3]+offset[1])) + region_size = (region_source[2]-region_source[0], region_source[3]-region_source[1]) + num_pixels = int(np.prod(region_size)) + bsize = target.size(0) + + t = target[:, :, region_target[0]:region_target[2], region_target[1]:region_target[3]] + s = source[:, :, region_source[0]:region_source[2], region_source[1]:region_source[3]] + # t = t.transpose(0,1).transpose(1,2).contiguous().view(num_pixels, -1) # 4096 x 3 + # s = s.transpose(0,1).transpose(1,2).contiguous().view(num_pixels, -1) # 4096 x 3 + t = t.view(bsize, self.nchannels, -1).transpose(0, 2).contiguous().view(-1, self.nchannels*bsize) # 4096 x 3N + s = s.view(bsize, self.nchannels, -1).transpose(0, 2).contiguous().view(-1, self.nchannels*bsize) # 4096 x 3N + + #create b + b = torch.matmul(self.P, s) # (4096 x 4096) x (4096 x 3N) = (4096 x 3N) + b[self.non_mask_pixels] = t[self.non_mask_pixels] + + # # solve Ax=b + # x = torch.gesv(b, self.A)[0] # 4096 x 3N + x = torch.matmul(self.Ainv, b) # 4096 x 3N + + # assign x to target image + # x = x.transpose(0, 1).view(bsize, self.nchannels, region_size[0], region_size[1]) + x = x.transpose(0, 1).contiguous().view(self.nchannels, bsize, region_size[0], region_size[1]).transpose(0, 1) + # x = x.transpose(0, 1).view(-1, region_size[0], region_size[1]) + # x.data.clamp_(-1, 1) + # target[:, :, region_target[0]:region_target[2], region_target[1]:region_target[3]].data.copy_(x.data) + + return x + + +def blend(img_target, img_source, img_mask, offset=(0, 0)): + # compute regions to be blended + region_source = ( + max(-offset[0], 0), + max(-offset[1], 0), + min(img_target.shape[1]-offset[0], img_source.shape[1]), + min(img_target.shape[2]-offset[1], img_source.shape[2])) + region_target = ( + max(offset[0], 0), + max(offset[1], 0), + min(img_target.shape[1], img_source.shape[1]+offset[0]), + min(img_target.shape[2], img_source.shape[2]+offset[1])) + region_size = (region_source[2]-region_source[0], region_source[3]-region_source[1]) + # region_source = (offset[0], offset[0], offset[1], offset[1]) + # region_target = (offset[0], offset[0], offset[1], offset[1]) + # region_size = (region_source[2]-region_source[0], region_source[3]-region_source[1]) + + # clip and normalize mask image + img_mask = img_mask[region_source[0]:region_source[2], region_source[1]:region_source[3]] + img_mask[img_mask==0] = False + img_mask[img_mask!=False] = True + + # create coefficient matrix + A = scipy.sparse.identity(np.prod(region_size), format='lil') + for y in range(region_size[0]): + for x in range(region_size[1]): + if img_mask[y,x]: + index = x+y*region_size[1] + A[index, index] = 4 + if index+1 < np.prod(region_size): + A[index, index+1] = -1 + if index-1 >= 0: + A[index, index-1] = -1 + if index+region_size[1] < np.prod(region_size): + A[index, index+region_size[1]] = -1 + if index-region_size[1] >= 0: + A[index, index-region_size[1]] = -1 + A = A.tocsr() + + # create poisson matrix for b + P = pyamg.gallery.poisson(img_mask.shape) + + # for each layer (ex. RGB) + for num_layer in range(img_target.shape[0]): + # get subimages + t = img_target[num_layer, region_target[0]:region_target[2],region_target[1]:region_target[3]] + s = img_source[num_layer, region_source[0]:region_source[2], region_source[1]:region_source[3]] + t = t.flatten() + s = s.flatten() + + # create b + b = P * s + for y in range(region_size[0]): + for x in range(region_size[1]): + if not img_mask[y,x]: + index = x+y*region_size[1] + b[index] = t[index] + + # solve Ax = b + x = pyamg.solve(A,b,verb=False,tol=1e-10) + + # assign x to target image + x = np.reshape(x, region_size) + x[x>1] = 1 + x[x<-1] = -1 + x = np.array(x, img_target.dtype) + img_target[num_layer, region_target[0]:region_target[2],region_target[1]:region_target[3]] = x + + return img_target + +def unwrap_self(arg, **kwarg): + return Evaluate.process_stage(*arg, **kwarg) + + +def process_stage(netG, netD, fake, real, mask, noise, loss, wcom, optimizer, scheduler): + fake = netG.forward(noise) + score = netD.forward(fake) + Contextual_Loss = loss(real, fake, mask) + disc_loss = -score.mean(0).view(1) + loss = Contextual_Loss + wcom*disc_loss + loss.backward() + optimizer.step() + scheduler.step(Contextual_Loss.data[0]) + return Contextual_Loss.data[0], disc_loss.data[0] + +def normalize(image): + image = (image - image.min()) + return image / image.max() + +def get_disc_loss(scores, type='wgan'): + if type == 'wgan': + return -scores.mean(0).view(1) + elif type == 'lsgan': + diff = scores - 1 + loss = 0.5 * torch.pow(diff, 2).mean(0).sum() + return loss + else: + raise("Disc loss should be specified") diff --git a/generation.py b/generation.py new file mode 100644 index 0000000..3ea07c4 --- /dev/null +++ b/generation.py @@ -0,0 +1,223 @@ +from torch.autograd import Variable +# evaluate.py + +import torch +import plugins +import torchvision +import numpy as np +import scipy +import torch.nn as nn +import torch.optim as optim +from torch.autograd import Variable +from skimage.measure import compare_ssim as ssim +from skimage.measure import compare_psnr as psnr +import pyamg +import PIL.Image +from torchvision.utils import save_image +from joblib import Parallel, delayed +import warnings +warnings.filterwarnings("ignore") + + +class Generator(): + + def __init__(self, args, netD, netG, netE): + self.netD = netD + self.netG = netG + self.netE = netE + + self.args = args + self.nchannels = args.nchannels + self.resolution_high = args.resolution_high + self.resolution_wide = args.resolution_wide + self.nz = args.nz + self.wcom = args.disc_loss_weight + self.cuda = args.cuda + self.citers = args.citers + self.lr = args.learning_rate_vae + self.momentum = args.momentum + self.batch_size = args.batch_size + self.use_encoder = args.use_encoder + + self.input = Variable(torch.FloatTensor(self.batch_size,self.nchannels,self.resolution_high,self.resolution_wide), volatile=True).cuda() + self.epsilon = Variable(torch.randn(self.batch_size, self.nz), volatile=True).cuda() + self.noise = Variable(torch.FloatTensor(self.batch_size, self.nz, 1, 1).normal_(0, 1), volatile=True) + + if args.cuda: + self.input = self.input.cuda() + self.epsilon = self.epsilon.cuda() + self.noise = self.noise.cuda() + + self.log_eval_loss = plugins.Logger(args.logs, 'Generation.txt') + self.params_eval_loss = ['Image', 'SSIM_1', 'SSIM_2', 'SSIM_3', 'SSIM_4', 'PSNR_1', 'PSNR_2', 'PSNR_3', 'PSNR_4', 'DiscScore_1', 'DiscScore_2', 'DiscScore_3', 'DiscScore_4'] + self.log_eval_loss.register(self.params_eval_loss) + + self.losses = {} + self.log_eval_monitor = plugins.Monitor() + self.params_eval_monitor = ['Image', 'SSIM_1', 'SSIM_2', 'SSIM_3', 'SSIM_4', 'PSNR_1', 'PSNR_2', 'PSNR_3', 'PSNR_4', 'DiscScore_1', 'DiscScore_2', 'DiscScore_3', 'DiscScore_4'] + self.log_eval_monitor.register(self.params_eval_monitor) + + self.print = '[%d/%d] ' + for item in self.params_eval_loss: + self.print = self.print + item + " %.4f " + + def ssim(self, data1, data2): + num = data1.size(0) + nchannels = data1.size(1) + + data1 = data1.transpose(1, 2).transpose(2, 3) + data2 = data2.transpose(1, 2).transpose(2, 3) + + score = 0 + + if nchannels > 1: + for i in range(num): + img1 = data1[i].numpy() + img2 = data2[i].numpy() + + range1 = img1.max() - img1.min() + range2 = img2.max() - img2.min() + range3 = max(range1, range2) + score += ssim(img1, img2, dynamic_range=range3, + multichannel=True) + else: + for i in range(num): + img1 = data1[i].numpy() + img2 = data2[i].numpy() + score += ssim(img1, img2, dynamic_range=self.range) + return score/num + + def psnr(self, data1, data2): + num = data1.size(0) + nchannels = data1.size(1) + + data1 = data1.transpose(1, 2).transpose(2, 3) + data2 = data2.transpose(1, 2).transpose(2, 3) + + score = 0 + + for i in range(num): + img1 = data1[i].numpy() + img2 = data2[i].numpy() + + range1 = img1.max() - img1.min() + range2 = img2.max() - img2.min() + range3 = max(range1, range2) + score += psnr(img1, img2, dynamic_range=range3) + + return score/num + + def generate(self, dataloader): + data_iter = iter(dataloader) + + data_i = 0 + while data_i < len(dataloader): + data_real = data_iter.next()[0] + data_i += 1 + + batch_size = data_real.size(0) + self.input.data.resize_(data_real.size()).copy_(data_real) + + if self.use_encoder: + self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1) + noise_mu, noise_logvar = self.netE(self.input) + noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5)) + latents = noise_mu + torch.mul(noise_sigma, self.epsilon) + latents = latents.unsqueeze(-1).unsqueeze(-1) + self.noise.data.copy_(latents.data) + else: + self.noise.data.normal_(0, 1) + + self.output = [self.netG[i].forward(self.noise) for i in range(4)] + ssims = [self.ssim(self.input.data.cpu(), self.output[i].data.cpu()) for i in range(4)] + psnrs = [self.psnr(self.input.data.cpu(), self.output[i].data.cpu()) for i in range(4)] + disc_scores = [self.netD(self.output[i]).mean(0).data[0] for i in range(4)] + + save_image(normalize(self.input.data[0].cpu()), "{}/{}_Original.png".format(self.args.save, data_i), padding=0, normalize=True) + for k in range(4): + save_image(normalize(self.output[k].data[0].cpu()), "{}/{}_Fake_Stage_{}.png".format(self.args.save, data_i, k+1), padding=0, normalize=True) + + self.losses['Image'] = float(data_i) + for k in range(4): + self.losses['SSIM_{}'.format(k+1)] = ssims[k] + self.losses['PSNR_{}'.format(k+1)] = psnrs[k] + self.losses['DiscScore_{}'.format(k+1)] = disc_scores[k] + self.log_eval_monitor.update(self.losses, batch_size, keepsame=True) + print(self.print % tuple([data_i, len(dataloader)] + [self.losses[key] + for key in self.params_eval_monitor])) + loss = self.log_eval_monitor.getvalues() + self.log_eval_loss.update(loss) + + def generate_one(self, dataloader): + data_iter = iter(dataloader) + + data_i = 0 + while data_i < len(dataloader): + data_real = data_iter.next()[0] + data_i += 1 + + batch_size = data_real.size(0) + self.input.data.resize_(data_real.size()).copy_(data_real) + + if self.use_encoder: + self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1) + noise_mu, noise_logvar = self.netE(self.input) + noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5)) + latents = noise_mu + torch.mul(noise_sigma, self.epsilon) + latents = latents.unsqueeze(-1).unsqueeze(-1) + self.noise.data.copy_(latents.data) + else: + self.noise.data.normal_(0, 1) + + self.output = self.netG.forward(self.noise) + # ssims = [self.ssim(self.input.data.cpu(), self.output[i].data.cpu()) for i in range(4)] + # psnrs = [self.psnr(self.input.data.cpu(), self.output[i].data.cpu()) for i in range(4)] + # disc_scores = [self.netD(self.output[i]).mean(0).data[0] for i in range(4)] + + # save_image(normalize(self.input.data[0].cpu()), "{}/{}_Original.png".format(self.args.save, data_i), padding=0, normalize=True) + # for k in range(4): + save_image(normalize(self.output.data[0].cpu()), "{}/{}_wgan_fake.png".format(self.args.save, data_i), padding=0, normalize=True) + + def interpolate(self, dataloader): + if self.use_encoder: + data_iter = iter(dataloader) + data_real = data_iter.next()[0] + batch_size = data_real.size(0) + + self.input.data.resize_(data_real.size()).copy_(data_real) + self.epsilon.data.resize_(batch_size, self.nz).normal_(0, 1) + noise_mu, noise_logvar = self.netE(self.input) + noise_sigma = torch.exp(torch.mul(noise_logvar, 0.5)) + latents = noise_mu + torch.mul(noise_sigma, self.epsilon) + latents = latents.unsqueeze(-1).unsqueeze(-1) + self.noise.data.copy_(latents.data) + + z1 = self.noise[0:3] + z2 = self.noise[3:6] + + for i in range(8): + z = z1 + (z2-z1)*i/7 + + self.output = [self.netG[i].forward(z) for i in range(4)] + for stage in range(4): + for anchor in range(3): + save_image(normalize(self.output[stage].data[anchor].cpu()), "{}/Montage_{}_Stage_{}_Z_{}.png".format(self.args.save, anchor+1, stage+1, i+1), padding=0, normalize=True) + + + else: + z1 = Variable(torch.FloatTensor(3, self.nz, 1, 1).normal_(0, 1), volatile=True).cuda() + z2 = Variable(torch.FloatTensor(3, self.nz, 1, 1).normal_(0, 1), volatile=True).cuda() + + for i in range(8): + z = z1 + (z2-z1)*i/7 + + self.output = [self.netG[i].forward(z) for i in range(4)] + for stage in range(4): + for anchor in range(3): + save_image(normalize(self.output[stage].data[anchor].cpu()), "{}/Montage_{}_Stage_{}_Z_{}.png".format(self.args.save, anchor+1, stage+1, i+1), padding=0, normalize=True) + + +def normalize(image): + image = (image - image.min()) + return image / image.max() + diff --git a/images/flowchart_red.png b/images/flowchart_red.png new file mode 100644 index 0000000000000000000000000000000000000000..fb7533525293d6c7ec364c658e13b315499997f1 GIT binary patch literal 10991740 zcmZ^Kby!qe`}Q6XP*Mcx6a|!S1SvrmJ6Zhtdje?p20F*_OT$mH!-d}ThY~Tg}jF6!Dep?wlJSnc4(Nf4{~9 zkx&ypV=FqS`JdOCU1l=7?FZrZzc2XxvtL^&gMIFTN9eDw{Pq8%xZ%MVC**yg`M=9F 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