├── datasources ├── datasource.lua ├── get_datasource.py ├── classdatasource.lua ├── thread.lua ├── augment.lua └── ucf101.lua ├── expand.lua ├── upsample.lua ├── README.txt ├── image_error_measures.lua ├── test-frame-prediction-on-ucf-rec_gdl.lua ├── get_model.lua ├── train_iclr_model.lua └── LICENSE /datasources/datasource.lua: -------------------------------------------------------------------------------- 1 | require 'torch' 2 | 3 | require 'datasources.classdatasource' -------------------------------------------------------------------------------- /expand.lua: -------------------------------------------------------------------------------- 1 | require 'nn' 2 | 3 | local ExpandDim, parent = torch.class('nn.ExpandDim', 'nn.Module') 4 | 5 | -- expand dim d (must be 1 in the input) k times 6 | function ExpandDim:__init(d, k) 7 | parent:__init(self) 8 | self.d = d 9 | self.k = k 10 | --self.output = torch.Tensor() 11 | self.gradInput = torch.Tensor() 12 | end 13 | 14 | function ExpandDim:updateOutput(input) 15 | assert(input:size(self.d) == 1) 16 | local dims = input:size():totable() 17 | dims[self.d] = self.k 18 | --self.output:resize(unpack(dims)) 19 | --self.output:copy(input:expand(unpack(dims))) 20 | self.output = input:expand(unpack(dims)) 21 | return self.output 22 | end 23 | 24 | function ExpandDim:updateGradInput(input, gradOutput) 25 | self.gradInput:resizeAs(input) 26 | self.gradInput:sum(gradOutput, self.d) 27 | return self.gradInput 28 | end 29 | -------------------------------------------------------------------------------- /datasources/get_datasource.py: -------------------------------------------------------------------------------- 1 | #!/usr/local/bin 2 | import os 3 | 4 | datafolder = 'ucf101' 5 | 6 | os.system(""" 7 | cd %s 8 | wget http://crcv.ucf.edu/data/UCF101/UCF101.rar 9 | unrar e UCF101.rar 10 | wget http://crcv.ucf.edu/data/UCF101/UCF101TrainTestSplits-RecognitionTask.zip 11 | unzip UCF101TrainTestSplits-RecognitionTask.zip 12 | mv ucfTrainTestlist/* . 13 | rmdir ucfTrainTestlist 14 | """%(datafolder)) 15 | 16 | files = os.listdir(datafolder) 17 | classes = set() 18 | for f in files: 19 | if f.find('.avi') != -1: 20 | cl = f[:f.find('_g')] 21 | assert(cl[:2] == 'v_') 22 | cl = cl[2:] 23 | classes.add(cl) 24 | 25 | for cl in classes: 26 | os.mkdir('%s/%s'%(datafolder, cl)) 27 | 28 | for f in files: 29 | if f.find('.avi') != -1: 30 | cl = f[:f.find('_g')] 31 | assert(cl[:2] == 'v_') 32 | cl = cl[2:] 33 | os.system('mv %s/%s %s/%s'%(datafolder, f, datafolder, cl)) 34 | 35 | # The following line fixes a bug in the dataset. 36 | os.system('mv %s/HandStandPushups %s/HandstandPushups'%(datafolder, datafolder)) 37 | -------------------------------------------------------------------------------- /upsample.lua: -------------------------------------------------------------------------------- 1 | require 'nn' 2 | require 'math' 3 | 4 | local SpatialUpSample, parent = torch.class('nn.SpatialUpSample', 'nn.Module') 5 | 6 | -- for now, assume square input 7 | function SpatialUpSample:__init(inputH, outputH) 8 | parent.__init(self) 9 | self.h = inputH 10 | self.H = outputH 11 | self.M = torch.zeros(self.H, self.h) 12 | local s = self.H / self.h 13 | for k = 1, self.h do 14 | for x = 1, self.H do 15 | local v = math.max(0, 1 - math.abs((x-1) / s - (s-1)/(2*s) - k + 1)) 16 | self.M[x][k] = v 17 | end 18 | end 19 | -- fix the first and last lines: 20 | self.M:cdiv(self.M:sum(2):expandAs(self.M)) 21 | self.output = torch.Tensor() 22 | self.tmp = torch.Tensor() 23 | self.tmp2 = torch.Tensor() 24 | self.gradInput = torch.Tensor() 25 | end 26 | 27 | function SpatialUpSample:updateOutput(input) 28 | assert(input:dim() == 4) 29 | local bsz, nfeature = input:size(1), input:size(2) 30 | local h, w = input:size(3), input:size(4) 31 | assert(h == self.h) 32 | assert(w == self.h) 33 | self.tmp:resize(bsz*nfeature*h, self.H) 34 | self.tmp:mm(input:view(bsz*nfeature*h, w), self.M:t()) 35 | self.tmp = self.tmp:view(bsz*nfeature, h, self.H) 36 | self.tmp2:resize(bsz*nfeature*self.H, h) 37 | self.tmp2:copy(self.tmp:transpose(2, 3)) 38 | self.tmp:resize(bsz*nfeature*self.H, self.H) 39 | self.tmp:mm(self.tmp2, self.M:t()) 40 | self.output:resize(bsz, nfeature, self.H, self.H) 41 | self.output:copy(self.tmp:view(-1, self.H, self.H):transpose(2, 3)) 42 | return self.output 43 | end 44 | 45 | function SpatialUpSample:updateGradInput(input, gradOutput) 46 | local bsz, nfeature = input:size(1), input:size(2) 47 | local h, w = input:size(3), input:size(4) 48 | self.tmp:resize(bsz*nfeature*self.H, self.H) 49 | self.tmp:copy(gradOutput:view(-1, self.H, self.H):transpose(2, 3)) 50 | self.tmp2:resize(bsz*nfeature*self.H, h) 51 | self.tmp2:mm(self.tmp, self.M) 52 | self.tmp2 = self.tmp2:view(bsz*nfeature, self.H, h) 53 | self.tmp:resize(bsz*nfeature*h, self.H) 54 | self.tmp:copy(self.tmp2:transpose(2,3)) 55 | self.gradInput:resize(bsz*nfeature*h, w) 56 | self.gradInput:mm(self.tmp, self.M) 57 | self.gradInput = self.gradInput:view(bsz, nfeature, h, w) 58 | return self.gradInput 59 | end 60 | -------------------------------------------------------------------------------- /README.txt: -------------------------------------------------------------------------------- 1 | July 2016 2 | Authors: Michael Mathieu, Camille Couprie 3 | 4 | Update: due to large files that could not be stored on github, the trained models and dataset may be found at: 5 | http://perso.esiee.fr/~coupriec/MathieuICLR16TestCode.zip 6 | 7 | This repository contains: 8 | 9 | - Test code for the ICLR 2016 paper: 10 | [1] Michael Mathieu, Camille Couprie, Yann LeCun: 11 | "Deep multi-scale video prediction beyond mean square error". 12 | http://arxiv.org/abs/1511.05440 13 | http://cs.nyu.edu/~mathieu/iclr2016.html 14 | 15 | - Two trained models (using adversarial+l2norm training or 16 | adversarial+l1norm+gdl training). 17 | 18 | - A subset of the UCF101 test dataset [2] with optical flow results to perform 19 | an evaluation in moving area as described in [1]. 20 | 21 | - A training script for the model. Because the Sports1m dataset is hard to get, 22 | we cannot provide an easy script to train on it. Instead, we propose a script 23 | to train on UCF101, which is significantly smaller. 24 | 25 | Main files: 26 | - For testing: test-frame-prediction-on-ucf-rec_gdl.lua 27 | Script to test 2 trained models to predict future frames in video from 4 28 | previous ones on a subset of the UCF101 test dataset. 29 | 30 | - For training: - For training: train_iclr_model.lua 31 | Script to train a model from scratch on the UCF101 dataset. If you want to 32 | train on the Sports1m dataset, you will need to download it and write a 33 | datareader, similar to datasources/ucf101.lua . 34 | 35 | Usage: 36 | 37 | 1- Install torch and the packages (standard packages + nngraph, cudnn.torch, gfx.js) 38 | 39 | For testing: 40 | 2- Uncompress the provided archives. 41 | 3- Run the main script : 42 | th test-frame-prediction-on-ucf-rec_gdl.lua 43 | 44 | It generates results (2 predicted images + animated gifs) 45 | in a directory named 'AdvGDL'. 46 | It also display the average PSNR and SSIM of the 2 first predicted frames 47 | following the evaluation presented in [1]. 48 | 49 | For training: 50 | 2- Get the UCF101 dataset (requires unrar, modify the script if you have another .rar extractor): 51 | cd datasources 52 | python get_datasource.py 53 | 3- Get thffpmeg from https://github.com/MichaelMathieu/THFFmpeg 54 | 4- Run the training script: 55 | th train_iclr_model.lua 56 | 5- For visualizing the intermediate results, start the gfx.js server 57 | th -lgfx.start 58 | And go to http://localhost:8000 in your internet browser. 59 | 60 | [2]:Khurram Soomro, Amir Roshan Zamir and Mubarak Shah, 61 | UCF101: A Dataset of 101 Human Action Classes From Videos in The Wild., 62 | CRCV-TR-12-01, November, 2012. 63 | 64 | 65 | 66 | 67 | -------------------------------------------------------------------------------- /datasources/classdatasource.lua: -------------------------------------------------------------------------------- 1 | local ClassDatasource = torch.class('ClassDatasource') 2 | 3 | function ClassDatasource:__init() 4 | self.tensortype = torch.getdefaulttensortype() 5 | self.output_cpu, self.labels_cpu = torch.Tensor(), torch.LongTensor() 6 | end 7 | 8 | function ClassDatasource:center(trainset, sets) 9 | -- unused, TODO move 10 | error("shouldn't be used for now") 11 | if trainset:dim() == 3 then 12 | local mean = trainset:mean() 13 | local std = trainset:std() 14 | for _, set in pairs(sets) do 15 | set:add(-mean):div(std) 16 | end 17 | else 18 | assert(trainset:dim() == 4) 19 | for iChannel = 1, trainset:size(2) do 20 | local mean = trainset[{{},iChannel}]:mean() 21 | local std = trainset[{{},iChannel}]:std() 22 | for _, set in pairs(sets) do 23 | set[{{},iChannel}]:add(-mean):div(std) 24 | end 25 | end 26 | end 27 | end 28 | 29 | function ClassDatasource:normalize(trainset, sets, fullset) 30 | local function getminmax(set) 31 | if fullset or set:size(1) < 100 then 32 | return set:min(), set:max() 33 | else 34 | set = set[{{1,100}}]--:contiguous() 35 | return set:min(), set:max() 36 | end 37 | end 38 | -- scales the data between -1 and 1 39 | if trainset:dim() == 3 then 40 | -- grayscale 41 | local mini, maxi = getminmax(trainset) 42 | for _, set in pairs(sets) do 43 | set:add(-mini):mul(2/(maxi-mini)):add(-1) 44 | end 45 | else 46 | -- rgb (or multichannel) 47 | assert(trainset:dim() == 4) 48 | for iChannel = 1, trainset:size(2) do 49 | local mini, maxi = getminmax(trainset[{{},iChannel}]) 50 | for _, set in pairs(sets) do 51 | set[{{},iChannel}]:add(-mini):mul(2/(maxi-mini)):add(-1) 52 | end 53 | end 54 | end 55 | end 56 | 57 | function ClassDatasource:typeResults(output, labels) 58 | if self.tensortype == 'torch.CudaTensor' then 59 | self.output_gpu:resize(output:size()):copy(output) 60 | self.labels_gpu:resize(labels:size()):copy(labels) 61 | return self.output_gpu, self.labels_gpu 62 | else 63 | return output, labels 64 | end 65 | end 66 | 67 | function ClassDatasource:type(typ) 68 | self.tensortype = typ 69 | if typ == 'torch.CudaTensor' then 70 | self.output_gpu = torch.CudaTensor() 71 | self.labels_gpu = torch.CudaTensor() 72 | else 73 | self.output_cpu = self.output_cpu:type(typ) 74 | self.output_gpu = nil 75 | self.labels_gpu = nil 76 | collectgarbage() 77 | end 78 | end 79 | 80 | function ClassDatasource:cuda() 81 | self:type('torch.CudaTensor') 82 | end 83 | 84 | function ClassDatasource:float() 85 | self:type('torch.FloatTensor') 86 | end 87 | 88 | function ClassDatasource:double() 89 | self:type('torch.DoubleTensor') 90 | end -------------------------------------------------------------------------------- /datasources/thread.lua: -------------------------------------------------------------------------------- 1 | --[[ 2 | Note that it costs time to switch from set (train/test/valid) 3 | and change the batch size. If you intend to do it a lot, create 4 | multiple instances of datasources, with constant set/batchSize 5 | params: 6 | nDonkeys [4] 7 | --]] 8 | 9 | require 'datasources.datasource' 10 | local threads = require 'threads' 11 | 12 | local ThreadedDatasource, parent = torch.class('ThreadedDatasource', 'ClassDatasource') 13 | 14 | function ThreadedDatasource:__init(getDatasourceFun, params) 15 | parent.__init(self) 16 | self.nDonkeys = params.nDonkeys or 4 17 | --threads.Threads.serialization('threads.sharedserialize') --TODO 18 | self.donkeys = threads.Threads(self.nDonkeys, 19 | function(threadid) 20 | require 'torch' 21 | require 'math' 22 | require 'os' 23 | torch.manualSeed(threadid*os.clock()) 24 | math.randomseed(threadid*os.clock()*1.7) 25 | torch.setnumthreads(1) 26 | threadid_t = threadid 27 | datasource_t = getDatasourceFun() 28 | end) 29 | self.donkeys:addjob( 30 | function() 31 | return datasource_t.nChannels, datasource_t.nClasses, datasource_t.h, datasource_t.w 32 | end, 33 | function(nChannels, nClasses, h, w) 34 | self.nChannels, self.nClasses = nChannels, nClasses 35 | self.h, self.w = h, w 36 | end) 37 | self.donkeys:synchronize() 38 | self.started = false 39 | self.output, self.labels = self.output_cpu, self.labels_cpu 40 | 41 | -- TODO? does that overrides the parent __gc?: 42 | if newproxy then 43 | --lua <= 5.1 44 | self.__gc__ = newproxy(true) 45 | getmetatable(self.__gc__).__gc = 46 | function() self.output = nil end 47 | else 48 | self.__gc = function() self.output = nil end 49 | end 50 | end 51 | 52 | function ThreadedDatasource:type(typ) 53 | parent.type(self, typ) 54 | if typ == 'torch.CudaTensor' then 55 | self.output, self.labels = self.output_gpu, self.labels_gpu 56 | else 57 | self.output, self.labels = self.output_cpu, self.labels_cpu 58 | end 59 | end 60 | 61 | function ThreadedDatasource:nextBatch(batchSize, set) 62 | assert(batchSize ~= nil, 'nextBatch: must specify batchSize') 63 | assert(set ~= nil, 'nextBatch: must specify set') 64 | local function addjob() 65 | self.donkeys:addjob( 66 | function() 67 | collectgarbage() 68 | local batch, labels = datasource_t:nextBatch(batchSize, set) 69 | return batch, labels 70 | end, 71 | function(outputs, labels) 72 | if self.output ~= nil then 73 | self.output:resize(outputs:size()):copy(outputs) 74 | self.labels:resize(labels:size()):copy(labels) 75 | self.last_config = {batchSize, set} 76 | end 77 | end) 78 | end 79 | if not self.started then 80 | self.donkeys:synchronize() 81 | self.donkeys:specific(false) 82 | for i = 1, self.nDonkeys do 83 | if self.donkeys:acceptsjob() then 84 | addjob() 85 | end 86 | end 87 | self.started = true 88 | end 89 | 90 | if self.donkeys:haserror() then 91 | print("ThreadedDatasource: There is an error in a donkey") 92 | self.donkeys:terminate() 93 | os.exit(0) 94 | end 95 | 96 | self.last_config = {} 97 | while (self.last_config[1] ~= batchSize) or (self.last_config[2] ~= set) do 98 | addjob() 99 | self.donkeys:dojob() 100 | end 101 | return self.output, self.labels 102 | end 103 | 104 | function ThreadedDatasource:orderedIterator(batchSize, set) 105 | -- this one doesn't parallelize on more than one thread 106 | -- (this might be a TODO but seems hard) 107 | assert(batchSize ~= nil, 'nextBatch: must specify batchSize') 108 | assert(set ~= nil, 'nextBatch: must specify set') 109 | self.donkeys:synchronize() 110 | self.donkeys:specific(true) 111 | self.started = false 112 | self.donkeys:addjob( 113 | 1, function() 114 | collectgarbage() 115 | it_t = datasource_t:orderedIterator(batchSize, set) 116 | end) 117 | local finished = false 118 | local function addjob() 119 | self.donkeys:addjob( 120 | 1, 121 | function() 122 | return it_t() 123 | end, 124 | function(output, labels) 125 | if output == nil then 126 | finished = true 127 | else 128 | if self.output ~= nil then --TODO: why is the line useful? 129 | self.output:resize(output:size()):copy(output) 130 | self.labels:resize(labels:size()):copy(labels) 131 | end 132 | end 133 | end) 134 | end 135 | return function() 136 | self.donkeys:synchronize() 137 | if finished then 138 | self.donkeys:addjob(1, function() it_t = nil collectgarbage() end) 139 | self.donkeys:synchronize() 140 | else 141 | addjob() 142 | return self.output, self.labels 143 | end 144 | end 145 | end -------------------------------------------------------------------------------- /image_error_measures.lua: -------------------------------------------------------------------------------- 1 | 2 | local iscuda=... 3 | 4 | -- useful to fast image gradient computation 5 | dy = nn.Sequential() 6 | dy:add(nn.SpatialZeroPadding(0,0,1, -1)) 7 | 8 | 9 | dx = nn.Sequential() 10 | dx:add(nn.SpatialZeroPadding(1, -1, 0, 0)) 11 | 12 | if iscuda==true then 13 | dy:cuda() 14 | dx:cuda() 15 | end 16 | 17 | 18 | -------------------------------------------------------------------------------- 19 | -- Calcul du PSNR entre 2 images 20 | function PSNR(true_frame, pred) 21 | 22 | local eps = 0.0001 23 | -- if true_frame:size(1) == 1 then true_frame = true_frame[1] end 24 | -- if pred:size(1) == 1 then pred = pred[1] end 25 | 26 | local prediction_error = 0 27 | for i = 1, pred:size(2) do 28 | for j = 1, pred:size(3) do 29 | for c = 1, pred:size(1) do 30 | -- put image from -1 to 1 to 0 and 255 31 | prediction_error = prediction_error + 32 | (pred[c][i][j] - true_frame[c][i][j])^2 33 | end 34 | end 35 | end 36 | --MSE 37 | prediction_error=128*128*prediction_error/(pred:size(1)*pred:size(2)*pred:size(3)) 38 | 39 | --PSNR 40 | if prediction_error>eps then 41 | prediction_error = 10*torch.log((255*255)/ prediction_error)/torch.log(10) 42 | else 43 | prediction_error = 10*torch.log((255*255)/ eps)/torch.log(10) 44 | end 45 | return prediction_error 46 | end 47 | 48 | -------------------------------------------------------------------------------- 49 | -- Calcul du SSIM 50 | function SSIM(img1, img2) 51 | --[[ 52 | %This is an implementation of the algorithm for calculating the 53 | %Structural SIMilarity (SSIM) index between two images. Please refer 54 | %to the following paper: 55 | % 56 | %Z. Wang, A. C. Bovik, H. R. Sheikh, and E. P. Simoncelli, "Image 57 | %quality assessment: From error visibility to structural similarity" 58 | %IEEE Transactios on Image Processing, vol. 13, no. 4, pp.600-612, 59 | %Apr. 2004. 60 | % 61 | 62 | %Input : (1) img1: the first image being compared 63 | % (2) img2: the second image being compared 64 | % (3) K: constants in the SSIM index formula (see the above 65 | % reference). defualt value: K = [0.01 0.03] 66 | % (4) window: local window for statistics (see the above 67 | % reference). default widnow is Gaussian given by 68 | % window = fspecial('gaussian', 11, 1.5); 69 | % (5) L: dynamic range of the images. default: L = 255 70 | % 71 | %Output: mssim: the mean SSIM index value between 2 images. 72 | % If one of the images being compared is regarded as 73 | % perfect quality, then mssim can be considered as the 74 | % quality measure of the other image. 75 | % If img1 = img2, then mssim = 1.]] 76 | 77 | 78 | if img1:size(1) > 2 then 79 | img1 = image.rgb2y(img1) 80 | img1 = img1[1] 81 | img2 = image.rgb2y(img2) 82 | img2 = img2[1] 83 | end 84 | 85 | 86 | 87 | -- place images between 0 and 255. 88 | img1:add(1):div(2):mul(255) 89 | img2:add(1):div(2):mul(255) 90 | 91 | local K1 = 0.01; 92 | local K2 = 0.03; 93 | local L = 255; 94 | 95 | local C1 = (K1*L)^2; 96 | local C2 = (K2*L)^2; 97 | local window = image.gaussian(11, 1.5/11,0.0708); 98 | 99 | local window = window:div(torch.sum(window)); 100 | 101 | local mu1 = image.convolve(img1, window, 'full') 102 | local mu2 = image.convolve(img2, window, 'full') 103 | 104 | local mu1_sq = torch.cmul(mu1,mu1); 105 | local mu2_sq = torch.cmul(mu2,mu2); 106 | local mu1_mu2 = torch.cmul(mu1,mu2); 107 | 108 | local sigma1_sq = image.convolve(torch.cmul(img1,img1),window,'full')-mu1_sq 109 | local sigma2_sq = image.convolve(torch.cmul(img2,img2),window,'full')-mu2_sq 110 | local sigma12 = image.convolve(torch.cmul(img1,img2),window,'full')-mu1_mu2 111 | 112 | local ssim_map = torch.cdiv( torch.cmul((mu1_mu2*2 + C1),(sigma12*2 + C2)) , 113 | torch.cmul((mu1_sq + mu2_sq + C1),(sigma1_sq + sigma2_sq + C2))); 114 | local mssim = torch.mean(ssim_map); 115 | return mssim 116 | end 117 | 118 | 119 | 120 | ------------------------------------------------------------------------------ 121 | -- image sharpeness difference measure 122 | 123 | function computel1difference(img_pred, img_true ) 124 | s = img_true:size() 125 | 126 | if img_pred:size(1)==2 then 127 | img_pred = img_pred[{{1},{},{}}] 128 | end 129 | 130 | local eps = 0.0001 131 | local diff_gradients = torch.abs( 132 | torch.abs(dx:forward(img_pred)-img_pred)[{{},{2,s[2]-1},{2,s[3]-1}}] - 133 | torch.abs(dx:forward(img_true)-img_true)[{{},{2,s[2]-1},{2,s[3]-1}}]) + 134 | torch.abs( 135 | torch.abs(dy:forward(img_pred)-img_pred)[{{},{2,s[2]-1},{2,s[3]-1}}] - 136 | torch.abs(dy:forward(img_true)-img_true)[{{},{2,s[2]-1},{2,s[3]-1}}]) 137 | local prediction_error = torch.sum(diff_gradients) 138 | 139 | -- Mean 140 | prediction_error=128*128*prediction_error/(s[1]*s[2]*s[3]) 141 | 142 | if prediction_error>eps then 143 | prediction_error = 10*torch.log((255*255)/ prediction_error)/torch.log(10) 144 | else 145 | prediction_error = 10*torch.log((255*255)/ eps)/torch.log(10) 146 | end 147 | 148 | return prediction_error 149 | end 150 | 151 | 152 | 153 | -------------------------------------------------------------------------------- /datasources/augment.lua: -------------------------------------------------------------------------------- 1 | require 'datasources.datasource' 2 | require 'paths' 3 | require 'image' 4 | require 'math' 5 | 6 | local function round(x) 7 | return math.floor(x+0.5) 8 | end 9 | 10 | local AugmentDatasource, parent = torch.class('AugmentDatasource', 'ClassDatasource') 11 | 12 | function AugmentDatasource:__init(datasource, params) 13 | parent.__init(self) 14 | self.datasource = datasource 15 | self.nChannels, self.nClasses = datasource.nChannels, datasource.nClasses 16 | if params.crop then 17 | assert(#(params.crop) == 2) 18 | self.h, self.w = params.crop[1], params.crop[2] 19 | else 20 | self.h, self.w = datasource.h, datasource.w 21 | end 22 | 23 | if self.datasource.tensortype == 'torch.CudaTensor' then 24 | print("Warning: AugmentDatasource used with a cuda datasource. Might break") 25 | end 26 | 27 | self.params = { 28 | flip = params.flip or 0, --1 for vflip, 2 for hflip, 3 for both 29 | crop = params.crop or {self.h, self.w}, 30 | scaleup = params.scaleup or 1, 31 | rotate = params.rotate or 0, 32 | cropMinimumMotion = params.cropMinimumMotion or nil, 33 | cropMinimumMotionNTries = params.cropMinimumMotionNTries or 25, 34 | } 35 | end 36 | 37 | local function flatten3d(x) 38 | -- if x is a video, flatten it 39 | if x:dim() == 4 then 40 | return x:view(x:size(1)*x:size(2), x:size(3), x:size(4)) 41 | else 42 | assert(x:dim() == 3) 43 | return x 44 | end 45 | end 46 | 47 | local function dimxy(x) 48 | assert((x:dim() == 3) or (x:dim() == 4)) 49 | if x:dim() == 4 then 50 | return 3, 4 51 | else 52 | return 2, 3 53 | end 54 | end 55 | 56 | local flip_out1, flip_out2 = torch.Tensor(), torch.Tensor() 57 | local function flip(patch, mode) 58 | local out = patch 59 | if (mode == 1) or (mode == 3) then 60 | if torch.bernoulli(0.5) == 1 then 61 | flip_out1:typeAs(out):resizeAs(out) 62 | image.vflip(flatten3d(flip_out1), flatten3d(out)) 63 | out = flip_out1 64 | end 65 | end 66 | if (mode == 2) or (mode == 3) then 67 | if torch.bernoulli(0.5) == 1 then 68 | flip_out2:typeAs(out):resizeAs(out) 69 | image.hflip(flatten3d(flip_out2), flatten3d(out)) 70 | out = flip_out2 71 | end 72 | end 73 | return out 74 | end 75 | 76 | local function crop(patch, hTarget, wTarget, minMotion, minMotionNTries) 77 | local dimy, dimx = dimxy(patch) 78 | local h, w = patch:size(dimy), patch:size(dimx) 79 | assert((h >= hTarget) and (w >= wTarget)) 80 | if (h == hTarget) and (w == wTarget) then 81 | return patch 82 | else 83 | if minMotion then 84 | assert(patch:dim() == 4) 85 | local x, y 86 | for i = 1, minMotionNTries do 87 | y = torch.random(1, h-hTarget+1) 88 | x = torch.random(1, w-wTarget+1) 89 | local cropped = patch:narrow(dimy, y, hTarget):narrow(dimx, x, wTarget) 90 | if (cropped[-1] - cropped[-2]):norm() > math.sqrt(minMotion * cropped[-1]:nElement()) then 91 | break 92 | end 93 | end 94 | return patch:narrow(dimy, y, hTarget):narrow(dimx, x, wTarget) 95 | else 96 | local y = torch.random(1, h-hTarget+1) 97 | local x = torch.random(1, w-wTarget+1) 98 | return patch:narrow(dimy, y, hTarget):narrow(dimx, x, wTarget) 99 | end 100 | end 101 | end 102 | 103 | local scaleup_out = torch.Tensor() 104 | local function scaleup(patch, maxscale, mode) 105 | mode = mode or 'bilinear' 106 | local dimy, dimx = dimxy(patch) 107 | assert(maxscale >= 1) 108 | local h, w = patch:size(dimy), patch:size(dimx) 109 | local maxH, maxW = round(h*maxscale), round(w*maxscale) 110 | if (maxH == h) and (maxW == w) then 111 | return patch 112 | else 113 | local scaleH = torch.random(h, maxH) 114 | local scaleW = torch.random(w, maxW) 115 | if patch:dim() == 3 then 116 | scaleup_out:typeAs(patch):resize(patch:size(1), scaleH, scaleW) 117 | else 118 | scaleup_out:typeAs(patch):resize(patch:size(1), patch:size(2), scaleH, scaleW) 119 | end 120 | return image.scale(flatten3d(scaleup_out), flatten3d(patch), mode) 121 | end 122 | end 123 | 124 | local rotate_out = torch.Tensor() 125 | local function rotate(patch, thetamax, mode) 126 | mode = mode or 'bilinear' 127 | assert(thetamax >= 0) 128 | if thetamax == 0 then 129 | return patch 130 | else 131 | local theta = torch.uniform(-thetamax, thetamax) 132 | rotate_out:typeAs(patch):resizeAs(patch) 133 | return image.rotate(flatten3d(rotate_out), flatten3d(patch), theta, mode) 134 | end 135 | end 136 | 137 | local input2_out = torch.Tensor() 138 | function AugmentDatasource:nextBatch(batchSize, set) 139 | local input, target = self.datasource:nextBatch(batchSize, set) 140 | if input:dim() == 4 then 141 | input2_out:resize(batchSize, input:size(2), 142 | self.params.crop[1], self.params.crop[2]) 143 | else 144 | input2_out:resize(batchSize, input:size(2), input:size(3), 145 | self.params.crop[1], self.params.crop[2]) 146 | end 147 | for i = 1, batchSize do 148 | local x = input[i] 149 | x = flip(x, self.params.flip) 150 | x = rotate(x, self.params.rotate) 151 | x = scaleup(x, self.params.scaleup) 152 | x = crop(x, self.params.crop[1], self.params.crop[2], 153 | self.params.cropMinimumMotion, self.params.cropMinimumMotionNTries) 154 | input2_out[i]:copy(x) 155 | end 156 | return self:typeResults(input2_out, target) 157 | end 158 | 159 | --This has NO data augmentation (you can't iterate over augmented data, it's infinite) 160 | function AugmentDatasource:orderedIterator(batchSize, set) 161 | local it = self.datasource:orderedIterator(batchSize, set) 162 | return function() 163 | local input, label = it() 164 | if input ~= nil then 165 | return self:typeResults(input, label) 166 | else 167 | return nil 168 | end 169 | end 170 | end 171 | -------------------------------------------------------------------------------- /datasources/ucf101.lua: -------------------------------------------------------------------------------- 1 | --[[ 2 | params: 3 | nInputFrames 4 | minimumMotion [nil] 5 | --]] 6 | 7 | require 'torch' 8 | require 'io' 9 | require 'paths' 10 | require 'thffmpeg' 11 | require 'math' 12 | require 'datasources.datasource' 13 | 14 | local UCF101Datasource, parent = torch.class('UCF101Datasource', 'ClassDatasource') 15 | 16 | function UCF101Datasource:__init(params) 17 | parent.__init(self) 18 | assert(params.nInputFrames ~= nil, "UCF101Dataset: must specify nInputFrames") 19 | self.datapath = params.datapath or 'datasources/ucf101/' 20 | local setfiles = {train = 'trainlist01.txt', test = 'testlist01.txt'} 21 | assert(paths.dirp(self.datapath), 'Path ' .. self.datapath .. ' does not exist') 22 | local classes = paths.dir(self.datapath) 23 | self.classes = {} 24 | self.sets = {train = {}, test = {}} 25 | for _, set in pairs{'train', 'test'} do 26 | local f = io.open(paths.concat(self.datapath, setfiles[set]), 'r') 27 | assert(f ~= nil, 'File ' .. paths.concat(self.datapath, setfiles[set]) .. ' not found.') 28 | for line in f:lines() do 29 | if string.byte(line:sub(-1,-1)) == 13 then 30 | --remove the windows carriage return 31 | line = line:sub(1,-2) 32 | end 33 | local filename, class 34 | if set == 'train' then 35 | filename = line:sub(1, line:find(' ')-1) 36 | classidx = tonumber(line:sub(line:find(' ')+1, -1)) 37 | class = filename:sub(1, filename:find('/')-1) 38 | self.classes[classidx] = class 39 | else 40 | filename = line 41 | class = filename:sub(1, filename:find('/')-1) 42 | end 43 | local avifile = filename:sub(filename:find('/')+1,-1) 44 | if self.sets[set][class] == nil then 45 | self.sets[set][class] = {} 46 | end 47 | table.insert(self.sets[set][class], avifile) 48 | end 49 | f:close() 50 | local n = 0 51 | for _, _ in pairs(self.sets[set]) do 52 | n = n + 1 53 | end 54 | assert(n == 101) 55 | end 56 | self.nbframes = {} 57 | assert(#self.classes == 101) 58 | self.nInputFrames = params.nInputFrames 59 | self.minimumMotion = params.minimumMotion 60 | assert((self.minimumMotion == nil) or (self.minimumMotion > 0)) 61 | self.nChannels, self.nClasses = 3, 101 62 | self.h, self.w = 240, 320 63 | self.thffmpeg = THFFmpeg() 64 | end 65 | 66 | function UCF101Datasource:testEnoughMotion(frame1, frame2) 67 | if self.minimumMotion == nil then 68 | return true 69 | else 70 | return (frame1 - frame2):norm() > math.sqrt(self.minimumMotion * frame1:nElement()) 71 | end 72 | end 73 | 74 | function UCF101Datasource:nextBatch(batchSize, set) 75 | assert(batchSize ~= nil, 'nextBatch: must specify batchSize') 76 | assert(self.sets[set] ~= nil, 'Unknown set ' .. set) 77 | self.output_cpu:resize(batchSize, self.nInputFrames, self.nChannels, self.h, self.w) 78 | self.labels_cpu:resize(batchSize) 79 | for i = 1, batchSize do 80 | local done = false 81 | while not done do 82 | local iclass = torch.random(self.nClasses) 83 | local class = self.classes[iclass] 84 | local idx = torch.random(#self.sets[set][class]) 85 | local filepath = paths.concat(self.datapath, class, self.sets[set][class][idx]) 86 | local result = self.thffmpeg:open(filepath) 87 | if result then 88 | if self.nbframes[filepath] == nil then 89 | self.nbframes[filepath] = self.thffmpeg:length() 90 | end 91 | local nframes = self.nbframes[filepath] 92 | if nframes >= self.nInputFrames then 93 | self.labels_cpu[i] = iclass 94 | local istart = torch.random(nframes - self.nInputFrames + 1) 95 | self.thffmpeg:seek(istart-1) 96 | for j = 1, self.nInputFrames do 97 | self.thffmpeg:next_frame(self.output_cpu[i][j]) 98 | end 99 | done = self:testEnoughMotion(self.output_cpu[i][-2], self.output_cpu[i][-1]) 100 | end 101 | else 102 | print("can't open", i, threadid_t, filepath) 103 | end 104 | end 105 | end 106 | self.thffmpeg:close() 107 | self.output_cpu:mul(2/255):add(-1) 108 | return self:typeResults(self.output_cpu, self.labels_cpu) 109 | end 110 | 111 | function UCF101Datasource:orderedIterator(batchSize, set) 112 | assert(batchSize ~= nil, 'nextBatch: must specify batchSize') 113 | assert(self.sets[set] ~= nil, 'Unknown set ' .. set) 114 | local class_idx = 1 115 | local video_idx = 1 116 | local frame_idx = 1 117 | local thffmpeg2 = THFFmpeg() 118 | return function() 119 | self.output_cpu:resize(batchSize, self.nInputFrames, self.nChannels, 120 | self.h, self.w) 121 | self.labels_cpu:resize(batchSize) 122 | for i = 1, batchSize do 123 | local done = false 124 | while not done do 125 | local class = self.classes[class_idx] 126 | local filepath = paths.concat(self.datapath, class, self.sets[set][class][video_idx]) 127 | local goodvid = true 128 | if frame_idx == 1 then 129 | goodvid = thffmpeg2:open(filepath) 130 | end 131 | if goodvid then 132 | self.labels_cpu[i] = class_idx 133 | for j = 1, self.nInputFrames do 134 | if not thffmpeg2:next_frame(self.output_cpu[i][j]) then 135 | done, goodvid = false, false 136 | break 137 | end 138 | end 139 | done = true 140 | frame_idx = frame_idx + self.nInputFrames 141 | end 142 | if not goodvid then 143 | video_idx = video_idx + 1 144 | if video_idx > #self.sets[set][class] then 145 | class_idx = class_idx + 1 146 | video_idx = 1 147 | if class_idx > self.nClasses then 148 | thffmpeg2:close() 149 | return nil 150 | end 151 | end 152 | frame_idx = 1 153 | end 154 | end 155 | end 156 | self.output_cpu:mul(2/255):add(-1) 157 | return self:typeResults(self.output_cpu, self.labels_cpu) 158 | end 159 | end 160 | 161 | function UCF101Datasource:orderedVideoIterator(batchSize, set) 162 | --returns only one sample (the first frames) per video 163 | assert(batchSize ~= nil, 'nextBatch: must specify batchSize') 164 | assert(self.sets[set] ~= nil, 'Unknown set ' .. set) 165 | local class_idx = 1 166 | local video_idx = 1 167 | local thffmpeg2 = THFFmpeg() 168 | return function() 169 | self.output_cpu:resize(batchSize, self.nInputFrames, self.nChannels, 170 | self.h, self.w) 171 | self.labels_cpu:resize(batchSize) 172 | for i = 1, batchSize do 173 | local done = false 174 | while not done do 175 | done = true 176 | local class = self.classes[class_idx] 177 | local filepath = paths.concat(self.datapath, class, self.sets[set][class][video_idx]) 178 | if not thffmpeg2:open(filepath) then 179 | done = false 180 | else 181 | self.labels_cpu[i] = class_idx 182 | for j = 1, self.nInputFrames do 183 | if not thffmpeg2:next_frame(self.output_cpu[i][j]) then 184 | done = false 185 | break 186 | end 187 | end 188 | end 189 | video_idx = video_idx + 1 190 | if video_idx > #self.sets[set][class] then 191 | class_idx = class_idx + 1 192 | video_idx = 1 193 | if class_idx > self.nClasses then 194 | thffmpeg2:close() 195 | return nil 196 | end 197 | end 198 | end 199 | end 200 | self.output_cpu:mul(2/255):add(-1) 201 | return self:typeResults(self.output_cpu, self.labels_cpu) 202 | end 203 | end 204 | -------------------------------------------------------------------------------- /test-frame-prediction-on-ucf-rec_gdl.lua: -------------------------------------------------------------------------------- 1 | --[[ 2 | July 2016 3 | Authors: Michael Mathieu, Camille Couprie 4 | Script to test 2 trained models to predict future frames in video from 4 5 | previous ones on a subset of the UCF101 test dataset. 6 | --]] 7 | 8 | require('torch') 9 | require('nngraph') 10 | require('image') 11 | --require('fbtorch') 12 | require('gfx.js') 13 | require('cunn') 14 | require('cudnn') 15 | 16 | paths.dofile('upsample.lua') 17 | paths.dofile('expand.lua') 18 | --dofile('ucf101.lua') 19 | 20 | torch.manualSeed(1) 21 | torch.setnumthreads(4) 22 | iscuda = false 23 | assert(loadfile("image_error_measures.lua"))(iscuda) 24 | 25 | opt_default = { 26 | full = false, -- display previous frames and target, otherwise the prediction 27 | with_pyr = true, 28 | with_delta = true, 29 | with_cuda = true, 30 | network_dir = 'AdvGDL', 31 | delay_gif = 25, 32 | totalNbiters=1, 33 | nChannels= 3, 34 | margin = 5, --for display 35 | nOutputFrames = 1, 36 | nOutputFramesRec = 2, 37 | interv = 1, 38 | flow_im_used=true 39 | } 40 | 41 | op = op or {} 42 | for k, v in pairs(opt_default) do 43 | if op[k] == nil then 44 | op[k] = v 45 | end 46 | end 47 | 48 | local inputH, inputW = 240, 320 49 | local netsize = 64 50 | opt = {batchsize = 1} 51 | 52 | -- loading trained network 53 | 54 | local flow_pth = 'UCF101frm10p/' 55 | local predloaded 56 | if op.network_dir=='Adv' then 57 | predloaded = torch.load('trained_models/new_adv_big_64_smalladv.t7') 58 | elseif op.network_dir=='AdvGDL' then 59 | predloaded = torch.load('trained_models/new_adv_big_gdl_64.t7') 60 | end 61 | local opt = predloaded.opt 62 | local model = predloaded.model 63 | opt.nOutputFrames = 1 64 | opt.batchsize = 1 65 | 66 | ------------------------------------------------------------------------------ 67 | -- init multiscale model with dsnet 68 | local dsnet = nn.ConcatTable() 69 | dsnet:add(nn.SpatialAveragePooling(8,8,8,8)) 70 | dsnet:add(nn.SpatialAveragePooling(4,4,4,4)) 71 | dsnet:add(nn.SpatialAveragePooling(2,2,2,2)) 72 | dsnet:add(nn.SpatialAveragePooling(1,1,1,1)) 73 | dsnet:cuda() 74 | local dsnetInput = dsnet 75 | local dsnetTarget = dsnet:clone() 76 | 77 | -------------------------------------------------------------------------------- 78 | -- network size adaptation for models fine-tuned on larger patchs 79 | for i = 1, #model.modules do 80 | if torch.type(model.modules[i]) == 'nn.ExpandDim' then 81 | local xH = math.floor(math.sqrt(model.modules[i].k) /netsize * inputH + 0.5) 82 | local xW = math.floor(math.sqrt(model.modules[i].k) /netsize * inputW + 0.5) 83 | model.modules[i].k = xH*xW 84 | end 85 | if torch.type(model.modules[i]) == 'nn.View' then 86 | if model.modules[i].numInputDims == 2 then 87 | local s1 = model.modules[i].size[1] 88 | local s2 = math.floor(model.modules[i].size[2] /netsize * inputH + 0.5) 89 | local s3 = math.floor(model.modules[i].size[3] /netsize * inputW + 0.5) 90 | model.modules[i].size = torch.LongStorage{s1, s2, s3} 91 | model.modules[i].numElements = s1*s2*s3 92 | --print(model.modules.size) 93 | end 94 | end 95 | end 96 | 97 | local delta = {torch.CudaTensor(opt.batchsize, 2):zero(), 98 | torch.CudaTensor(opt.batchsize, 4):zero(), 99 | torch.CudaTensor(opt.batchsize, 6):zero(), 100 | torch.CudaTensor(opt.batchsize, 8):zero()} 101 | 102 | ------------------------------------------------------------------------------ 103 | 104 | function display_frames(my_array,nbframes) 105 | 106 | local inter = torch.Tensor(op.nChannels,my_array:size(2),op.margin):fill(1) 107 | local todisp = torch.Tensor(op.nChannels,my_array:size(2),op.margin):fill(1) 108 | local todisp2 = torch.Tensor(nbframes,op.nChannels,my_array:size(2), 109 | my_array:size(3)) 110 | for i = 1, nbframes do 111 | for j = 1, op.nChannels do 112 | todisp2[i][j]= my_array[(i-1)*3+j] 113 | end 114 | todisp = torch.cat(todisp, todisp2[i], 3) 115 | todisp = torch.cat(todisp, inter, 3) 116 | end 117 | gfx.image(todisp) 118 | end 119 | 120 | function save_frames(prediction, nbframes, filename) 121 | for i = 1, opt.nInputFrames do 122 | prediction[i]:add(1):div(2) 123 | 124 | image.save(filename..'/pred_'..i..'.png',prediction[i]) 125 | end 126 | local new_img = torch.Tensor(op.nChannels,inputH, inputW):fill(0) 127 | new_img[1]:fill(1) 128 | for i = opt.nInputFrames+1, opt.nInputFrames+op.nOutputFramesRec do 129 | prediction[i]:add(1):div(2) 130 | new_img[{{},{3,inputH-2},{3,inputW-2}}]= 131 | prediction[i][{{},{3,inputH-2},{3,inputW-2}}] 132 | image.save(filename..'/pred_'..i..'.png',new_img) 133 | end 134 | end 135 | 136 | ------------------------------------------------------------------------------ 137 | -- Main job 138 | 139 | local sum_PSNR=torch.Tensor(op.nOutputFramesRec):fill(0) 140 | local sum_err_sharp2=torch.Tensor(op.nOutputFramesRec):fill(0) 141 | local sum_SSIM=torch.Tensor(op.nOutputFramesRec):fill(0) 142 | local nbimagestosave = op.nOutputFramesRec+opt.nInputFrames 143 | local array_to_save= torch.Tensor(nbimagestosave,op.nChannels,inputH,inputW) 144 | local target_to_save = 145 | torch.Tensor(op.nOutputFramesRec,op.nChannels,inputH,inputW) 146 | 147 | local input, output, target 148 | local batch=1 149 | local nbvideos = 3783 150 | local nbframes, nbpartvid 151 | local nbvid = torch.Tensor(op.nOutputFramesRec):fill(0) 152 | 153 | local index = 154 | torch.range(1,(opt.nInputFrames+op.nOutputFramesRec)*op.interv, op.interv) 155 | 156 | 157 | for videoidx = 1,nbvideos,10 do 158 | --local vid, label --= datasets[set]:nextTestImage(videoidx) 159 | local vid = 160 | torch.Tensor(opt.nInputFrames+ op.nOutputFramesRec, op.nChannels, 240,320) 161 | for fr=1,opt.nInputFrames do 162 | im_name = flow_pth..videoidx..'/pred_'..fr..'.png' 163 | vid[fr] = (image.load(im_name)) 164 | end 165 | for fr = 1,op.nOutputFramesRec do 166 | im_name = flow_pth..videoidx..'/target_'..fr..'.png' 167 | vid[fr+opt.nInputFrames] = (image.load(im_name)) 168 | end 169 | 170 | vid:mul(2):add(-1) 171 | nbframes = vid:size(1) 172 | nbpartvid = torch.abs(nbframes/opt.nInputFrames) 173 | 174 | local filename_out = op.network_dir..'/'..videoidx 175 | for ii = 1,op.nOutputFramesRec do 176 | 177 | -- extract the first frames 178 | input = vid[{{1 , opt.nInputFrames}}] 179 | for f=1,opt.nInputFrames-ii+1 do 180 | input[f] = vid[index[ii+f-1]] 181 | end 182 | for j=1,ii-1 do 183 | if j> opt.nInputFrames then break end 184 | input[opt.nInputFrames+1-j] = array_to_save[ii-j+opt.nInputFrames] 185 | end 186 | target = torch.Tensor(op.nOutputFrames, op.nChannels, 240,320) 187 | for f=1,op.nOutputFrames do 188 | target[f] = vid[index[opt.nInputFrames+ii+f-1]] 189 | end 190 | 191 | input = input:view(1, op.nChannels*opt.nInputFrames, 192 | input:size(3), input:size(4)) 193 | target = target:view(1, op.nChannels*opt.nOutputFrames, 194 | target:size(3), target:size(4)) 195 | if op.with_pyr == true then 196 | input = dsnetInput:forward(input:cuda()) 197 | target = dsnetTarget:forward(target:cuda()) 198 | end 199 | if op.with_delta == true then 200 | output = model:forward({input, delta})[1] 201 | elseif op.with_pyr == false then 202 | output = model:forward(input:cuda()) 203 | else 204 | output = model:forward(input) 205 | end 206 | if op.with_pyr == true then 207 | output = output[4] -- the largest scale output[1][4] 208 | end 209 | output = output:double() 210 | if op.with_pyr == true then 211 | input = input[4] 212 | input = input[{{1},{},{},{}}]:float() 213 | target = target[4]:double() 214 | end 215 | output = output[batch] 216 | 217 | -- replace target and input in same space than the output 218 | target = target[batch] 219 | 220 | if ii==1 then 221 | array_to_save[{{1,opt.nInputFrames}}]=input 222 | end 223 | array_to_save[opt.nInputFrames+ii]=output -- target 224 | 225 | -- extract moving pixels for SNR computations 226 | if op.flow_im_used then 227 | local flow_im_name 228 | local moutput = torch.Tensor(3,240,320):fill(-1) 229 | local mtarget = torch.Tensor(3,240,320):fill(-1) 230 | if ii==1 then 231 | flow_im_name = flow_pth..videoidx..'/pred_4_flow.png' 232 | else 233 | flow_im_name = flow_pth..videoidx..'/target_'..(ii-1)..'_flow.png' 234 | end 235 | 236 | local flow_im = image.load(flow_im_name) 237 | local s = output[{{1,3}}]:size() 238 | 239 | for j=1, s[2] do 240 | for k=1, s[3] do 241 | if flow_im[1][j][k]< 0.2 or flow_im[2][j][k]< 0.2 242 | or flow_im[3][j][k]< 0.2 then -- moving 243 | for i=1,s[1] do 244 | moutput[i][j][k] = output[i][j][k] 245 | mtarget[i][j][k] = target[i][j][k] 246 | end 247 | end 248 | end 249 | end 250 | 251 | local psnr = PSNR(moutput, mtarget) 252 | if psnr < 50 then 253 | sum_PSNR[ii] = sum_PSNR[ii]+psnr 254 | sum_SSIM[ii] = sum_SSIM[ii]+SSIM(moutput, mtarget) 255 | sum_err_sharp2[ii] = sum_err_sharp2[ii] + 256 | computel1difference(moutput, mtarget) 257 | nbvid[ii] = nbvid[ii]+1 258 | end 259 | else 260 | sum_PSNR[ii] = sum_PSNR[ii]+PSNR(output[{{1,3}}], target[{{1,3}}]) 261 | sum_SSIM[ii] = sum_SSIM[ii]+SSIM(output[{{1,3}}], target[{{1,3}}]) 262 | sum_err_sharp2[ii] = sum_err_sharp2[ii] + 263 | computel1difference(output[{{1,3}}], target[{{1,3}}]) 264 | nbvid[ii] = nbvid[ii]+1 265 | end 266 | end --for ii = 1,op.nOutputFramesRec 267 | 268 | print(filename_out) 269 | os.execute('mkdir -p "' .. filename_out .. '"; ') 270 | save_frames(array_to_save, nbimagestosave, filename_out) 271 | 272 | for i= 1,op.nOutputFramesRec do 273 | print('******** video '..videoidx..', '..i..' th frame pred *************') 274 | print(string.format("score sharp diff: %.2f",sum_err_sharp2[i]/nbvid[i])) 275 | print(string.format("PSNR: %.2f",sum_PSNR[i]/nbvid[i])) 276 | print(string.format("SSIM: %.2f",sum_SSIM[i]/nbvid[i])) 277 | end 278 | 279 | os.execute('convert $(for ((a=1; a<'..nbimagestosave.. 280 | '; a++)); do printf -- "-delay '..op.delay_gif..' '..filename_out.. 281 | '/pred_%s.png " $a; done;) '..filename_out..'result.gif') 282 | 283 | end --for videoidx = 1,nbvideos,10 284 | 285 | -------------------------------------------------------------------------------- /get_model.lua: -------------------------------------------------------------------------------- 1 | require('nngraph') 2 | require('cunn') 3 | require('cudnn') 4 | require('nnx') 5 | 6 | 7 | local function getConvNet(struct, nChannels, h, w, nOutputChannels, nOutputElements) 8 | local isInFCMode, nElements = false, nil 9 | local input = nn.Identity()() 10 | local x = nn.Identity()(input) 11 | local feature = nil 12 | for i = 1, #struct do 13 | if struct[i][1] == 'conv' then 14 | local nOutputs = struct[i][3] or nOutputChannels 15 | assert(not isInFCMode) -- no convolutions after FC 16 | assert(nOutputs ~= nil) -- no nil if nOutputChannels is nil 17 | assert((struct[i][3] ~= nil) or (i == #struct)) -- no nil except in last layer 18 | x = cudnn.SpatialConvolution(nChannels, nOutputs, 19 | struct[i][2], struct[i][2], 20 | struct[i][4], struct[i][4]):cuda()(x) 21 | if struct[i][4] ~= nil then 22 | nChannels, h, w = nOutputs, math.floor((h - struct[i][2])/struct[i][4]) + 1, math.floor((w - struct[i][2])/struct[i][4]) + 1 23 | else 24 | nChannels, h, w = nOutputs, h - struct[i][2] + 1, w - struct[i][2] + 1 25 | end 26 | elseif struct[i][1] == 'convp' then 27 | local nOutputs = struct[i][3] or nOutputChannels 28 | assert(struct[i][2] % 2 == 1) -- no even kernel sizes when padding! 29 | assert(not isInFCMode) -- no convolutions after FC 30 | assert(nOutputs ~= nil) -- no nil if nOutputChannels is nil 31 | assert((struct[i][3] ~= nil) or (i == #struct)) -- no nil except in last layer 32 | x = cudnn.SpatialConvolution(nChannels, nOutputs, 33 | struct[i][2], struct[i][2], 34 | 1, 1, (struct[i][2]-1)/2, 35 | (struct[i][2]-1)/2):cuda()(x) 36 | nChannels = nOutputs 37 | elseif struct[i][1] == 'maxpool' then 38 | assert(not isInFCMode) -- no pooling after FC 39 | x = cudnn.SpatialMaxPooling(struct[i][2], struct[i][2], 40 | struct[i][3], struct[i][3])(x) 41 | h = math.floor((h - struct[i][2])/struct[i][3] + 1) 42 | w = math.floor((w - struct[i][2])/struct[i][3] + 1) 43 | elseif struct[i][1] == 'fc' then 44 | local nOutputs = struct[i][2] or nOutputElements 45 | assert(nOutputs ~= nil) -- no nil if nOutputElements is nil 46 | assert((struct[i][2] ~= nil) or (i == #struct)) -- no nil except in last layer 47 | if not isInFCMode then 48 | nElements = h*w*nChannels 49 | x = nn.View(nElements):setNumInputDims(3)(x) 50 | isInFCMode = true 51 | end 52 | x = nn.Linear(nElements, nOutputs):cuda()(x) 53 | nElements = nOutputs 54 | elseif struct[i][1] == 'feature' then 55 | assert(feature == nil) -- only one feature layer (for now) 56 | feature = x 57 | elseif struct[i][1] == 'spatialbatchnorm' then 58 | x = nn.SpatialBatchNormalization(nChannels)(x) 59 | else 60 | error('Unknown network element ' .. struct[i][1]) 61 | end 62 | if i ~= #struct then 63 | x = nn.ReLU()(x) 64 | end 65 | end 66 | local net = nn.gModule({input}, {x, feature}) 67 | if isInFCMode then 68 | return net, nElements 69 | else 70 | return net, nChannels, h, w 71 | end 72 | end 73 | 74 | function getPyrModel(opt, dataset, in_modules) 75 | -- assume input/target is between -1 and 1 76 | local out_modules = {} 77 | local function getPred(imagesScaled, inputGuess, scale, scaleRatio, in_module) 78 | -- input: images(scale res), guess(scale/2 res) 79 | local ws, hs = opt.w / scale, opt.h / scale 80 | local guessScaled, x = nil, nil 81 | local nInputChannels = opt.nInputFrames*dataset.nChannels 82 | if inputGuess ~= nil then 83 | guessScaled = nn.SpatialUpSamplingNearest(scaleRatio)(inputGuess) 84 | nInputChannels = nInputChannels +opt.nTargetFrames*dataset.nChannels 85 | x = nn.JoinTable(2){imagesScaled, guessScaled} 86 | else 87 | x = imagesScaled 88 | end 89 | local mod = in_module 90 | if not mod then 91 | mod = getConvNet(opt.modelStruct[scale], nInputChannels, 92 | hs, ws, opt.nTargetFrames*dataset.nChannels) 93 | end 94 | mod = mod:cuda() 95 | x = mod(x) 96 | out_modules[scale] = mod 97 | local x, features = x:split(2) 98 | if inputGuess ~= nil then 99 | x = nn.CAddTable(){x, guessScaled} 100 | end 101 | x = nn.Tanh()(x) 102 | return x, features 103 | end 104 | 105 | local inputImages = nn.Identity()() 106 | local pred, features = {}, {} 107 | for i = 1, #opt.scaleList do 108 | local scale = opt.scaleList[i] 109 | local mod = nil 110 | if in_modules then 111 | mod = in_modules[scale] 112 | end 113 | pred[i], features[i] = 114 | getPred(nn.SelectTable(i)(inputImages), 115 | pred[i-1], --nil if i == 0, on purpose 116 | scale, 117 | (i == 1) or (opt.scaleList[i-1] / scale), 118 | mod) 119 | end 120 | pred = nn.Identity()(pred) 121 | features = nn.Identity()(features) 122 | model = nn.gModule({inputImages}, {pred, features}) 123 | model = model:cuda() 124 | return model, out_modules 125 | end 126 | 127 | function getRecModel(opt, model, datasource) 128 | assert(opt.h == opt.w) 129 | local input = nn.Identity()() 130 | local output = {} 131 | local lastinput = input 132 | for i = 1, opt.nRec do 133 | local netoutput = model:clone('weight', 'bias', 'gradWeight', 'gradBias')(lastinput) 134 | netoutput = nn.SelectTable(1)(netoutput) 135 | output[i] = netoutput 136 | if i ~= opt.nRec then 137 | local newinput = {} 138 | for j = 1, #opt.scaleList do 139 | local npix = opt.h / opt.scaleList[j] 140 | local x1 = nn.SelectTable(j)(lastinput) 141 | x1 = nn.View(opt.batchsize, opt.nInputFrames, datasource.nChannels, npix, npix)(x1) 142 | x1 = nn.Narrow(2, 2, opt.nInputFrames-1)(x1) 143 | local x2 = nn.SelectTable(j)(netoutput) 144 | x2 = nn.View(opt.batchsize, 1, datasource.nChannels, npix, npix)(x2) 145 | local y = nn.JoinTable(2){x1, x2} 146 | newinput[j] = 147 | nn.View(opt.batchsize, opt.nInputFrames*datasource.nChannels, npix, npix)(y) 148 | end 149 | lastinput = newinput 150 | end 151 | end 152 | if #output == 1 then 153 | local dummy = nn.ConcatTable() 154 | dummy:add(nn.Identity()) 155 | output = dummy(output) 156 | return nn.gModule({input}, {output}):cuda() 157 | else 158 | return nn.gModule({input}, output):cuda() 159 | end 160 | end 161 | 162 | function getPyrAdv(opt, dataset) 163 | local inputImages = nn.Identity()() 164 | local inputPred = nn.Identity()() 165 | local adv = {} 166 | for i = 1, #opt.scaleList do 167 | assert(opt.advStruct[opt.scaleList[i] ] ~= nil) -- model and adv must have same scales 168 | local x = nn.JoinTable(2){nn.SelectTable(i)(inputImages), 169 | nn.SelectTable(i)(inputPred)} 170 | x = getConvNet(opt.advStruct[opt.scaleList[i] ], 171 | (opt.nInputFrames+opt.nTargetFrames)*dataset.nChannels, 172 | opt.w / opt.scaleList[i], opt.h / opt.scaleList[i], nil, 1)(x) 173 | adv[i] = nn.Sigmoid()(x) 174 | end 175 | 176 | advmodel = nn.gModule({inputImages, inputPred}, adv) 177 | advmodel = advmodel:cuda() 178 | return advmodel 179 | end 180 | 181 | function getRecAdv(opt, advmodel, datasource, in_modules) 182 | assert((advmodel == nil) ~= (in_modules == nil)) 183 | local input1 = nn.Identity()() 184 | local input2 = nn.Identity()() 185 | local output = {} 186 | local input1b = input1 187 | out_modules = {} 188 | for i = 1, opt.nRec do 189 | local input2b = nn.SelectTable(i)(input2) 190 | local mod = nil 191 | if advmodel ~= nil then 192 | if opt.advshare == true then 193 | mod = advmodel:clone('weight', 'bias', 'gradWeight', 'gradBias') 194 | else 195 | mod = advmodel:clone() 196 | print("====================================================================") 197 | print("================= CLONING ADVMODEL =====================") 198 | print("====================================================================") 199 | end 200 | else 201 | if in_modules[i] ~= nil then 202 | mod = in_modules[i] 203 | else 204 | if opt.advshare == true then 205 | mod = in_modules[#in_modules]:clone('weight', 'bias', 'gradWeight', 'gradBias') 206 | print("====================================================================") 207 | print("================= SHARING LAST ADVMODEL =====================") 208 | print("====================================================================") 209 | else 210 | mod = in_modules[#in_modules]:clone() 211 | print("====================================================================") 212 | print("================= CLONING LAST ADVMODEL =====================") 213 | print("====================================================================") 214 | end 215 | end 216 | end 217 | for i, node in ipairs(mod.backwardnodes) do 218 | --TODO: somehow :cuda() fails otherwise 219 | node.data.gradOutputBuffer = nil 220 | end 221 | out_modules[i] = mod 222 | output[i] = mod{input1b, input2b} 223 | if i ~= opt.nRec then 224 | local newinput1b = {} 225 | for j = 1, #opt.scaleList do 226 | local npix = opt.h / opt.scaleList[j] 227 | local x1 = nn.SelectTable(j)(input1b) 228 | x1 = nn.View(opt.batchsize, opt.nInputFrames, datasource.nChannels, npix, npix)(x1) 229 | x1 = nn.Narrow(2, 2, opt.nInputFrames-1)(x1) 230 | local x2 = nn.SelectTable(j)(input2b) 231 | x2 = nn.View(opt.batchsize, 1, datasource.nChannels, npix, npix)(x2) 232 | local y = nn.JoinTable(2){x1, x2} 233 | newinput1b[j] = 234 | nn.View(opt.batchsize, opt.nInputFrames*datasource.nChannels, npix, npix)(y) 235 | end 236 | input1b = nn.Identity()(newinput1b) 237 | end 238 | end 239 | if #output == 1 then 240 | local dummy = nn.ConcatTable() 241 | dummy:add(nn.Identity()) 242 | output = dummy(output) 243 | return nn.gModule({input1, input2}, {output}):cuda(), out_modules 244 | else 245 | return nn.gModule({input1, input2}, output):cuda(), out_modules 246 | end 247 | end 248 | 249 | function getPyrPreprocessor(opt, dataset) 250 | local net = nn.ConcatTable() 251 | for i = 1, #opt.scaleList do 252 | local net2 = nn.Sequential() 253 | net:add(net2) 254 | net2:add(nn.FunctionWrapper( 255 | function(self) end, 256 | function(self, input) 257 | return input:view(input:size(1), 258 | -1, input:size(input:dim()-1), 259 | input:size(input:dim())) 260 | end, 261 | function(self, input, gradOutput) 262 | return gradOutput:viewAs(input) 263 | end)) 264 | scale = opt.scaleList[i] 265 | net2:add(nn.SpatialAveragePooling(scale, scale, scale, scale)) 266 | end 267 | net:cuda() 268 | return net 269 | end 270 | 271 | -- replicated the criterion into a sort of parallel criterion 272 | -- TODO: is this used? 273 | function getPyrCriterion(opt, simpleCriterion) 274 | local output = {} 275 | output.criterion = nn.ParallelCriterion() 276 | for i = 1, #opt.scaleList do 277 | output.criterion:add(simpleCriterion:clone()) 278 | end 279 | output.criterion:cuda() 280 | output.dsnet = nn.ConcatTable() 281 | for i = 1, #opt.scaleList do 282 | local scale = opt.scaleList[i] 283 | output.dsnet:add(nn.SpatialAveragePooling(scale, scale, scale, scale)) 284 | end 285 | output.dsnet:cuda() 286 | function output:forward(input, target) 287 | return output.criterion:forward(input, output.dsnet:forward(target)) 288 | end 289 | function output:updateGradInput(input, target) 290 | return output.criterion:backward(input, output.dsnet.output) 291 | end 292 | output.backward = output.updateGradInput 293 | return output 294 | end 295 | 296 | GDL, gdlparent = torch.class('nn.GDLCriterion', 'nn.Criterion') 297 | 298 | function GDL:__init(alpha) 299 | gdlparent:__init(self) 300 | self.alpha = alpha or 1 301 | assert(alpha == 1) --for now 302 | local Y = nn.Identity()() 303 | local Yhat = nn.Identity()() 304 | local Yi1 = nn.SpatialZeroPadding(0,0,0,-1)(Y) 305 | local Yj1 = nn.SpatialZeroPadding(0,0,-1,0)(Y) 306 | local Yi2 = nn.SpatialZeroPadding(0,-1,0,0)(Y) 307 | local Yj2 = nn.SpatialZeroPadding(-1,0,0,0)(Y) 308 | local Yhati1 = nn.SpatialZeroPadding(0,0,0,-1)(Yhat) 309 | local Yhatj1 = nn.SpatialZeroPadding(0,0,-1,0)(Yhat) 310 | local Yhati2 = nn.SpatialZeroPadding(0,-1,0,0)(Yhat) 311 | local Yhatj2 = nn.SpatialZeroPadding(-1,0,0,0)(Yhat) 312 | local term1 = nn.Abs()(nn.CSubTable(){Yi2, Yi1}) 313 | local term2 = nn.Abs()(nn.CSubTable(){Yhati2, Yhati1}) 314 | local term3 = nn.Abs()(nn.CSubTable(){Yj2, Yj1}) 315 | local term4 = nn.Abs()(nn.CSubTable(){Yhatj2, Yhatj1}) 316 | local term12 = nn.CSubTable(){term1, term2} 317 | local term34 = nn.CSubTable(){term3, term4} 318 | self.net = nn.gModule({Yhat, Y}, {term12, term34}) 319 | self.net:cuda() 320 | self.crit = nn.ParallelCriterion() 321 | self.crit:add(nn.AbsCriterion()) 322 | self.crit:add(nn.AbsCriterion()) 323 | self.crit:cuda() 324 | self.target1 = torch.CudaTensor() 325 | self.target2 = torch.CudaTensor() 326 | end 327 | 328 | function GDL:updateOutput(input, target) 329 | self.netoutput = self.net:updateOutput{input, target} 330 | self.target1:resizeAs(self.netoutput[1]):zero() 331 | self.target2:resizeAs(self.netoutput[2]):zero() 332 | self.target = {self.target1, self.target2} 333 | self.loss = self.crit:updateOutput(self.netoutput, self.target) 334 | return self.loss 335 | end 336 | 337 | function GDL:updateGradInput(input, target) 338 | local gradInput = 339 | self.crit:updateGradInput(self.netoutput, self.target) 340 | self.gradInput = 341 | self.net:updateGradInput({input, target}, gradInput)[1] 342 | return self.gradInput 343 | end 344 | -------------------------------------------------------------------------------- /train_iclr_model.lua: -------------------------------------------------------------------------------- 1 | --[[ 2 | Trains an L2 + adversarial network (can be only L2 by setting advweight to 0) 3 | to predict next frame. 4 | The network uses a multi-resolution pyramid (hardcoded to 4 levels for now) 5 | Uses latent variable in additive mode at each level 6 | Supports sgd and adagrad optimization 7 | --]] 8 | 9 | require('torch') 10 | require('optim') 11 | require('get_model') 12 | require 'gfx.js' 13 | 14 | nngraph.setDebug(false) 15 | gfx.verbose = false 16 | torch.setnumthreads(2) 17 | torch.manualSeed(1) 18 | 19 | opt_default = { 20 | -- general 21 | devid = 2, -- GPU id 22 | saveName = 'model.t7', -- save file name 23 | loadName = '', 24 | loadOpt=false, 25 | dataset = 'ucf101', -- dataset name 26 | -- training 27 | nEpoches = 10000, -- number of "epoches" per training 28 | nIters = 100, -- number of minibatches per "epoch" 29 | batchsize = 8, -- number of samples per minibatches 30 | -- model 31 | h = 32, 32 | w = 32, -- size of the patches 33 | modelOptim = 'sgd', -- delta(adadelta), grad(adagrad) or sgd 34 | modelConfig = { 35 | learningRate = 0.02, 36 | --learningRateDecay = 0, 37 | --weightDecay = 0, 38 | --momentum = 0 39 | }, 40 | nInputFrames = 4, -- number of *input* frames (excluding target) 41 | nTargetFrames = 1, -- number of frames to predict 42 | 43 | modelStruct = { 44 | [8] = { 45 | {'convp', 3, 16}, 46 | {'convp', 3, 32}, 47 | {'feature'}, 48 | {'convp', 3, 16}, 49 | {'convp', 3, nil}}, 50 | [4] = { 51 | {'convp', 5, 16}, 52 | {'convp', 3, 32}, 53 | {'feature'}, 54 | {'convp', 3, 16}, 55 | {'convp', 5, nil}}, 56 | [2] = { 57 | {'convp', 5, 16}, 58 | {'convp', 3, 32}, 59 | {'convp', 3, 64}, 60 | {'feature'}, 61 | {'convp', 3, 32}, 62 | {'convp', 3, 16}, 63 | {'convp', 5, nil}}, 64 | [1] = { 65 | {'convp', 7, 16}, 66 | {'convp', 5, 32}, 67 | {'convp', 5, 64}, 68 | {'feature'}, 69 | {'convp', 5, 32}, 70 | {'convp', 5, 16}, 71 | {'convp', 7, nil}}}, 72 | -- adv 73 | advOptim = 'sgd', -- see modelOptim 74 | advConfig = { 75 | learningRate = 0.02, 76 | }, 77 | l2weight = 1, -- L2 weight in the loss 78 | advweight = 0.01, -- adversarial weight in the loss 79 | advNIter = 1, -- number of adversarial training iterations 80 | advExt = 'full', -- extend adv training to fake "real" examples [none|cheap|full] 81 | advStruct = { 82 | [8] = { 83 | {'conv', 3, 32}, 84 | {'fc', 256}, 85 | {'fc', 128}, 86 | {'fc', nil}}, 87 | [4] = { 88 | {'conv', 3, 32}, 89 | {'conv', 3, 32}, 90 | {'conv', 3, 64}, 91 | {'fc', 256}, 92 | {'fc', 128}, 93 | {'fc', nil}}, 94 | [2] = { 95 | {'conv', 5, 32}, 96 | {'conv', 5, 32}, 97 | {'conv', 5, 64}, 98 | {'fc', 256}, 99 | {'fc', 128}, 100 | {'fc', nil}}, 101 | [1] = { 102 | {'conv', 7, 32}, 103 | {'conv', 7, 32}, 104 | {'conv', 5, 64}, 105 | {'conv', 5, 128}, 106 | {'maxpool', 2, 2}, 107 | --TODO: shared weights with two last layers 108 | {'fc', 256}, 109 | {'fc', 128}, 110 | {'fc', nil}}, 111 | }, 112 | } 113 | opt = opt or {} 114 | for k, v in pairs(opt_default) do 115 | if opt[k] == nil then 116 | opt[k] = v 117 | end 118 | end 119 | modelState = nil 120 | advState = nil 121 | assert((opt.advweight == 0) ~= (opt.advNIter ~= 0)) -- if not, it's probably a mistake 122 | 123 | cutorch.setDevice(opt.devid) 124 | 125 | loaded = {} 126 | if opt.loadName ~= '' then 127 | loaded = torch.load(opt.loadName) 128 | model = loaded.model 129 | advmodel = loaded.advmodel 130 | if loaded.opt.h ~= opt.h then 131 | advmodel = nil 132 | end 133 | end 134 | if opt.loadOpt == true then 135 | local oldopt = opt 136 | opt = loaded.opt 137 | --opt.devid = oldopt.devid 138 | --opt.saveName = oldopt.saveName 139 | for k, v in pairs(opt_override) do 140 | opt[k] = v 141 | end 142 | end 143 | 144 | local w, h = opt.h, opt.w 145 | local winput = w 146 | local hinput = h 147 | if opt.dataset == 'sports1m' then 148 | error("no sports1m dataset") 149 | elseif opt.dataset == 'ucf101' then 150 | require('datasources.thread') 151 | local optt = opt -- need local var, opt is global 152 | dataset = ThreadedDatasource( 153 | function() 154 | require('datasources.augment') 155 | require('datasources.ucf101') 156 | local ucfdataset = UCF101Datasource{ 157 | nInputFrames = optt.nInputFrames+optt.nTargetFrames 158 | } 159 | return AugmentDatasource(ucfdataset, {crop = {h, w}}) 160 | end, {nDonkeys = 8}) 161 | dataset:cuda() 162 | else 163 | error("Unknown dataset " .. opt.dataset) 164 | end 165 | 166 | opt.scaleList = {} 167 | for k, v in pairs(opt.modelStruct) do 168 | opt.scaleList[1+#opt.scaleList] = k 169 | end 170 | table.sort(opt.scaleList, function(a,b) return b 5 | Everyone is permitted to copy and distribute verbatim copies 6 | of this license document, but changing it is not allowed. 7 | 8 | Preamble 9 | 10 | The GNU General Public License is a free, copyleft license for 11 | software and other kinds of works. 12 | 13 | The licenses for most software and other practical works are designed 14 | to take away your freedom to share and change the works. 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If the Program does not specify a version number of the 576 | GNU General Public License, you may choose any version ever published 577 | by the Free Software Foundation. 578 | 579 | If the Program specifies that a proxy can decide which future 580 | versions of the GNU General Public License can be used, that proxy's 581 | public statement of acceptance of a version permanently authorizes you 582 | to choose that version for the Program. 583 | 584 | Later license versions may give you additional or different 585 | permissions. However, no additional obligations are imposed on any 586 | author or copyright holder as a result of your choosing to follow a 587 | later version. 588 | 589 | 15. Disclaimer of Warranty. 590 | 591 | THERE IS NO WARRANTY FOR THE PROGRAM, TO THE EXTENT PERMITTED BY 592 | APPLICABLE LAW. EXCEPT WHEN OTHERWISE STATED IN WRITING THE COPYRIGHT 593 | HOLDERS AND/OR OTHER PARTIES PROVIDE THE PROGRAM "AS IS" WITHOUT WARRANTY 594 | OF ANY KIND, EITHER EXPRESSED OR IMPLIED, INCLUDING, BUT NOT LIMITED TO, 595 | THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR 596 | PURPOSE. THE ENTIRE RISK AS TO THE QUALITY AND PERFORMANCE OF THE PROGRAM 597 | IS WITH YOU. SHOULD THE PROGRAM PROVE DEFECTIVE, YOU ASSUME THE COST OF 598 | ALL NECESSARY SERVICING, REPAIR OR CORRECTION. 599 | 600 | 16. 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Interpretation of Sections 15 and 16. 613 | 614 | If the disclaimer of warranty and limitation of liability provided 615 | above cannot be given local legal effect according to their terms, 616 | reviewing courts shall apply local law that most closely approximates 617 | an absolute waiver of all civil liability in connection with the 618 | Program, unless a warranty or assumption of liability accompanies a 619 | copy of the Program in return for a fee. 620 | 621 | END OF TERMS AND CONDITIONS 622 | 623 | How to Apply These Terms to Your New Programs 624 | 625 | If you develop a new program, and you want it to be of the greatest 626 | possible use to the public, the best way to achieve this is to make it 627 | free software which everyone can redistribute and change under these terms. 628 | 629 | To do so, attach the following notices to the program. It is safest 630 | to attach them to the start of each source file to most effectively 631 | state the exclusion of warranty; and each file should have at least 632 | the "copyright" line and a pointer to where the full notice is found. 633 | 634 | {one line to give the program's name and a brief idea of what it does.} 635 | Copyright (C) {year} {name of author} 636 | 637 | This program is free software: you can redistribute it and/or modify 638 | it under the terms of the GNU General Public License as published by 639 | the Free Software Foundation, either version 3 of the License, or 640 | (at your option) any later version. 641 | 642 | This program is distributed in the hope that it will be useful, 643 | but WITHOUT ANY WARRANTY; without even the implied warranty of 644 | MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the 645 | GNU General Public License for more details. 646 | 647 | You should have received a copy of the GNU General Public License 648 | along with this program. If not, see . 649 | 650 | Also add information on how to contact you by electronic and paper mail. 651 | 652 | If the program does terminal interaction, make it output a short 653 | notice like this when it starts in an interactive mode: 654 | 655 | {project} Copyright (C) {year} {fullname} 656 | This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'. 657 | This is free software, and you are welcome to redistribute it 658 | under certain conditions; type `show c' for details. 659 | 660 | The hypothetical commands `show w' and `show c' should show the appropriate 661 | parts of the General Public License. Of course, your program's commands 662 | might be different; for a GUI interface, you would use an "about box". 663 | 664 | You should also get your employer (if you work as a programmer) or school, 665 | if any, to sign a "copyright disclaimer" for the program, if necessary. 666 | For more information on this, and how to apply and follow the GNU GPL, see 667 | . 668 | 669 | The GNU General Public License does not permit incorporating your program 670 | into proprietary programs. If your program is a subroutine library, you 671 | may consider it more useful to permit linking proprietary applications with 672 | the library. If this is what you want to do, use the GNU Lesser General 673 | Public License instead of this License. But first, please read 674 | . 675 | --------------------------------------------------------------------------------