timing_util restored

timing_util.lua

@@ -0,0 +1,259 @@
+-- Copyright 2016 Anurag Ranjan and the Max Planck Gesellschaft.
+-- All rights reserved.
+-- This software is provided for research purposes only.  
+-- By using this software you agree to the terms of the license file 
+-- in the root folder.
+-- For commercial use, please contact [email protected].
+
+require 'image'
+local TF = require 'transforms'
+require 'cutorch'
+require 'nn'
+require 'cunn'
+require 'cudnn'
+require 'nngraph'
+require 'stn'
+require 'spy'
+local flowX = require 'flowExtensions'
+
+local M = {}
+
+local eps = 1e-6
+local meanstd = {
+   mean = { 0.485, 0.456, 0.406 },
+   std = { 0.229, 0.224, 0.225 },
+}
+local pca = {
+   eigval = torch.Tensor{ 0.2175, 0.0188, 0.0045 },
+   eigvec = torch.Tensor{
+      { -0.5675,  0.7192,  0.4009 },
+      { -0.5808, -0.0045, -0.8140 },
+      { -0.5836, -0.6948,  0.4203 },
+   },
+}
+
+local mean = meanstd.mean
+local std = meanstd.std
+------------------------------------------
+local function createWarpModel()
+  local imgData = nn.Identity()()
+  local floData = nn.Identity()()
+
+  local imgOut = nn.Transpose({2,3},{3,4})(imgData)
+  local floOut = nn.Transpose({2,3},{3,4})(floData)
+
+  local warpImOut = nn.Transpose({3,4},{2,3})(nn.BilinearSamplerBHWD()({imgOut, floOut}))
+  local model = nn.gModule({imgData, floData}, {warpImOut})
+
+  return model
+end
+
+local down2 = nn.SpatialAveragePooling(2,2,2,2):cuda()
+local down3 = nn.SpatialAveragePooling(2,2,2,2):cuda()
+local down4 = nn.SpatialAveragePooling(2,2,2,2):cuda()
+local down5 = nn.SpatialAveragePooling(2,2,2,2):cuda()
+
+local up2 =  nn.Sequential():add(nn.Transpose({2,3},{3,4})):add(nn.ScaleBHWD(2)):add(nn.Transpose({3,4},{2,3})):cuda()
+local up3 =  nn.Sequential():add(nn.Transpose({2,3},{3,4})):add(nn.ScaleBHWD(2)):add(nn.Transpose({3,4},{2,3})):cuda()
+local up4 =  nn.Sequential():add(nn.Transpose({2,3},{3,4})):add(nn.ScaleBHWD(2)):add(nn.Transpose({3,4},{2,3})):cuda()
+local up5 =  nn.Sequential():add(nn.Transpose({2,3},{3,4})):add(nn.ScaleBHWD(2)):add(nn.Transpose({3,4},{2,3})):cuda()
+
+local warpmodel2 = createWarpModel():cuda()
+local warpmodel3 = createWarpModel():cuda()
+local warpmodel4 = createWarpModel():cuda()
+local warpmodel5 = createWarpModel():cuda()
+
+down2:evaluate()
+down3:evaluate()
+down4:evaluate()
+down5:evaluate()
+
+up2:evaluate()
+up3:evaluate()
+up4:evaluate()
+up5:evaluate()
+
+warpmodel2:evaluate()
+warpmodel3:evaluate()
+warpmodel4:evaluate()
+warpmodel5:evaluate()
+
+-------------------------------------------------
+local  modelL0, modelL1, modelL2, modelL3, modelL4, modelL5
+local modelL1path, modelL2path, modelL3path, modelL4path, modelL5path
+
+modelL1path = paths.concat('models', 'modelL1_4.t7')
+modelL2path = paths.concat('models', 'modelL2_4.t7')
+modelL3path = paths.concat('models', 'modelL3_4.t7')
+modelL4path = paths.concat('models', 'modelL4_4.t7')
+modelL5path = paths.concat('models', 'modelL5_4.t7')
+
+modelL1 = torch.load(modelL1path)
+if torch.type(modelL1) == 'nn.DataParallelTable' then
+   modelL1 = modelL1:get(1)
+end
+modelL1:evaluate()
+
+modelL2 = torch.load(modelL2path)
+if torch.type(modelL2) == 'nn.DataParallelTable' then
+   modelL2 = modelL2:get(1)
+end
+modelL2:evaluate()
+
+modelL3 = torch.load(modelL3path)
+if torch.type(modelL3) == 'nn.DataParallelTable' then
+   modelL3 = modelL3:get(1)
+end
+modelL3:evaluate()
+
+modelL4 = torch.load(modelL4path)
+if torch.type(modelL4) == 'nn.DataParallelTable' then
+   modelL4 = modelL4:get(1)
+end
+modelL4:evaluate()
+
+modelL5 = torch.load(modelL5path)
+if torch.type(modelL5) == 'nn.DataParallelTable' then
+   modelL5 = modelL5:get(1)
+end
+modelL5:evaluate()
+
+local function getTrainValidationSplits(path)
+   local numSamples = sys.fexecute( "ls " .. opt.data .. "| wc -l")/3
+   local ff = torch.DiskFile(path, 'r')
+   local trainValidationSamples = torch.IntTensor(numSamples)
+   ff:readInt(trainValidationSamples:storage())
+   ff:close()
+
+   local train_samples = trainValidationSamples:eq(1):nonzero()
+   local validation_samples = trainValidationSamples:eq(2):nonzero()
+
+   return train_samples, validation_samples
+  -- body
+end
+M.getTrainValidationSplits = getTrainValidationSplits
+
+local function loadImage(path)
+   local input = image.load(path, 3, 'float')
+   return input
+end
+M.loadImage = loadImage
+
+local function loadFlow(filename)
+  TAG_FLOAT = 202021.25 
+  local ff = torch.DiskFile(filename):binary()
+  local tag = ff:readFloat()
+  if tag ~= TAG_FLOAT then
+    xerror('unable to read '..filename..
+     ' perhaps bigendian error','readflo()')
+  end
+
+  local w = ff:readInt()
+  local h = ff:readInt()
+  local nbands = 2
+  local tf = torch.FloatTensor(h, w, nbands)
+  ff:readFloat(tf:storage())
+  ff:close()
+
+  local flow = tf:permute(3,1,2)  
+  return flow
+end
+M.loadFlow = loadFlow
+
+
+local function computeInitFlowL1(imagesL1)
+  local h = imagesL1:size(3)
+  local w = imagesL1:size(4)
+
+  local _flowappend = torch.zeros(opt.batchSize, 2, h, w):cuda()
+  local images_in = torch.cat(imagesL1, _flowappend, 2)
+
+  local flow_est = modelL1:forward(images_in)
+  return flow_est
+end
+M.computeInitFlowL1 = computeInitFlowL1
+
+local function computeInitFlowL2(imagesL2)
+  local imagesL1 = down2:forward(imagesL2:clone())  
+  local _flowappend = up2:forward(computeInitFlowL1(imagesL1))*2    
+  local _img2 = imagesL2[{{},{4,6},{},{}}]
+  imagesL2[{{},{4,6},{},{}}]:copy(warpmodel2:forward({_img2, _flowappend}))
+
+  local images_in = torch.cat(imagesL2, _flowappend, 2)
+
+  local  flow_est = modelL2:forward(images_in)
+  return flow_est:add(_flowappend)
+end
+M.computeInitFlowL2 = computeInitFlowL2
+
+local function computeInitFlowL3(imagesL3)
+  local imagesL2 = down3:forward(imagesL3:clone())
+  local _flowappend = up3:forward(computeInitFlowL2(imagesL2))*2  
+  local _img2 = imagesL3[{{},{4,6},{},{}}]
+  imagesL3[{{},{4,6},{},{}}]:copy(warpmodel3:forward({_img2, _flowappend}))
+
+  local images_in = torch.cat(imagesL3, _flowappend, 2)
+
+  local  flow_est = modelL3:forward(images_in)
+  return flow_est:add(_flowappend)
+end
+M.computeInitFlowL3 = computeInitFlowL3
+
+local  function computeInitFlowL4(imagesL4)
+  local imagesL3 = down4:forward(imagesL4)
+  local _flowappend = up4:forward(computeInitFlowL3(imagesL3))*2  
+  local _img2 = imagesL4[{{},{4,6},{},{}}]
+  imagesL4[{{},{4,6},{},{}}]:copy(warpmodel4:forward({_img2, _flowappend}))
+
+  local images_in = torch.cat(imagesL4, _flowappend, 2)
+
+  local  flow_est = modelL4:forward(images_in)
+  return flow_est:add(_flowappend)
+end
+M.computeInitFlowL4 = computeInitFlowL4
+
+local function computeInitFlowL5(imagesL5)  
+  local imagesL4 = down5:forward(imagesL5)
+  local _flowappend = up5:forward(computeInitFlowL4(imagesL4))*2  
+
+  local _img2 = imagesL5[{{},{4,6},{},{}}]
+  imagesL5[{{},{4,6},{},{}}]:copy(warpmodel5:forward({_img2, _flowappend}))
+
+  local images_in = torch.cat(imagesL5, _flowappend, 2)
+
+  local  flow_est = modelL5:forward(images_in)
+  return flow_est:add(_flowappend)
+end
+M.computeInitFlowL5 = computeInitFlowL5
+
+local function getRawData(id)
+   local path1 = paths.concat(opt.data, (string.format("%05i", id) .."_img1.ppm"))
+   local path2 = paths.concat(opt.data, (string.format("%05i", id) .."_img2.ppm"))
+
+   local img1 = loadImage(path1)
+   local img2 = loadImage(path2)
+
+   local pathF = paths.concat(opt.data, (string.format("%05i", id) .."_flow.flo"))
+   local flow = loadFlow(pathF)
+
+   return img1, img2, flow
+end
+M.getRawData = getRawData
+
+local testHook = function(id)
+   local path1 = paths.concat(opt.data, (string.format("%05i", id) .."_img1.ppm"))
+   local path2 = paths.concat(opt.data, (string.format("%05i", id) .."_img2.ppm"))
+
+   local img1 = loadImage(path1)
+   local img2 = loadImage(path2)
+   local images = torch.cat(img1, img2, 1)
+
+   local pathF = paths.concat(opt.data, (string.format("%05i", id) .."_flow.flo"))
+   local flow = loadFlow(pathF)
+
+   images = TF.ColorNormalize(meanstd)(images)
+   return images, flow
+end
+M.testHook = testHook
+
+return M