X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=dyncnn.git;a=blobdiff_plain;f=fftb.lua;fp=fftb.lua;h=86a05a8c1de83d8ae7f4607475deef6761f22ad0;hp=0000000000000000000000000000000000000000;hb=8cce872485111eaa79ce60041715227a8ff4d45f;hpb=c2ae78c4cfa3628c9695a08f60daeec09d9c2aad

diff --git a/fftb.lua b/fftb.lua
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+++ b/fftb.lua
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+
+-- Francois Fleuret's Torch Toolbox
+
+require 'torch'
+require 'nn'
+
+----------------------------------------------------------------------
+
+colors = sys.COLORS
+
+function printf(f, ...)
+   print(string.format(f, unpack({...})))
+end
+
+function printfc(c, f, ...)
+   printf(c .. string.format(f, unpack({...})) .. colors.black)
+end
+
+function logCommand(c)
+   print(colors.blue .. '[' .. c .. '] -> [' .. sys.execute(c) .. ']' .. colors.black)
+end
+
+----------------------------------------------------------------------
+-- Environment variables
+
+defaultNbThreads = 1
+defaultUseGPU = false
+
+if os.getenv('TORCH_NB_THREADS') then
+   defaultNbThreads = os.getenv('TORCH_NB_THREADS')
+   print('Environment variable TORCH_NB_THREADS is set and equal to ' .. defaultNbThreads)
+else
+   print('Environment variable TORCH_NB_THREADS is not set, default is ' .. defaultNbThreads)
+end
+
+if os.getenv('TORCH_USE_GPU') then
+   defaultUseGPU = os.getenv('TORCH_USE_GPU') == 'yes'
+   print('Environment variable TORCH_USE_GPU is set and evaluated as ' .. tostring(defaultUseGPU))
+else
+   print('Environment variable TORCH_USE_GPU is not set, default is ' .. tostring(defaultUseGPU))
+end
+
+----------------------------------------------------------------------
+
+function fftbInit(cmd, params)
+
+   torch.setnumthreads(params.nbThreads)
+   torch.setdefaulttensortype('torch.FloatTensor')
+   torch.manualSeed(params.seed)
+
+   -- Logging
+
+   if params.rundir == '' then
+      params.rundir = cmd:string('experiment', params, { })
+   end
+
+   paths.mkdir(params.rundir)
+
+   if not params.noLog then
+      -- Append to the log if there is one
+      cmd:log(io.open(params.rundir .. '/log', 'a'), params)
+   end
+
+   -- Dealing with the CPU/GPU
+
+   ffnn = {}
+
+   -- By default, ffnn returns the entries from nn
+   local mt = {}
+   function mt.__index(table, key)
+      return (cudnn and cudnn[key]) or (cunn and cunn[key]) or nn[key]
+   end
+   setmetatable(ffnn, mt)
+
+   -- These are the tensors that can be kept on the CPU
+   ffnn.SlowTensor = torch.Tensor
+   ffnn.SlowStorage = torch.Storage
+   -- These are the tensors that should be moved to the GPU
+   ffnn.FastTensor = torch.Tensor
+   ffnn.FastStorage = torch.Storage
+
+   if params.useGPU then
+      require 'cutorch'
+      require 'cunn'
+      require 'cudnn'
+
+      if params.fastGPU then
+         cudnn.benchmark = true
+         cudnn.fastest = true
+      end
+
+      ffnn.FastTensor = torch.CudaTensor
+      ffnn.FastStorage = torch.CudaStorage
+   end
+end
+
+----------------------------------------------------------------------
+
+function dimAtThatPoint(model, input)
+   if params.useGPU then
+      model:cuda()
+   end
+   local i = ffnn.FastTensor(input:narrow(1, 1, 1):size()):copy(input:narrow(1, 1, 1))
+   return model:forward(i):nElement()
+end
+
+----------------------------------------------------------------------
+
+function sizeForBatch(n, x)
+   local size = x:size()
+   size[1] = n
+   return size
+end
+
+function fillBatch(data, first, batch, permutation)
+   local actualBatchSize = math.min(params.batchSize, data.input:size(1) - first + 1)
+
+   if batch.input then
+      if actualBatchSize ~= batch.input:size(1) then
+         batch.input:resize(sizeForBatch(actualBatchSize, batch.input))
+      end
+   else
+      if torch.isTypeOf(data.input, ffnn.SlowTensor) then
+         batch.input = ffnn.FastTensor(sizeForBatch(actualBatchSize, data.input));
+      else
+         batch.input = data.input.new():resize(sizeForBatch(actualBatchSize, data.input));
+      end
+   end
+
+   if batch.target then
+      if actualBatchSize ~= batch.target:size(1) then
+         batch.target:resize(sizeForBatch(actualBatchSize, batch.target))
+      end
+   else
+      if torch.isTypeOf(data.target, ffnn.SlowTensor) then
+         batch.target = ffnn.FastTensor(sizeForBatch(actualBatchSize, data.target));
+      else
+         batch.target = data.target.new():resize(sizeForBatch(actualBatchSize, data.target));
+      end
+   end
+
+   for k = 1, actualBatchSize do
+      local i
+      if permutation then
+         i = permutation[first + k - 1]
+      else
+         i = first + k - 1
+      end
+      batch.input[k] = data.input[i]
+      batch.target[k] = data.target[i]
+   end
+end
+
+----------------------------------------------------------------------
+
+--[[
+
+The combineImage function takes as input a parameter c which is the
+value to use for the background of the resulting image (padding and
+such), and t which is either a 2d tensor, a 3d tensor, or a table.
+
+ * If t is a 3d tensor, it is returned unchanged.
+
+ * If t is a 2d tensor [r x c], it is reshaped to [1 x r x c] and
+   returned.
+
+ * If t is a table, combineImage first calls itself recursively on
+   t[1], t[2], etc.
+
+   It then creates a new tensor by concatenating the results
+   horizontally if t.vertical is nil, vertically otherwise.
+
+   It adds a padding of t.pad pixels if this field is set.
+
+ * Example
+
+   x = torch.Tensor(64, 64):fill(0.5)
+   y = torch.Tensor(100, 30):fill(0.85)
+
+   i = combineImages(1.0,
+      {
+         pad = 1,
+         vertical = true,
+         { pad = 1, x },
+         {
+            y,
+            { pad = 4, torch.Tensor(32, 16):fill(0.25) },
+            { pad = 1, torch.Tensor(45, 54):uniform(0.25, 0.9) },
+         }
+      }
+   )
+
+   image.save('example.png', i)
+
+]]--
+
+function combineImages(c, t)
+
+   if torch.isTensor(t) then
+
+      if t:dim() == 3 then
+         return t
+      elseif t:dim() == 2 then
+         return torch.Tensor(1, t:size(1), t:size(2)):copy(t)
+      else
+         error('can only deal with [height x width] or [channel x height x width] tensors.')
+      end
+
+   else
+
+      local subImages = {} -- The subimages
+      local nc = 0 -- Nb of columns
+      local nr = 0 -- Nb of rows
+
+      for i, x in ipairs(t) do
+         subImages[i] = combineImages(c, x)
+         if t.vertical then
+            nr = nr + subImages[i]:size(2)
+            nc = math.max(nc, subImages[i]:size(3))
+         else
+            nr = math.max(nr, subImages[i]:size(2))
+            nc = nc + subImages[i]:size(3)
+         end
+      end
+
+      local pad = t.pad or 0
+      local result = torch.Tensor(subImages[1]:size(1), nr + 2 * pad, nc + 2 * pad):fill(c)
+      local co = 1 + pad -- Origin column
+      local ro = 1 + pad -- Origin row
+
+      for i in ipairs(t) do
+
+         result
+            :sub(1, subImages[1]:size(1),
+                 ro, ro + subImages[i]:size(2) - 1,
+                 co, co + subImages[i]:size(3) - 1)
+            :copy(subImages[i])
+
+         if t.vertical then
+            ro = ro + subImages[i]:size(2)
+         else
+            co = co + subImages[i]:size(3)
+         end
+
+      end
+
+      return result
+
+   end
+
+end
+
+--[[
+
+The imageFromTensors function gets as input a list of tensors of
+arbitrary dimensions each, but whose two last dimensions stand for
+height x width. It creates an image tensor (2d, one channel) with each
+argument tensor unfolded per row.
+
+]]--
+
+function imageFromTensors(bt, signed)
+   local gap = 1
+   local tgap = -1
+   local width = 0
+   local height = gap
+
+   for _, t in pairs(bt) do
+      local d = t:dim()
+      local h, w = t:size(d - 1), t:size(d)
+      local n = t:nElement() / (w * h)
+      width = math.max(width, gap + n * (gap + w))
+      height = height + gap + tgap + gap + h
+   end
+
+   local e = torch.Tensor(3, height, width):fill(1.0)
+   local y0 = 1 + gap
+
+   for _, t in pairs(bt) do
+      local d = t:dim()
+      local h, w = t:size(d - 1), t:size(d)
+      local n = t:nElement() / (w * h)
+      local z = t:norm() / math.sqrt(t:nElement())
+
+      local x0 = 1 + gap + math.floor( (width - n * (w + gap)) /2 )
+      local u = torch.Tensor(t:size()):copy(t):resize(n, h, w)
+      for m = 1, n do
+
+         for c = 1, 3 do
+            for y = 0, h+1 do
+               e[c][y0 + y - 1][x0     - 1] = 0.0
+               e[c][y0 + y - 1][x0 + w    ] = 0.0
+            end
+            for x = 0, w+1 do
+               e[c][y0     - 1][x0 + x - 1] = 0.0
+               e[c][y0 + h    ][x0 + x - 1] = 0.0
+            end
+         end
+
+         for y = 1, h do
+            for x = 1, w do
+               local v = u[m][y][x] / z
+               local r, g, b
+               if signed then
+                  if v < -1 then
+                     r, g, b = 0.0, 0.0, 1.0
+                  elseif v > 1 then
+                     r, g, b = 1.0, 0.0, 0.0
+                  elseif v >= 0 then
+                     r, g, b = 1.0, 1.0 - v, 1.0 - v
+                  else
+                     r, g, b = 1.0 + v, 1.0 + v, 1.0
+                  end
+               else
+                  if v <= 0 then
+                     r, g, b = 1.0, 1.0, 1.0
+                  elseif v > 1 then
+                     r, g, b = 0.0, 0.0, 0.0
+                  else
+                     r, g, b = 1.0 - v, 1.0 - v, 1.0 - v
+                  end
+               end
+               e[1][y0 + y - 1][x0 + x - 1] = r
+               e[2][y0 + y - 1][x0 + x - 1] = g
+               e[3][y0 + y - 1][x0 + x - 1] = b
+            end
+         end
+         x0 = x0 + w + gap
+      end
+      y0 = y0 + h + gap + tgap + gap
+   end
+
+   return e
+end