hello-world.lua

-- Copyright 2016-present, Facebook, Inc.
-- All rights reserved.
--
-- This source code is licensed under the license found in the
-- LICENSE file in the root directory of this source tree.

--Train on the GPU if there is one, otherwise CPU
scn=require 'sparseconvnet'
tensorType = scn.cutorch and 'torch.CudaTensor' or 'torch.FloatTensor'


model = scn.Sequential()
:add(scn.SparseVggNet(2,1,{ --dimension 2, 1 input plane
      {'C', 8}, -- 3x3 VSC convolution, 8 output planes, batchnorm, ReLU
      {'C', 8}, -- and another
      {'MP', 3, 2}, --max pooling, size 3, stride 2
      {'C', 16}, -- etc
      {'C', 16},
      {'MP', 3, 2},
      {'C', 24},
      {'C', 24},
      {'MP', 3, 2}}))
:add(scn.Convolution(2,24,32,3,1,false)) --an SC convolution on top
:add(scn.BatchNormReLU(32))
:add(scn.SparseToDense(2))
:type(tensorType)

--[[
To use the network we must create an scn.InputBatch with right dimensionality.
If we want the output to have spatial size 10x10, we can find the appropriate
input size, give that we uses three layers of MP3/2 max-pooling, and finish
with a SC convoluton
]]

inputSpatialSize=model:suggestInputSize(torch.LongTensor{10,10}) --103x103
input=scn.InputBatch(2,inputSpatialSize)

--Now we build the input batch, sample by sample, and active site by active site.
msg={
  " O   O  OOO  O    O    OO     O       O   OO   OOO   O    OOO   ",
  " O   O  O    O    O   O  O    O       O  O  O  O  O  O    O  O  ",
  " OOOOO  OO   O    O   O  O    O   O   O  O  O  OOO   O    O   O ",
  " O   O  O    O    O   O  O     O O O O   O  O  O  O  O    O  O  ",
  " O   O  OOO  OOO  OOO  OO       O   O     OO   O  O  OOO  OOO   ",
}

input:addSample()

local locations = {}
local featureVectors = {}

for y,line in ipairs(msg) do
  for x = 1,string.len(line) do
    if string.sub(line,x,x) == 'O' then
      table.insert(locations, {x,y})
      table.insert(featureVectors, {1})
    end
  end
end

input:setLocations(
  torch.LongTensor(locations),
  torch.FloatTensor(featureVectors),
  0)

--[[
Optional: allow metadata preprocessing to be done in batch preparation threads
to improve GPU utilization.

Parameter:
3 if using MP3/2 or size-3 stride-2 convolutions for downsizeing,
2 if using MP2
]]
input:precomputeMetadata(3)

model:evaluate()
input:type(tensorType)
output = model:forward(input)

--[[
Output is 1x32x10x10: our minibatch has 1 sample, the network has 32 output
feature planes, and 10x10 is the spatial size of the output.
]]
print(output:size(), output:type())