initial commit

examples/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()
+for y,line in ipairs(msg) do
+  for x = 1,string.len(line) do
+    if string.sub(line,x,x) == 'O' then
+      local location = torch.LongTensor{x,y}
+      local featureVector = torch.FloatTensor{1}
+      input:setLocation(location,featureVector,0)
+    end
+  end
+end
+
+--[[
+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())

examples/hello-world.py

+# 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.
+
+import torch
+import sparseconvnet.legacy as scn
+
+# Use the GPU if there is one, otherwise CPU
+tensorType = 'torch.cuda.FloatTensor' if torch.cuda.is_available() else 'torch.FloatTensor'
+
+model = scn.Sequential().add(
+    scn.SparseVggNet(2, 1,
+                     [['C', 8], ['C', 8], ['MP', 3, 2],
+                      ['C', 16], ['C', 16], ['MP', 3, 2],
+                         ['C', 24], ['C', 24], ['MP', 3, 2]])
+).add(
+    scn.ValidConvolution(2, 24, 32, 3, False)
+).add(
+    scn.BatchNormReLU(32)
+).add(
+    scn.SparseToDense(2)
+).type(tensorType)
+
+# output will be 10x10
+inputSpatialSize = model.suggestInputSize(torch.LongTensor([10, 10]))
+input = scn.InputBatch(2, inputSpatialSize)
+
+msg = [
+    " X   X  XXX  X    X    XX     X       X   XX   XXX   X    XXX   ",
+    " X   X  X    X    X   X  X    X       X  X  X  X  X  X    X  X  ",
+    " XXXXX  XX   X    X   X  X    X   X   X  X  X  XXX   X    X   X ",
+    " X   X  X    X    X   X  X     X X X X   X  X  X  X  X    X  X  ",
+    " X   X  XXX  XXX  XXX  XX       X   X     XX   X  X  XXX  XXX   "]
+input.addSample()
+for y, line in enumerate(msg):
+    for x, c in enumerate(line):
+        if c == 'X':
+            location = torch.LongTensor([x, y])
+            featureVector = torch.FloatTensor([1])
+            input.setLocation(location, featureVector, 0)
+
+# Optional: allow metadata preprocessing to be done in batch preparation threads
+# to improve GPU utilization.
+#
+# Parameter:
+#    3 if using MP3/2 pooling or C3/2 convolutions for downsizing,
+#    2 if using MP2 pooling for downsizing.
+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())