X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?p=profiler-torch.git;a=blobdiff_plain;f=test-profiler.lua;h=92f186e5cd8be1170916c8ac60b4050353f65bf9;hp=b394a332898ac52f72e80201382e50ce95922ef6;hb=HEAD;hpb=21c3bd2eb990e3fa58aa36b0e8fcd8901de5569c

diff --git a/test-profiler.lua b/test-profiler.lua
index b394a33..92f186e 100755
--- a/test-profiler.lua
+++ b/test-profiler.lua
@@ -1,25 +1,86 @@
 #!/usr/bin/env luajit
 
+--[[
+
+   Copyright (c) 2016 Idiap Research Institute, http://www.idiap.ch/
+   Written by Francois Fleuret <francois.fleuret@idiap.ch>
+
+   This file is free software: you can redistribute it and/or modify
+   it under the terms of the GNU General Public License version 3 as
+   published by the Free Software Foundation.
+
+   It is distributed in the hope that it will be useful, but WITHOUT
+   ANY WARRANTY; without even the implied warranty of MERCHANTABILITY
+   or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU General Public
+   License for more details.
+
+   You should have received a copy of the GNU General Public License
+   along with this file.  If not, see <http://www.gnu.org/licenses/>.
+
+]]--
+
 require 'torch'
 require 'nn'
 
 require 'profiler'
 
+-- Create a model
+
+local w, h, fs = 50, 50, 3
+local nhu =  (w - fs + 1) * (h - fs + 1)
+
 local model = nn.Sequential()
-model:add(nn.Linear(1000, 1000))
-model:add(nn.ReLU())
-model:add(nn.Linear(1000, 100))
+   :add(nn.Sequential()
+           :add(nn.SpatialConvolution(1, 1, fs, fs))
+           :add(nn.Reshape(nhu))
+           :add(nn.Linear(nhu, 1000))
+           :add(nn.ReLU())
+       )
+   :add(nn.Linear(1000, 100))
+
+-- Decorate it for profiling
+
+profiler.decorate(model)
+
+-- Create the data and criterion
 
-profiler.decor(model)
+local input = torch.Tensor(1000, 1, h, w)
+local target = torch.Tensor(input:size(1), 100)
+local criterion = nn.MSECriterion()
+
+local nbSamples = 0
+local modelTime = 0
+local dataTime = 0
+
+-- Loop five times through the data forward and backward
+
+for k = 1, 5 do
+   local t1 = sys.clock()
+
+   input:uniform(-1, 1)
+   target:uniform()
+
+   local t2 = sys.clock()
 
-for k = 1, 10 do
-   local input = torch.Tensor(1000, 1000):uniform(-1, 1)
-   local target = torch.Tensor(input:size(1), 100):uniform()
-   local criterion = nn.MSECriterion()
    local output = model:forward(input)
    local loss = criterion:forward(output, target)
    local dloss = criterion:backward(output, target)
    model:backward(input, dloss)
+
+   local t3 = sys.clock()
+
+   dataTime = dataTime + (t2 - t1)
+   modelTime = modelTime + (t3 - t2)
+
+   nbSamples = nbSamples + input:size(1)
 end
 
-profiler.print(model)
+-- Print the accumulated timings
+
+print()
+-- profiler.color = false
+profiler.print(model, nbSamples)
+-- profiler.print(model)
+
+print(string.format('Total model time %.02fs', modelTime))
+print(string.format('Total data time %.02fs', dataTime))