From: Francois Fleuret Date: Sun, 4 Dec 2016 16:03:29 +0000 (+0100) Subject: Added comments, a slightly more complicated model, and the header. X-Git-Url: https://fleuret.org/cgi-bin/gitweb/gitweb.cgi?a=commitdiff_plain;h=1cc41a13ae500ceaf037b3ff880a0eeb25472633;p=profiler-torch.git Added comments, a slightly more complicated model, and the header. --- diff --git a/test-profiler.lua b/test-profiler.lua index 44bbee1..2f1f0ec 100755 --- a/test-profiler.lua +++ b/test-profiler.lua @@ -1,17 +1,57 @@ #!/usr/bin/env luajit +--[[ + + Written by Francois Fleuret (francois@fleuret.org) + + This is free and unencumbered software released into the public + domain. + + Anyone is free to copy, modify, publish, use, compile, sell, or + distribute this software, either in source code form or as a + compiled binary, for any purpose, commercial or non-commercial, and + by any means. + + In jurisdictions that recognize copyright laws, the author or + authors of this software dedicate any and all copyright interest in + the software to the public domain. We make this dedication for the + benefit of the public at large and to the detriment of our heirs + and successors. We intend this dedication to be an overt act of + relinquishment in perpetuity of all present and future rights to + this software under copyright law. + + THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, + EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF + MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND + NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS BE LIABLE FOR ANY + CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF + CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION + WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + + For more information, please refer to + +]]-- + require 'torch' require 'nn' require 'profiler' +-- Create a model + 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.Linear(1000, 1000)) + :add(nn.ReLU()) + ) + :add(nn.Linear(1000, 100)) + +-- Decor it for profiling profiler.decor(model) +-- Create the data and criterion + local input = torch.Tensor(1000, 1000) local target = torch.Tensor(input:size(1), 100) local criterion = nn.MSECriterion() @@ -20,8 +60,11 @@ 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() @@ -40,6 +83,8 @@ for k = 1, 5 do nbSamples = nbSamples + input:size(1) end +-- Print the accumulated timings + profiler.print(model, nbSamples) print('----------------------------------------------------------------------')