So, back to decorating the classes and not the objects so that torch.save() does...

diff --git a/profiler.lua b/profiler.lua
index 34e180b..4e45787 100644 (file)
--- a/profiler.lua
+++ b/profiler.lua
@@ -50,9 +50,18 @@ function profiler.decorate(model, functionsToDecorate)
 
       local nameOrig = name .. '__orig'
 
 
       local nameOrig = name .. '__orig'
 

-      if model[name] and not model[nameOrig] then
-         model[nameOrig] = model[name]
-         model[name] = function(self, ...)
+      -- We decorate the class and not the object, otherwise we cannot
+      -- save models anymore.
+
+      if rawget(model, name) then
+         error('We decorate the class, not the objects, and there is a ' .. name .. ' in ' .. model)
+      end
+
+      local toDecorate = getmetatable(model)
+
+      if toDecorate[name] and not toDecorate[nameOrig] then
+         toDecorate[nameOrig] = toDecorate[name]
+         toDecorate[name] = function(self, ...)

             local startTime = sys.clock()
             local result = { self[nameOrig](self, unpack({...})) }
             local endTime = sys.clock()
             local startTime = sys.clock()
             local result = { self[nameOrig](self, unpack({...})) }
             local endTime = sys.clock()


@@ -71,24 +80,27 @@ function profiler.decorate(model, functionsToDecorate)
 
 end
 
 
 end
 

-function profiler.print(model, nbSamples, indent)
+function profiler.print(model, nbSamples, totalTime, indent)

    local indent = indent or ''
 
    print(string.format('%s* %s', indent, model.__typename))
 
    for l, t in pairs(model.accTime) do
    local indent = indent or ''
 
    print(string.format('%s* %s', indent, model.__typename))
 
    for l, t in pairs(model.accTime) do

-      local s
+      local s = string.format('%s  %s %.02fs', indent, l, t)
+      if totalTime then
+         s = s .. string.format(' [%.02f%%]', 100 * t / totalTime)
+      end

       if nbSamples then
       if nbSamples then

-         s = string.format(' (%.01fmus/sample)', 1e6 * t / nbSamples)
-      else
-         s = ''
+         s = s .. string.format(' (%.01fmus/sample)', 1e6 * t / nbSamples)

       end
       end

-      print(string.format('%s  %s %.02fs%s', indent, l, t, s))
+      print(s)

    end
 
    end
 

+   print()
+

    if torch.isTypeOf(model, nn.Container) then
       for _, m in ipairs(model.modules) do
    if torch.isTypeOf(model, nn.Container) then
       for _, m in ipairs(model.modules) do

-         profiler.print(m, nbSamples, indent .. '  ')
+         profiler.print(m, nbSamples, totalTime, indent .. '  ')

       end
    end
 end
       end
    end
 end

diff --git a/test-profiler.lua b/test-profiler.lua
index a78c944..18677ec 100755 (executable)
--- a/test-profiler.lua
+++ b/test-profiler.lua
@@ -39,9 +39,14 @@ require 'profiler'
 
 -- Create a model
 
 
 -- Create a model
 

+local w, h, fs = 50, 50, 3
+local nhu =  (w - fs + 1) * (h - fs + 1)
+

 local model = nn.Sequential()
    :add(nn.Sequential()
 local model = nn.Sequential()
    :add(nn.Sequential()

-           :add(nn.Linear(1000, 1000))
+           :add(nn.SpatialConvolution(1, 1, fs, fs))
+           :add(nn.Reshape(nhu))
+           :add(nn.Linear(nhu, 1000))

            :add(nn.ReLU())
        )
    :add(nn.Linear(1000, 100))
            :add(nn.ReLU())
        )
    :add(nn.Linear(1000, 100))


@@ -55,7 +60,7 @@ torch.save('model.t7', model)
 
 -- Create the data and criterion
 
 
 -- Create the data and criterion
 

-local input = torch.Tensor(1000, 1000)
+local input = torch.Tensor(1000, 1, h, w)

 local target = torch.Tensor(input:size(1), 100)
 local criterion = nn.MSECriterion()
 
 local target = torch.Tensor(input:size(1), 100)
 local criterion = nn.MSECriterion()
 


@@ -88,7 +93,7 @@ end
 
 -- Print the accumulated timings
 
 
 -- Print the accumulated timings
 

-profiler.print(model, nbSamples)
+profiler.print(model, nbSamples, modelTime)

 -- profiler.print(model)
 
 print()
 -- profiler.print(model)
 
 print()