Spacing.

data.lua

@@ -6,29 +6,29 @@ Threads.serialization('threads.sharedserialize')
 -- For the data-loading details, look at donkey.lua
 -------------------------------------------------------------------------------
 do -- start K datathreads (donkeys)
-	if opt.nDonkeys > 0 then
-		local options = opt -- make an upvalue to serialize over to donkey threads
-		donkeys = Threads(
-		opt.nDonkeys,
-		function()
-			require 'torch'
-			paths.dofile('trainplot/TrainPlotter.lua')
-		end,
-		function(idx)
-			opt = options -- pass to all donkeys via upvalue
-			tid = idx
- 			local seed = opt.manualSeed + idx
-			torch.manualSeed(seed)
-			print(string.format('Starting donkey with id: %d seed: %d', tid, seed))
-			paths.dofile('donkey.lua')
-		end
-		);
-	else -- single threaded data loading. useful for debugging
-		paths.dofile('donkey.lua')
-		donkeys = {}
-		function donkeys:addjob(f1, f2) f2(f1()) end
-		function donkeys:synchronize() end
-	end
+    if opt.nDonkeys > 0 then
+        local options = opt -- make an upvalue to serialize over to donkey threads
+        donkeys = Threads(
+        opt.nDonkeys,
+        function()
+            require 'torch'
+            paths.dofile('trainplot/TrainPlotter.lua')
+        end,
+        function(idx)
+            opt = options -- pass to all donkeys via upvalue
+            tid = idx
+            local seed = opt.manualSeed + idx
+            torch.manualSeed(seed)
+            print(string.format('Starting donkey with id: %d seed: %d', tid, seed))
+            paths.dofile('donkey.lua')
+        end
+        );
+    else -- single threaded data loading. useful for debugging
+        paths.dofile('donkey.lua')
+        donkeys = {}
+        function donkeys:addjob(f1, f2) f2(f1()) end
+        function donkeys:synchronize() end
+    end
 end
 
 nClasses = nil

dataset.lua

@@ -379,41 +379,41 @@ function dataset:get(i1, i2)
 end
 
 function dataset:computeAccuracy(scores)
-	local N, M, P, S, X 
-	-- Load dataLoader for videos ('self' dataLoader is for clips)
-	local testCacheVid = torch.load(paths.concat(opt.logRoot, opt.dataset, 'cache', opt.stream, 'testCache.t7'))
-	--assert(self.numSamples == scores:size(1))
-
-	-- Find clip-video correspondence
-	local clips = torch.Tensor(scores:size(1))
-	for i = 1, scores:size(1) do
-		N = ffi.string(torch.data(self.imagePath[i])) --v_ApplyEyeMakeup_g01_c01.avi_0017.avi
-		N = paths.basename(N, 'avi')                  --v_ApplyEyeMakeup_g01_c01.avi_0017
-		N = paths.basename(N, paths.extname(N))       --v_ApplyEyeMakeup_g01_c01
+   local N, M, P, S, X 
+   -- Load dataLoader for videos ('self' dataLoader is for clips)
+   local testCacheVid = torch.load(paths.concat(opt.logRoot, opt.dataset, 'cache', opt.stream, 'testCache.t7'))
+   --assert(self.numSamples == scores:size(1))
+
+   -- Find clip-video correspondence
+   local clips = torch.Tensor(scores:size(1))
+   for i = 1, scores:size(1) do
+      N = ffi.string(torch.data(self.imagePath[i])) --v_ApplyEyeMakeup_g01_c01.avi_0017.avi
+      N = paths.basename(N, 'avi')                  --v_ApplyEyeMakeup_g01_c01.avi_0017
+      N = paths.basename(N, paths.extname(N))       --v_ApplyEyeMakeup_g01_c01
 
-		for j = 1, testCacheVid.numSamples do
-			if(N == paths.basename(ffi.string(torch.data(testCacheVid.imagePath[j])), 'avi')) then
-				clips[i] = j
-				break
-			end
-		end
-	end
-
-	-- Loop over videos to take mean over clip scores and compute accuracy
-	local acc = 0
-	for i = 1, testCacheVid.numSamples do
-		X = clips:eq(i)                       -- 0,1 vector of clips belonging to same video
-		if(X:any()) then                      -- normally always true
-			X = torch.nonzero(X)              -- indices of clips belonging to same video
-			S = scores:index(1,X:select(2,1)) -- scores of clips from the same video
-			S = S:mean(1)                     -- mean over clips
-			M, P = torch.max(S, 2)            -- max score -> predicted class
-			if(P:squeeze() == testCacheVid.imageClass[i]) then -- pred==gt
-				acc = acc + 1
-			end
-		end
-	end
-	return 100*acc/testCacheVid.numSamples
+      for j = 1, testCacheVid.numSamples do
+         if(N == paths.basename(ffi.string(torch.data(testCacheVid.imagePath[j])), 'avi')) then
+            clips[i] = j
+            break
+         end
+      end
+   end
+
+   -- Loop over videos to take mean over clip scores and compute accuracy
+   local acc = 0
+   for i = 1, testCacheVid.numSamples do
+      X = clips:eq(i)                       -- 0,1 vector of clips belonging to same video
+      if(X:any()) then                      -- normally always true
+         X = torch.nonzero(X)              -- indices of clips belonging to same video
+         S = scores:index(1,X:select(2,1)) -- scores of clips from the same video
+         S = S:mean(1)                     -- mean over clips
+         M, P = torch.max(S, 2)            -- max score -> predicted class
+         if(P:squeeze() == testCacheVid.imageClass[i]) then -- pred==gt
+            acc = acc + 1
+         end
+      end
+   end
+   return 100*acc/testCacheVid.numSamples
 end
 
 return dataset