Merge pull request #52 from sergomezcol/master

Extra checks and cleanup in C code

7 files changed, 18 insertions, 6 deletions

diff --git a/SpatialDownSampling.lua b/SpatialDownSampling.lua
index b18849f..2aa4216 100644
--- a/SpatialDownSampling.lua
+++ b/SpatialDownSampling.lua
@@ -26,6 +26,11 @@ function SpatialDownSampling:__init(...)
 end
 
 function SpatialDownSampling:updateOutput(input)
+   if (input:size(2) / self.rH) < 1 then
+      error('input too small in dimension 2')
+   elseif (input:size(3) / self.rW) < 1 then
+      error('input too small in dimension 3')
+   end
    self.output:resize(input:size(1), math.floor(input:size(2) / self.rH),
 		      math.floor(input:size(3) / self.rW))
    input.nn.SpatialDownSampling_updateOutput(self, input)
diff --git a/SpatialPadding.lua b/SpatialPadding.lua
index 07bacf8..9c59e9a 100644
--- a/SpatialPadding.lua
+++ b/SpatialPadding.lua
@@ -22,6 +22,8 @@ function SpatialPadding:__init(pad_l, pad_r, pad_t, pad_b, y_dim, x_dim, val)
    self.pad_b = pad_b or self.pad_l
    self.x_dim = x_dim or 3
    self.y_dim = y_dim or 2
+   if (self.x_dim % 1) ~= 0 then error('x_dim must be integer') end
+   if (self.y_dim % 1) ~= 0 then error('y_dim must be integer') end
    self.val = val or 0
 end
 
diff --git a/SpatialReSampling.lua b/SpatialReSampling.lua
index 7324098..e83f8f2 100644
--- a/SpatialReSampling.lua
+++ b/SpatialReSampling.lua
@@ -31,6 +31,9 @@ function SpatialReSampling:__init(...)
 end
 
 function SpatialReSampling:updateOutput(input)
+   assert(input:dim() == 3 or input:dim() == 4,
+          'input to SpatialReSampling must be 3D or 4D, received: [' ..
+          table.concat(input:size():totable(), ', ') .. ']')
    local hDim, wDim = 2, 3
    if input:dim() == 4 then
       hDim, wDim = 3, 4
diff --git a/generic/SoftMaxTree.c b/generic/SoftMaxTree.c
index 9e66a58..074c74e 100644
--- a/generic/SoftMaxTree.c
+++ b/generic/SoftMaxTree.c
@@ -126,13 +126,12 @@ static int nn_(SoftMaxTree_updateGradInput)(lua_State *L)
   THTensor *logsoftOutput = luaT_getfieldcheckudata(L, 1, "_multiBuffer", torch_Tensor);
   
   THTensor *weight = luaT_getfieldcheckudata(L, 1, "weight", torch_Tensor);
-  THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_Tensor);
   THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "_gradInput", torch_Tensor);
   
   THIntTensor *node;
   THTensor *nodeWeight, *nodeOutput;
-  THTensor *nodeGradInput, *nodeGradOutput, *weightTranspose;
-  real *gradInput_data, *output_data;
+  THTensor *nodeGradInput, *weightTranspose;
+  real *output_data;
 
   long i, d;
   
@@ -231,7 +230,7 @@ static int nn_(SoftMaxTree_accGradParameters)(lua_State *L)
   THIntTensor *node;
   THTensor *nodeGradWeight, *nodeGradBias, *nodeInput, *nodeGradOutput;
 
-  long i, d;
+  long i;
   
   luaL_argcheck(L, input->nDimension == 2, 2, "2D(batch mode) tensor expected");
   luaL_argcheck(L, input->size[1] == inputSize, 2, "invalid input size");
diff --git a/generic/SpatialDownSampling.c b/generic/SpatialDownSampling.c
index e5c3c47..4e1fc5a 100644
--- a/generic/SpatialDownSampling.c
+++ b/generic/SpatialDownSampling.c
@@ -48,6 +48,8 @@ static int nn_(SpatialDownSampling_updateGradInput)(lua_State *L) {
   int rW = luaT_getfieldcheckint(L, 1, "rW");
   int rH = luaT_getfieldcheckint(L, 1, "rH");
 
+  THArgCheck(gradOutput->nDimension == 3, 2, "gradOutput must be 3D Tensor");
+
   // dims
   int owidth = gradOutput->size[2];
   int oheight = gradOutput->size[1];
diff --git a/generic/SpatialLinear.c b/generic/SpatialLinear.c
index 8a8b756..113eb85 100644
--- a/generic/SpatialLinear.c
+++ b/generic/SpatialLinear.c
@@ -105,6 +105,7 @@ static int nn_(SpatialLinear_updateGradInput)(lua_State *L)
   THTensor_(free)(gradOutput_y);
   THTensor_(free)(input_xy);
   THTensor_(free)(input_y);
+  THTensor_(free)(weight_t);
   return 1;
 }
 
diff --git a/test/test-all.lua b/test/test-all.lua
index 760d452..27efc37 100644
--- a/test/test-all.lua
+++ b/test/test-all.lua
@@ -137,8 +137,8 @@ end
 
 function nnxtest.SpatialDownSampling()
    local fanin = math.random(1,4)
-   local sizex = math.random(1,4)
-   local sizey = math.random(1,4)
+   local sizex = math.random(11,4)
+   local sizey = math.random(11,4)
    local mx = math.random(2,6)
    local my = math.random(2,6)
    local module = nn.SpatialDownSampling(mx,my)