Introducing: SpatialClassifier, to replace all Spatial modules.

The goal is simple: instead of writing tons of Spatial*** modules,
we make sure all modules (Linear,SoftMax,...) can handle batches,
and use SpatialClassifier to replicate these modules spatially.

5 files changed, 2 insertions, 140 deletions

diff --git a/SpatialLogSoftMax.lua b/SpatialLogSoftMax.lua
deleted file mode 100644
index d145caa..0000000
--- a/SpatialLogSoftMax.lua
+++ /dev/null
@@ -1,17 +0,0 @@
-local SpatialLogSoftMax, parent = torch.class('nn.SpatialLogSoftMax', 'nn.Module')
-
-function SpatialLogSoftMax:__init()
-   parent.__init(self)
-end
-
-function SpatialLogSoftMax:forward(input)
-   self.output:resizeAs(input)
-   input.nn.SpatialLogSoftMax_forward(self, input)
-   return self.output
-end
-
-function SpatialLogSoftMax:backward(input, gradOutput)
-   self.gradInput:resizeAs(input)
-   input.nn.SpatialLogSoftMax_backward(self, input, gradOutput)
-   return self.gradInput
-end
diff --git a/generic/SpatialLogSoftMax.c b/generic/SpatialLogSoftMax.c
deleted file mode 100644
index d0ec07a..0000000
--- a/generic/SpatialLogSoftMax.c
+++ /dev/null
@@ -1,116 +0,0 @@
-#ifndef TH_GENERIC_FILE
-#define TH_GENERIC_FILE "generic/SpatialLogSoftMax.c"
-#else
-
-static int nn_(SpatialLogSoftMax_forward)(lua_State *L)
-{
-  // get all params
-  THTensor *input = luaT_checkudata(L, 2, torch_(Tensor_id));
-  THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_(Tensor_id));
-
-  // dims
-  int width = input->size[2];
-  int height = input->size[1];
-
-  // select planes
-  THTensor *input_row = THTensor_(new)();
-  THTensor *input_point = THTensor_(new)();
-  THTensor *output_row = THTensor_(new)();
-  THTensor *output_point = THTensor_(new)();
-
-  // process the whole plane
-  int x,y;
-  for (y=0; y<height; y++) {
-    THTensor_(select)(input_row, input, 1, y);
-    THTensor_(select)(output_row, output, 1, y);
-    for (x=0; x<width; x++) {
-      THTensor_(select)(input_point, input_row, 1, x);
-      THTensor_(select)(output_point, output_row, 1, x);
-      
-      real sum = THLogZero;
-      
-      TH_TENSOR_APPLY2(real, output_point, real, input_point,           \
-                       real z = *input_point_data;                      \
-                       *output_point_data = z;                          \
-                       sum = THLogAdd(sum, z);)
-
-      THTensor_(add)(output_point, -sum);
-    }
-  }
-
-  // cleanup
-  THTensor_(free)(input_row);
-  THTensor_(free)(input_point);
-  THTensor_(free)(output_row);
-  THTensor_(free)(output_point);
-  return 1;
-}
-
-static int nn_(SpatialLogSoftMax_backward)(lua_State *L)
-{
-  // get all params
-  THTensor *input = luaT_checkudata(L, 2, torch_(Tensor_id));
-  THTensor *gradOutput = luaT_checkudata(L, 3, torch_(Tensor_id));
-  THTensor *gradInput = luaT_getfieldcheckudata(L, 1, "gradInput", torch_(Tensor_id));
-  THTensor *output = luaT_getfieldcheckudata(L, 1, "output", torch_(Tensor_id));
-
-  // dims
-  int width = input->size[2];
-  int height = input->size[1];
-
-  // zero gradInput
-  THTensor_(zero)(gradInput);
-
-  // select planes
-  THTensor *gradOutput_row = THTensor_(new)();
-  THTensor *gradOutput_point = THTensor_(new)();
-  THTensor *gradInput_row = THTensor_(new)();
-  THTensor *gradInput_point = THTensor_(new)();
-  THTensor *output_row = THTensor_(new)();
-  THTensor *output_point = THTensor_(new)();
-
-  // compute gradients for each point
-  int x,y;
-  for (y=0; y<height; y++) {
-    THTensor_(select)(gradInput_row, gradInput, 1, y);
-    THTensor_(select)(gradOutput_row, gradOutput, 1, y);
-    THTensor_(select)(output_row, output, 1, y);
-    for (x=0; x<width; x++) {
-      THTensor_(select)(gradInput_point, gradInput_row, 1, x);
-      THTensor_(select)(gradOutput_point, gradOutput_row, 1, x);
-      THTensor_(select)(output_point, output_row, 1, x);
-      
-      real sum = THTensor_(sum)(gradOutput_point);
-      
-      TH_TENSOR_APPLY3(real, gradInput_point,        \
-                       real, gradOutput_point,       \
-                       real, output_point,                            \
-                       *gradInput_point_data = *gradOutput_point_data - exp(*output_point_data)*sum;);
-    }
-  }
-  
-  // cleanup
-  THTensor_(free)(gradInput_row);
-  THTensor_(free)(gradInput_point);
-  THTensor_(free)(gradOutput_row);
-  THTensor_(free)(gradOutput_point);
-  THTensor_(free)(output_row);
-  THTensor_(free)(output_point);
-  
-  return 1;
-}
-
-static const struct luaL_Reg nn_(SpatialLogSoftMax__) [] = {
-  {"SpatialLogSoftMax_forward", nn_(SpatialLogSoftMax_forward)},
-  {"SpatialLogSoftMax_backward", nn_(SpatialLogSoftMax_backward)},
-  {NULL, NULL}
-};
-
-static void nn_(SpatialLogSoftMax_init)(lua_State *L)
-{
-  luaT_pushmetaclass(L, torch_(Tensor_id));
-  luaT_registeratname(L, nn_(SpatialLogSoftMax__), "nn");
-  lua_pop(L,1);
-}
-
-#endif
diff --git a/init.c b/init.c
index 8ae2b3c..b079723 100644
--- a/init.c
+++ b/init.c
@@ -21,9 +21,6 @@ static const void* torch_DoubleTensor_id = NULL;
 #include "generic/SpatialMaxPooling.c"
 #include "THGenerateFloatTypes.h"
 
-#include "generic/SpatialLogSoftMax.c"
-#include "THGenerateFloatTypes.h"
-
 #include "generic/SpatialUpSampling.c"
 #include "THGenerateFloatTypes.h"
 
@@ -60,7 +57,6 @@ DLL_EXPORT int luaopen_libnnx(lua_State *L)
   nn_FloatHardShrink_init(L);
   nn_FloatAbs_init(L);
   nn_FloatThreshold_init(L);
-  nn_FloatSpatialLogSoftMax_init(L);
   nn_FloatSpatialMaxPooling_init(L);
   nn_FloatSpatialUpSampling_init(L);
   nn_FloatSpatialReSampling_init(L);
@@ -75,7 +71,6 @@ DLL_EXPORT int luaopen_libnnx(lua_State *L)
   nn_DoubleHardShrink_init(L);
   nn_DoubleAbs_init(L);
   nn_DoubleThreshold_init(L);
-  nn_DoubleSpatialLogSoftMax_init(L);
   nn_DoubleSpatialMaxPooling_init(L);
   nn_DoubleSpatialUpSampling_init(L);
   nn_DoubleSpatialReSampling_init(L);
diff --git a/init.lua b/init.lua
index 199075c..50d4317 100644
--- a/init.lua
+++ b/init.lua
@@ -78,7 +78,7 @@ end
 
 -- spatial (images) operators:
 torch.include('nnx', 'SpatialLinear.lua')
-torch.include('nnx', 'SpatialLogSoftMax.lua')
+torch.include('nnx', 'SpatialClassifier.lua')
 torch.include('nnx', 'SpatialMaxPooling.lua')
 torch.include('nnx', 'SpatialPadding.lua')
 torch.include('nnx', 'SpatialNormalization.lua')
diff --git a/nnx-1.0-1.rockspec b/nnx-1.0-1.rockspec
index a5ee7f6..1b90f21 100644
--- a/nnx-1.0-1.rockspec
+++ b/nnx-1.0-1.rockspec
@@ -72,7 +72,7 @@ build = {
          install_files(/lua/nnx Sqrt.lua)
          install_files(/lua/nnx Threshold.lua)
          install_files(/lua/nnx OmpModule.lua)
-         install_files(/lua/nnx SpatialLogSoftMax.lua)
+         install_files(/lua/nnx SpatialClassifier.lua)
          install_files(/lua/nnx SpatialMaxPooling.lua)
          install_files(/lua/nnx SpatialLinear.lua)
          install_files(/lua/nnx SpatialPadding.lua)