Fix many spelling errors with tool codespell.

This patch is imported from Debian Package.

CONTRIBUTING.md

@@ -22,7 +22,7 @@ restrictions:
   [mailing-list](http://groups.google.com/forum/#!forum/torch7)).
 
 * Please **do not** open issues regarding the code in a torch package 
-  outside the core. For example dont open issues about the 
+  outside the core. For example don't open issues about the 
   REPL in the nn issue tracker, use the trepl issue tracker for that.
 
 <a name="bugs"></a>

ClassSimplexCriterion.lua

@@ -64,7 +64,7 @@ function ClassSimplexCriterion:__init(nClasses)
 end
 
 -- handle target being both 1D tensor, and
--- target being 2D tensor (2D tensor means dont do anything)
+-- target being 2D tensor (2D tensor means don't do anything)
 local function transformTarget(self, target)
     if torch.type(target) == 'number' then
         self._target:resize(self.nClasses)

Container.lua

@@ -22,7 +22,7 @@ end
 
 -- Check if passing arguments through xpcall is supported in this Lua interpreter.
 local _, XPCALL_ARGS = xpcall(function(x) return x ~= nil end, function() end, 1)
-local TRACEBACK_WARNING = "WARNING: If you see a stack trace below, it doesn't point to the place where this error occured. Please use only the one above."
+local TRACEBACK_WARNING = "WARNING: If you see a stack trace below, it doesn't point to the place where this error occurred. Please use only the one above."
 -- module argument can be retrieved with moduleIndex, but code is cleaner when
 -- it has to be specified anyway.
 function Container:rethrowErrors(module, moduleIndex, funcName, ...)

LookupTable.lua

@@ -125,7 +125,7 @@ function LookupTable:renorm(input)
    if not self.maxNorm then
       return
    end
-   -- copy input into _input, so _input is continous.
+   -- copy input into _input, so _input is continuous.
    -- The copied _input will be modified in the C code.
    self._input:resize(input:size()):copy(input)
    local row_idx = self._input

SpatialDropout.lua

@@ -19,7 +19,7 @@ function SpatialDropout:updateOutput(input)
       end
       self.noise:bernoulli(1-self.p)
       -- We expand the random dropouts to the entire feature map because the
-      -- features are likely correlated accross the map and so the dropout
+      -- features are likely correlated across the map and so the dropout
       -- should also be correlated.
       self.output:cmul(torch.expandAs(self.noise, input))
    else

Sum.lua

@@ -36,8 +36,8 @@ end
 
 function Sum:updateGradInput(input, gradOutput)
     local dimension = self:_getPositiveDimension(input)
-    -- zero-strides dont work with MKL/BLAS, so
-    -- dont set self.gradInput to zero-stride tensor.
+    -- zero-strides don't work with MKL/BLAS, so
+    -- don't set self.gradInput to zero-stride tensor.
     -- Instead, do a deepcopy
     local size      = input:size()
     size[dimension] = 1

VolumetricDropout.lua

@@ -19,7 +19,7 @@ function VolumetricDropout:updateOutput(input)
       end
       self.noise:bernoulli(1-self.p)
       -- We expand the random dropouts to the entire feature map because the
-      -- features are likely correlated accross the map and so the dropout
+      -- features are likely correlated across the map and so the dropout
       -- should also be correlated.
       self.output:cmul(torch.expandAs(self.noise, input))
    else

doc/simple.md

@@ -598,7 +598,7 @@ end
 module = nn.Copy(inputType, outputType, [forceCopy, dontCast])
 ```
 
-This layer copies the input to output with type casting from `inputType` to `outputType`. Unless `forceCopy` is true, when the first two arguments are the same, the input isn't copied, only transfered as the output. The default `forceCopy` is false.
+This layer copies the input to output with type casting from `inputType` to `outputType`. Unless `forceCopy` is true, when the first two arguments are the same, the input isn't copied, only transferred as the output. The default `forceCopy` is false.
 When `dontCast` is true, a call to `nn.Copy:type(type)` will not cast the module's `output` and `gradInput` Tensors to the new type. The default is false.
 
 <a name="nn.Narrow"></a>
@@ -1432,10 +1432,10 @@ gpustr = torch.serialize(gpu)
 ``` 
 
 The module is located in the __nn__ package instead of __cunn__ as this allows
-it to be used in CPU-only enviroments, which are common for production models.
+it to be used in CPU-only environments, which are common for production models.
 
 The module supports nested table `input` and `gradOutput` tensors originating from multiple devices.
-Each nested tensor in the returned `gradInput` will be transfered to the device its commensurate tensor in the `input`.
+Each nested tensor in the returned `gradInput` will be transferred to the device its commensurate tensor in the `input`.
 
 The intended use-case is not for model-parallelism where the models are executed in parallel on multiple devices, but 
 for sequential models where a single GPU doesn't have enough memory. 

lib/THNN/generic/SpatialUpSamplingNearest.c

@@ -14,7 +14,7 @@ void THNN_(SpatialUpSamplingNearest_updateOutput)(
   int yDim = input->nDimension-1;
 
   // dims
-  int idim = input->nDimension;  // Gauranteed to be between 3 and 5
+  int idim = input->nDimension;  // Guaranteed to be between 3 and 5
   int osz0 = output->size[0];
   int osz1 = output->size[1];
   int osz2 = output->size[2];
@@ -80,7 +80,7 @@ void THNN_(SpatialUpSamplingNearest_updateGradInput)(
   int yDim = gradInput->nDimension-1;
 
   // dims
-  int idim = gradInput->nDimension;  // Gauranteed to be between 3 and 5
+  int idim = gradInput->nDimension;  // Guaranteed to be between 3 and 5
   int isz0 = gradInput->size[0];
   int isz1 = gradInput->size[1];
   int isz2 = gradInput->size[2];

test.lua

@@ -6306,9 +6306,9 @@ function nntest.addSingletonDimension()
    local resultArg = torch.Tensor()
    local resultR = nn.utils.addSingletonDimension(resultArg, tensor, dim)
    mytester:eq(resultArg:size():totable(), resultSize,
-               'wrong content for random singleton dimention '..
+               'wrong content for random singleton dimension '..
                'when the result is passed as argument')
-   mytester:eq(resultArg, result, 'wrong content for random singleton dimention '..
+   mytester:eq(resultArg, result, 'wrong content for random singleton dimension '..
                'when the result is passed as argument')
 
    mytester:eq(resultR == resultArg, true,