added more documentation and assertion check on input dim

1 files changed, 11 insertions, 1 deletions

diff --git a/CTCCriterion.lua b/CTCCriterion.lua
index 1449db0..7f628de 100644
--- a/CTCCriterion.lua
+++ b/CTCCriterion.lua
@@ -1,7 +1,16 @@
 require 'warp_ctc'
-
+------------------------------------------------------------------------
+--[[ CTCCriterion ]]--
+-- CTC Alignment for sequence data where input and labels do not align.
+-- Useful for speech recognition on a phoneme/character level basis.
+-- Inputs assumed are in the form of batch x time x inputdim.
+-- Targets assumed in the form of {{1,2},{3,4}} where {1,2} is for the first
+-- element.
+------------------------------------------------------------------------
 local CTCCriterion, parent = torch.class('nn.CTCCriterion', 'nn.Criterion')
 
+CTCCriterion.dim = 3
+
 function CTCCriterion:__init()
     parent.__init(self)
     self.acts = torch.Tensor()
@@ -9,6 +18,7 @@ function CTCCriterion:__init()
 end
 
 function CTCCriterion:updateOutput(output, labels)
+    assert(output:nDimension() == CTCCriterion.dim, "Output must be a tensor of (batch x time x inputdim), recieved " .. output:nDimension() .. " dimensions")
     local tensorSizes = output:size()
     local acts = self:createCTCBatch(output, tensorSizes)
     local sizes = {} -- For each batch we state the number of time steps.