Rowconv repull (#1120)

* Added TemporalRowConvolutionMM layer, tests, and documentation

1 files changed, 120 insertions, 0 deletions

diff --git a/TemporalRowConvolution.lua b/TemporalRowConvolution.lua
new file mode 100644
index 0000000..7c9d6a2
--- /dev/null
+++ b/TemporalRowConvolution.lua
@@ -0,0 +1,120 @@
+local THNN = require "nn.THNN"
+
+local TemporalRowConvolution, parent = torch.class("nn.TemporalRowConvolution", "nn.Module")
+
+function TemporalRowConvolution:__init(inputFrameSize, kW, dW, featFirst)
+  parent.__init(self)
+
+  self.inputFrameSize = inputFrameSize
+  self.kW = kW
+  self.dW = dW or 1
+
+  self.weight = torch.Tensor(inputFrameSize, 1, kW)
+  self.bias = torch.Tensor(inputFrameSize)
+  self.gradWeight = torch.Tensor(inputFrameSize, 1, kW)
+  self.gradBias = torch.Tensor(inputFrameSize)
+
+  -- Set to true for batch x inputFrameSize x nInputFrame
+  self.featFirst = featFirst and true or false
+  self:reset()
+end
+
+function TemporalRowConvolution:noBias()
+  self.bias = nil
+  self.gradBias = nil
+  return self
+end
+
+function TemporalRowConvolution:reset(stdv)
+  if stdv then
+    stdv = stdv * math.sqrt(3)
+  else
+    stdv = 1 / math.sqrt(self.kW * self.inputFrameSize)
+  end
+  self.weight:uniform(-stdv, stdv)
+  self.bias:uniform(-stdv, stdv)
+end
+
+function TemporalRowConvolution:updateOutput(input)
+  assert(input.THNN, torch.type(input)..".THNN backend not imported")
+  self.finput = self.finput or input.new()
+  self.fgradInput = self.fgradInput or input.new()
+
+  input.THNN.TemporalRowConvolution_updateOutput(
+    input:cdata(),
+    self.output:cdata(),
+    self.weight:cdata(),
+    THNN.optionalTensor(self.bias),
+    self.finput:cdata(),
+    self.fgradInput:cdata(),
+    self.kW,
+    self.dW,
+    0, -- would be self.padW
+    self.featFirst
+  )
+
+  return self.output
+end
+
+function TemporalRowConvolution:updateGradInput(input, gradOutput)
+  assert(input.THNN, torch.type(input)..".THNN backend not imported")
+
+  if self.gradInput then
+    input.THNN.TemporalRowConvolution_updateGradInput(
+      input:cdata(),
+      gradOutput:cdata(),
+      self.gradInput:cdata(),
+      self.weight:cdata(),
+      self.finput:cdata(),
+      self.fgradInput:cdata(),
+      self.kW,
+      self.dW,
+      0, -- would be self.padW
+      self.featFirst
+    )
+    return self.gradInput
+  end
+end
+
+function TemporalRowConvolution:accGradParameters(input, gradOutput, scale)
+  assert(input.THNN, torch.type(input)..".THNN backend not imported")
+
+  input.THNN.TemporalRowConvolution_accGradParameters(
+    input:cdata(),
+    gradOutput:cdata(),
+    self.gradWeight:cdata(),
+    THNN.optionalTensor(self.gradBias),
+    self.finput:cdata(),
+    self.fgradInput:cdata(),
+    self.kW,
+    self.dW,
+    0, -- would be self.padW
+    self.featFirst,
+    scale or 1)
+end
+
+function TemporalRowConvolution:type(type, tensorCache)
+  if self.finput then self.finput:set() end
+  if self.fgradInput then self.fgradInput:set() end
+  return parent.type(self, type, tensorCache)
+end
+
+function TemporalRowConvolution:__tostring__()
+  local s = string.format("%s(%d, %d", torch.type(self), self.inputFrameSize, self.kW)
+  if self.dW ~= 1 then
+    s = s .. string.format(", %d", self.dW)
+  end
+  if self.padW and self.padW ~= 0 then -- currently padding is not supported
+    s = s .. ", " .. self.padW
+  end
+  if self.bias then
+    return s .. ")"
+  else
+    return s .. ") without bias"
+  end
+end
+
+function TemporalRowConvolution:clearState()
+  nn.utils.clear(self, "finput", "fgradInput", "_input", "_gradOutput")
+  return parent.clearState(self)
+end