model_cnn_depth5.lua

dofile('optim-rmsprop-single.lua')
dofile('MapTable.lua')

L_cnn = nn.LookupTableMaskZero(mapWordIdx2Vector:size()[1], opt.embeddingDim)
L_cnn.weight:sub(2,-1):copy(mapWordIdx2Vector)


cnn = nn.Sequential()
cnn:add(L_cnn)
if opt.dropout > 0 then
   cnn:add(nn.Dropout(opt.dropout))
end
if cudnnok then
   conv = cudnn.TemporalConvolution(opt.embeddingDim, opt.numFilters, opt.contConvWidth)
elseif fbok then
   conv = nn.TemporalConvolutionFB(opt.embeddingDim, opt.numFilters, opt.contConvWidth)
else
   conv = nn.TemporalConvolution(opt.embeddingDim, opt.numFilters, opt.contConvWidth)
end

cnn:add(conv)

if opt.conv1Norm then
  norm=nn.Sequential()
  if opt.normTrans then
    norm:add(nn.Transpose({1,2}))
    norm:add(nn.Normalize(2))
    norm:add(nn.Transpose({2,1}))
  else
    norm:add(nn.Normalize(2))
  end
  cnn:add(nn.SplitTable(1))
  cnn:add(nn.MapTable():add(norm))
  cnn:add(nn.JoinTable(1))
  cnn:add(nn.View(opt.batchSize, -1,opt.numFilters ))
end

cnn:add(nn.ReLU())

if cudnnok then
   conv2 = cudnn.TemporalConvolution(opt.numFilters, opt.numFilters, opt.contConvWidth)
elseif fbok then
   conv2 = nn.TemporalConvolutionFB(opt.numFilters, opt.numFilters, opt.contConvWidth)
else
   conv2 = nn.TemporalConvolution(opt.numFilters, opt.numFilters, opt.contConvWidth)
end
cnn:add(conv2)

if opt.conv2Norm then
  norm=nn.Sequential()
  if opt.normTrans then
    norm:add(nn.Transpose({1,2}))
    norm:add(nn.Normalize(2))
    norm:add(nn.Transpose({2,1}))
  else
    norm:add(nn.Normalize(2))
  end
  cnn:add(nn.SplitTable(1))
  cnn:add(nn.MapTable():add(norm))
  cnn:add(nn.JoinTable(1))
  cnn:add(nn.View(opt.batchSize, -1,opt.numFilters ))
end

cnn:add(nn.ReLU())
--cnn:add(nn.TemporalMaxPooling(2))

if cudnnok then
   conv3 = cudnn.TemporalConvolution(opt.numFilters, opt.numFilters, opt.contConvWidth)
elseif fbok then
   conv3 = nn.TemporalConvolutionFB(opt.numFilters, opt.numFilters, opt.contConvWidth)
else
   conv3 = nn.TemporalConvolution(opt.numFilters, opt.numFilters, opt.contConvWidth)
end
cnn:add(conv3)

if opt.conv3Norm then
  norm=nn.Sequential()
  if opt.normTrans then
    norm:add(nn.Transpose({1,2}))
    norm:add(nn.Normalize(2))
    norm:add(nn.Transpose({2,1}))
  else
    norm:add(nn.Normalize(2))
  end
  cnn:add(nn.SplitTable(1))
  cnn:add(nn.MapTable():add(norm))
  cnn:add(nn.JoinTable(1))
  cnn:add(nn.View(opt.batchSize, -1,opt.numFilters ))
end

conv4_filters = opt.numFilters
cnn:add(nn.ReLU())
if opt.TMP then
  cnn:add(nn.TemporalMaxPooling(opt.contConvWidth, opt.contConvWidth-1))
  conv4_filters=opt.numFilters*2
end
if cudnnok then
   conv4 = cudnn.TemporalConvolution(opt.numFilters, conv4_filters, opt.contConvWidth)
elseif fbok then
   conv4 = nn.TemporalConvolutionFB(opt.numFilters, conv4_filters, opt.contConvWidth)
else
   conv4 = nn.TemporalConvolution(opt.numFilters, conv4_filters, opt.contConvWidth)
end
cnn:add(conv4)

if opt.conv4Norm then
  norm=nn.Sequential()
  if opt.normTrans then
    norm:add(nn.Transpose({1,2}))
    norm:add(nn.Normalize(2))
    norm:add(nn.Transpose({2,1}))
  else
    norm:add(nn.Normalize(2))
  end
  cnn:add(nn.SplitTable(1))
  cnn:add(nn.MapTable():add(norm))
  cnn:add(nn.JoinTable(1))
  cnn:add(nn.View(opt.batchSize, -1, conv4_filters ))
end

cnn:add(nn.ReLU())

conv5_filters = conv4_filters
if cudnnok then
   conv5 = cudnn.TemporalConvolution(conv4_filters, conv5_filters, opt.contConvWidth)
elseif fbok then
   conv5 = nn.TemporalConvolutionFB(conv4_filters, conv5_filters, opt.contConvWidth)
else
   conv5 = nn.TemporalConvolution(conv4_filters, conv5_filters, opt.contConvWidth)
end
cnn:add(conv5)

if opt.conv5Norm then
  norm=nn.Sequential()
  if opt.normTrans then
    norm:add(nn.Transpose({1,2}))
    norm:add(nn.Normalize(2))
    norm:add(nn.Transpose({2,1}))
  else
    norm:add(nn.Normalize(2))
  end
  cnn:add(nn.SplitTable(1))
  cnn:add(nn.MapTable():add(norm))
  cnn:add(nn.JoinTable(1))
  cnn:add(nn.View(opt.batchSize, -1, conv5_filters ))
end

cnn:add(nn.ReLU())

cnn:add(nn.TopK(opt.topk, 2, true, true))
cnn:add(nn.View(opt.batchSize, -1))
--cnn:add(nn.Max(2))
cnn:add(nn.Linear(conv5_filters*opt.topk, opt.hiddenDim))
cnn:add(nn.ReLU())


model = nn.Sequential()
model:add(cnn)
--model:add(nn.Dropout(0.5))
--model:add(cudnn.BatchNormalization(opt.hiddenDim + 2*opt.LSTMhiddenSize))
model:add(nn.Linear(opt.hiddenDim, opt.numLabels))
model:add(nn.LogSoftMax())

if opt.twoCriterion then
  prob_idx = nn.ConcatTable()
  prob_idx:add(nn.Identity())
  prob_idx:add(nn.ArgMax(2,opt.numLabels, false))
  model:add(prob_idx)

  nll = nn.ClassNLLCriterion()
  abs = nn.AbsCriterion()
  criterion = nn.ParallelCriterion(true):add(nll, opt.criterionWeight):add(abs)
else
  criterion = nn.ClassNLLCriterion()
end


if opt.type == 'cuda' then
   model:cuda()
   criterion:cuda()
end
if model then
   parameters,gradParameters = model:getParameters()
   print("Model Size: ", parameters:size()[1])
   parametersClone = parameters:clone()
end
print(model)
print(criterion)

if opt.optimization == 'CG' then
   optimState = {
      maxIter = opt.maxIter
   }
   optimMethod = optim.cg

elseif opt.optimization == 'LBFGS' then
   optimState = {
      learningRate = opt.learningRate,
      maxIter = opt.maxIter,
      nCorrection = 10
   }
   optimMethod = optim.lbfgs
elseif opt.optimization == 'sgd' then
    optimState = {
      lr = opt.learningRate,
      lrd = opt.weightDecay,
      mom = opt.momentum,
   }
   optimMethod = optim.msgd
elseif opt.optimization == 'SGD' then
   optimState = {
      learningRate = opt.learningRate,
      learningRateDecay = opt.learningRateDecay,
      momentum = opt.momentum,
      learningRateDecay = 0,
      dampening = 0,
      nesterov = opt.nesterov
   }
   optimMethod = optim.sgd

elseif opt.optimization == 'RMSPROP' then
   optimState = {
      decay = opt.decayRMSProp,
      lr = opt.lrRMSProp,
      momentum = opt.momentumRMSProp,
      epsilon = opt.epsilonRMSProp
   }
   optimMethod = optim.rmspropsingle
else
   error('unknown optimization method')
end

function saveModel(s)
   torch.save(opt.outputprefix .. string.format("_%010.2f_model", s), parameters)
end

function loadModel(m)
   parameters:copy(torch.load(m))
end

function cleanMemForRuntime()
   parametersClone = nil
   gradParameters = nil
   model:get(1).gradWeight = nil
   model:get(3).gradWeight = nil 
   model:get(3).gradBias = nil
   model:get(6).gradWeight = nil
   model:get(6).gradBias = nil
   model:get(9).gradWeight = nil
   model:get(9).gradBias = nil
   model:get(11).gradWeight = nil
   model:get(11).gradBias = nil
   collectgarbage()
   collectgarbage()
end


function train()
    epoch = epoch or 1
    if optimState.evalCounter then
        optimState.evalCounter = optimState.evalCounter + 1
    end
--    optimState.learningRate = opt.learningRate
    local time = sys.clock()
    model:training()
   
    local batches = trainDataTensor:size()[1]/opt.batchSize
    local bs = opt.batchSize
    shuffle = torch.randperm(batches)
    for t = 1,batches,1 do
        local begin = (shuffle[t] - 1)*bs + 1
        local input = trainDataTensor:narrow(1, begin , bs) 
        local target = trainDataTensor_y:narrow(1, begin , bs)
        local input_lstm_fwd = trainDataTensor_lstm_fwd:narrow(1, begin , bs)
        local input_lstm_bwd = trainDataTensor_lstm_bwd:narrow(1, begin , bs)

        if cudnnok then
          conv_nodes = model:findModules('cudnn.TemporalConvolution')
        else
          conv_nodes = model:findModules('nn.TemporalConvolution')
        end
        for i = 1, #conv_nodes do
          conv_nodes[i].bias:fill(0)
        end
        
   --     print(conv_nodes)
   --     print(container_nodes)
   --     model:get(1):get(2).bias:fill(0)
   --     model:get(1):get(4).bias:fill(0)
        
        
        local feval = function(x)
            if x ~= parameters then
                parameters:copy(x)
            end
            gradParameters:zero()
            local f = 0
            if true then
               local output = model:forward(input)
               f = criterion:forward(output, target)
               local df_do = criterion:backward(output, target)
               model:backward(input, df_do)
            else
               local output = model:forward(input)
               f = criterion:forward(output, target)
               local df_do = criterion:backward(output, target)
               model:backward(input, df_do) 
            end
            --cutorch.synchronize()
            if opt.L1reg ~= 0 then
               local norm, sign = torch.norm, torch.sign
               f = f + opt.L1reg * norm(parameters,1)
               gradParameters:add( sign(parameters):mul(opt.L1reg) )
            end
            if opt.L2reg ~= 0 then
    --           local norm, sign = torch.norm, torch.sign
    --           f = f + opt.L2reg * norm(parameters,2)^2/2
               parametersClone:copy(parameters)
               gradParameters:add( parametersClone:mul(opt.L2reg) )
            end
            gradParameters:clamp(-opt.gradClip, opt.gradClip)
            return f,gradParameters
        end

        if optimMethod == optim.asgd then
            _,_,average = optimMethod(feval, parameters, optimState)
        else
--            a,b = model:parameters()
         --   print('a ' .. a[1][1][1]);
            optimMethod(feval, parameters, optimState)
         --   print('  ' .. a[1][1][1]);
        end
    end

    time = sys.clock() - time
    print("\n==> time for 1 epoch = " .. (time) .. ' seconds')
end

function test(inputDataTensor, inputDataTensor_lstm_fwd, inputDataTensor_lstm_bwd, inputTarget, state)
    local time = sys.clock()
    model:evaluate()
    local bs = opt.batchSizeTest
    local batches = inputDataTensor:size()[1]/bs
    local correct = 0
    local correct2 = 0
    local curr = -1
    if cudnnok then
      conv_nodes = model:findModules('cudnn.TemporalConvolution')
    else
      conv_nodes = model:findModules('nn.TemporalConvolution')
    end
    for i = 1, #conv_nodes do
       conv_nodes[i].bias:fill(0)
    end
    for t = 1,batches,1 do
        curr = t
        local begin = (t - 1)*bs + 1
        local input = inputDataTensor:narrow(1, begin , bs)
        local input_lstm_fwd = inputDataTensor_lstm_fwd:narrow(1, begin , bs)
        local input_lstm_bwd = inputDataTensor_lstm_bwd:narrow(1, begin , bs)
        local pred        
        pred = model:forward(input)
   
        local prob, pos
        if opt.twoCriterion then
           prob, pos = torch.max(pred[1], 2)
        else
           prob, pos = torch.max(pred, 2)
        end
        for m = 1,bs do
          for k,v in ipairs(inputTarget[begin+m-1]) do
            if pos[m][1] == v then
                correct = correct + 1
                break
            end
          end
          for k,v in ipairs(inputTarget[begin+m-1]) do
            if torch.abs(pos[m][1] - v) < 2 then
              correct2 = correct2 + 1
              break
            end
          end 
        end     
    end

    local rest_size = inputDataTensor:size()[1] - curr * bs
    if rest_size > 0 then
       local input
       local input_lstm_fwd
       local input_lstm_bwd
       if opt.type == 'cuda' then
          input = torch.CudaTensor(bs, inputDataTensor:size(2)):zero()
          input_lstm_fwd = torch.CudaTensor(bs, inputDataTensor_lstm_fwd:size(2)):zero()
          input_lstm_bwd = torch.CudaTensor(bs, inputDataTensor_lstm_bwd:size(2)):zero()
       else
          input = torch.FloatTensor(bs, inputDataTensor:size(2)):zero()
          input_lstm_fwd = torch.FloatTensor(bs, inputDataTensor_lstm_fwd:size(2)):zero()
          input_lstm_bwd = torch.FloatTensor(bs, inputDataTensor_lstm_bwd:size(2)):zero()
       end
       input:narrow(1,1,rest_size):copy(inputDataTensor:narrow(1, curr*bs + 1, rest_size))
       input_lstm_fwd:narrow(1,1,rest_size):copy(inputDataTensor_lstm_fwd:narrow(1, curr*bs + 1, rest_size))
       input_lstm_bwd:narrow(1,1,rest_size):copy(inputDataTensor_lstm_bwd:narrow(1, curr*bs + 1, rest_size))
       local pred
       pred = model:forward(input)

       local prob, pos 
       if opt.twoCriterion then
           prob, pos = torch.max(pred[1], 2)
       else
           prob, pos = torch.max(pred, 2)
       end
       for m = 1,rest_size do
          for k,v in ipairs(inputTarget[curr*bs+m]) do
            if pos[m][1] == v then
                correct = correct + 1
                break
            end
          end
          for k,v in ipairs(inputTarget[curr*bs+m]) do
            if torch.abs(pos[m][1] - v) < 2 then
                correct2 = correct2 + 1
                break
            end
          end
       end
    end
     
    state.bestAccuracy = state.bestAccuracy or 0
    state.bestEpoch = state.bestEpoch or 0
    state.bestAccuracy2 = state.bestAccuracy2 or 0
    state.bestEpoch2 = state.bestEpoch2 or 0
    local currAccuracy = correct/(inputDataTensor:size()[1])
    local currAccuracy2 = correct2/(inputDataTensor:size()[1])
    if currAccuracy > state.bestAccuracy then state.bestAccuracy = currAccuracy; state.bestEpoch = epoch end
    if currAccuracy2 > state.bestAccuracy2 then state.bestAccuracy2 = currAccuracy2; state.bestEpoch2 = epoch end
    print(string.format("Epoch %s Accuracy: %s, best Accuracy: %s on epoch %s at time %s", epoch, currAccuracy, state.bestAccuracy, state.bestEpoch, sys.toc() ))
    print(string.format("Epoch %s Accuracy2: %s, best Accuracy: %s on epoch %s at time %s", epoch, currAccuracy2, state.bestAccuracy2, state.bestEpoch2, sys.toc() ))
end