nn.SequencerNot Implement nn.Sequencer; Not Implement JoinTable; Not Implement Mean

[Andybert]

when I try to convert the following torch net to pytorch,

require 'torch' require 'nn' require 'nnx' require 'optim' require 'rnn'

function buildModel_MeanPool_RNN(nFltrs1,nFltrs2,nFltrs3,nPersonsTrain)

local nFilters = {nFltrs1,nFltrs2,nFltrs3}

local filtsize = {5,5,5}
local poolsize = {2,2,2}
local stepSize = {2,2,2}

-- remember this adds padding to ALL SIDES of the image
local padDim = 4

local cnn = nn.Sequential()

local ninputChannels = 5
cnn:add(nn.SpatialZeroPadding(padDim, padDim, padDim, padDim))
cnn:add(nn.SpatialConvolutionMM(ninputChannels, nFilters[1], filtsize[1], filtsize[1], 1, 1))
cnn:add(nn.Tanh())
cnn:add(nn.SpatialMaxPooling(poolsize[1],poolsize[1],stepSize[1],stepSize[1]))

ninputChannels = nFilters[1]
cnn:add(nn.SpatialZeroPadding(padDim, padDim, padDim, padDim))
cnn:add(nn.SpatialConvolutionMM(ninputChannels, nFilters[2], filtsize[2], filtsize[2], 1, 1))
cnn:add(nn.Tanh())
cnn:add(nn.SpatialMaxPooling(poolsize[2],poolsize[2],stepSize[2],stepSize[2]))

ninputChannels = nFilters[2]
cnn:add(nn.SpatialZeroPadding(padDim, padDim, padDim, padDim))
cnn:add(nn.SpatialConvolutionMM(ninputChannels, nFilters[3], filtsize[3], filtsize[3], 1, 1))
cnn:add(nn.Tanh())
cnn:add(nn.SpatialMaxPooling(poolsize[3],poolsize[3],stepSize[3],stepSize[3]))

local nFullyConnected = nFilters[3]*10*8    

cnn:add(nn.Reshape(1,nFullyConnected))
cnn:add(nn.Dropout(0.6))    
cnn:add(nn.Linear(nFullyConnected,128))
-- cnn:cuda()

local h2h = nn.Sequential()
h2h:add(nn.Tanh())
h2h:add(nn.Dropout(0.6))
h2h:add(nn.Linear(128,128))
-- h2h:cuda()

local r1 = nn.Recurrent(
    128,
    cnn,
    h2h,
    nn.Identity(),
    16)

local rnn1 = nn.Sequencer(
    nn.Sequential()
    :add(r1)
    )

Combined_CNN_RNN_1 = nn.Sequential()
Combined_CNN_RNN_1:add(rnn1)
Combined_CNN_RNN_1:add(nn.JoinTable(1))
Combined_CNN_RNN_1:add(nn.Mean(1))

local r2 = nn.Recurrent(
    128,
    cnn:clone('weight','bias','gradWeight','gradBias'),
    h2h:clone('weight','bias','gradWeight','gradBias'),
    nn.Identity(),
    16)

local rnn2 = nn.Sequencer(
    nn.Sequential()
    :add(r2)
    )

Combined_CNN_RNN_2 = nn.Sequential()
Combined_CNN_RNN_2:add(rnn2)
Combined_CNN_RNN_2:add(nn.JoinTable(1))
Combined_CNN_RNN_2:add(nn.Mean(1))

-- Combined_CNN_RNN_2 = Combined_CNN_RNN_1:clone('weight','bias','gradWeight','gradBias')

local mlp2 = nn.ParallelTable()
mlp2:add(Combined_CNN_RNN_1)
mlp2:add(Combined_CNN_RNN_2)
-- mlp2:cuda()

local mlp3 = nn.ConcatTable()
mlp3:add(nn.Identity())
mlp3:add(nn.Identity())
mlp3:add(nn.Identity())
-- mlp3:cuda()

local mlp4 = nn.ParallelTable()
mlp4:add(nn.Identity())
mlp4:add(nn.SelectTable(1))
mlp4:add(nn.SelectTable(2))
-- mlp4:cuda()

-- used to predict the identity of each person
local classifierLayer = nn.Linear(128,nPersonsTrain)

-- identification
local mlp6 = nn.Sequential()
mlp6:add(classifierLayer)
mlp6:add(nn.LogSoftMax())
-- mlp6:cuda()

local mlp7 = nn.Sequential()
mlp7:add(classifierLayer:clone('weight','bias','gradWeight','gradBias'))
mlp7:add(nn.LogSoftMax())
-- mlp7:cuda()

local mlp5 = nn.ParallelTable()
mlp5:add(nn.PairwiseDistance(2))
mlp5:add(mlp6)
mlp5:add(mlp7)
-- mlp5:cuda()

local fullModel = nn.Sequential()
fullModel:add(mlp2)
fullModel:add(mlp3)
fullModel:add(mlp4)
fullModel:add(mlp5)
-- fullModel:cuda()

local crit = nn.SuperCriterion()
crit:add(nn.HingeEmbeddingCriterion(2),1)
crit:add(nn.ClassNLLCriterion(),1)
crit:add(nn.ClassNLLCriterion(),1)

return fullModel, crit, Combined_CNN_RNN_1, cnn

end

fullModel, crit, Combined_CNN_RNN_1, cnn = buildModel_MeanPool_RNN(16,32,32,150) torch.save('Combined_CNN_RNN.t7',Combined_CNN_RNN_1)

But it is not working: Not Implement nn.Sequencer Not Implement JoinTable Not Implement Mean

[rkosti]

Why did you ( @clcarwin ) close this issue? @Andybert did you solve this problem? I am having similar issue: Not Implement ParallelTable Not Implement JoinTable Not Implement BatchNormalization

[Andybert]

No, I haven't solved it. I don't know why this issue is closed.