def __init__(self, params):
self.parent = super(Questioner, self)
# input-output for current bot
params['inVocabSize'] = params['qInVocab']
params['outVocabSize'] = params['qOutVocab']
self.parent.__init__(params)
# always condition on task
#self.rnn = nn.LSTMCell(2*self.embedSize, self.hiddenSize)
self.rnn = nn.LSTMCell(self.embedSize, self.hiddenSize)
# additional prediction network
# start token included
numPreds = sum([len(ii) for ii in self.props.values()])
# network for predicting
# embedsize *2 because we embed the two components separately
self.predictRNN = nn.LSTMCell(self.embedSize * 2, self.hiddenSize)
self.predictNet = nn.Linear(self.hiddenSize, numPreds)
initializeWeights([self.predictNet, self.predictRNN, self.rnn],
'xavier')
# setting offset
self.taskOffset = params['aOutVocab'] + params['qOutVocab']
self.listenOffset = params['aOutVocab']
self.tasklist_per_component = torch.LongTensor([[0, 1], [0, 2], [0, 3], \
[1, 0], [1, 2], [1, 3], \
[2, 0], [2, 1], [2, 3], \
[3, 0], [3, 1], [3, 2]]) \
def __init__(self, params):
self.parent = super(Questioner, self)
# input-output for current bot
params['inVocabSize'] = params['qInVocab']
if params['overhear']:
params['inVocabSize'] += params['aOutVocab']
if params['overhearTask']:
params['inVocabSize'] += params['numTasks']
params['outVocabSize'] = params['qOutVocab']
self.parent.__init__(params)
# always condition on task
#self.rnn = nn.LSTMCell(2*self.embedSize, self.hiddenSize);
self.rnn = nn.LSTMCell(self.embedSize, self.hiddenSize)
# additional prediction network
# start token included
numPreds = sum([len(ii) for ii in self.props.values()])
# network for predicting
self.predictRNN = nn.LSTMCell(self.embedSize, self.hiddenSize)
self.predictNet = nn.Linear(self.hiddenSize, numPreds)
initializeWeights([self.predictNet, self.predictRNN, self.rnn],
'xavier')
# setting offset
# aOut | qOut | tasks | none | noneTask | aOut | tasks
self.listenOffset = params['aOutVocab']
self.taskOffset = params['aOutVocab'] + params['qOutVocab']
self.noneToken = self.taskOffset + params['numTasks']
self.noneTaskToken = self.noneToken + 1
self.overhearOffset = self.noneTaskToken + 1
self.overhearTaskOffset = params['numTasks'] + 2 + params['aOutVocab']
def __init__(self, params):
self.parent = super(Answerer, self)
# input-output for current bot
params['inVocabSize'] = params['aInVocab']
if params['overhear']:
params['inVocabSize'] += params['qOutVocab']
params['outVocabSize'] = params['aOutVocab']
self.parent.__init__(params)
# number of attribute values
numAttrs = sum([len(ii) for ii in self.props.values()])
# number of unique attributes
numUniqAttr = len(self.props)
# rnn inputSize
rnnInputSize = numUniqAttr * self.imgFeatSize + self.embedSize
self.imgNet = nn.Embedding(numAttrs, self.imgFeatSize)
self.rnn = nn.LSTMCell(rnnInputSize, self.hiddenSize)
initializeWeights([self.rnn, self.imgNet], 'xavier')
# set offset
self.listenOffset = params['qOutVocab']
self.noneToken = self.listenOffset + params['aOutVocab']
self.overhearOffset = self.noneToken + 1
def __init__(self, params):
super(ChatBot, self).__init__()
# absorb all parameters to self
for attr in params:
setattr(self, attr, params[attr])
# standard initializations
self.hState = torch.Tensor()
self.cState = torch.Tensor()
self.actions = []
self.evalFlag = False
# modules (common)
self.inNet = nn.Embedding(self.inVocabSize, self.embedSize)
self.outNet = nn.Linear(self.hiddenSize, self.outVocabSize)
# initialize weights
initializeWeights([self.inNet, self.outNet], 'xavier')
def __init__(self, params):
self.parent = super(Questioner, self)
# input-output for current bot
params['inVocabSize'] = params['qInVocab']
params['outVocabSize'] = params['qOutVocab']
self.parent.__init__(params)
self.bot_name = 'qbot'
# always condition on task
#self.rnn = nn.LSTMCell(2*self.embedSize, self.hiddenSize);
self.rnn = nn.LSTMCell(self.embedSize, self.hiddenSize)
# additional prediction network
# start token included
numPreds = sum([len(ii) for ii in self.props.values()])
# network for predicting
self.predictRNN = nn.LSTMCell(self.embedSize, self.hiddenSize)
self.predictNet = nn.Linear(self.hiddenSize, numPreds)
initializeWeights([self.predictNet, self.predictRNN, self.rnn],
'xavier')
# setting offset
self.taskOffset = params['aOutVocab'] + params['qOutVocab']
self.listenOffset = params['aOutVocab']