def Test28():
Trans, BatchDatas, SrcDict, TgtDict, MaxLength = T.TrainBuildTransformer()
for BatchInd, Batch in enumerate(BatchDatas):
SrcSent = Batch["SrcSent"]
SrcLength = Batch["SrcLength"]
TgtSent = Batch["TgtSent"]
TgtLength = Batch["TgtLength"]
SrcMask = T.BatchLengthToBoolTensorMask(SrcLength, MaxLength)
TgtMask = T.BatchLengthToBoolTensorMask(TgtLength, MaxLength)
Output = Trans(SrcSent, TgtSent, SrcMask, TgtMask)
print("Step")
print(BatchInd + 1)
print(Output.size())
print(Output[0][2])
def Do(self):
for BatchInd, Batch in enumerate(self.BatchDatas):
SrcSent = Batch["SrcSent"]
SrcLength = Batch["SrcLength"]
BatchSampleNum = SrcSent.size()[0]
#print(type(BatchSampleNum))
CurrentBatchOuput = TranslateOutput(
self.TgtDict, self.MaxLength).Init(BatchSampleNum)
TgtIndexSent = CurrentBatchOuput.GetCurrentIndexTensor()
SrcMask = Tr.BatchLengthToBoolTensorMask(SrcLength, self.MaxLength)
for Step in range(self.MaxLength):
TgtMask = self.GetTgtMask(Step, BatchSampleNum)
if self.UseGPU:
SrcSent=SrcSent.cuda()
TgtMask=TgtMask.cuda()
SrcMask=SrcMask.cuda()
TgtMask=TgtMask.cuda()
ProOutput = self.Model(SrcSent, TgtIndexSent, SrcMask, TgtMask)
ProOutput=ProOutput.cpu()
LocalMaxPro, Idx = self.PickWord(ProOutput, Step)
CurrentBatchOuput.Add(Idx)
if CurrentBatchOuput.AllFinish():
print("Appending Ouput of Batch "+str(BatchInd))
CurrentBatchOuput.ToFile(self.ResultPath)
break
TgtIndexSent = CurrentBatchOuput.GetCurrentIndexTensor()
def Test29():
Trans, BatchDatas, SrcDict, TgtDict, MaxLength = T.TestBuildTransformer()
for BatchInd, Batch in enumerate(BatchDatas):
SrcSent = Batch["SrcSent"]
SrcLength = Batch["SrcLength"]
SrcMask = T.BatchLengthToBoolTensorMask(SrcLength, MaxLength)
#Output=Trans(SrcSent,TgtSent,SrcMask,TgtMask)
print("Step")
print(BatchInd + 1)
def Test26():
MaxLength = 30
BatchSize = 2
EmbeddingSize = 4
HeadNum = 2
EnLayer = 2
DeLayer = 2
SrcIndSentences, SrcLength, SrcDict = DL.LoadData("src.sents", "src.vocab",
MaxLength)
TgtIndSentences, TgtLength, TgtDict = DL.LoadData("tgt.sents", "tgt.vocab",
MaxLength)
TrainDataset = DL.TrainCorpusDataset(SrcIndSentences, SrcLength,
TgtIndSentences, TgtLength)
BatchDatas = DL.TrainDataLoaderCreator(TrainDataset, BatchSize)
SrcVocabularySize = SrcDict.VocabularySize()
TgtVocabularySize = TgtDict.VocabularySize()
Trans = T.TransformerNMTModel(HeadNum, EmbeddingSize, SrcVocabularySize,
TgtVocabularySize, MaxLength, EnLayer,
DeLayer)
for BatchInd, Batch in enumerate(BatchDatas):
print("BegingBatch")
SrcSent = Batch["SrcSent"]
print(SrcSent.size())
SrcLength = Batch["SrcLength"]
#print(SrcLength.size())
TgtSent = Batch["TgtSent"]
print(TgtSent.size())
TgtLength = Batch["TgtLength"]
#print(TgtLength.size())
SrcMask = T.BatchLengthToBoolTensorMask(SrcLength, MaxLength)
TgtMask = T.BatchLengthToBoolTensorMask(TgtLength, MaxLength)
Output = Trans(SrcSent, TgtSent, SrcMask, TgtMask)
print("Step")
print(BatchInd + 1)
print(Output.size())
print(Output[0][2])
def Test24():
print(T.BatchLengthToBoolTensorMask([2, 3, 4], 6))