def forward(self, tenFirst, tenSecond, objPrevious):
tenFlow = None
tenFeat = None
if objPrevious is None:
tenFlow = None
tenFeat = None
tenVolume = torch.nn.functional.leaky_relu(input=correlation.FunctionCorrelation(tenFirst=tenFirst, tenSecond=tenSecond), negative_slope=0.1, inplace=False)
tenFeat = torch.cat([ tenVolume ], 1)
elif objPrevious is not None:
tenFlow = self.netUpflow(objPrevious['tenFlow'])
tenFeat = self.netUpfeat(objPrevious['tenFeat'])
tenVolume = torch.nn.functional.leaky_relu(input=correlation.FunctionCorrelation(tenFirst=tenFirst, tenSecond=backwarp(tenInput=tenSecond, tenFlow=tenFlow * self.fltBackwarp)), negative_slope=0.1, inplace=False)
tenFeat = torch.cat([ tenVolume, tenFirst, tenFlow, tenFeat ], 1)
# end
tenFeat = torch.cat([ self.netOne(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netTwo(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netThr(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netFou(tenFeat), tenFeat ], 1)
tenFeat = torch.cat([ self.netFiv(tenFeat), tenFeat ], 1)
tenFlow = self.netSix(tenFeat)
return {
'tenFlow': tenFlow,
'tenFeat': tenFeat
}
def forward(self, tenFirst, tenSecond, tenFeaturesFirst, tenFeaturesSecond, tenFlow):
tenFeaturesFirst = self.netFeat(tenFeaturesFirst)
tenFeaturesSecond = self.netFeat(tenFeaturesSecond)
if tenFlow is not None:
tenFlow = self.netUpflow(tenFlow)
# end
if tenFlow is not None:
tenFeaturesSecond = backwarp(tenInput=tenFeaturesSecond, tenFlow=tenFlow * self.fltBackwarp)
# end
if self.netUpcorr is None:
tenCorrelation = torch.nn.functional.leaky_relu(
input=correlation.FunctionCorrelation(tenFirst=tenFeaturesFirst, tenSecond=tenFeaturesSecond,
intStride=1), negative_slope=0.1, inplace=False)
elif self.netUpcorr is not None:
tenCorrelation = self.netUpcorr(torch.nn.functional.leaky_relu(
input=correlation.FunctionCorrelation(tenFirst=tenFeaturesFirst, tenSecond=tenFeaturesSecond,
intStride=2), negative_slope=0.1, inplace=False))
# end
return (tenFlow if tenFlow is not None else 0.0) + self.netMain(tenCorrelation)
def forward(self, tensorFirst, tensorSecond, objectPrevious):
tensorFlow = None
tensorFeat = None
if objectPrevious is None:
tensorFlow = None
tensorFeat = None
tensorVolume = torch.nn.functional.leaky_relu(
input=correlation.FunctionCorrelation(
tensorFirst=tensorFirst,
tensorSecond=tensorSecond),
negative_slope=0.1,
inplace=False)
tensorFeat = torch.cat([tensorVolume], 1)
elif objectPrevious is not None:
tensorFlow = self.moduleUpflow(
objectPrevious['tensorFlow'])
tensorFeat = self.moduleUpfeat(
objectPrevious['tensorFeat'])
tensorVolume = torch.nn.functional.leaky_relu(
input=correlation.FunctionCorrelation(
tensorFirst=tensorFirst,
tensorSecond=backwarp(tensorInput=tensorSecond,
tensorFlow=tensorFlow *
self.dblBackwarp)),
negative_slope=0.1,
inplace=False)
tensorFeat = torch.cat(
[tensorVolume, tensorFirst, tensorFlow, tensorFeat], 1)
# end
tensorFeat = torch.cat(
[self.moduleOne(tensorFeat), tensorFeat], 1)
tensorFeat = torch.cat(
[self.moduleTwo(tensorFeat), tensorFeat], 1)
tensorFeat = torch.cat(
[self.moduleThr(tensorFeat), tensorFeat], 1)
tensorFeat = torch.cat(
[self.moduleFou(tensorFeat), tensorFeat], 1)
tensorFeat = torch.cat(
[self.moduleFiv(tensorFeat), tensorFeat], 1)
tensorFlow = self.moduleSix(tensorFeat)
return {'tensorFlow': tensorFlow, 'tensorFeat': tensorFeat}
def forward(self, tensorFirst, tensorSecond, tensorFeaturesFirst,
tensorFeaturesSecond, tensorFlow):
tensorFeaturesFirst = self.moduleFeat(tensorFeaturesFirst)
tensorFeaturesSecond = self.moduleFeat(tensorFeaturesSecond)
if tensorFlow is not None:
tensorFlow = self.moduleUpflow(tensorFlow)
# end
if tensorFlow is not None:
tensorFeaturesSecond = Backward(
tensorInput=tensorFeaturesSecond,
tensorFlow=tensorFlow * self.dblBackward)
# end
if self.moduleUpcorr is None:
tensorCorrelation = torch.nn.functional.leaky_relu(
input=correlation.FunctionCorrelation(
tensorFirst=tensorFeaturesFirst,
tensorSecond=tensorFeaturesSecond,
intStride=1),
negative_slope=0.1,
inplace=False)
elif self.moduleUpcorr is not None:
tensorCorrelation = self.moduleUpcorr(
torch.nn.functional.leaky_relu(
input=correlation.FunctionCorrelation(
tensorFirst=tensorFeaturesFirst,
tensorSecond=tensorFeaturesSecond,
intStride=2),
negative_slope=0.1,
inplace=False))
# end
return (tensorFlow if tensorFlow is not None else
0.0) + self.moduleMain(tensorCorrelation)
