def forward(self, tensorFirst, tensorSecond):
tensorJoin = torch.cat([tensorFirst, tensorSecond], 1)
tensorConv1 = self.moduleConv1(tensorJoin)
tensorPool1 = self.modulePool1(tensorConv1)
tensorConv2 = self.moduleConv2(tensorPool1)
tensorPool2 = self.modulePool2(tensorConv2)
tensorConv3 = self.moduleConv3(tensorPool2)
tensorPool3 = self.modulePool3(tensorConv3)
tensorConv4 = self.moduleConv4(tensorPool3)
tensorPool4 = self.modulePool4(tensorConv4)
tensorConv5 = self.moduleConv5(tensorPool4)
tensorPool5 = self.modulePool5(tensorConv5)
tensorDeconv5 = self.moduleDeconv5(tensorPool5)
tensorUpsample5 = self.moduleUpsample5(tensorDeconv5)
tensorCombine = tensorUpsample5 + tensorConv5
tensorDeconv4 = self.moduleDeconv4(tensorCombine)
tensorUpsample4 = self.moduleUpsample4(tensorDeconv4)
tensorCombine = tensorUpsample4 + tensorConv4
tensorDeconv3 = self.moduleDeconv3(tensorCombine)
tensorUpsample3 = self.moduleUpsample3(tensorDeconv3)
tensorCombine = tensorUpsample3 + tensorConv3
tensorDeconv2 = self.moduleDeconv2(tensorCombine)
tensorUpsample2 = self.moduleUpsample2(tensorDeconv2)
tensorCombine = tensorUpsample2 + tensorConv2
tensorDot1 = sepconv.FunctionSepconv()(
self.modulePad(tensorFirst), self.moduleVertical1(tensorCombine),
self.moduleHorizontal1(tensorCombine))
tensorDot2 = sepconv.FunctionSepconv()(
self.modulePad(tensorSecond), self.moduleVertical2(tensorCombine),
self.moduleHorizontal2(tensorCombine))
return tensorDot1 + tensorDot2
def forward(self, tensorFirst, tensorSecond): tensorConv1 = self.moduleConv1(torch.cat([ tensorFirst, tensorSecond ], 1)) tensorConv2 = self.moduleConv2(torch.nn.functional.avg_pool2d(input=tensorConv1, kernel_size=2, stride=2, count_include_pad=False)) tensorConv3 = self.moduleConv3(torch.nn.functional.avg_pool2d(input=tensorConv2, kernel_size=2, stride=2, count_include_pad=False)) tensorConv4 = self.moduleConv4(torch.nn.functional.avg_pool2d(input=tensorConv3, kernel_size=2, stride=2, count_include_pad=False)) tensorConv5 = self.moduleConv5(torch.nn.functional.avg_pool2d(input=tensorConv4, kernel_size=2, stride=2, count_include_pad=False)) tensorDeconv5 = self.moduleUpsample5(self.moduleDeconv5(torch.nn.functional.avg_pool2d(input=tensorConv5, kernel_size=2, stride=2, count_include_pad=False))) tensorDeconv4 = self.moduleUpsample4(self.moduleDeconv4(tensorDeconv5 + tensorConv5)) tensorDeconv3 = self.moduleUpsample3(self.moduleDeconv3(tensorDeconv4 + tensorConv4)) tensorDeconv2 = self.moduleUpsample2(self.moduleDeconv2(tensorDeconv3 + tensorConv3)) tensorCombine = tensorDeconv2 + tensorConv2 tensorFirst = torch.nn.functional.pad(input=tensorFirst, pad=[ int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)) ], mode='replicate') tensorSecond = torch.nn.functional.pad(input=tensorSecond, pad=[ int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)), int(math.floor(51 / 2.0)) ], mode='replicate') tensorDot1 = sepconv.FunctionSepconv(tensorInput=tensorFirst, tensorVertical=self.moduleVertical1(tensorCombine), tensorHorizontal=self.moduleHorizontal1(tensorCombine)) tensorDot2 = sepconv.FunctionSepconv(tensorInput=tensorSecond, tensorVertical=self.moduleVertical2(tensorCombine), tensorHorizontal=self.moduleHorizontal2(tensorCombine)) return tensorDot1 + tensorDot2
def forward(self, tensorFirst, tensorSecond):
tensorJoin = torch.cat([tensorFirst, tensorSecond], 1)
tensorConv1 = self.moduleConv1(tensorJoin)
tensorPool1 = self.modulePool1(tensorConv1)
tensorConv2 = self.moduleConv2(tensorPool1)
tensorPool2 = self.modulePool2(tensorConv2)
tensorConv3 = self.moduleConv3(tensorPool2)
tensorPool3 = self.modulePool3(tensorConv3)
tensorConv4 = self.moduleConv4(tensorPool3)
tensorPool4 = self.modulePool4(tensorConv4)
tensorConv5 = self.moduleConv5(tensorPool4)
tensorPool5 = self.modulePool5(tensorConv5)
tensorDeconv5 = self.moduleDeconv5(tensorPool5)
tensorUpsample5 = self.moduleUpsample5(tensorDeconv5)
tensorCombine = tensorUpsample5 + tensorConv5
tensorDeconv4 = self.moduleDeconv4(tensorCombine)
tensorUpsample4 = self.moduleUpsample4(tensorDeconv4)
tensorCombine = tensorUpsample4 + tensorConv4
tensorDeconv3 = self.moduleDeconv3(tensorCombine)
tensorUpsample3 = self.moduleUpsample3(tensorDeconv3)
tensorCombine = tensorUpsample3 + tensorConv3
tensorDeconv2 = self.moduleDeconv2(tensorCombine)
tensorUpsample2 = self.moduleUpsample2(tensorDeconv2)
tensorCombine = tensorUpsample2 + tensorConv2
tensorFirst = torch.nn.functional.pad(input=tensorFirst,
pad=[
int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0))
],
mode='replicate')
tensorSecond = torch.nn.functional.pad(input=tensorSecond,
pad=[
int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0)),
int(math.floor(51 / 2.0))
],
mode='replicate')
tensorDot1 = sepconv.FunctionSepconv(
tensorInput=tensorFirst,
tensorVertical=self.moduleVertical1(tensorCombine),
tensorHorizontal=self.moduleHorizontal1(tensorCombine))
tensorDot2 = sepconv.FunctionSepconv(
tensorInput=tensorSecond,
tensorVertical=self.moduleVertical2(tensorCombine),
tensorHorizontal=self.moduleHorizontal2(tensorCombine))
return tensorDot1 + tensorDot2