e87597e058df8aedf24bcd3a1cfe537c44c1d713,models.py,FlowNet2SD,forward,#FlowNet2SD#,292

Before Change

        if self.training:
            return flow2,flow3,flow4,flow5,flow6
        else:
            return flow2

class FlowNet2CS(nn.Module):

After Change

    def forward(self, inputs):
        rgb_mean = inputs.contiguous().view(inputs.size()[:2]+(-1,)).mean(dim=-1).view(inputs.size()[:2] + (1,1,1,))
        x = (inputs - rgb_mean) / self.rgb_max
        x = torch.cat( (x[:,:,0,:,:], x[:,:,1,:,:]), dim = 1)

        out_conv0 = self.conv0(x)
        out_conv1 = self.conv1_1(self.conv1(out_conv0))
        out_conv2 = self.conv2_1(self.conv2(out_conv1))

        out_conv3 = self.conv3_1(self.conv3(out_conv2))
        out_conv4 = self.conv4_1(self.conv4(out_conv3))
        out_conv5 = self.conv5_1(self.conv5(out_conv4))
        out_conv6 = self.conv6_1(self.conv6(out_conv5))

        flow6       = self.predict_flow6(out_conv6)
        flow6_up    = self.upsampled_flow6_to_5(flow6)
        out_deconv5 = self.deconv5(out_conv6)
        
        concat5 = torch.cat((out_conv5,out_deconv5,flow6_up),1)
        out_interconv5 = self.inter_conv5(concat5)
        flow5       = self.predict_flow5(out_interconv5)

        flow5_up    = self.upsampled_flow5_to_4(flow5)
        out_deconv4 = self.deconv4(concat5)
        
        concat4 = torch.cat((out_conv4,out_deconv4,flow5_up),1)
        out_interconv4 = self.inter_conv4(concat4)
        flow4       = self.predict_flow4(out_interconv4)
        flow4_up    = self.upsampled_flow4_to_3(flow4)
        out_deconv3 = self.deconv3(concat4)
        
        concat3 = torch.cat((out_conv3,out_deconv3,flow4_up),1)
        out_interconv3 = self.inter_conv3(concat3)
        flow3       = self.predict_flow3(out_interconv3)
        flow3_up    = self.upsampled_flow3_to_2(flow3)
        out_deconv2 = self.deconv2(concat3)

        concat2 = torch.cat((out_conv2,out_deconv2,flow3_up),1)
        out_interconv2 = self.inter_conv2(concat2)
        flow2 = self.predict_flow2(out_interconv2)

        if self.training:
            return flow2,flow3,flow4,flow5,flow6
        else:
            return self.upsample1(flow2*self.div_flow)

class FlowNet2CS(nn.Module):

Instances