diff options
| -rw-r--r-- | README.md | 1 | ||||
| -rw-r--r-- | run.py | 16 | ||||
| -rw-r--r-- | src/SeparableConvolution_kernel.o | bin | 10984 -> 0 bytes |
3 files changed, 12 insertions, 5 deletions
@@ -4,6 +4,7 @@ This is a reference implementation of Video Frame Interpolation via Adaptive Sep <a href="https://arxiv.org/abs/1708.01692" rel="Paper"><img src="http://content.sniklaus.com/SepConv/Paper.jpg" alt="Paper" width="100%"></a> For the Torch version of this work, please see: https://github.com/sniklaus/torch-sepconv +For a third-party fork with video support, consider: https://github.com/dagf2101/pytorch-sepconv ## setup To build the implementation and download the pre-trained networks, run `bash install.bash` and make sure that you configured the `CUDA_HOME` environment variable. After successfully completing this step, run `python run.py` to test it. Should you receive an error message regarding an invalid device function during execution, configure the utilized CUDA architecture within `install.bash` to something your graphics card supports. @@ -142,13 +142,19 @@ class Network(torch.nn.Module): variableDeconv5 = self.moduleDeconv5(variablePool5) variableUpsample5 = self.moduleUpsample5(variableDeconv5) - variableDeconv4 = self.moduleDeconv4(variableUpsample5 + variableConv5) + variableCombine = variableUpsample5 + variableConv5 + + variableDeconv4 = self.moduleDeconv4(variableCombine) variableUpsample4 = self.moduleUpsample4(variableDeconv4) - variableDeconv3 = self.moduleDeconv3(variableUpsample4 + variableConv4) + variableCombine = variableUpsample4 + variableConv4 + + variableDeconv3 = self.moduleDeconv3(variableCombine) variableUpsample3 = self.moduleUpsample3(variableDeconv3) - variableDeconv2 = self.moduleDeconv2(variableUpsample3 + variableConv3) + variableCombine = variableUpsample3 + variableConv3 + + variableDeconv2 = self.moduleDeconv2(variableCombine) variableUpsample2 = self.moduleUpsample2(variableDeconv2) variableCombine = variableUpsample2 + variableConv2 @@ -181,8 +187,8 @@ intPaddingLeft = int(math.floor(51 / 2.0)) intPaddingTop = int(math.floor(51 / 2.0)) intPaddingRight = int(math.floor(51 / 2.0)) intPaddingBottom = int(math.floor(51 / 2.0)) -modulePaddingInput = torch.nn.Module() -modulePaddingOutput = torch.nn.Module() +modulePaddingInput = torch.nn.Sequential() +modulePaddingOutput = torch.nn.Sequential() if True: intPaddingWidth = intPaddingLeft + intWidth + intPaddingRight diff --git a/src/SeparableConvolution_kernel.o b/src/SeparableConvolution_kernel.o Binary files differdeleted file mode 100644 index 396aba1..0000000 --- a/src/SeparableConvolution_kernel.o +++ /dev/null |