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Commit 9aea4e05 authored by Benjamin Thomas Graham's avatar Benjamin Thomas Graham
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unet stride, readme

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README.md
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...@@ -136,7 +136,7 @@ conda install google-sparsehash -c bioconda # OR apt-get install libsparsehash ...@@ -136,7 +136,7 @@ conda install google-sparsehash -c bioconda # OR apt-get install libsparsehash
conda install -c anaconda pillow conda install -c anaconda pillow
git clone git@github.com:facebookresearch/SparseConvNet.git git clone git@github.com:facebookresearch/SparseConvNet.git
cd SparseConvNet/ cd SparseConvNet/
bash build.sh bash develop.sh
``` ```
To run the examples you may also need to install unrar: To run the examples you may also need to install unrar:
``` ```
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examples/3d_segmentation/unet.py
View file @ 9aea4e05
...@@ -26,7 +26,7 @@ class Model(nn.Module): ...@@ -26,7 +26,7 @@ class Model(nn.Module):
self.sparseModel = scn.Sequential().add( self.sparseModel = scn.Sequential().add(
scn.InputLayer(dimension, data.spatialSize, mode=3)).add( scn.InputLayer(dimension, data.spatialSize, mode=3)).add(
scn.SubmanifoldConvolution(dimension, 1, m, 3, False)).add( scn.SubmanifoldConvolution(dimension, 1, m, 3, False)).add(
scn.UNet(dimension, reps, nPlanes, residual_blocks=False, downsample=[3,2])).add( scn.UNet(dimension, reps, nPlanes, residual_blocks=False, downsample=[2,2])).add(
scn.BatchNormReLU(m)).add( scn.BatchNormReLU(m)).add(
scn.OutputLayer(dimension)) scn.OutputLayer(dimension))
self.linear = nn.Linear(m, data.nClassesTotal) self.linear = nn.Linear(m, data.nClassesTotal)
......
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