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about ConvType #54

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suyunzzz opened this issue Dec 1, 2021 · 0 comments
Open

about ConvType #54

suyunzzz opened this issue Dec 1, 2021 · 0 comments

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@suyunzzz
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suyunzzz commented Dec 1, 2021

anyone could tell me difference of these ConvType?
i.e. ME.RegionType.HYPERCUBE,...,

if i want to build a Spatio Module, which ConvType should i use?

i dont know why these are two kind of ConvType in MinkUNet32?

Res16UNet34(
  (conv0p1s1): MinkowskiConvolution(in=6, out=32, region_type=RegionType.HYPERCUBE, kernel_size=[5, 5, 5], stride=[1, 1, 1], dilation=[1, 1, 1])
  (bn0): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (conv1p1s2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block1): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (conv2p2s2): MinkowskiConvolution(in=32, out=32, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn2): MinkowskiBatchNorm(32, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block2): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=32, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=32, out=64, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (2): BasicBlock(
      (conv1): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (conv3p4s2): MinkowskiConvolution(in=64, out=64, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn3): MinkowskiBatchNorm(64, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block3): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=64, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=64, out=128, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (2): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (3): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (conv4p8s2): MinkowskiConvolution(in=128, out=128, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bn4): MinkowskiBatchNorm(128, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block4): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=128, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=128, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (2): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (3): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (4): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
    (5): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr4p16s2): MinkowskiConvolutionTranspose(in=256, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr4): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block5): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=384, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=384, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr5p8s2): MinkowskiConvolutionTranspose(in=256, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr5): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block6): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=320, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=320, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr6p4s2): MinkowskiConvolutionTranspose(in=256, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr6): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block7): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=288, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=288, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (convtr7p2s2): MinkowskiConvolutionTranspose(in=256, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[2, 2, 2], stride=[2, 2, 2], dilation=[1, 1, 1])
  (bntr7): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
  (block8): Sequential(
    (0): BasicBlock(
      (conv1): MinkowskiConvolution(in=288, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
      (downsample): Sequential(
        (0): MinkowskiConvolution(in=288, out=256, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
        (1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.02, affine=True, track_running_stats=True)
      )
    )
    (1): BasicBlock(
      (conv1): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm1): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (conv2): MinkowskiConvolution(in=256, out=256, region_type=RegionType.HYBRID, kernel_volume=27, stride=[1, 1, 1], dilation=[1, 1, 1])
      (norm2): MinkowskiBatchNorm(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (relu): MinkowskiReLU()
    )
  )
  (final): MinkowskiConvolution(in=256, out=13, region_type=RegionType.HYPERCUBE, kernel_size=[1, 1, 1], stride=[1, 1, 1], dilation=[1, 1, 1])
  (relu): MinkowskiReLU()
)

SpatioTemporalSegmentation/models/modules/common.py

Line 27 in 4afee29

class ConvType(Enum):

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