def __init__(self, in_nchannel, out_nchannel, D):
ME.MinkowskiNetwork.__init__(self, D)
channels = [in_nchannel, 16, 32]
self.net = nn.Sequential(
ME.MinkowskiStackSum(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=1,
dimension=D,
),
nn.Sequential(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=2,
dimension=D,
),
ME.MinkowskiStackSum(
nn.Identity(),
nn.Sequential(
ME.MinkowskiConvolution(
channels[1],
channels[2],
kernel_size=3,
stride=2,
dimension=D,
),
ME.MinkowskiConvolutionTranspose(
channels[2],
channels[1],
kernel_size=3,
stride=1,
dimension=D,
),
ME.MinkowskiPoolingTranspose(
kernel_size=2, stride=2, dimension=D
),
),
),
ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D),
),
),
ME.MinkowskiToFeature(),
nn.Linear(channels[1], out_nchannel, bias=True),
)
def test_sum(self):
coords, colors, pcd = load_file("1.ply")
device = "cuda"
D = 3
batch_size = 16
voxel_size = 0.02
channels = [3, 64, 128]
dcoords = torch.from_numpy(np.floor(coords / voxel_size)).int()
bcoords = batched_coordinates([dcoords for i in range(batch_size)])
in_feats = torch.rand(len(bcoords), 3).to(0)
layer = MinkowskiStackSum(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=1,
dimension=3,
),
nn.Sequential(
ME.MinkowskiConvolution(
channels[0],
channels[1],
kernel_size=3,
stride=2,
dimension=3,
),
ME.MinkowskiStackSum(
nn.Identity(),
nn.Sequential(
ME.MinkowskiConvolution(
channels[1],
channels[2],
kernel_size=3,
stride=2,
dimension=3,
),
ME.MinkowskiConvolutionTranspose(
channels[2],
channels[1],
kernel_size=3,
stride=1,
dimension=3,
),
ME.MinkowskiPoolingTranspose(
kernel_size=2, stride=2, dimension=D
),
),
),
ME.MinkowskiPoolingTranspose(kernel_size=2, stride=2, dimension=D),
),
).cuda()
for i in range(1000):
torch.cuda.empty_cache()
sinput = ME.SparseTensor(in_feats, coordinates=bcoords, device=device)
layer(sinput)
