def __init__(self, num_layers, ndim, shape, kernel_size, stride, padding,
dilation):
super().__init__()
layers = [
spconv.SparseMaxPool3d(kernel_size, stride, padding, dilation)
]
for i in range(1, num_layers):
layers.append(
spconv.SparseMaxPool3d(kernel_size, stride, padding, dilation))
self.net = spconv.SparseSequential(*layers, )
self.shape = shape
def __init__(self, shape, algo):
super().__init__()
self.net = spconv.SparseSequential(
spconv.SubMConv3d(3,
128,
3,
bias=False,
indice_key="c0",
algo=algo),
# spconv.SubMConv3d(32,
# 32,
# 3,
# bias=False,
# indice_key="c0",
# algo=algo),
# # nn.BatchNorm1d(32),
# # nn.ReLU(),
# # spconv.SparseConv3d(64, 64, 2, 2, bias=False,
# # algo=algo),
# spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0",
# algo=algo),
spconv.SubMConv3d(128,
128,
3,
bias=False,
indice_key="c0",
algo=algo),
# nn.BatchNorm1d(32),
# nn.ReLU(),
# spconv.SparseMaxPool3d(2, 2),
# spconv.SubMConv3d(256,
# 512,
# 3,
# bias=False,
# indice_key="c1",
# algo=algo),
# spconv.SubMConv3d(512,
# 512,
# 3,
# bias=False,
# indice_key="c1",
# algo=algo),
)
max_batch_size = 1
# grid (dense map) is used for indice generation. use pre-allocated grid can run faster.
self.grid = torch.full([max_batch_size, *shape], -1,
dtype=torch.int32).cuda()
# self.grid = None
self.shape = shape
def __init__(self):
super(Net, self).__init__()
self.net = spconv.SparseSequential(
nn.BatchNorm1d(1),
spconv.SubMConv2d(1, 32, 3, 1),
nn.ReLU(),
spconv.SubMConv2d(32, 64, 3, 1),
nn.ReLU(),
spconv.SparseMaxPool2d(2, 2),
spconv.ToDense(),
)
self.fc1 = nn.Linear(14 * 14 * 64, 128)
self.fc2 = nn.Linear(128, 10)
self.dropout1 = nn.Dropout2d(0.25)
self.dropout2 = nn.Dropout2d(0.5)
def __init__(self, num_layers, ndim, shape, in_channels, out_channels,
kernel_size, stride):
super().__init__()
self.net = spconv.SparseSequential(
spconv.SparseConv3d(in_channels,
out_channels,
kernel_size,
stride,
indice_key="cp0",
bias=False),
spconv.SparseInverseConv3d(out_channels,
in_channels,
kernel_size,
indice_key="cp0",
bias=False),
)
self.todense = spconv.ToDense()
self.shape = shape
def __init__(self, num_layers, ndim, shape, in_channels, out_channels,
kernel_size, stride, padding, dilation):
super().__init__()
layers = [
spconv.SparseConvTranspose3d(in_channels,
out_channels,
kernel_size,
stride,
padding=padding,
dilation=dilation,
bias=False)
]
for i in range(1, num_layers):
layers.append(
spconv.SparseConvTranspose3d(out_channels,
out_channels,
kernel_size,
stride,
padding=padding,
dilation=dilation,
bias=False))
self.net = spconv.SparseSequential(*layers, )
self.shape = shape
def __init__(self,
num_layers,
ndim,
shape,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
algo=spconv.ConvAlgo.MaskSplitImplicitGemm):
super().__init__()
self.algo = algo
layers = [
spconv.SparseConv3d(in_channels,
out_channels,
kernel_size,
stride,
padding=padding,
dilation=dilation,
bias=False,
algo=algo)
]
for i in range(1, num_layers):
layers.append(
spconv.SparseConv3d(out_channels,
out_channels,
kernel_size,
stride,
padding=padding,
dilation=dilation,
bias=False,
algo=algo))
self.net = spconv.SparseSequential(*layers, )
# self.grid = torch.full([3, *shape], -1, dtype=torch.int32).cuda()
self.grid = None
self.shape = shape
def __init__(self, shape, algo):
super().__init__()
pool_algo = algo
# pool_algo = ConvAlgo.Native
self.net = spconv.SparseSequential(
spconv.SubMConv3d(3, 64, 3, bias=False, indice_key="c0",
algo=algo),
# spconv.SubMConv3d(32,
# 32,
# 3,
# bias=False,
# indice_key="c0",
# algo=algo),
# # nn.BatchNorm1d(32),
# # nn.ReLU(),
# # spconv.SparseConv3d(64, 64, 2, 2, bias=False,
# # algo=algo),
# spconv.SubMConv3d(32, 64, 3, bias=False, indice_key="c0",
# algo=algo),
spconv.SubMConv3d(64,
64,
3,
bias=False,
indice_key="c0",
algo=algo),
# nn.BatchNorm1d(32),
# nn.ReLU(),
# spconv.SparseConv3d(64, 64, 2, 2, bias=False, indice_key="m0"),
spconv.SparseMaxPool3d(2, 2, algo=pool_algo),
spconv.SubMConv3d(64,
96,
3,
bias=False,
indice_key="c1",
algo=algo),
spconv.SubMConv3d(96,
96,
3,
bias=False,
indice_key="c1",
algo=algo),
# nn.BatchNorm1d(64),
# nn.ReLU(),
# spconv.SparseConv3d(96, 96, 2, 2, bias=False, indice_key="m1"),
spconv.SparseMaxPool3d(2, 2, algo=pool_algo),
spconv.SubMConv3d(96,
128,
3,
bias=False,
indice_key="c2",
algo=algo),
spconv.SubMConv3d(128,
128,
3,
bias=False,
indice_key="c2",
algo=algo),
# nn.BatchNorm1d(128),
# nn.ReLU(),
# spconv.SparseConv3d(128, 128, 2, 2, bias=False, indice_key="m2"),
spconv.SparseMaxPool3d(2, 2, algo=pool_algo),
spconv.SubMConv3d(128,
160,
3,
bias=False,
indice_key="c3",
algo=algo),
spconv.SubMConv3d(160,
160,
3,
bias=False,
indice_key="c3",
algo=algo),
# nn.BatchNorm1d(128),
# nn.ReLU(),
# spconv.SparseConv3d(160, 160, 2, 2, bias=False, indice_key="m3"),
spconv.SparseMaxPool3d(2, 2, algo=pool_algo),
spconv.SubMConv3d(160,
192,
3,
bias=False,
indice_key="c4",
algo=algo),
spconv.SubMConv3d(192,
192,
3,
bias=False,
indice_key="c4",
algo=algo),
# nn.BatchNorm1d(128),
# nn.ReLU(),
spconv.SparseMaxPool3d(2, 2, indice_key="m4", algo=pool_algo),
# spconv.SparseConv3d(192, 192, 2, 2, bias=False, indice_key="m4"),
spconv.SubMConv3d(192,
224,
3,
bias=False,
indice_key="c5",
algo=algo),
spconv.SubMConv3d(224,
224,
3,
bias=False,
indice_key="c5",
algo=algo),
# nn.BatchNorm1d(224),
# nn.ReLU(),
# spconv.SparseConv3d(224, 224, 2, 2, bias=False, indice_key="m5"),
spconv.SparseMaxPool3d(2, 2, indice_key="m5", algo=pool_algo),
spconv.SubMConv3d(224,
256,
3,
bias=False,
indice_key="c6",
algo=algo),
spconv.SubMConv3d(256,
256,
3,
bias=False,
indice_key="c6",
algo=algo),
# nn.BatchNorm1d(256),
# nn.ReLU(),
# spconv.SparseInverseConv3d(256, 128, 2, indice_key="m5", bias=False, algo=algo),
# # nn.BatchNorm1d(128),
# # nn.ReLU(),
# spconv.SparseInverseConv3d(128, 64, 2, indice_key="m4", bias=False, algo=algo),
)
max_batch_size = 1
# grid (dense map) is used for indice generation. use pre-allocated grid can run faster.
self.grid = torch.full([max_batch_size, *shape], -1,
dtype=torch.int32).cuda()
# self.grid = None
self.shape = shape