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 conv1x1(in_planes, out_planes, stride=1, indice_key=None):
return spconv.SubMConv3d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
padding=1,
bias=False,
indice_key=indice_key)
def conv1x1x3(in_planes, out_planes, stride=1, indice_key=None):
return spconv.SubMConv3d(in_planes,
out_planes,
kernel_size=(1, 1, 3),
stride=stride,
padding=(0, 0, 1),
bias=False,
indice_key=indice_key)
def __init__(self, num_layers, ndim, shape, in_channels, out_channels,
kernel_size, stride):
super().__init__()
layers = [
spconv.SubMConv3d(in_channels,
out_channels,
kernel_size,
bias=False,
indice_key="subm0")
]
for i in range(1, num_layers):
layers.append(
spconv.SubMConv3d(out_channels,
out_channels,
kernel_size,
bias=False))
self.net = nn.Sequential(*layers, )
self.shape = shape
def __init__(self,
num_layers,
ndim,
shape,
in_channels,
out_channels,
kernel_size,
stride,
padding,
dilation,
algo=spconv.ConvAlgo.Native):
super().__init__()
layers = [
spconv.SubMConv3d(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.SubMConv3d(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
def __init__(self,
output_shape,
use_norm=True,
num_input_features=128,
nclasses=20,
n_height=32,
strict=False,
init_size=16):
super(Asymm_3d_spconv, self).__init__()
self.nclasses = nclasses
self.nheight = n_height
self.strict = False
sparse_shape = np.array(output_shape)
# sparse_shape[0] = 11
print(sparse_shape)
self.sparse_shape = sparse_shape
self.downCntx = ResContextBlock(num_input_features,
init_size,
indice_key="pre")
self.resBlock2 = ResBlock(init_size,
2 * init_size,
0.2,
height_pooling=True,
indice_key="down2")
self.resBlock3 = ResBlock(2 * init_size,
4 * init_size,
0.2,
height_pooling=True,
indice_key="down3")
self.resBlock4 = ResBlock(4 * init_size,
8 * init_size,
0.2,
pooling=True,
height_pooling=False,
indice_key="down4")
self.resBlock5 = ResBlock(8 * init_size,
16 * init_size,
0.2,
pooling=True,
height_pooling=False,
indice_key="down5")
self.upBlock0 = UpBlock(16 * init_size,
16 * init_size,
indice_key="up0",
up_key="down5")
self.upBlock1 = UpBlock(16 * init_size,
8 * init_size,
indice_key="up1",
up_key="down4")
self.upBlock2 = UpBlock(8 * init_size,
4 * init_size,
indice_key="up2",
up_key="down3")
self.upBlock3 = UpBlock(4 * init_size,
2 * init_size,
indice_key="up3",
up_key="down2")
self.ReconNet = ReconBlock(2 * init_size,
2 * init_size,
indice_key="recon")
self.logits = spconv.SubMConv3d(4 * init_size,
nclasses,
indice_key="logit",
kernel_size=3,
stride=1,
padding=1,
bias=True)