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import torch
import torch.nn as nn
import MinkowskiEngine as ME
from MinkowskiEngine import SparseTensor
from timm.models.layers import trunc_normal_
from .mink_layers import MinkConvBNRelu, MinkResBlock
from .swin3d_layers import GridDownsample, GridKNNDownsample, BasicLayer, Upsample
from pointcept.models.builder import MODELS
from pointcept.models.utils import offset2batch, batch2offset
@MODELS.register_module("Swin3D-v1m1")
class Swin3DUNet(nn.Module):
def __init__(
self,
in_channels,
num_classes,
base_grid_size,
depths,
channels,
num_heads,
window_sizes,
quant_size,
drop_path_rate=0.2,
up_k=3,
num_layers=5,
stem_transformer=True,
down_stride=2,
upsample="linear",
knn_down=True,
cRSE="XYZ_RGB",
fp16_mode=0,
):
super().__init__()
dpr = [
x.item() for x in torch.linspace(0, drop_path_rate, sum(depths))
] # stochastic depth decay rule
if knn_down:
downsample = GridKNNDownsample
else:
downsample = GridDownsample
self.cRSE = cRSE
if stem_transformer:
self.stem_layer = MinkConvBNRelu(
in_channels=in_channels,
out_channels=channels[0],
kernel_size=3,
stride=1,
)
self.layer_start = 0
else:
self.stem_layer = nn.Sequential(
MinkConvBNRelu(
in_channels=in_channels,
out_channels=channels[0],
kernel_size=3,
stride=1,
),
MinkResBlock(in_channels=channels[0], out_channels=channels[0]),
)
self.downsample = downsample(
channels[0], channels[1], kernel_size=down_stride, stride=down_stride
)
self.layer_start = 1
self.layers = nn.ModuleList(
[
BasicLayer(
dim=channels[i],
depth=depths[i],
num_heads=num_heads[i],
window_size=window_sizes[i],
quant_size=quant_size,
drop_path=dpr[sum(depths[:i]) : sum(depths[: i + 1])],
downsample=downsample if i < num_layers - 1 else None,
down_stride=down_stride if i == 0 else 2,
out_channels=channels[i + 1] if i < num_layers - 1 else None,
cRSE=cRSE,
fp16_mode=fp16_mode,
)
for i in range(self.layer_start, num_layers)
]
)
if "attn" in upsample:
up_attn = True
else:
up_attn = False
self.upsamples = nn.ModuleList(
[
Upsample(
channels[i],
channels[i - 1],
num_heads[i - 1],
window_sizes[i - 1],
quant_size,
attn=up_attn,
up_k=up_k,
cRSE=cRSE,
fp16_mode=fp16_mode,
)
for i in range(num_layers - 1, 0, -1)
]
)
self.classifier = nn.Sequential(
nn.Linear(channels[0], channels[0]),
nn.BatchNorm1d(channels[0]),
nn.ReLU(inplace=True),
nn.Linear(channels[0], num_classes),
)
self.num_classes = num_classes
self.base_grid_size = base_grid_size
self.init_weights()
def forward(self, data_dict):
grid_coord = data_dict["grid_coord"]
feat = data_dict["feat"]
coord_feat = data_dict["coord_feat"]
coord = data_dict["coord"]
offset = data_dict["offset"]
batch = offset2batch(offset)
in_field = ME.TensorField(
features=torch.cat(
[
batch.unsqueeze(-1),
coord / self.base_grid_size,
coord_feat / 1.001,
feat,
],
dim=1,
),
coordinates=torch.cat([batch.unsqueeze(-1).int(), grid_coord.int()], dim=1),
quantization_mode=ME.SparseTensorQuantizationMode.UNWEIGHTED_AVERAGE,
minkowski_algorithm=ME.MinkowskiAlgorithm.SPEED_OPTIMIZED,
device=feat.device,
)
sp = in_field.sparse()
coords_sp = SparseTensor(
features=sp.F[:, : coord_feat.shape[-1] + 4],
coordinate_map_key=sp.coordinate_map_key,
coordinate_manager=sp.coordinate_manager,
)
sp = SparseTensor(
features=sp.F[:, coord_feat.shape[-1] + 4 :],
coordinate_map_key=sp.coordinate_map_key,
coordinate_manager=sp.coordinate_manager,
)
sp_stack = []
coords_sp_stack = []
sp = self.stem_layer(sp)
if self.layer_start > 0:
sp_stack.append(sp)
coords_sp_stack.append(coords_sp)
sp, coords_sp = self.downsample(sp, coords_sp)
for i, layer in enumerate(self.layers):
coords_sp_stack.append(coords_sp)
sp, sp_down, coords_sp = layer(sp, coords_sp)
sp_stack.append(sp)
assert (coords_sp.C == sp_down.C).all()
sp = sp_down
sp = sp_stack.pop()
coords_sp = coords_sp_stack.pop()
for i, upsample in enumerate(self.upsamples):
sp_i = sp_stack.pop()
coords_sp_i = coords_sp_stack.pop()
sp = upsample(sp, coords_sp, sp_i, coords_sp_i)
coords_sp = coords_sp_i
output = self.classifier(sp.slice(in_field).F)
return output
def init_weights(self):
"""Initialize the weights in backbone."""
def _init_weights(m):
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=0.02)
if isinstance(m, nn.Linear) and m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.LayerNorm) or isinstance(m, nn.BatchNorm1d):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
self.apply(_init_weights)
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