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import inspect
import torch.nn as nn
try:
from mamba_ssm import Mamba
except ImportError as exc:
raise ImportError(
"mamba_ssm is required for SegMamba. Install it via the SegMamba repo (mamba/ setup.py install) "
"or pip install mamba-ssm."
) from exc
from .unet import TwoConv
class MambaLayer(nn.Module):
def __init__(self, dim, d_state=16, d_conv=4, expand=2, num_slices=None):
super().__init__()
self.dim = dim
self.norm = nn.LayerNorm(dim)
kwargs = {
"d_model": dim,
"d_state": d_state,
"d_conv": d_conv,
"expand": expand,
}
sig = inspect.signature(Mamba.__init__)
if "bimamba_type" in sig.parameters:
kwargs["bimamba_type"] = "v3"
if num_slices is not None and "nslices" in sig.parameters:
kwargs["nslices"] = num_slices
self.mamba = Mamba(**kwargs)
def forward(self, x):
b, c = x.shape[:2]
x_skip = x
if c != self.dim:
raise ValueError(f"Expected {self.dim} channels, got {c}")
n_tokens = x.shape[2:].numel()
img_dims = x.shape[2:]
x_flat = x.reshape(b, c, n_tokens).transpose(-1, -2)
x_norm = self.norm(x_flat)
x_mamba = self.mamba(x_norm)
out = x_mamba.transpose(-1, -2).reshape(b, c, *img_dims)
return out + x_skip
class MlpChannel(nn.Module):
def __init__(self, hidden_size, mlp_dim):
super().__init__()
self.fc1 = nn.Conv3d(hidden_size, mlp_dim, 1)
self.act = nn.GELU()
self.fc2 = nn.Conv3d(mlp_dim, hidden_size, 1)
def forward(self, x):
x = self.fc1(x)
x = self.act(x)
x = self.fc2(x)
return x
class GSC(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.proj = nn.Conv3d(in_channels, in_channels, 3, 1, 1)
self.norm = nn.InstanceNorm3d(in_channels)
self.nonliner = nn.ReLU()
self.proj2 = nn.Conv3d(in_channels, in_channels, 3, 1, 1)
self.norm2 = nn.InstanceNorm3d(in_channels)
self.nonliner2 = nn.ReLU()
self.proj3 = nn.Conv3d(in_channels, in_channels, 1, 1, 0)
self.norm3 = nn.InstanceNorm3d(in_channels)
self.nonliner3 = nn.ReLU()
self.proj4 = nn.Conv3d(in_channels, in_channels, 1, 1, 0)
self.norm4 = nn.InstanceNorm3d(in_channels)
self.nonliner4 = nn.ReLU()
def forward(self, x):
x_residual = x
x1 = self.proj(x)
x1 = self.norm(x1)
x1 = self.nonliner(x1)
x1 = self.proj2(x1)
x1 = self.norm2(x1)
x1 = self.nonliner2(x1)
x2 = self.proj3(x)
x2 = self.norm3(x2)
x2 = self.nonliner3(x2)
x = x1 + x2
x = self.proj4(x)
x = self.norm4(x)
x = self.nonliner4(x)
return x + x_residual
class MambaEncoder(nn.Module):
def __init__(
self,
in_chans=1,
depths=(2, 2, 2, 2),
dims=(48, 96, 192, 384),
drop_path_rate=0.0,
layer_scale_init_value=1e-6,
out_indices=(0, 1, 2, 3),
img_size=128,
):
super().__init__()
self.downsample_layers = nn.ModuleList()
stem = nn.Sequential(
nn.Conv3d(in_chans, dims[0], kernel_size=7, stride=2, padding=3),
)
self.downsample_layers.append(stem)
for i in range(3):
downsample_layer = nn.Sequential(
nn.InstanceNorm3d(dims[i]),
nn.Conv3d(dims[i], dims[i + 1], kernel_size=2, stride=2),
)
self.downsample_layers.append(downsample_layer)
self.stages = nn.ModuleList()
self.gscs = nn.ModuleList()
num_slices_list = [max(1, img_size // (2 ** (i + 1))) for i in range(4)]
for i in range(4):
gsc = GSC(dims[i])
stage = nn.Sequential(
*[MambaLayer(dim=dims[i], num_slices=num_slices_list[i]) for _ in range(depths[i])]
)
self.stages.append(stage)
self.gscs.append(gsc)
self.out_indices = out_indices
self.mlps = nn.ModuleList()
for i_layer in range(4):
layer = nn.InstanceNorm3d(dims[i_layer])
self.add_module(f"norm{i_layer}", layer)
self.mlps.append(MlpChannel(dims[i_layer], 2 * dims[i_layer]))
def forward_features(self, x):
outs = []
for i in range(4):
x = self.downsample_layers[i](x)
x = self.gscs[i](x)
x = self.stages[i](x)
if i in self.out_indices:
norm_layer = getattr(self, f"norm{i}")
x_out = norm_layer(x)
x_out = self.mlps[i](x_out)
outs.append(x_out)
return tuple(outs)
def forward(self, x):
return self.forward_features(x)
class ImageEncoderSegMamba(nn.Module):
def __init__(
self,
args,
img_size=128,
in_chans=1,
embed_dim=384,
depths=(2, 2, 2, 2),
dims=(48, 96, 192, 384),
):
super().__init__()
self.args = args
self.img_size = img_size
self.backbone = MambaEncoder(
in_chans=in_chans,
depths=depths,
dims=dims,
img_size=img_size,
)
act = ("LeakyReLU", {"negative_slope": 0.1, "inplace": True})
norm = ("instance", {"affine": True})
self.stem = TwoConv(3, in_chans, 32, act, norm, True, 0.0)
self.proj1 = self._proj_block(dims[0], 32)
self.proj2 = self._proj_block(dims[1], 64)
self.proj3 = self._proj_block(dims[2], 128)
self.out_proj = None
if dims[3] != embed_dim:
self.out_proj = nn.Conv3d(dims[3], embed_dim, kernel_size=1, bias=False)
self.out_norm = nn.InstanceNorm3d(embed_dim)
def _proj_block(self, in_ch, out_ch):
return nn.Sequential(
nn.Conv3d(in_ch, out_ch, kernel_size=1, bias=False),
nn.InstanceNorm3d(out_ch),
nn.GELU(),
)
def forward(self, x):
x0 = self.stem(x)
x1, x2, x3, x4 = self.backbone(x)
f1 = self.proj1(x1)
f2 = self.proj2(x2)
f3 = self.proj3(x3)
if self.out_proj is not None:
x4 = self.out_norm(self.out_proj(x4))
feature_list = [x0, f1, f2, f3]
return x4, feature_list
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