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# Copyright (c) MONAI Consortium
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import annotations
import os
import sys
import torch.nn as nn
import torch
from functools import partial
# Prefer pip-installed MONAI over the local monai/ folder.
os.environ.setdefault("MONAI_SKIP_SUBMODULES", "1")
_repo_root = os.path.abspath(os.path.join(os.path.dirname(__file__), ".."))
if "" in sys.path:
sys.path.remove("")
if _repo_root in sys.path:
sys.path.remove(_repo_root)
import monai # noqa: E402
sys.path.insert(0, _repo_root)
from monai.networks.blocks.dynunet_block import UnetOutBlock
from monai.networks.blocks.unetr_block import UnetrBasicBlock, UnetrUpBlock
from mamba_ssm import Mamba
import torch.nn.functional as F
class LayerNorm(nn.Module):
r""" LayerNorm that supports two data formats: channels_last (default) or channels_first.
The ordering of the dimensions in the inputs. channels_last corresponds to inputs with
shape (batch_size, height, width, channels) while channels_first corresponds to inputs
with shape (batch_size, channels, height, width).
"""
def __init__(self, normalized_shape, eps=1e-6, data_format="channels_last"):
super().__init__()
self.weight = nn.Parameter(torch.ones(normalized_shape))
self.bias = nn.Parameter(torch.zeros(normalized_shape))
self.eps = eps
self.data_format = data_format
if self.data_format not in ["channels_last", "channels_first"]:
raise NotImplementedError
self.normalized_shape = (normalized_shape, )
def forward(self, x):
if self.data_format == "channels_last":
return F.layer_norm(x, self.normalized_shape, self.weight, self.bias, self.eps)
elif self.data_format == "channels_first":
u = x.mean(1, keepdim=True)
s = (x - u).pow(2).mean(1, keepdim=True)
x = (x - u) / torch.sqrt(s + self.eps)
x = self.weight[:, None, None, None] * x + self.bias[:, None, None, None]
return x
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)
self.mamba = Mamba(
d_model=dim, # Model dimension d_model
d_state=d_state, # SSM state expansion factor
d_conv=d_conv, # Local convolution width
expand=expand, # Block expansion factor
bimamba_type="v3",
nslices=num_slices,
)
def forward(self, x):
B, C = x.shape[:2]
x_skip = x
assert C == self.dim
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)
out = out + x_skip
return out
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_channles) -> None:
super().__init__()
self.proj = nn.Conv3d(in_channles, in_channles, 3, 1, 1)
self.norm = nn.InstanceNorm3d(in_channles)
self.nonliner = nn.ReLU()
self.proj2 = nn.Conv3d(in_channles, in_channles, 3, 1, 1)
self.norm2 = nn.InstanceNorm3d(in_channles)
self.nonliner2 = nn.ReLU()
self.proj3 = nn.Conv3d(in_channles, in_channles, 1, 1, 0)
self.norm3 = nn.InstanceNorm3d(in_channles)
self.nonliner3 = nn.ReLU()
self.proj4 = nn.Conv3d(in_channles, in_channles, 1, 1, 0)
self.norm4 = nn.InstanceNorm3d(in_channles)
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., layer_scale_init_value=1e-6, out_indices=[0, 1, 2, 3]):
super().__init__()
self.downsample_layers = nn.ModuleList() # stem and 3 intermediate downsampling conv layers
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(
# LayerNorm(dims[i], eps=1e-6, data_format="channels_first"),
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 = [64, 32, 16, 8]
cur = 0
for i in range(4):
gsc = GSC(dims[i])
stage = nn.Sequential(
*[MambaLayer(dim=dims[i], num_slices=num_slices_list[i]) for j in range(depths[i])]
)
self.stages.append(stage)
self.gscs.append(gsc)
cur += depths[i]
self.out_indices = out_indices
self.mlps = nn.ModuleList()
for i_layer in range(4):
layer = nn.InstanceNorm3d(dims[i_layer])
layer_name = f'norm{i_layer}'
self.add_module(layer_name, 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):
x = self.forward_features(x)
return x
class SegMamba(nn.Module):
def __init__(
self,
in_chans=1,
out_chans=13,
depths=[2, 2, 2, 2],
feat_size=[48, 96, 192, 384],
drop_path_rate=0,
layer_scale_init_value=1e-6,
hidden_size: int = 768,
norm_name = "instance",
conv_block: bool = True,
res_block: bool = True,
spatial_dims=3,
) -> None:
super().__init__()
self.hidden_size = hidden_size
self.in_chans = in_chans
self.out_chans = out_chans
self.depths = depths
self.drop_path_rate = drop_path_rate
self.feat_size = feat_size
self.layer_scale_init_value = layer_scale_init_value
self.spatial_dims = spatial_dims
self.vit = MambaEncoder(in_chans,
depths=depths,
dims=feat_size,
drop_path_rate=drop_path_rate,
layer_scale_init_value=layer_scale_init_value,
)
self.encoder1 = UnetrBasicBlock(
spatial_dims=spatial_dims,
in_channels=self.in_chans,
out_channels=self.feat_size[0],
kernel_size=3,
stride=1,
norm_name=norm_name,
res_block=res_block,
)
self.encoder2 = UnetrBasicBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[0],
out_channels=self.feat_size[1],
kernel_size=3,
stride=1,
norm_name=norm_name,
res_block=res_block,
)
self.encoder3 = UnetrBasicBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[1],
out_channels=self.feat_size[2],
kernel_size=3,
stride=1,
norm_name=norm_name,
res_block=res_block,
)
self.encoder4 = UnetrBasicBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[2],
out_channels=self.feat_size[3],
kernel_size=3,
stride=1,
norm_name=norm_name,
res_block=res_block,
)
self.encoder5 = UnetrBasicBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[3],
out_channels=self.hidden_size,
kernel_size=3,
stride=1,
norm_name=norm_name,
res_block=res_block,
)
self.decoder5 = UnetrUpBlock(
spatial_dims=spatial_dims,
in_channels=self.hidden_size,
out_channels=self.feat_size[3],
kernel_size=3,
upsample_kernel_size=2,
norm_name=norm_name,
res_block=res_block,
)
self.decoder4 = UnetrUpBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[3],
out_channels=self.feat_size[2],
kernel_size=3,
upsample_kernel_size=2,
norm_name=norm_name,
res_block=res_block,
)
self.decoder3 = UnetrUpBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[2],
out_channels=self.feat_size[1],
kernel_size=3,
upsample_kernel_size=2,
norm_name=norm_name,
res_block=res_block,
)
self.decoder2 = UnetrUpBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[1],
out_channels=self.feat_size[0],
kernel_size=3,
upsample_kernel_size=2,
norm_name=norm_name,
res_block=res_block,
)
self.decoder1 = UnetrBasicBlock(
spatial_dims=spatial_dims,
in_channels=self.feat_size[0],
out_channels=self.feat_size[0],
kernel_size=3,
stride=1,
norm_name=norm_name,
res_block=res_block,
)
self.out = UnetOutBlock(spatial_dims=spatial_dims, in_channels=48, out_channels=self.out_chans)
def proj_feat(self, x):
new_view = [x.size(0)] + self.proj_view_shape
x = x.view(new_view)
x = x.permute(self.proj_axes).contiguous()
return x
def forward(self, x_in):
outs = self.vit(x_in)
enc1 = self.encoder1(x_in)
x2 = outs[0]
enc2 = self.encoder2(x2)
x3 = outs[1]
enc3 = self.encoder3(x3)
x4 = outs[2]
enc4 = self.encoder4(x4)
enc_hidden = self.encoder5(outs[3])
dec3 = self.decoder5(enc_hidden, enc4)
dec2 = self.decoder4(dec3, enc3)
dec1 = self.decoder3(dec2, enc2)
dec0 = self.decoder2(dec1, enc1)
out = self.decoder1(dec0)
return self.out(out)
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