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from __future__ import annotations
import torch
import torch.nn as nn
import torch.nn.functional as F
from monai.networks.blocks.convolutions import Convolution
from monai.networks.blocks.segresnet_block import get_conv_layer, get_upsample_layer
from monai.networks.layers.factories import Dropout
from monai.networks.layers.utils import get_act_layer, get_norm_layer
from monai.utils import UpsampleMode
from einops import rearrange
from models.mamba_customer import ConvMamba, M3, PatchEmbed, PatchUnEmbed
from models.Blocks import CAB, SAB, VSSBlock, ShallowFusionAttnBlock
import warnings
warnings.filterwarnings("ignore")
def get_dwconv_layer(
spatial_dims: int, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1,
bias: bool = False
):
depth_conv = Convolution(spatial_dims=spatial_dims, in_channels=in_channels, out_channels=in_channels,
strides=stride, kernel_size=kernel_size, bias=bias, conv_only=True, groups=in_channels)
point_conv = Convolution(spatial_dims=spatial_dims, in_channels=in_channels, out_channels=out_channels,
strides=stride, kernel_size=1, bias=bias, conv_only=True, groups=1)
return torch.nn.Sequential(depth_conv, point_conv)
class SRCMLayer(nn.Module):
def __init__(self, input_dim, output_dim, d_state=16, d_conv=4, expand=2, conv_mode='deepwise'):
super().__init__()
self.input_dim = input_dim
self.output_dim = output_dim
self.norm = nn.LayerNorm(input_dim)
self.convmamba = ConvMamba(
d_model=input_dim,
d_state=d_state,
d_conv=d_conv,
expand=expand,
bimamba_type="v2",
conv_mode=conv_mode
)
self.proj = nn.Linear(input_dim, output_dim)
self.skip_scale = nn.Parameter(torch.ones(1))
def forward(self, x):
if x.dtype == torch.float16:
x = x.type(torch.float32)
B, C = x.shape[:2]
assert C == self.input_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.convmamba(x_norm) + self.skip_scale * x_flat
x_mamba = self.norm(x_mamba)
x_mamba = self.proj(x_mamba)
out = x_mamba.transpose(-1, -2).reshape(B, self.output_dim, *img_dims)
return out
def get_srcm_layer(
spatial_dims: int, in_channels: int, out_channels: int, stride: int = 1, conv_mode: str = "deepwise"
):
srcm_layer = SRCMLayer(input_dim=in_channels, output_dim=out_channels, conv_mode=conv_mode)
if stride != 1:
if spatial_dims == 2:
return nn.Sequential(srcm_layer, nn.MaxPool2d(kernel_size=stride, stride=stride))
return srcm_layer
class SRCMBlock(nn.Module):
def __init__(
self,
spatial_dims: int,
in_channels: int,
norm: tuple | str,
kernel_size: int = 3,
conv_mode: str = "deepwise",
act: tuple | str = ("RELU", {"inplace": True}),
) -> None:
"""
Args:
spatial_dims: number of spatial dimensions, could be 1, 2 or 3.
in_channels: number of input channels.
norm: feature normalization type and arguments.
kernel_size: convolution kernel size, the value should be an odd number. Defaults to 3.
act: activation type and arguments. Defaults to ``RELU``.
"""
super().__init__()
if kernel_size % 2 != 1:
raise AssertionError("kernel_size should be an odd number.")
# print(conv_mode)
self.norm1 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels)
self.norm2 = get_norm_layer(name=norm, spatial_dims=spatial_dims, channels=in_channels)
self.act = get_act_layer(act)
self.conv1 = get_srcm_layer(
spatial_dims, in_channels=in_channels, out_channels=in_channels, conv_mode=conv_mode
)
self.conv2 = get_srcm_layer(
spatial_dims, in_channels=in_channels, out_channels=in_channels, conv_mode=conv_mode
)
def forward(self, x):
identity = x
x = self.norm1(x)
x = self.act(x)
x = self.conv1(x)
x = self.norm2(x)
x = self.act(x)
x = self.conv2(x)
x += identity
return x
class CSI(nn.Module):
def __init__(self, dim):
super(CSI, self).__init__()
self.shallow_fusion_attn = ShallowFusionAttnBlock(dim)
self.m3 = M3(dim)
self.vss = VSSBlock(hidden_dim=dim)
self.patch_embed = PatchEmbed(in_chans=dim, embed_dim=dim)
self.patch_unembed = PatchUnEmbed(in_chans=dim, embed_dim=dim)
def forward(self, I1, I2, h, w):
I1_fuse, I2_fuse = self.shallow_fusion_attn(I1, I2, h, w)
fusion = torch.abs(I1_fuse - I2_fuse)
I1_token = self.patch_embed(I1_fuse)
I2_token = self.patch_embed(I2_fuse)
fusion_token = self.patch_embed(fusion)
test_h, test_w = fusion.shape[2], fusion.shape[3]
fusion_token, _ = self.m3(I1_token, I2_token, fusion_token, test_h, test_w)
fusion_out = self.patch_unembed(fusion_token, (h, w))
return fusion_out
class STNR(nn.Module):
def __init__(
self,
spatial_dims: int = 2,
init_filters: int = 16,
in_channels: int = 1,
out_channels: int = 2,
conv_mode: str = "deepwise",
local_query_model = "orignal_dinner",
dropout_prob: float | None = None,
act: tuple | str = ("RELU", {"inplace": True}),
norm: tuple | str = ("GROUP", {"num_groups": 8}),
norm_name: str = "",
num_groups: int = 8,
use_conv_final: bool = True,
blocks_down: tuple = (1, 2, 2, 4),
blocks_up: tuple = (1, 1, 1),
mode: str = "",
up_mode="ResMamba",
up_conv_mode="deepwise",
resdiual=False,
stage = 4,
diff_abs="later",
mamba_act = "silu",
upsample_mode: UpsampleMode | str = UpsampleMode.NONTRAINABLE,
):
super().__init__()
if spatial_dims not in (2, 3):
raise ValueError("`spatial_dims` can only be 2 or 3.")
self.mode = mode
self.stage = stage
self.up_conv_mode = up_conv_mode
self.mamba_act = mamba_act
self.resdiual = resdiual
self.up_mode = up_mode
self.diff_abs = diff_abs
self.conv_mode = conv_mode
self.local_query_model = local_query_model
self.spatial_dims = spatial_dims
self.init_filters = init_filters
self.channels_list = [self.init_filters, self.init_filters*2, self.init_filters*4, self.init_filters*8]
self.in_channels = in_channels
self.blocks_down = blocks_down
self.blocks_up = blocks_up
print(self.blocks_up)
self.dropout_prob = dropout_prob
self.act = act # input options
self.act_mod = get_act_layer(act)
if norm_name:
if norm_name.lower() != "group":
raise ValueError(f"Deprecating option 'norm_name={norm_name}', please use 'norm' instead.")
norm = ("group", {"num_groups": num_groups})
self.norm = norm
print(self.norm)
self.upsample_mode = UpsampleMode(upsample_mode)
self.use_conv_final = use_conv_final
self.convInit = get_conv_layer(spatial_dims, in_channels, init_filters)
self.srcm_encoder_layers = self._make_srcm_encoder_layers()
self.srcm_decoder_layers, self.up_samples = self._make_srcm_decoder_layers(up_mode=self.up_mode)
self.conv_final = self._make_final_conv(out_channels)
self.fusion_blocks = nn.ModuleList(
[CSI(self.channels_list[i]) for i in range(self.stage)]
)
self.cab_layers = nn.ModuleList([
CAB(ch) for ch in self.channels_list[::-1][1:]
])
self.sab_layers = nn.ModuleList([
SAB(kernel_size=7) for _ in range(len(self.blocks_up))
])
self.conv_down_layers = nn.ModuleList([
nn.Conv2d(ch * 2, ch, kernel_size=1, stride=1, padding=0) for ch in self.channels_list[::-1][1:]
])
if dropout_prob is not None:
self.dropout = Dropout[Dropout.DROPOUT, spatial_dims](dropout_prob)
def _make_srcm_encoder_layers(self):
srcm_encoder_layers = nn.ModuleList()
blocks_down, spatial_dims, filters, norm, conv_mode = (self.blocks_down, self.spatial_dims, self.init_filters, self.norm, self.conv_mode)
for i, item in enumerate(blocks_down):
layer_in_channels = filters * 2 ** i
downsample_mamba = (
get_srcm_layer(spatial_dims, layer_in_channels // 2, layer_in_channels, stride=2, conv_mode=conv_mode)
if i > 0
else nn.Identity()
)
down_layer = nn.Sequential(
downsample_mamba,
*[SRCMBlock(spatial_dims, layer_in_channels, norm=norm, act=self.act, conv_mode=conv_mode) for _ in range(item)]
)
srcm_encoder_layers.append(down_layer)
return srcm_encoder_layers
def _make_srcm_decoder_layers(self, up_mode):
srcm_decoder_layers, up_samples = nn.ModuleList(), nn.ModuleList()
upsample_mode, blocks_up, spatial_dims, filters, norm = (
self.upsample_mode,
self.blocks_up,
self.spatial_dims,
self.init_filters,
self.norm,
)
if up_mode == 'SRCM':
Block_up = SRCMBlock
n_up = len(blocks_up)
for i in range(n_up):
sample_in_channels = filters * 2 ** (n_up - i)
srcm_decoder_layers.append(
nn.Sequential(
*[
Block_up(spatial_dims, sample_in_channels // 2, norm=norm, act=self.act, conv_mode=self.up_conv_mode)
for _ in range(blocks_up[i])
]
)
)
up_samples.append(
nn.Sequential(
*[
get_conv_layer(spatial_dims, sample_in_channels, sample_in_channels // 2, kernel_size=1),
get_upsample_layer(spatial_dims, sample_in_channels // 2, upsample_mode=upsample_mode),
]
)
)
return srcm_decoder_layers, up_samples
def _make_final_conv(self, out_channels: int):
return nn.Sequential(
get_norm_layer(name=self.norm, spatial_dims=self.spatial_dims, channels=self.init_filters),
self.act_mod,
get_conv_layer(self.spatial_dims, self.init_filters, out_channels, kernel_size=1, bias=True),
)
def encode(self, x: torch.Tensor) -> tuple[torch.Tensor, list[torch.Tensor]]:
x = self.convInit(x)
if self.dropout_prob is not None:
x = self.dropout(x)
down_x = []
for down in self.srcm_encoder_layers:
x = down(x)
down_x.append(x)
return x, down_x
def decode(self, x: torch.Tensor, down_x: list[torch.Tensor]) -> torch.Tensor:
for i, (up, upl) in enumerate(zip(self.up_samples, self.srcm_decoder_layers)):
skip = down_x[i + 1]
x_up = up(x) + skip
x_cab = self.cab_layers[i](x_up) * x_up
x_sab = self.sab_layers[i](x_cab) * x_cab
x_srcm = upl(x_up)
combined_out = torch.cat([x_sab, x_srcm], dim=1)
final_out = self.conv_down_layers[i](combined_out)
x = final_out
if self.use_conv_final:
x = self.conv_final(x)
return x
def forward(self, x1: torch.Tensor, x2:torch.Tensor) -> torch.Tensor:
b, c, h, w = x1.shape
x1, down_x1 = self.encode(x1)
x2, down_x2 = self.encode(x2)
down_x = []
for i in range(len(down_x1)):
x1_level, x2_level = down_x1[i], down_x2[i]
H_i, W_i = x1_level.shape[2], x1_level.shape[3]
if self.diff_abs == "later":
if self.mode == "FUSION":
if i < self.stage:
zero_res = torch.zeros_like(x1_level)
fusion = self.fusion_blocks[i](x1_level, x2_level, H_i, W_i)
else:
fusion = torch.abs(x1_level - x2_level)
else:
fusion = torch.abs(x1_level - x2_level)
down_x.append(fusion)
down_x.reverse()
x = self.decode(down_x[0], down_x)
return x
if __name__ == "__main__":
device = "cuda:0"
CDMamba = STNR(spatial_dims=2, in_channels=3, out_channels=2, init_filters=16, norm=("GROUP", {"num_groups": 8}),
mode="FUSION", conv_mode='orignal', local_query_model="orignal_dinner",
stage=4, mamba_act="silu", up_mode="SRCM", up_conv_mode='deepwise', blocks_down=(1, 2, 2, 4), blocks_up=(1, 1, 1),
resdiual=False, diff_abs="later").to(device)
x = torch.randn(1, 3, 256, 256).to(device)
y = CDMamba(x, x)
print(y.shape) |