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import math
from typing import List
import torch
import torch.nn as nn
from timm.models.layers import trunc_normal_
class UNetBlock(nn.Module):
def __init__(self, cin, cout, bn3d):
"""
a UNet block with 2x up sampling
"""
super().__init__()
self.up_sample = nn.ConvTranspose3d(cin, cin, kernel_size=2, stride=2, padding=0, bias=True)
self.conv = nn.Sequential(
nn.Conv3d(cin, cout, kernel_size=3, stride=1, padding=1, bias=True), bn3d(cout), nn.ReLU(inplace=True),
nn.Conv3d(cout, cout, kernel_size=3, stride=1, padding=1, bias=True), bn3d(cout), nn.ReLU(inplace=True),
)
def forward(self, x):
x = self.up_sample(x)
return self.conv(x)
class FusionBlock(nn.Module):
def __init__(self, cin, cout, bn3d):
"""
a fusionBlock block with 2x up sampling
"""
super().__init__()
self.conv = nn.Sequential(
nn.Conv3d(cin, cout, kernel_size=3, stride=1, padding=1, bias=True), bn3d(cout), nn.ReLU(inplace=True),
nn.Conv3d(cout, cout, kernel_size=3, stride=1, padding=1, bias=True), bn3d(cout), nn.ReLU(inplace=True),
)
def forward(self, x):
return self.conv(x)
class LightDecoder(nn.Module):
def __init__(self, up_sample_ratio, width=768,
sbn=True): # todo: the decoder's width follows a simple halfing rule; you can change it to any other rule
super().__init__()
self.width = width
n = round(math.log2(up_sample_ratio))
channels = [self.width // 2 ** i for i in range(
n + 1)] # todo: the decoder's width follows a simple halfing rule; you can change it to any other rule
bn3d = nn.BatchNorm3d
self.dec = nn.ModuleList([UNetBlock(cin, cout, bn3d) for (cin, cout) in zip(channels[:-1], channels[1:])])
self.fuse = nn.ModuleList([FusionBlock(cin * 2, cin, bn3d) for (cin, cout) in zip(channels[:-1], channels[1:])])
self.proj = nn.Conv3d(channels[-1], 1, kernel_size=1, stride=1, bias=True)
self.initialize()
def forward(self, to_dec: List[torch.Tensor]):
x = 0
for i, d in enumerate(self.dec):
if i < len(to_dec) and to_dec[i] is not None:
if isinstance(x, int):
x = x + to_dec[i]
else:
x = torch.cat((x, to_dec[i]), dim=1)
x = self.fuse[i](x)
x = self.dec[i](x)
return self.proj(x)
def extra_repr(self) -> str:
return f'width={self.width}'
def initialize(self):
for m in self.modules():
if isinstance(m, nn.Linear):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Conv3d):
trunc_normal_(m.weight, std=.02)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, (nn.Conv3d, nn.ConvTranspose3d)):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0.)
elif isinstance(m, (nn.LayerNorm, nn.BatchNorm2d, nn.BatchNorm3d, nn.SyncBatchNorm)):
nn.init.constant_(m.bias, 0)
nn.init.constant_(m.weight, 1.0)
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