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"""
Modified by Nikita Selin (OPHoperHPO)[https://github.com/OPHoperHPO].
Source url: https://github.com/xuebinqin/U-2-Net
License: Apache License 2.0
"""
from typing import Union
import torch
import torch.nn as nn
import math
__all__ = ["U2NETArchitecture"]
def _upsample_like(x, size):
return nn.Upsample(size=size, mode="bilinear", align_corners=False)(x)
def _size_map(x, height):
# {height: size} for Upsample
size = list(x.shape[-2:])
sizes = {}
for h in range(1, height):
sizes[h] = size
size = [math.ceil(w / 2) for w in size]
return sizes
class REBNCONV(nn.Module):
def __init__(self, in_ch=3, out_ch=3, dilate=1):
super(REBNCONV, self).__init__()
self.conv_s1 = nn.Conv2d(
in_ch, out_ch, 3, padding=1 * dilate, dilation=1 * dilate
)
self.bn_s1 = nn.BatchNorm2d(out_ch)
self.relu_s1 = nn.ReLU(inplace=True)
def forward(self, x):
return self.relu_s1(self.bn_s1(self.conv_s1(x)))
class RSU(nn.Module):
def __init__(self, name, height, in_ch, mid_ch, out_ch, dilated=False):
super(RSU, self).__init__()
self.name = name
self.height = height
self.dilated = dilated
self._make_layers(height, in_ch, mid_ch, out_ch, dilated)
def forward(self, x):
sizes = _size_map(x, self.height)
x = self.rebnconvin(x)
# U-Net like symmetric encoder-decoder structure
def unet(x, height=1):
if height < self.height:
x1 = getattr(self, f"rebnconv{height}")(x)
if not self.dilated and height < self.height - 1:
x2 = unet(getattr(self, "downsample")(x1), height + 1)
else:
x2 = unet(x1, height + 1)
x = getattr(self, f"rebnconv{height}d")(torch.cat((x2, x1), 1))
return (
_upsample_like(x, sizes[height - 1])
if not self.dilated and height > 1
else x
)
else:
return getattr(self, f"rebnconv{height}")(x)
return x + unet(x)
def _make_layers(self, height, in_ch, mid_ch, out_ch, dilated=False):
self.add_module("rebnconvin", REBNCONV(in_ch, out_ch))
self.add_module("downsample", nn.MaxPool2d(2, stride=2, ceil_mode=True))
self.add_module("rebnconv1", REBNCONV(out_ch, mid_ch))
self.add_module("rebnconv1d", REBNCONV(mid_ch * 2, out_ch))
for i in range(2, height):
dilate = 1 if not dilated else 2 ** (i - 1)
self.add_module(f"rebnconv{i}", REBNCONV(mid_ch, mid_ch, dilate=dilate))
self.add_module(
f"rebnconv{i}d", REBNCONV(mid_ch * 2, mid_ch, dilate=dilate)
)
dilate = 2 if not dilated else 2 ** (height - 1)
self.add_module(f"rebnconv{height}", REBNCONV(mid_ch, mid_ch, dilate=dilate))
class U2NETArchitecture(nn.Module):
def __init__(self, cfg_type: Union[dict, str] = "full", out_ch: int = 1):
super(U2NETArchitecture, self).__init__()
if isinstance(cfg_type, str):
if cfg_type == "full":
layers_cfgs = {
# cfgs for building RSUs and sides
# {stage : [name, (height(L), in_ch, mid_ch, out_ch, dilated), side]}
"stage1": ["En_1", (7, 3, 32, 64), -1],
"stage2": ["En_2", (6, 64, 32, 128), -1],
"stage3": ["En_3", (5, 128, 64, 256), -1],
"stage4": ["En_4", (4, 256, 128, 512), -1],
"stage5": ["En_5", (4, 512, 256, 512, True), -1],
"stage6": ["En_6", (4, 512, 256, 512, True), 512],
"stage5d": ["De_5", (4, 1024, 256, 512, True), 512],
"stage4d": ["De_4", (4, 1024, 128, 256), 256],
"stage3d": ["De_3", (5, 512, 64, 128), 128],
"stage2d": ["De_2", (6, 256, 32, 64), 64],
"stage1d": ["De_1", (7, 128, 16, 64), 64],
}
else:
raise ValueError("Unknown U^2-Net architecture conf. name")
elif isinstance(cfg_type, dict):
layers_cfgs = cfg_type
else:
raise ValueError("Unknown U^2-Net architecture conf. type")
self.out_ch = out_ch
self._make_layers(layers_cfgs)
def forward(self, x):
sizes = _size_map(x, self.height)
maps = [] # storage for maps
# side saliency map
def unet(x, height=1):
if height < 6:
x1 = getattr(self, f"stage{height}")(x)
x2 = unet(getattr(self, "downsample")(x1), height + 1)
x = getattr(self, f"stage{height}d")(torch.cat((x2, x1), 1))
side(x, height)
return _upsample_like(x, sizes[height - 1]) if height > 1 else x
else:
x = getattr(self, f"stage{height}")(x)
side(x, height)
return _upsample_like(x, sizes[height - 1])
def side(x, h):
# side output saliency map (before sigmoid)
x = getattr(self, f"side{h}")(x)
x = _upsample_like(x, sizes[1])
maps.append(x)
def fuse():
# fuse saliency probability maps
maps.reverse()
x = torch.cat(maps, 1)
x = getattr(self, "outconv")(x)
maps.insert(0, x)
return [torch.sigmoid(x) for x in maps]
unet(x)
maps = fuse()
return maps
def _make_layers(self, cfgs):
self.height = int((len(cfgs) + 1) / 2)
self.add_module("downsample", nn.MaxPool2d(2, stride=2, ceil_mode=True))
for k, v in cfgs.items():
# build rsu block
self.add_module(k, RSU(v[0], *v[1]))
if v[2] > 0:
# build side layer
self.add_module(
f"side{v[0][-1]}", nn.Conv2d(v[2], self.out_ch, 3, padding=1)
)
# build fuse layer
self.add_module(
"outconv", nn.Conv2d(int(self.height * self.out_ch), self.out_ch, 1)
)
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