briarmbg.py

# RMBG1.4 (diffusers implementation)
# Found on huggingface space of several projects
# Not sure which project is the source of this file

import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin


class REBNCONV(nn.Module):
    def __init__(self, in_ch=3, out_ch=3, dirate=1, stride=1):
        super(REBNCONV, self).__init__()

        self.conv_s1 = nn.Conv2d(
            in_ch, out_ch, 3, padding=1 * dirate, dilation=1 * dirate, stride=stride
        )
        self.bn_s1 = nn.BatchNorm2d(out_ch)
        self.relu_s1 = nn.ReLU(inplace=True)

    def forward(self, x):
        hx = x
        xout = self.relu_s1(self.bn_s1(self.conv_s1(hx)))

        return xout


def _upsample_like(src, tar):
    src = F.interpolate(src, size=tar.shape[2:], mode="bilinear")
    return src


### RSU-7 ###
class RSU7(nn.Module):
    def __init__(self, in_ch=3, mid_ch=12, out_ch=3, img_size=512):
        super(RSU7, self).__init__()

        self.in_ch = in_ch
        self.mid_ch = mid_ch
        self.out_ch = out_ch

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)  ## 1 -> 1/2

        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool5 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=1)

        self.rebnconv7 = REBNCONV(mid_ch, mid_ch, dirate=2)

        self.rebnconv6d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        b, c, h, w = x.shape

        hx = x
        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)

        hx4 = self.rebnconv4(hx)
        hx = self.pool4(hx4)

        hx5 = self.rebnconv5(hx)
        hx = self.pool5(hx5)

        hx6 = self.rebnconv6(hx)

        hx7 = self.rebnconv7(hx6)

        hx6d = self.rebnconv6d(torch.cat((hx7, hx6), 1))
        hx6dup = _upsample_like(hx6d, hx5)

        hx5d = self.rebnconv5d(torch.cat((hx6dup, hx5), 1))
        hx5dup = _upsample_like(hx5d, hx4)

        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))

        return hx1d + hxin


### RSU-6 ###
class RSU6(nn.Module):
    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU6, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)

        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=1)

        self.rebnconv6 = REBNCONV(mid_ch, mid_ch, dirate=2)

        self.rebnconv5d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)

        hx4 = self.rebnconv4(hx)
        hx = self.pool4(hx4)

        hx5 = self.rebnconv5(hx)

        hx6 = self.rebnconv6(hx5)

        hx5d = self.rebnconv5d(torch.cat((hx6, hx5), 1))
        hx5dup = _upsample_like(hx5d, hx4)

        hx4d = self.rebnconv4d(torch.cat((hx5dup, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))

        return hx1d + hxin


### RSU-5 ###
class RSU5(nn.Module):
    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU5, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)

        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool3 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=1)

        self.rebnconv5 = REBNCONV(mid_ch, mid_ch, dirate=2)

        self.rebnconv4d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)
        hx = self.pool3(hx3)

        hx4 = self.rebnconv4(hx)

        hx5 = self.rebnconv5(hx4)

        hx4d = self.rebnconv4d(torch.cat((hx5, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))

        return hx1d + hxin


### RSU-4 ###
class RSU4(nn.Module):
    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU4, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)

        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.pool1 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=1)
        self.pool2 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=1)

        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=2)

        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=1)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx = self.pool1(hx1)

        hx2 = self.rebnconv2(hx)
        hx = self.pool2(hx2)

        hx3 = self.rebnconv3(hx)

        hx4 = self.rebnconv4(hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)

        hx2d = self.rebnconv2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)

        hx1d = self.rebnconv1d(torch.cat((hx2dup, hx1), 1))

        return hx1d + hxin


### RSU-4F ###
class RSU4F(nn.Module):
    def __init__(self, in_ch=3, mid_ch=12, out_ch=3):
        super(RSU4F, self).__init__()

        self.rebnconvin = REBNCONV(in_ch, out_ch, dirate=1)

        self.rebnconv1 = REBNCONV(out_ch, mid_ch, dirate=1)
        self.rebnconv2 = REBNCONV(mid_ch, mid_ch, dirate=2)
        self.rebnconv3 = REBNCONV(mid_ch, mid_ch, dirate=4)

        self.rebnconv4 = REBNCONV(mid_ch, mid_ch, dirate=8)

        self.rebnconv3d = REBNCONV(mid_ch * 2, mid_ch, dirate=4)
        self.rebnconv2d = REBNCONV(mid_ch * 2, mid_ch, dirate=2)
        self.rebnconv1d = REBNCONV(mid_ch * 2, out_ch, dirate=1)

    def forward(self, x):
        hx = x

        hxin = self.rebnconvin(hx)

        hx1 = self.rebnconv1(hxin)
        hx2 = self.rebnconv2(hx1)
        hx3 = self.rebnconv3(hx2)

        hx4 = self.rebnconv4(hx3)

        hx3d = self.rebnconv3d(torch.cat((hx4, hx3), 1))
        hx2d = self.rebnconv2d(torch.cat((hx3d, hx2), 1))
        hx1d = self.rebnconv1d(torch.cat((hx2d, hx1), 1))

        return hx1d + hxin


class myrebnconv(nn.Module):
    def __init__(
        self,
        in_ch=3,
        out_ch=1,
        kernel_size=3,
        stride=1,
        padding=1,
        dilation=1,
        groups=1,
    ):
        super(myrebnconv, self).__init__()

        self.conv = nn.Conv2d(
            in_ch,
            out_ch,
            kernel_size=kernel_size,
            stride=stride,
            padding=padding,
            dilation=dilation,
            groups=groups,
        )
        self.bn = nn.BatchNorm2d(out_ch)
        self.rl = nn.ReLU(inplace=True)

    def forward(self, x):
        return self.rl(self.bn(self.conv(x)))


class BriaRMBG(nn.Module, PyTorchModelHubMixin):
    def __init__(self, config: dict = {"in_ch": 3, "out_ch": 1}):
        super(BriaRMBG, self).__init__()
        in_ch = config["in_ch"]
        out_ch = config["out_ch"]
        self.conv_in = nn.Conv2d(in_ch, 64, 3, stride=2, padding=1)
        self.pool_in = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.stage1 = RSU7(64, 32, 64)
        self.pool12 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.stage2 = RSU6(64, 32, 128)
        self.pool23 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.stage3 = RSU5(128, 64, 256)
        self.pool34 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.stage4 = RSU4(256, 128, 512)
        self.pool45 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.stage5 = RSU4F(512, 256, 512)
        self.pool56 = nn.MaxPool2d(2, stride=2, ceil_mode=True)

        self.stage6 = RSU4F(512, 256, 512)

        # decoder
        self.stage5d = RSU4F(1024, 256, 512)
        self.stage4d = RSU4(1024, 128, 256)
        self.stage3d = RSU5(512, 64, 128)
        self.stage2d = RSU6(256, 32, 64)
        self.stage1d = RSU7(128, 16, 64)

        self.side1 = nn.Conv2d(64, out_ch, 3, padding=1)
        self.side2 = nn.Conv2d(64, out_ch, 3, padding=1)
        self.side3 = nn.Conv2d(128, out_ch, 3, padding=1)
        self.side4 = nn.Conv2d(256, out_ch, 3, padding=1)
        self.side5 = nn.Conv2d(512, out_ch, 3, padding=1)
        self.side6 = nn.Conv2d(512, out_ch, 3, padding=1)

        # self.outconv = nn.Conv2d(6*out_ch,out_ch,1)

    def forward(self, x):
        hx = x

        hxin = self.conv_in(hx)
        # hx = self.pool_in(hxin)

        # stage 1
        hx1 = self.stage1(hxin)
        hx = self.pool12(hx1)

        # stage 2
        hx2 = self.stage2(hx)
        hx = self.pool23(hx2)

        # stage 3
        hx3 = self.stage3(hx)
        hx = self.pool34(hx3)

        # stage 4
        hx4 = self.stage4(hx)
        hx = self.pool45(hx4)

        # stage 5
        hx5 = self.stage5(hx)
        hx = self.pool56(hx5)

        # stage 6
        hx6 = self.stage6(hx)
        hx6up = _upsample_like(hx6, hx5)

        # -------------------- decoder --------------------
        hx5d = self.stage5d(torch.cat((hx6up, hx5), 1))
        hx5dup = _upsample_like(hx5d, hx4)

        hx4d = self.stage4d(torch.cat((hx5dup, hx4), 1))
        hx4dup = _upsample_like(hx4d, hx3)

        hx3d = self.stage3d(torch.cat((hx4dup, hx3), 1))
        hx3dup = _upsample_like(hx3d, hx2)

        hx2d = self.stage2d(torch.cat((hx3dup, hx2), 1))
        hx2dup = _upsample_like(hx2d, hx1)

        hx1d = self.stage1d(torch.cat((hx2dup, hx1), 1))

        # side output
        d1 = self.side1(hx1d)
        d1 = _upsample_like(d1, x)

        d2 = self.side2(hx2d)
        d2 = _upsample_like(d2, x)

        d3 = self.side3(hx3d)
        d3 = _upsample_like(d3, x)

        d4 = self.side4(hx4d)
        d4 = _upsample_like(d4, x)

        d5 = self.side5(hx5d)
        d5 = _upsample_like(d5, x)

        d6 = self.side6(hx6)
        d6 = _upsample_like(d6, x)

        return [
            F.sigmoid(d1),
            F.sigmoid(d2),
            F.sigmoid(d3),
            F.sigmoid(d4),
            F.sigmoid(d5),
            F.sigmoid(d6),
        ], [hx1d, hx2d, hx3d, hx4d, hx5d, hx6]