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Zero
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import torch
import torch.nn as nn
import torch.nn.functional as F
from huggingface_hub import PyTorchModelHubMixin
import numpy as np
from PIL import Image
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
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)
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):
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
class BriaRMBG(nn.Module, PyTorchModelHubMixin):
def __init__(self, config: dict = {"in_ch": 3, "out_ch": 1}):
super(BriaRMBG, self).__init__()
self.config = config
# Simplified architecture for fallback
self.stage1 = RSU7(3, 32, 64)
self.stage2 = RSU7(64, 32, 128)
self.stage3 = RSU7(128, 64, 256)
self.stage4 = RSU7(256, 128, 512)
# Decoder
self.stage4d = RSU7(1024, 128, 256)
self.stage3d = RSU7(512, 64, 128)
self.stage2d = RSU7(256, 32, 64)
self.stage1d = RSU7(128, 16, 64)
self.side1 = nn.Conv2d(64, 1, 3, padding=1)
self.side2 = nn.Conv2d(64, 1, 3, padding=1)
self.side3 = nn.Conv2d(128, 1, 3, padding=1)
self.side4 = nn.Conv2d(256, 1, 3, padding=1)
self.side5 = nn.Conv2d(512, 1, 3, padding=1)
self.side6 = nn.Conv2d(256, 1, 3, padding=1)
self.outconv = nn.Conv2d(6, 1, 1)
def forward(self, x):
hx = x
# Encoder
hx1 = self.stage1(hx)
hx = F.max_pool2d(hx1, 2, stride=2, ceil_mode=True)
hx2 = self.stage2(hx)
hx = F.max_pool2d(hx2, 2, stride=2, ceil_mode=True)
hx3 = self.stage3(hx)
hx = F.max_pool2d(hx3, 2, stride=2, ceil_mode=True)
hx4 = self.stage4(hx)
hx = F.max_pool2d(hx4, 2, stride=2, ceil_mode=True)
# Decoder
hx4d = self.stage4d(torch.cat((hx, 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 outputs
side1 = self.side1(hx1d)
side2 = _upsample_like(self.side2(hx2d), side1)
side3 = _upsample_like(self.side3(hx3d), side1)
side4 = _upsample_like(self.side4(hx4d), side1)
side5 = _upsample_like(self.side5(hx), side1)
side6 = _upsample_like(self.side6(hx4d), side1)
# Fusion
out = self.outconv(torch.cat((side1, side2, side3, side4, side5, side6), 1))
return torch.sigmoid(out)
def simple_background_removal(image):
"""
Simple fallback background removal using edge detection and thresholding
"""
if isinstance(image, np.ndarray):
img = image
else:
img = np.array(image)
# Convert to grayscale
gray = np.mean(img, axis=2)
# Simple edge detection
from scipy import ndimage
edges = ndimage.sobel(gray)
# Create a simple mask based on intensity
mask = np.ones_like(gray)
# Simple thresholding - assume foreground is in center and has more edges
h, w = gray.shape
center_mask = np.zeros_like(gray)
center_mask[h//4:3*h//4, w//4:3*w//4] = 1
# Combine edge information with center bias
mask = (edges > np.percentile(edges, 70)) * center_mask
mask = ndimage.binary_fill_holes(mask)
mask = ndimage.gaussian_filter(mask.astype(float), sigma=2)
return mask |