400 lines
15 KiB
Python
400 lines
15 KiB
Python
from PIL import Image
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import cv2
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import os
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision.transforms as transforms
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from PIL import Image
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from skimage import transform
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from scipy.io import loadmat
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import comfy
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import comfy.model_management
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import folder_paths
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# from .utils import tensor2pil, pil2tensor
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def conv3x3(in_planes, out_planes, stride=1):
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"""3x3 convolution with padding"""
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return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
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padding=1, bias=False)
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class BasicBlock(nn.Module):
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def __init__(self, in_chan, out_chan, stride=1):
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super(BasicBlock, self).__init__()
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self.conv1 = conv3x3(in_chan, out_chan, stride)
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self.bn1 = nn.BatchNorm2d(out_chan)
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self.conv2 = conv3x3(out_chan, out_chan)
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self.bn2 = nn.BatchNorm2d(out_chan)
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self.relu = nn.ReLU(inplace=True)
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self.downsample = None
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if in_chan != out_chan or stride != 1:
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self.downsample = nn.Sequential(
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nn.Conv2d(in_chan, out_chan,
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kernel_size=1, stride=stride, bias=False),
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nn.BatchNorm2d(out_chan),
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)
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def forward(self, x):
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residual = self.conv1(x)
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residual = F.relu(self.bn1(residual))
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residual = self.conv2(residual)
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residual = self.bn2(residual)
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shortcut = x
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if self.downsample is not None:
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shortcut = self.downsample(x)
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out = shortcut + residual
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out = self.relu(out)
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return out
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def create_layer_basic(in_chan, out_chan, bnum, stride=1):
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layers = [BasicBlock(in_chan, out_chan, stride=stride)]
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for i in range(bnum - 1):
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layers.append(BasicBlock(out_chan, out_chan, stride=1))
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return nn.Sequential(*layers)
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class Resnet18(nn.Module):
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def __init__(self):
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super(Resnet18, self).__init__()
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self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
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bias=False)
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self.bn1 = nn.BatchNorm2d(64)
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self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
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self.layer1 = create_layer_basic(64, 64, bnum=2, stride=1)
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self.layer2 = create_layer_basic(64, 128, bnum=2, stride=2)
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self.layer3 = create_layer_basic(128, 256, bnum=2, stride=2)
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self.layer4 = create_layer_basic(256, 512, bnum=2, stride=2)
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def forward(self, x):
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x = self.conv1(x)
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x = F.relu(self.bn1(x))
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x = self.maxpool(x)
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x = self.layer1(x)
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feat8 = self.layer2(x) # 1/8
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feat16 = self.layer3(feat8) # 1/16
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feat32 = self.layer4(feat16) # 1/32
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return feat8, feat16, feat32
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def get_params(self):
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wd_params, nowd_params = [], []
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for name, module in self.named_modules():
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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wd_params.append(module.weight)
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if not module.bias is None:
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nowd_params.append(module.bias)
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elif isinstance(module, nn.BatchNorm2d):
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nowd_params += list(module.parameters())
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return wd_params, nowd_params
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class ConvBNReLU(nn.Module):
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def __init__(self, in_chan, out_chan, ks=3, stride=1, padding=1, *args, **kwargs):
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super(ConvBNReLU, self).__init__()
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self.conv = nn.Conv2d(in_chan,
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out_chan,
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kernel_size=ks,
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stride=stride,
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padding=padding,
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bias=False)
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self.bn = nn.BatchNorm2d(out_chan)
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self.init_weight()
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def forward(self, x):
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x = self.conv(x)
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x = F.relu(self.bn(x))
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return x
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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class BiSeNetOutput(nn.Module):
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def __init__(self, in_chan, mid_chan, n_classes, *args, **kwargs):
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super(BiSeNetOutput, self).__init__()
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self.conv = ConvBNReLU(in_chan, mid_chan, ks=3, stride=1, padding=1)
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self.conv_out = nn.Conv2d(mid_chan, n_classes, kernel_size=1, bias=False)
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self.init_weight()
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def forward(self, x):
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x = self.conv(x)
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x = self.conv_out(x)
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return x
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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def get_params(self):
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wd_params, nowd_params = [], []
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for name, module in self.named_modules():
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if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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wd_params.append(module.weight)
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if not module.bias is None:
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nowd_params.append(module.bias)
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elif isinstance(module, nn.BatchNorm2d):
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nowd_params += list(module.parameters())
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return wd_params, nowd_params
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class AttentionRefinementModule(nn.Module):
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def __init__(self, in_chan, out_chan, *args, **kwargs):
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super(AttentionRefinementModule, self).__init__()
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self.conv = ConvBNReLU(in_chan, out_chan, ks=3, stride=1, padding=1)
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self.conv_atten = nn.Conv2d(out_chan, out_chan, kernel_size=1, bias=False)
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self.bn_atten = nn.BatchNorm2d(out_chan)
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self.sigmoid_atten = nn.Sigmoid()
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self.init_weight()
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def forward(self, x):
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feat = self.conv(x)
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atten = F.avg_pool2d(feat, feat.size()[2:])
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atten = self.conv_atten(atten)
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atten = self.bn_atten(atten)
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atten = self.sigmoid_atten(atten)
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out = torch.mul(feat, atten)
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return out
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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class ContextPath(nn.Module):
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def __init__(self, *args, **kwargs):
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super(ContextPath, self).__init__()
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self.resnet = Resnet18()
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self.arm16 = AttentionRefinementModule(256, 128)
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self.arm32 = AttentionRefinementModule(512, 128)
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self.conv_head32 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
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self.conv_head16 = ConvBNReLU(128, 128, ks=3, stride=1, padding=1)
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self.conv_avg = ConvBNReLU(512, 128, ks=1, stride=1, padding=0)
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self.init_weight()
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def forward(self, x):
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H0, W0 = x.size()[2:]
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feat8, feat16, feat32 = self.resnet(x)
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H8, W8 = feat8.size()[2:]
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H16, W16 = feat16.size()[2:]
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H32, W32 = feat32.size()[2:]
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avg = F.avg_pool2d(feat32, feat32.size()[2:])
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avg = self.conv_avg(avg)
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avg_up = F.interpolate(avg, (H32, W32), mode='nearest')
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feat32_arm = self.arm32(feat32)
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feat32_sum = feat32_arm + avg_up
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feat32_up = F.interpolate(feat32_sum, (H16, W16), mode='nearest')
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feat32_up = self.conv_head32(feat32_up)
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feat16_arm = self.arm16(feat16)
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feat16_sum = feat16_arm + feat32_up
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feat16_up = F.interpolate(feat16_sum, (H8, W8), mode='nearest')
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feat16_up = self.conv_head16(feat16_up)
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return feat8, feat16_up, feat32_up # x8, x8, x16
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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def get_params(self):
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wd_params, nowd_params = [], []
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for name, module in self.named_modules():
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if isinstance(module, (nn.Linear, nn.Conv2d)):
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wd_params.append(module.weight)
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if not module.bias is None:
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nowd_params.append(module.bias)
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elif isinstance(module, nn.BatchNorm2d):
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nowd_params += list(module.parameters())
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return wd_params, nowd_params
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### This is not used, since I replace this with the resnet feature with the same size
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class SpatialPath(nn.Module):
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def __init__(self, *args, **kwargs):
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super(SpatialPath, self).__init__()
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self.conv1 = ConvBNReLU(3, 64, ks=7, stride=2, padding=3)
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self.conv2 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
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self.conv3 = ConvBNReLU(64, 64, ks=3, stride=2, padding=1)
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self.conv_out = ConvBNReLU(64, 128, ks=1, stride=1, padding=0)
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self.init_weight()
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def forward(self, x):
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feat = self.conv1(x)
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feat = self.conv2(feat)
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feat = self.conv3(feat)
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feat = self.conv_out(feat)
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return feat
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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def get_params(self):
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wd_params, nowd_params = [], []
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for name, module in self.named_modules():
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if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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wd_params.append(module.weight)
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if not module.bias is None:
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nowd_params.append(module.bias)
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elif isinstance(module, nn.BatchNorm2d):
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nowd_params += list(module.parameters())
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return wd_params, nowd_params
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class FeatureFusionModule(nn.Module):
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def __init__(self, in_chan, out_chan, *args, **kwargs):
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super(FeatureFusionModule, self).__init__()
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self.convblk = ConvBNReLU(in_chan, out_chan, ks=1, stride=1, padding=0)
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self.conv1 = nn.Conv2d(out_chan,
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out_chan // 4,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=False)
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self.conv2 = nn.Conv2d(out_chan // 4,
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out_chan,
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kernel_size=1,
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stride=1,
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padding=0,
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bias=False)
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self.relu = nn.ReLU(inplace=True)
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self.sigmoid = nn.Sigmoid()
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self.init_weight()
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def forward(self, fsp, fcp):
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fcat = torch.cat([fsp, fcp], dim=1)
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feat = self.convblk(fcat)
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atten = F.avg_pool2d(feat, feat.size()[2:])
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atten = self.conv1(atten)
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atten = self.relu(atten)
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atten = self.conv2(atten)
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atten = self.sigmoid(atten)
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feat_atten = torch.mul(feat, atten)
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feat_out = feat_atten + feat
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return feat_out
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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def get_params(self):
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wd_params, nowd_params = [], []
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for name, module in self.named_modules():
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if isinstance(module, nn.Linear) or isinstance(module, nn.Conv2d):
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wd_params.append(module.weight)
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if not module.bias is None:
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nowd_params.append(module.bias)
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elif isinstance(module, nn.BatchNorm2d):
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nowd_params += list(module.parameters())
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return wd_params, nowd_params
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class BiSeNet(nn.Module):
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def __init__(self, n_classes, *args, **kwargs):
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super(BiSeNet, self).__init__()
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self.cp = ContextPath()
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## here self.sp is deleted
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self.ffm = FeatureFusionModule(256, 256)
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self.conv_out = BiSeNetOutput(256, 256, n_classes)
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self.conv_out16 = BiSeNetOutput(128, 64, n_classes)
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self.conv_out32 = BiSeNetOutput(128, 64, n_classes)
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self.init_weight()
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def forward(self, x):
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H, W = x.size()[2:]
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feat_res8, feat_cp8, feat_cp16 = self.cp(x) # here return res3b1 feature
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feat_sp = feat_res8 # use res3b1 feature to replace spatial path feature
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feat_fuse = self.ffm(feat_sp, feat_cp8)
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feat_out = self.conv_out(feat_fuse)
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feat_out16 = self.conv_out16(feat_cp8)
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feat_out32 = self.conv_out32(feat_cp16)
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feat_out = F.interpolate(feat_out, (H, W), mode='bilinear', align_corners=True)
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feat_out16 = F.interpolate(feat_out16, (H, W), mode='bilinear', align_corners=True)
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feat_out32 = F.interpolate(feat_out32, (H, W), mode='bilinear', align_corners=True)
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return feat_out, feat_out16, feat_out32
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def init_weight(self):
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for ly in self.children():
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if isinstance(ly, nn.Conv2d):
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nn.init.kaiming_normal_(ly.weight, a=1)
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if not ly.bias is None: nn.init.constant_(ly.bias, 0)
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def get_params(self):
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wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params = [], [], [], []
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for name, child in self.named_children():
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child_wd_params, child_nowd_params = child.get_params()
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if isinstance(child, FeatureFusionModule) or isinstance(child, BiSeNetOutput):
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lr_mul_wd_params += child_wd_params
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lr_mul_nowd_params += child_nowd_params
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else:
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wd_params += child_wd_params
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nowd_params += child_nowd_params
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return wd_params, nowd_params, lr_mul_wd_params, lr_mul_nowd_params
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class FaceSkin(object):
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_label_index = {
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'skin': 1, 'left_brow': 2, 'right_brow': 3,
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'left_eye': 4, 'right_eye': 5, 'eye_g': 6, 'left_ear': 7, 'right_ear': 8,
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'ear_r': 9, 'nose': 10, 'mouth': 11, 'upper_lip': 12, 'low_lip': 13, 'neck': 14, 'neck_l': 15, 'cloth': 17,
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'hair': 17, 'hat': 18
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}
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_skin_label = (1, 2, 3, 4, 5, 10, 11, 12, 13)
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def __init__(self, model_path) -> None:
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n_classes = 19
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self.model = BiSeNet(n_classes=n_classes)
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self.model.load_state_dict(torch.load(model_path, map_location='cpu'))
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self.model.eval()
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# transform for input image
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self.trans = transforms.Compose([
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# transforms.ToTensor(),
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transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)),
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])
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self.model.to(comfy.model_management.get_torch_device())
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@torch.inference_mode()
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def estimate(self, image):
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torch_img = image.to(comfy.model_management.get_torch_device())
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torch_img = image.cuda()
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mask, _, _ = self.model(torch_img)
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return mask.squeeze(0).argmax(0)
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def parse_mask(self, mask: torch, label_name=None):
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if label_name is None:
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label = self._skin_label
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else:
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label = [self._label_index[i] for i in label_name]
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total_mask = torch.zeros_like(mask, dtype=torch.uint8, device=mask.device)
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for index in label:
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total_mask += torch.tensor(mask == index,
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dtype=torch.uint8, device=mask.device)
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total_mask = torch.clip(total_mask, 0, 1) * 255
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return total_mask.unsqueeze(0) |