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import torch
import torch.nn as nn
import torch.nn.functional as F
from PIL import Image
import fnmatch
import cv2
import sys
import numpy as np
from modules import devices
from einops import rearrange
from annotator.annotator_path import models_path
import torchvision
from torchvision.models import MobileNet_V2_Weights
from torchvision import transforms
COLOR_BACKGROUND = (255,255,0)
COLOR_HAIR = (0,0,255)
COLOR_EYE = (255,0,0)
COLOR_MOUTH = (255,255,255)
COLOR_FACE = (0,255,0)
COLOR_SKIN = (0,255,255)
COLOR_CLOTHES = (255,0,255)
PALETTE = [COLOR_BACKGROUND,COLOR_HAIR,COLOR_EYE,COLOR_MOUTH,COLOR_FACE,COLOR_SKIN,COLOR_CLOTHES]
class UNet(nn.Module):
def __init__(self):
super(UNet, self).__init__()
self.NUM_SEG_CLASSES = 7 # Background, hair, face, eye, mouth, skin, clothes
mobilenet_v2 = torchvision.models.mobilenet_v2(weights=MobileNet_V2_Weights.IMAGENET1K_V1)
mob_blocks = mobilenet_v2.features
# Encoder
self.en_block0 = nn.Sequential( # in_ch=3 out_ch=16
mob_blocks[0],
mob_blocks[1]
)
self.en_block1 = nn.Sequential( # in_ch=16 out_ch=24
mob_blocks[2],
mob_blocks[3],
)
self.en_block2 = nn.Sequential( # in_ch=24 out_ch=32
mob_blocks[4],
mob_blocks[5],
mob_blocks[6],
)
self.en_block3 = nn.Sequential( # in_ch=32 out_ch=96
mob_blocks[7],
mob_blocks[8],
mob_blocks[9],
mob_blocks[10],
mob_blocks[11],
mob_blocks[12],
mob_blocks[13],
)
self.en_block4 = nn.Sequential( # in_ch=96 out_ch=160
mob_blocks[14],
mob_blocks[15],
mob_blocks[16],
)
# Decoder
self.de_block4 = nn.Sequential( # in_ch=160 out_ch=96
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(160, 96, kernel_size=3, padding=1),
nn.InstanceNorm2d(96),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block3 = nn.Sequential( # in_ch=96x2 out_ch=32
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(96*2, 32, kernel_size=3, padding=1),
nn.InstanceNorm2d(32),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block2 = nn.Sequential( # in_ch=32x2 out_ch=24
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(32*2, 24, kernel_size=3, padding=1),
nn.InstanceNorm2d(24),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block1 = nn.Sequential( # in_ch=24x2 out_ch=16
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(24*2, 16, kernel_size=3, padding=1),
nn.InstanceNorm2d(16),
nn.LeakyReLU(0.1),
nn.Dropout(p=0.2)
)
self.de_block0 = nn.Sequential( # in_ch=16x2 out_ch=7
nn.UpsamplingNearest2d(scale_factor=2),
nn.Conv2d(16*2, self.NUM_SEG_CLASSES, kernel_size=3, padding=1),
nn.Softmax2d()
)
def forward(self, x):
e0 = self.en_block0(x)
e1 = self.en_block1(e0)
e2 = self.en_block2(e1)
e3 = self.en_block3(e2)
e4 = self.en_block4(e3)
d4 = self.de_block4(e4)
d4 = F.interpolate(d4, size=e3.size()[2:], mode='bilinear', align_corners=True)
c4 = torch.cat((d4,e3),1)
d3 = self.de_block3(c4)
d3 = F.interpolate(d3, size=e2.size()[2:], mode='bilinear', align_corners=True)
c3 = torch.cat((d3,e2),1)
d2 = self.de_block2(c3)
d2 = F.interpolate(d2, size=e1.size()[2:], mode='bilinear', align_corners=True)
c2 =torch.cat((d2,e1),1)
d1 = self.de_block1(c2)
d1 = F.interpolate(d1, size=e0.size()[2:], mode='bilinear', align_corners=True)
c1 = torch.cat((d1,e0),1)
y = self.de_block0(c1)
return y
class AnimeFaceSegment:
model_dir = os.path.join(models_path, "anime_face_segment")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/bdsqlsz/qinglong_controlnet-lllite/resolve/main/Annotators/UNet.pth"
modelpath = os.path.join(self.model_dir, "UNet.pth")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
net = UNet()
ckpt = torch.load(modelpath, map_location=self.device)
for key in list(ckpt.keys()):
if 'module.' in key:
ckpt[key.replace('module.', '')] = ckpt[key]
del ckpt[key]
net.load_state_dict(ckpt)
net.eval()
self.model = net.to(self.device)
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
transform = transforms.Compose([
transforms.Resize(512,interpolation=transforms.InterpolationMode.BICUBIC),
transforms.ToTensor(),])
img = Image.fromarray(input_image)
with torch.no_grad():
img = transform(img).unsqueeze(dim=0).to(self.device)
seg = self.model(img).squeeze(dim=0)
seg = seg.cpu().detach().numpy()
img = rearrange(seg,'h w c -> w c h')
img = [[PALETTE[np.argmax(val)] for val in buf]for buf in img]
return np.array(img).astype(np.uint8)
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