File size: 9,248 Bytes
18dd6ad |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 |
import os
import torch
import torch.nn as nn
from PIL import Image
import fnmatch
import cv2
import sys
import numpy as np
from einops import rearrange
from modules import devices
from annotator.annotator_path import models_path
class _bn_relu_conv(nn.Module):
def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
super(_bn_relu_conv, self).__init__()
self.model = nn.Sequential(
nn.BatchNorm2d(in_filters, eps=1e-3),
nn.LeakyReLU(0.2),
nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2), padding_mode='zeros')
)
def forward(self, x):
return self.model(x)
# the following are for debugs
print("****", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
for i,layer in enumerate(self.model):
if i != 2:
x = layer(x)
else:
x = layer(x)
#x = nn.functional.pad(x, (1, 1, 1, 1), mode='constant', value=0)
print("____", np.max(x.cpu().numpy()), np.min(x.cpu().numpy()), np.mean(x.cpu().numpy()), np.std(x.cpu().numpy()), x.shape)
print(x[0])
return x
class _u_bn_relu_conv(nn.Module):
def __init__(self, in_filters, nb_filters, fw, fh, subsample=1):
super(_u_bn_relu_conv, self).__init__()
self.model = nn.Sequential(
nn.BatchNorm2d(in_filters, eps=1e-3),
nn.LeakyReLU(0.2),
nn.Conv2d(in_filters, nb_filters, (fw, fh), stride=subsample, padding=(fw//2, fh//2)),
nn.Upsample(scale_factor=2, mode='nearest')
)
def forward(self, x):
return self.model(x)
class _shortcut(nn.Module):
def __init__(self, in_filters, nb_filters, subsample=1):
super(_shortcut, self).__init__()
self.process = False
self.model = None
if in_filters != nb_filters or subsample != 1:
self.process = True
self.model = nn.Sequential(
nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample)
)
def forward(self, x, y):
#print(x.size(), y.size(), self.process)
if self.process:
y0 = self.model(x)
#print("merge+", torch.max(y0+y), torch.min(y0+y),torch.mean(y0+y), torch.std(y0+y), y0.shape)
return y0 + y
else:
#print("merge", torch.max(x+y), torch.min(x+y),torch.mean(x+y), torch.std(x+y), y.shape)
return x + y
class _u_shortcut(nn.Module):
def __init__(self, in_filters, nb_filters, subsample):
super(_u_shortcut, self).__init__()
self.process = False
self.model = None
if in_filters != nb_filters:
self.process = True
self.model = nn.Sequential(
nn.Conv2d(in_filters, nb_filters, (1, 1), stride=subsample, padding_mode='zeros'),
nn.Upsample(scale_factor=2, mode='nearest')
)
def forward(self, x, y):
if self.process:
return self.model(x) + y
else:
return x + y
class basic_block(nn.Module):
def __init__(self, in_filters, nb_filters, init_subsample=1):
super(basic_block, self).__init__()
self.conv1 = _bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
self.shortcut = _shortcut(in_filters, nb_filters, subsample=init_subsample)
def forward(self, x):
x1 = self.conv1(x)
x2 = self.residual(x1)
return self.shortcut(x, x2)
class _u_basic_block(nn.Module):
def __init__(self, in_filters, nb_filters, init_subsample=1):
super(_u_basic_block, self).__init__()
self.conv1 = _u_bn_relu_conv(in_filters, nb_filters, 3, 3, subsample=init_subsample)
self.residual = _bn_relu_conv(nb_filters, nb_filters, 3, 3)
self.shortcut = _u_shortcut(in_filters, nb_filters, subsample=init_subsample)
def forward(self, x):
y = self.residual(self.conv1(x))
return self.shortcut(x, y)
class _residual_block(nn.Module):
def __init__(self, in_filters, nb_filters, repetitions, is_first_layer=False):
super(_residual_block, self).__init__()
layers = []
for i in range(repetitions):
init_subsample = 1
if i == repetitions - 1 and not is_first_layer:
init_subsample = 2
if i == 0:
l = basic_block(in_filters=in_filters, nb_filters=nb_filters, init_subsample=init_subsample)
else:
l = basic_block(in_filters=nb_filters, nb_filters=nb_filters, init_subsample=init_subsample)
layers.append(l)
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class _upsampling_residual_block(nn.Module):
def __init__(self, in_filters, nb_filters, repetitions):
super(_upsampling_residual_block, self).__init__()
layers = []
for i in range(repetitions):
l = None
if i == 0:
l = _u_basic_block(in_filters=in_filters, nb_filters=nb_filters)#(input)
else:
l = basic_block(in_filters=nb_filters, nb_filters=nb_filters)#(input)
layers.append(l)
self.model = nn.Sequential(*layers)
def forward(self, x):
return self.model(x)
class res_skip(nn.Module):
def __init__(self):
super(res_skip, self).__init__()
self.block0 = _residual_block(in_filters=1, nb_filters=24, repetitions=2, is_first_layer=True)#(input)
self.block1 = _residual_block(in_filters=24, nb_filters=48, repetitions=3)#(block0)
self.block2 = _residual_block(in_filters=48, nb_filters=96, repetitions=5)#(block1)
self.block3 = _residual_block(in_filters=96, nb_filters=192, repetitions=7)#(block2)
self.block4 = _residual_block(in_filters=192, nb_filters=384, repetitions=12)#(block3)
self.block5 = _upsampling_residual_block(in_filters=384, nb_filters=192, repetitions=7)#(block4)
self.res1 = _shortcut(in_filters=192, nb_filters=192)#(block3, block5, subsample=(1,1))
self.block6 = _upsampling_residual_block(in_filters=192, nb_filters=96, repetitions=5)#(res1)
self.res2 = _shortcut(in_filters=96, nb_filters=96)#(block2, block6, subsample=(1,1))
self.block7 = _upsampling_residual_block(in_filters=96, nb_filters=48, repetitions=3)#(res2)
self.res3 = _shortcut(in_filters=48, nb_filters=48)#(block1, block7, subsample=(1,1))
self.block8 = _upsampling_residual_block(in_filters=48, nb_filters=24, repetitions=2)#(res3)
self.res4 = _shortcut(in_filters=24, nb_filters=24)#(block0,block8, subsample=(1,1))
self.block9 = _residual_block(in_filters=24, nb_filters=16, repetitions=2, is_first_layer=True)#(res4)
self.conv15 = _bn_relu_conv(in_filters=16, nb_filters=1, fh=1, fw=1, subsample=1)#(block7)
def forward(self, x):
x0 = self.block0(x)
x1 = self.block1(x0)
x2 = self.block2(x1)
x3 = self.block3(x2)
x4 = self.block4(x3)
x5 = self.block5(x4)
res1 = self.res1(x3, x5)
x6 = self.block6(res1)
res2 = self.res2(x2, x6)
x7 = self.block7(res2)
res3 = self.res3(x1, x7)
x8 = self.block8(res3)
res4 = self.res4(x0, x8)
x9 = self.block9(res4)
y = self.conv15(x9)
return y
class MangaLineExtration:
model_dir = os.path.join(models_path, "manga_line")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co./lllyasviel/Annotators/resolve/main/erika.pth"
modelpath = os.path.join(self.model_dir, "erika.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)
#norm_layer = functools.partial(nn.InstanceNorm2d, affine=False, track_running_stats=False)
net = res_skip()
ckpt = torch.load(modelpath)
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)
img = cv2.cvtColor(input_image, cv2.COLOR_RGB2GRAY)
img = np.ascontiguousarray(img.copy()).copy()
with torch.no_grad():
image_feed = torch.from_numpy(img).float().to(self.device)
image_feed = rearrange(image_feed, 'h w -> 1 1 h w')
line = self.model(image_feed)
line = 255 - line.cpu().numpy()[0, 0]
return line.clip(0, 255).astype(np.uint8)
|