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Running
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Zero
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# From https://github.com/carolineec/informative-drawings
# MIT License
'''
MIT License
Copyright (c) 2022 Caroline Chan
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.
'''
import os
import cv2
import torch
import numpy as np
import torch.nn as nn
from einops import rearrange
from .utils import load_file_from_url
norm_layer = nn.InstanceNorm2d
class ResidualBlock(nn.Module):
def __init__(self, in_features):
super(ResidualBlock, self).__init__()
conv_block = [ nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features),
nn.ReLU(inplace=True),
nn.ReflectionPad2d(1),
nn.Conv2d(in_features, in_features, 3),
norm_layer(in_features)
]
self.conv_block = nn.Sequential(*conv_block)
def forward(self, x):
return x + self.conv_block(x)
class Generator(nn.Module):
def __init__(self, input_nc, output_nc, n_residual_blocks=9, sigmoid=True):
super(Generator, self).__init__()
# Initial convolution block
model0 = [ nn.ReflectionPad2d(3),
nn.Conv2d(input_nc, 64, 7),
norm_layer(64),
nn.ReLU(inplace=True) ]
self.model0 = nn.Sequential(*model0)
# Downsampling
model1 = []
in_features = 64
out_features = in_features*2
for _ in range(2):
model1 += [ nn.Conv2d(in_features, out_features, 3, stride=2, padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True) ]
in_features = out_features
out_features = in_features*2
self.model1 = nn.Sequential(*model1)
model2 = []
# Residual blocks
for _ in range(n_residual_blocks):
model2 += [ResidualBlock(in_features)]
self.model2 = nn.Sequential(*model2)
# Upsampling
model3 = []
out_features = in_features//2
for _ in range(2):
model3 += [ nn.ConvTranspose2d(in_features, out_features, 3, stride=2, padding=1, output_padding=1),
norm_layer(out_features),
nn.ReLU(inplace=True) ]
in_features = out_features
out_features = in_features//2
self.model3 = nn.Sequential(*model3)
# Output layer
model4 = [ nn.ReflectionPad2d(3),
nn.Conv2d(64, output_nc, 7)]
if sigmoid:
model4 += [nn.Sigmoid()]
self.model4 = nn.Sequential(*model4)
def forward(self, x, cond=None):
out = self.model0(x)
out = self.model1(out)
out = self.model2(out)
out = self.model3(out)
out = self.model4(out)
return out
class LineartDetector:
def __init__(self, model_path="hf_download"):
self.model = self.load_model('sk_model.pth', model_path)
self.model_coarse = self.load_model('sk_model2.pth', model_path)
def load_model(self, name, model_path="hf_download"):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name
modelpath = os.path.join(model_path, name)
if not os.path.exists(modelpath):
load_file_from_url(remote_model_path, model_dir=model_path)
model = Generator(3, 1, 3)
model.load_state_dict(torch.load(modelpath, map_location=torch.device('cpu')))
model.eval()
model = model.cuda()
return model
def __call__(self, input_image, coarse=False):
model = self.model_coarse if coarse else self.model
assert input_image.ndim == 3
image = input_image
with torch.no_grad():
image = torch.from_numpy(image).float().cuda()
image = image / 255.0
image = rearrange(image, 'h w c -> 1 c h w')
line = model(image)[0][0]
line = line.cpu().numpy()
line = (line * 255.0).clip(0, 255).astype(np.uint8)
return line |