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Zero
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from controlnet_aux import LineartDetector
import torch
import cv2
import numpy as np
import torch.nn as nn
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 LineArt(nn.Module):
def __init__(self, input_nc=3, output_nc=1, n_residual_blocks=3, sigmoid=True):
super(LineArt, 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):
"""
input: tensor (B,C,H,W)
output: tensor (B,1,H,W) 0~1
"""
out = self.model0(x)
out = self.model1(out)
out = self.model2(out)
out = self.model3(out)
out = self.model4(out)
return out
if __name__ == '__main__':
import matplotlib.pyplot as plt
from tqdm import tqdm
apply_lineart = LineArt()
apply_lineart.load_state_dict(torch.load('condition/ckpts/model.pth', map_location=torch.device('cpu')))
img = cv2.imread('condition/car_448_768.jpg')
img = torch.from_numpy(img).permute(2,0,1).unsqueeze(0).repeat(8,1,1,1).float()
detected_map = apply_lineart(img)
print(img.shape, img.max(),img.min(),detected_map.shape, detected_map.max(),detected_map.min())
cv2.imwrite('condition/example_lineart.jpg', 255*detected_map[0,0].cpu().detach().numpy()) |