Spaces:
Running
on
Zero
Running
on
Zero
File size: 5,184 Bytes
459fa69 |
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 |
# From https://github.com/carolineec/informative-drawings
# MIT License
import os
import cv2
import torch
import numpy as np
import torch.nn as nn
from einops import rearrange
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, annotator_ckpts_path):
self.annotator_ckpts_path = annotator_ckpts_path
self.model = self.load_model('sk_model.pth')
self.model_coarse = self.load_model('sk_model2.pth')
def load_model(self, name):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name
modelpath = os.path.join(self.annotator_ckpts_path, name)
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.annotator_ckpts_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):
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
class BatchLineartDetector:
def __init__(self, annotator_ckpts_path):
self.annotator_ckpts_path = annotator_ckpts_path
self.model = self.load_model('sk_model.pth')
def load_model(self, name):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/" + name
modelpath = os.path.join(self.annotator_ckpts_path, name)
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.annotator_ckpts_path)
model = Generator(3, 1, 3)
model.load_state_dict(torch.load(modelpath, map_location=torch.device('cpu')))
model.eval()
return model
def to(self, device, dtype):
self.model.to(device, dtype=dtype)
def __call__(self, input_image, mean=-1., std=2.):
model = self.model
image = input_image
with torch.no_grad():
image = (image - mean) / std
line = model(image)
line = 1 - line
return line
|