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import os
import warnings
import cv2
import numpy as np
import torch
import torch.nn as nn
from einops import rearrange
from huggingface_hub import hf_hub_download
from PIL import Image
from ..util import HWC3, resize_image
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, coarse_model):
self.model = model
self.model_coarse = coarse_model
@classmethod
def from_pretrained(cls, pretrained_model_or_path, filename=None, coarse_filename=None, cache_dir=None, local_files_only=False):
filename = filename or "sk_model.pth"
coarse_filename = coarse_filename or "sk_model2.pth"
if os.path.isdir(pretrained_model_or_path):
model_path = os.path.join(pretrained_model_or_path, filename)
coarse_model_path = os.path.join(pretrained_model_or_path, coarse_filename)
else:
model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only)
coarse_model_path = hf_hub_download(pretrained_model_or_path, coarse_filename, cache_dir=cache_dir, local_files_only=local_files_only)
model = Generator(3, 1, 3)
model.load_state_dict(torch.load(model_path, map_location=torch.device('cpu')))
model.eval()
coarse_model = Generator(3, 1, 3)
coarse_model.load_state_dict(torch.load(coarse_model_path, map_location=torch.device('cpu')))
coarse_model.eval()
return cls(model, coarse_model)
def to(self, device):
self.model.to(device)
self.model_coarse.to(device)
return self
def __call__(self, input_image, coarse=False, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs):
if "return_pil" in kwargs:
warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning)
output_type = "pil" if kwargs["return_pil"] else "np"
if type(output_type) is bool:
warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions")
if output_type:
output_type = "pil"
device = next(iter(self.model.parameters())).device
if not isinstance(input_image, np.ndarray):
input_image = np.array(input_image, dtype=np.uint8)
input_image = HWC3(input_image)
input_image = resize_image(input_image, detect_resolution)
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().to(device)
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)
detected_map = line
detected_map = HWC3(detected_map)
img = resize_image(input_image, image_resolution)
H, W, C = img.shape
detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR)
detected_map = 255 - detected_map
if output_type == "pil":
detected_map = Image.fromarray(detected_map)
return detected_map
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