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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 | |
| 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 | |