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| import os | |
| import types | |
| import torch | |
| import numpy as np | |
| from einops import rearrange | |
| from .models.NNET import NNET | |
| from modules import devices | |
| from annotator.annotator_path import models_path | |
| import torchvision.transforms as transforms | |
| # load model | |
| def load_checkpoint(fpath, model): | |
| ckpt = torch.load(fpath, map_location='cpu')['model'] | |
| load_dict = {} | |
| for k, v in ckpt.items(): | |
| if k.startswith('module.'): | |
| k_ = k.replace('module.', '') | |
| load_dict[k_] = v | |
| else: | |
| load_dict[k] = v | |
| model.load_state_dict(load_dict) | |
| return model | |
| class NormalBaeDetector: | |
| model_dir = os.path.join(models_path, "normal_bae") | |
| def __init__(self): | |
| self.model = None | |
| self.device = devices.get_device_for("controlnet") | |
| def load_model(self): | |
| remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt" | |
| modelpath = os.path.join(self.model_dir, "scannet.pt") | |
| 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.model_dir) | |
| args = types.SimpleNamespace() | |
| args.mode = 'client' | |
| args.architecture = 'BN' | |
| args.pretrained = 'scannet' | |
| args.sampling_ratio = 0.4 | |
| args.importance_ratio = 0.7 | |
| model = NNET(args) | |
| model = load_checkpoint(modelpath, model) | |
| model.eval() | |
| self.model = model.to(self.device) | |
| self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| def unload_model(self): | |
| if self.model is not None: | |
| self.model.cpu() | |
| def __call__(self, input_image): | |
| if self.model is None: | |
| self.load_model() | |
| self.model.to(self.device) | |
| assert input_image.ndim == 3 | |
| image_normal = input_image | |
| with torch.no_grad(): | |
| image_normal = torch.from_numpy(image_normal).float().to(self.device) | |
| image_normal = image_normal / 255.0 | |
| image_normal = rearrange(image_normal, 'h w c -> 1 c h w') | |
| image_normal = self.norm(image_normal) | |
| normal = self.model(image_normal) | |
| normal = normal[0][-1][:, :3] | |
| # d = torch.sum(normal ** 2.0, dim=1, keepdim=True) ** 0.5 | |
| # d = torch.maximum(d, torch.ones_like(d) * 1e-5) | |
| # normal /= d | |
| normal = ((normal + 1) * 0.5).clip(0, 1) | |
| normal = rearrange(normal[0], 'c h w -> h w c').cpu().numpy() | |
| normal_image = (normal * 255.0).clip(0, 255).astype(np.uint8) | |
| return normal_image | |