| 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 |
|
|
|
|
| |
| 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] |
| |
| |
| |
| 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 |
|
|
