| 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 scripts.utils import load_file_from_url
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| 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]
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|
|
|
|
|
|
| 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
|
|
|
