| | 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
|
| |
|
