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