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Configuration error
Configuration error
| import os | |
| import types | |
| import warnings | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torchvision.transforms as transforms | |
| from einops import rearrange | |
| from PIL import Image | |
| from custom_controlnet_aux.util import HWC3, common_input_validate, resize_image_with_pad, custom_hf_download, HF_MODEL_NAME | |
| from .nets.NNET import NNET | |
| # 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: | |
| def __init__(self, model): | |
| self.model = model | |
| self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) | |
| self.device = "cpu" | |
| def from_pretrained(cls, pretrained_model_or_path=HF_MODEL_NAME, filename="scannet.pt"): | |
| model_path = custom_hf_download(pretrained_model_or_path, filename) | |
| 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(model_path, model) | |
| model.eval() | |
| return cls(model) | |
| def to(self, device): | |
| self.model.to(device) | |
| self.device = device | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, output_type="pil", upscale_method="INTER_CUBIC", **kwargs): | |
| input_image, output_type = common_input_validate(input_image, output_type, **kwargs) | |
| detected_map, remove_pad = resize_image_with_pad(input_image, detect_resolution, upscale_method) | |
| image_normal = detected_map | |
| 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) | |
| detected_map = remove_pad(HWC3(normal_image)) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |