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on
Zero
Running
on
Zero
| import os | |
| import types | |
| import warnings | |
| import cv2 | |
| import numpy as np | |
| import torch | |
| import torchvision.transforms as transforms | |
| from einops import rearrange | |
| from huggingface_hub import hf_hub_download | |
| from PIL import Image | |
| from ..util import HWC3, resize_image | |
| 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]) | |
| def from_pretrained(cls, pretrained_model_or_path, filename=None, cache_dir=None, local_files_only=False): | |
| filename = filename or "scannet.pt" | |
| if os.path.isdir(pretrained_model_or_path): | |
| model_path = os.path.join(pretrained_model_or_path, filename) | |
| else: | |
| model_path = hf_hub_download(pretrained_model_or_path, filename, cache_dir=cache_dir, local_files_only=local_files_only) | |
| 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) | |
| return self | |
| def __call__(self, input_image, detect_resolution=512, image_resolution=512, output_type="pil", **kwargs): | |
| if "return_pil" in kwargs: | |
| warnings.warn("return_pil is deprecated. Use output_type instead.", DeprecationWarning) | |
| output_type = "pil" if kwargs["return_pil"] else "np" | |
| if type(output_type) is bool: | |
| warnings.warn("Passing `True` or `False` to `output_type` is deprecated and will raise an error in future versions") | |
| if output_type: | |
| output_type = "pil" | |
| device = next(iter(self.model.parameters())).device | |
| if not isinstance(input_image, np.ndarray): | |
| input_image = np.array(input_image, dtype=np.uint8) | |
| input_image = HWC3(input_image) | |
| input_image = resize_image(input_image, detect_resolution) | |
| assert input_image.ndim == 3 | |
| image_normal = input_image | |
| with torch.no_grad(): | |
| image_normal = torch.from_numpy(image_normal).float().to(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 = normal_image | |
| detected_map = HWC3(detected_map) | |
| img = resize_image(input_image, image_resolution) | |
| H, W, C = img.shape | |
| detected_map = cv2.resize(detected_map, (W, H), interpolation=cv2.INTER_LINEAR) | |
| if output_type == "pil": | |
| detected_map = Image.fromarray(detected_map) | |
| return detected_map | |