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