controlnet_aux/normalbae/__init__.py

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])

 @classmethod
 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