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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
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