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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 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"
@classmethod
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
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