Spaces:
Paused
Paused
File size: 2,680 Bytes
5c32cd0 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 |
import os
import types
import torch
import numpy as np
from einops import rearrange
from .models.NNET import NNET
from modules import devices
from annotator.annotator_path import models_path
import torchvision.transforms as transforms
# 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:
model_dir = os.path.join(models_path, "normal_bae")
def __init__(self):
self.model = None
self.device = devices.get_device_for("controlnet")
def load_model(self):
remote_model_path = "https://huggingface.co/lllyasviel/Annotators/resolve/main/scannet.pt"
modelpath = os.path.join(self.model_dir, "scannet.pt")
if not os.path.exists(modelpath):
from basicsr.utils.download_util import load_file_from_url
load_file_from_url(remote_model_path, model_dir=self.model_dir)
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(modelpath, model)
model.eval()
self.model = model.to(self.device)
self.norm = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
def unload_model(self):
if self.model is not None:
self.model.cpu()
def __call__(self, input_image):
if self.model is None:
self.load_model()
self.model.to(self.device)
assert input_image.ndim == 3
image_normal = input_image
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)
return normal_image
|