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import os
import torch
import yaml
from lidm.utils.misc_utils import dict2namespace
from ..modules.rangenet.model import Model as rangenet
try:
from ..modules.spvcnn.model import Model as spvcnn
from ..modules.minkowskinet.model import Model as minkowskinet
except:
print('To install torchsparse 1.4.0, please refer to https://github.com/mit-han-lab/torchsparse/tree/74099d10a51c71c14318bce63d6421f698b24f24')
DEFAULT_ROOT = './pretrained_weights'
def build_model(dataset_name, model_name, device='cpu'):
# config
model_folder = os.path.join(DEFAULT_ROOT, dataset_name, model_name)
if not os.path.isdir(model_folder):
raise Exception('Not Available Pretrained Weights!')
config = yaml.safe_load(open(os.path.join(model_folder, 'config.yaml'), 'r'))
if model_name != 'rangenet':
config = dict2namespace(config)
# build model
model = eval(model_name)(config)
# load checkpoint
if model_name == 'rangenet':
model.load_pretrained_weights(model_folder)
else:
ckpt = torch.load(os.path.join(model_folder, 'model.ckpt'), map_location="cpu")
model.load_state_dict(ckpt['state_dict'], strict=False)
model.to(device)
model.eval()
return model
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