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model_zoo.py
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model_zoo.py
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import json
import os
import sys
from urllib.request import urlretrieve
import torch
from torchpack import distributed as dist
from core.models.semantic_kitti.minkunet import MinkUNet
from core.models.semantic_kitti.spvcnn import SPVCNN
from core.models.semantic_kitti.spvnas import SPVNAS
__all__ = ['spvnas_specialized', 'minkunet', 'spvcnn']
def download_url(url, model_dir='~/.torch/', overwrite=False):
target_dir = url.split('/')[-1]
model_dir = os.path.expanduser(model_dir)
if not os.path.exists(model_dir):
os.makedirs(model_dir)
model_dir = os.path.join(model_dir, target_dir)
cached_file = model_dir
if not os.path.exists(cached_file) or overwrite:
sys.stderr.write(f'Downloading: "{url}" to {cached_file}\n')
urlretrieve(url, cached_file)
return cached_file
def spvnas_specialized(net_id, pretrained=True, **kwargs):
url_base = 'https://hanlab.mit.edu/files/SPVNAS/spvnas_specialized/'
net_config = json.load(
open(
download_url(url_base + net_id + '/net.config',
model_dir='.torch/spvnas_specialized/%s/' % net_id)))
model = SPVNAS(
net_config['num_classes'],
macro_depth_constraint=1,
pres=net_config['pres'],
vres=net_config['vres']).to(
'cuda:%d'
% dist.local_rank() if torch.cuda.is_available() else 'cpu')
model.manual_select(net_config)
model = model.determinize()
if pretrained:
init = torch.load(download_url(url_base + net_id + '/init',
model_dir='.torch/spvnas_specialized/%s/'
% net_id),
map_location='cuda:%d' % dist.local_rank()
if torch.cuda.is_available() else 'cpu')['model']
model.load_state_dict(init)
return model
def spvnas_supernet(net_id, pretrained=True, **kwargs):
url_base = 'https://hanlab.mit.edu/files/SPVNAS/spvnas_supernet/'
net_config = json.load(
open(
download_url(url_base + net_id + '/net.config',
model_dir='.torch/spvnas_supernet/%s/' % net_id)))
model = SPVNAS(
net_config['num_classes'],
macro_depth_constraint=net_config['macro_depth_constraint'],
pres=net_config['pres'],
vres=net_config['vres']).to(
'cuda:%d'
% dist.local_rank() if torch.cuda.is_available() else 'cpu')
if pretrained:
init = torch.load(download_url(url_base + net_id + '/init',
model_dir='.torch/spvnas_supernet/%s/'
% net_id),
map_location='cuda:%d' % dist.local_rank()
if torch.cuda.is_available() else 'cpu')['model']
model.load_state_dict(init)
return model
def minkunet(net_id, pretrained=True, **kwargs):
url_base = 'https://hanlab.mit.edu/files/SPVNAS/minkunet/'
net_config = json.load(
open(
download_url(url_base + net_id + '/net.config',
model_dir='.torch/minkunet/%s/' % net_id)))
model = MinkUNet(
num_classes=net_config['num_classes'], cr=net_config['cr']).to(
'cuda:%d'
% dist.local_rank() if torch.cuda.is_available() else 'cpu')
if pretrained:
init = torch.load(download_url(url_base + net_id + '/init',
model_dir='.torch/minkunet/%s/'
% net_id),
map_location='cuda:%d' % dist.local_rank()
if torch.cuda.is_available() else 'cpu')['model']
model.load_state_dict(init)
return model
def spvcnn(net_id, pretrained=True, **kwargs):
url_base = 'https://hanlab.mit.edu/files/SPVNAS/spvcnn/'
net_config = json.load(
open(
download_url(url_base + net_id + '/net.config',
model_dir='.torch/spvcnn/%s/' % net_id)))
model = SPVCNN(
num_classes=net_config['num_classes'],
cr=net_config['cr'],
pres=net_config['pres'],
vres=net_config['vres']).to(
'cuda:%d'
% dist.local_rank() if torch.cuda.is_available() else 'cpu')
if pretrained:
init = torch.load(download_url(url_base + net_id + '/init',
model_dir='.torch/spvcnn/%s/' % net_id),
map_location='cuda:%d' % dist.local_rank()
if torch.cuda.is_available() else 'cpu')['model']
model.load_state_dict(init)
return model