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from email.policy import strict
import torch
import torchvision.models
import os.path as osp
import copy
from ...log_service import print_log
from .utils import \
get_total_param, get_total_param_sum, \
get_unit
# def load_state_dict(net, model_path):
# if isinstance(net, dict):
# for ni, neti in net.items():
# paras = torch.load(model_path[ni], map_location=torch.device('cpu'))
# new_paras = neti.state_dict()
# new_paras.update(paras)
# neti.load_state_dict(new_paras)
# else:
# paras = torch.load(model_path, map_location=torch.device('cpu'))
# new_paras = net.state_dict()
# new_paras.update(paras)
# net.load_state_dict(new_paras)
# return
# def save_state_dict(net, path):
# if isinstance(net, (torch.nn.DataParallel,
# torch.nn.parallel.DistributedDataParallel)):
# torch.save(net.module.state_dict(), path)
# else:
# torch.save(net.state_dict(), path)
def singleton(class_):
instances = {}
def getinstance(*args, **kwargs):
if class_ not in instances:
instances[class_] = class_(*args, **kwargs)
return instances[class_]
return getinstance
def preprocess_model_args(args):
# If args has layer_units, get the corresponding
# units.
# If args get backbone, get the backbone model.
args = copy.deepcopy(args)
if 'layer_units' in args:
layer_units = [
get_unit()(i) for i in args.layer_units
]
args.layer_units = layer_units
if 'backbone' in args:
args.backbone = get_model()(args.backbone)
return args
@singleton
class get_model(object):
def __init__(self):
self.model = {}
self.version = {}
def register(self, model, name, version='x'):
self.model[name] = model
self.version[name] = version
def __call__(self, cfg, verbose=True):
"""
Construct model based on the config.
"""
t = cfg.type
# the register is in each file
if t.find('ldm')==0:
from .. import ldm
elif t=='autoencoderkl':
from .. import autoencoder
elif t.find('clip')==0:
from .. import clip
elif t.find('sd')==0:
from .. import sd
elif t.find('vd')==0:
from .. import vd
elif t.find('openai_unet')==0:
from .. import openaimodel
elif t.find('optimus')==0:
from .. import optimus
args = preprocess_model_args(cfg.args)
net = self.model[t](**args)
if 'ckpt' in cfg:
checkpoint = torch.load(cfg.ckpt, map_location='cpu')
strict_sd = cfg.get('strict_sd', True)
net.load_state_dict(checkpoint['state_dict'], strict=strict_sd)
if verbose:
print_log('Load ckpt from {}'.format(cfg.ckpt))
elif 'pth' in cfg:
sd = torch.load(cfg.pth, map_location='cpu')
strict_sd = cfg.get('strict_sd', True)
net.load_state_dict(sd, strict=strict_sd)
if verbose:
print_log('Load pth from {}'.format(cfg.pth))
# display param_num & param_sum
if verbose:
print_log(
'Load {} with total {} parameters,'
'{:.3f} parameter sum.'.format(
t,
get_total_param(net),
get_total_param_sum(net) ))
return net
def get_version(self, name):
return self.version[name]
def register(name, version='x'):
def wrapper(class_):
get_model().register(class_, name, version)
return class_
return wrapper
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