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from email.policy import strict
import torch
import torchvision.models
import os.path as osp
import copy
from .utils import \
get_total_param, get_total_param_sum, \
get_unit
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('audioldm')==0:
from ..latent_diffusion.vae import audioldm
elif t.find('autoencoderkl')==0:
from ..latent_diffusion.vae import autokl
elif t.find('optimus')==0:
from ..latent_diffusion.vae import optimus
elif t.find('clip')==0:
from ..encoders import clip
elif t.find('clap')==0:
from ..encoders import clap
elif t.find('sd')==0:
from .. import sd
elif t.find('codi')==0:
from .. import codi
elif t.find('thesis_model')==0:
from .. import codi_2
elif t.find('openai_unet')==0:
from ..latent_diffusion import diffusion_unet
elif t.find('prova')==0:
from ..latent_diffusion import diffusion_unet
args = preprocess_model_args(cfg.args)
net = self.model[t](**args)
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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