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Running
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A10G
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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 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 = {}
def register(self, model, name):
self.model[name] = model
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 autokl
elif (t.find('clip')==0) or (t.find('openclip')==0):
from .. import clip
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)
map_location = cfg.get('map_location', 'cpu')
strict_sd = cfg.get('strict_sd', True)
if 'ckpt' in cfg:
checkpoint = torch.load(cfg.ckpt, map_location=map_location)
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=map_location)
net.load_state_dict(sd, strict=strict_sd)
if verbose:
print_log('Load pth from {}'.format(cfg.pth))
elif 'hfm' in cfg:
from huggingface_hub import hf_hub_download
temppath = hf_hub_download(cfg.hfm[0], cfg.hfm[1])
sd = torch.load(temppath, map_location='cpu')
strict_sd = cfg.get('strict_sd', True)
net.load_state_dict(sd, strict=strict_sd)
if verbose:
print_log('Load hfm from {}/{}'.format(*cfg.hfm))
# 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 register(name):
def wrapper(class_):
get_model().register(class_, name)
return class_
return wrapper
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