def __init__(self, ae_config, loss_config, ckpt_path=None, ignore_keys=[]):
super().__init__()
self.autoencoder = instantiate_from_config(ae_config)
self.loss = instantiate_from_config(loss_config)
if ckpt_path is not None:
print("Loading model from {}".format(ckpt_path))
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def get_data(config):
# get data
data = instantiate_from_config(config.data)
data.prepare_data()
data.setup()
dset = data.datasets["validation"]
return dset
def load_model_from_config(config, sd, gpu=True, eval_mode=True):
model = instantiate_from_config(config)
if sd is not None:
model.load_state_dict(sd)
if gpu:
model.cuda()
if eval_mode:
model.eval()
return {"model": model}
def init_preprocessing(self):
dqcfg = {
"target":
"net2net.modules.autoencoder.basic.BasicFullyConnectedVAE"
}
self.dequantizer = instantiate_from_config(dqcfg)
ckpt = get_ckpt_path("dequant_vae",
"net2net/modules/autoencoder/dequant_vae")
self.dequantizer.load_state_dict(torch.load(
ckpt, map_location=torch.device("cpu")),
strict=False)
self.dequantizer.eval()
self.dequantizer.train = disabled_train
def __init__(self,
flow_config,
first_stage_config,
cond_stage_config,
ckpt_path=None,
ignore_keys=[],
first_stage_key="image",
cond_stage_key="image",
interpolate_cond_size=-1):
super().__init__()
self.init_first_stage_from_ckpt(first_stage_config)
self.init_cond_stage_from_ckpt(cond_stage_config)
self.flow = instantiate_from_config(config=flow_config)
self.loss = NLL()
self.first_stage_key = first_stage_key
self.cond_stage_key = cond_stage_key
self.interpolate_cond_size = interpolate_cond_size
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def init_cond_stage_from_ckpt(self, config):
model = instantiate_from_config(config)
self.cond_stage_model = model.eval()
def __init__(self, flow_config):
super().__init__()
self.flow = instantiate_from_config(config=flow_config)
self.loss = NLL()
def init_first_stage_from_ckpt(self, config):
model = instantiate_from_config(config)
model = model.eval()
model.train = disabled_train
self.first_stage_model = model
def init_to_c_model(self, config):
model = instantiate_from_config(config)
model = model.eval()
model.train = disabled_train
self.to_c_model = model
