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import importlib
import numpy as np
import taming
import torch
import yaml
from omegaconf import OmegaConf
from PIL import Image
from taming.models.vqgan import VQModel
from utils import get_device
# import discriminator
def load_config(config_path, display=False):
config = OmegaConf.load(config_path)
if display:
print(yaml.dump(OmegaConf.to_container(config)))
return config
# def load_disc(device):
# dconf = load_config("disc_config.yaml")
# sd = torch.load("disc.pt", map_location=device)
# # print(sd.keys())
# model = discriminator.NLayerDiscriminator()
# model.load_state_dict(sd, strict=True)
# model.to(device)
# return model
# print(dconf.keys())
def load_default(device):
# device = get_device()
ckpt_path = "logs/2021-04-23T18-11-19_celebahq_transformer/checkpoints/last.ckpt"
conf_path = "./unwrapped.yaml"
config = load_config(conf_path, display=False)
model = taming.models.vqgan.VQModel(**config.model.params)
sd = torch.load("./vqgan_only.pt", map_location=device)
model.load_state_dict(sd, strict=True)
model.to(device)
return model
def load_vqgan(config, ckpt_path=None, is_gumbel=False):
if is_gumbel:
model = GumbelVQ(**config.model.params)
else:
model = VQModel(**config.model.params)
if ckpt_path is not None:
sd = torch.load(ckpt_path, map_location="cpu")["state_dict"]
missing, unexpected = model.load_state_dict(sd, strict=False)
return model.eval()
def load_ffhq():
conf = "2020-11-09T13-33-36_faceshq_vqgan/configs/2020-11-09T13-33-36-project.yaml"
ckpt = "2020-11-09T13-33-36_faceshq_vqgan/checkpoints/last.ckpt"
vqgan = load_model(load_config(conf), ckpt, True, True)[0]
def reconstruct_with_vqgan(x, model):
# could also use model(x) for reconstruction but use explicit encoding and decoding here
z, _, [_, _, indices] = model.encode(x)
print(f"VQGAN --- {model.__class__.__name__}: latent shape: {z.shape[2:]}")
xrec = model.decode(z)
return xrec
def get_obj_from_str(string, reload=False):
module, cls = string.rsplit(".", 1)
if reload:
module_imp = importlib.import_module(module)
importlib.reload(module_imp)
return getattr(importlib.import_module(module, package=None), cls)
def instantiate_from_config(config):
if not "target" in config:
raise KeyError("Expected key `target` to instantiate.")
return get_obj_from_str(config["target"])(**config.get("params", dict()))
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 load_model(config, ckpt, gpu, eval_mode):
# load the specified checkpoint
if ckpt:
pl_sd = torch.load(ckpt, map_location="cpu")
global_step = pl_sd["global_step"]
print(f"loaded model from global step {global_step}.")
else:
pl_sd = {"state_dict": None}
global_step = None
model = load_model_from_config(config.model, pl_sd["state_dict"], gpu=gpu, eval_mode=eval_mode)["model"]
return model, global_step |