-
Notifications
You must be signed in to change notification settings - Fork 0
/
loaders.py
79 lines (65 loc) · 2.3 KB
/
loaders.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
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
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_vqgan(device, conf_path=None, ckpt_path=None):
if conf_path is None:
conf_path = "./model_checkpoints/vqgan_only.yaml"
config = load_config(conf_path, display=False)
model = taming.models.vqgan.VQModel(**config.model.params)
if ckpt_path is None:
ckpt_path = "./model_checkpoints/vqgan_only.pt"
sd = torch.load(ckpt_path, map_location=device)
if ".ckpt" in ckpt_path:
sd = sd["state_dict"]
model.load_state_dict(sd, strict=True)
model.to(device)
del sd
return model
def reconstruct_with_vqgan(x, model):
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 "target" not 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