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Fix operator precedence bug in lvlb_weights and mutable default arguments #108

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Fix operator precedence bug in lvlb_weights and mutable default arguments #108

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4 changes: 3 additions & 1 deletion lvdm/models/autoencoder.py
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Original file line number Diff line number Diff line change
Expand Up @@ -77,7 +77,9 @@ def init_test(self,):
self.decodes = []
self.save_decode_samples = 2048

def init_from_ckpt(self, path, ignore_keys=list()):
def init_from_ckpt(self, path, ignore_keys=None):
if ignore_keys is None:
ignore_keys = []
sd = torch.load(path, map_location="cpu")
try:
self._cur_epoch = sd['epoch']
Expand Down
12 changes: 8 additions & 4 deletions lvdm/models/ddpm3d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -43,7 +43,7 @@ def __init__(self,
beta_schedule="linear",
loss_type="l2",
ckpt_path=None,
ignore_keys=[],
ignore_keys=None,
load_only_unet=False,
monitor=None,
use_ema=True,
Expand All @@ -67,6 +67,8 @@ def __init__(self,
logvar_init=0.
):
super().__init__()
if ignore_keys is None:
ignore_keys = []
assert parameterization in ["eps", "x0"], 'currently only supporting "eps" and "x0"'
self.parameterization = parameterization
mainlogger.info(f"{self.__class__.__name__}: Running in {self.parameterization}-prediction mode")
Expand Down Expand Up @@ -156,13 +158,13 @@ def register_schedule(self, given_betas=None, beta_schedule="linear", timesteps=
lvlb_weights = self.betas ** 2 / (
2 * self.posterior_variance * to_torch(alphas) * (1 - self.alphas_cumprod))
elif self.parameterization == "x0":
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * 1 - torch.Tensor(alphas_cumprod))
lvlb_weights = 0.5 * np.sqrt(torch.Tensor(alphas_cumprod)) / (2. * (1 - torch.Tensor(alphas_cumprod)))
else:
raise NotImplementedError("mu not supported")
# TODO how to choose this term
lvlb_weights[0] = lvlb_weights[1]
self.register_buffer('lvlb_weights', lvlb_weights, persistent=False)
assert not torch.isnan(self.lvlb_weights).all()
assert not torch.isnan(self.lvlb_weights).any()

@contextmanager
def ema_scope(self, context=None):
Expand All @@ -179,7 +181,9 @@ def ema_scope(self, context=None):
if context is not None:
mainlogger.info(f"{context}: Restored training weights")

def init_from_ckpt(self, path, ignore_keys=list(), only_model=False):
def init_from_ckpt(self, path, ignore_keys=None, only_model=False):
if ignore_keys is None:
ignore_keys = []
sd = torch.load(path, map_location="cpu")
if "state_dict" in list(sd.keys()):
sd = sd["state_dict"]
Expand Down