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import torch
import sys
import sys
sys.path.append(".")
from causalvideovae.model.causal_vae.modeling_causalvae import CausalVAEModel
from causalvideovae.model.modules import *
origin_path = "/remote-home1/lzj/causal-video-vae-github/results/test"
output_path = "models/latent8_3d"
print("Loading model!")
model = CausalVAEModel.from_pretrained(origin_path)
new_config = model.config.copy()
new_config['z_channels'] = 8
new_config['embed_dim'] = 8
reset_mix_factor = True
print("Building new model")
new_model = CausalVAEModel.from_config(new_config)
ckpt = new_model.state_dict()
old_ckpt = model.state_dict()
for name, parameter in new_model.named_parameters():
if name not in old_ckpt:
# ckpt[name] = torch.zeros_like(ckpt[name])
continue
shape1 = ckpt[name].shape
if sum(shape1) == 1:
if reset_mix_factor:
ckpt[name] = torch.tensor([0.])
continue
shape2 = old_ckpt[name].shape
slices = tuple(slice(0, s) for s in shape2)
mu = torch.mean(old_ckpt[name])
std = torch.std(old_ckpt[name])
ckpt[name] = torch.empty_like(ckpt[name]).normal_(mean=mu, std=std)
ckpt[name][slices] = old_ckpt[name]
new_model.load_state_dict(ckpt)
new_model.save_pretrained(output_path)
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