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