tomasvanoyen
commented
1 year ago The solution to the above is found by loading the state_dict of the checkpoint and not the entire checkpoint.
E.g.:
import torch
fn_ckpt = '../models/---.ckpt'
fn_ckpt_state_dict = '../models/state_dict_---.ckpt'
ckpt = torch.load(fn_ckpt)
torch.save(ckpt['state_dict'], fn_ckpt_state_dict )
Then the following works:
from ldcast.forecast import Forecast
fn_aut = 'models/autoenc/autoenc-32-0.01.pt'
fn_ckpt_state_dict = '../models/state_dict_---.ckpt'
fc = Forecast(ldm_weights_fn = fn_gen, autoenc_weights_fn=fn_ckpt_state_dict)
jleinonen
Hi @jleinonen ,
thank you for this nice work.
I am trying to retrain the model using the script
python train_genforecast.py --model_dir="../models/genforecast_trainto see if I can reproduce the weights and obtain somewhat similar results. However, I am failing to load the model ckpt's into the
Forecastclass. Please note that loading the pretrained weights coming the Zenodo data repository does work.Below I will provide the error message, but I also observed that the model size (on disk) is almost double for the ckpt's created by the
train_genforecast.pyscript vsgenforecast-radaronly-256x256-20step.pt.I guess I am missing an obvious step here?
Error message:
from ldcast.forecast import Forecastfn_aut = 'models/autoenc/autoenc-32-0.01.pt'fn_gen = 'models/genforecast_train/epoch=0-val_loss_ema=0.6150.ckpt'fc = Forecast(ldm_weights_fn = fn_gen, autoenc_weights_fn=fn_aut)Traceback (most recent call last): File "<stdin>", line 1, in <module> File "/workspace/thirdparty/meteoswiss/ldcast/forecast.py", line 49, in __init__ self.ldm = self._init_model() File "/workspace/thirdparty/meteoswiss/ldcast/forecast.py", line 99, in _init_model ldm.load_state_dict(torch.load(self.ldm_weights_fn)) File "/workspace/virtualenv/venv_ldcast/lib/python3.10/site-packages/torch/nn/modules/module.py", line 2041, in load_state_dict raise RuntimeError('Error(s) in loading state_dict for {}:\n\t{}'.format( RuntimeError: Error(s) in loading state_dict for LatentDiffusion: Missing key(s) in state_dict: "betas", "alphas_cumprod", "alphas_cumprod_prev", "sqrt_alphas_cumprod", "sqrt_one_minus_alphas_cumprod", "model.time_embed.0.weight", "model.time_embed.0.bias", 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"model_ema.output_blocks10in_layers2bias", "model_ema.output_blocks10emb_layers1weight", "model_ema.output_blocks10emb_layers1bias", "model_ema.output_blocks10out_layers3weight", "model_ema.output_blocks10out_layers3bias", "model_ema.output_blocks10skip_connectionweight", "model_ema.output_blocks10skip_connectionbias", "model_ema.output_blocks20in_layers2weight", "model_ema.output_blocks20in_layers2bias", "model_ema.output_blocks20emb_layers1weight", "model_ema.output_blocks20emb_layers1bias", "model_ema.output_blocks20out_layers3weight", "model_ema.output_blocks20out_layers3bias", "model_ema.output_blocks20skip_connectionweight", "model_ema.output_blocks20skip_connectionbias", "model_ema.output_blocks21convweight", "model_ema.output_blocks21convbias", "model_ema.output_blocks30in_layers2weight", "model_ema.output_blocks30in_layers2bias", "model_ema.output_blocks30emb_layers1weight", "model_ema.output_blocks30emb_layers1bias", "model_ema.output_blocks30out_layers3weight", 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"model_ema.output_blocks80skip_connectionweight", "model_ema.output_blocks80skip_connectionbias", "model_ema.output_blocks81pre_projweight", "model_ema.output_blocks81pre_projbias", "model_ema.output_blocks81filterw1", "model_ema.output_blocks81filterb1", "model_ema.output_blocks81filterw2", "model_ema.output_blocks81filterb2", "model_ema.output_blocks81mlpfc1weight", "model_ema.output_blocks81mlpfc1bias", "model_ema.output_blocks81mlpfc2weight", "model_ema.output_blocks81mlpfc2bias", "model_ema.out2weight", "model_ema.out2bias". Unexpected key(s) in state_dict: "epoch", "global_step", "pytorch-lightning_version", "state_dict", "loops", "callbacks", "optimizer_states", "lr_schedulers".