loading trained model
tomasvanoyen opened this issue · comments
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_train
to see if I can reproduce the weights and obtain somewhat similar results. However, I am failing to load the model ckpt's into the Forecast class. 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.py script vs genforecast-radaronly-256x256-20step.pt.
I guess I am missing an obvious step here?
Error message:
from ldcast.forecast import Forecast
fn_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", "model.time_embed.2.weight", "model.time_embed.2.bias", "model.input_blocks.0.0.weight", "model.input_blocks.0.0.bias", "model.input_blocks.1.0.in_layers.2.weight", "model.input_blocks.1.0.in_layers.2.bias", 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Unexpected key(s) in state_dict: "epoch", "global_step", "pytorch-lightning_version", "state_dict", "loops", "callbacks", "optimizer_states", "lr_schedulers".
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)
Hi @tomasvanoyen, thanks for figuring it out!