#%%
from torch.utils.data import DataLoader
from icae.tools.dataset.single import SingleWaveformDataset, SingleWaveformPreprocessing
from icae.tools.torch.gym import Gym
from icae.models.waveform.simple import ConvAE
# %%
raise NotImplementedError()
dataset = SingleWaveformDataset(load_waveform_only=False,transform=SingleWaveformPreprocessing(),batch_loading_size=64)
train = DataLoader(dataset, shuffle=True, batch_size=1,num_workers=1)
# TODO: check performanc
#%%
%%timeit
for i,j in zip(train,range(1000)):
pass
# %%
# %%
#%%
filename = config.root + config.MC.filename
device = torch.device("cuda")
dl_config = {
"batch_size": 32 * 10,
"num_workers": 12,
"shuffle": True,
# "collate_fn": MCDataset.collate_fn,
"pin_memory": True,
}
dataset_train = MCDataset(filename=filename,
key="train/waveforms",
transform=SingleWaveformPreprocessing())
dataset_val = MCDataset(filename=filename,
key="val/waveforms",
transform=SingleWaveformPreprocessing())
train = DataLoader(dataset_train, **dl_config)
val = DataLoader(dataset_val, batch_size=1024, num_workers=12)
#%%
runs = TrainingStability("Ribbles_w_outliers_1e6", None)
runs.load()
#%%
def argmedian(array):
return np.argsort(array)[len(array) // 2]