plt.ylim(-1, 1)
plt.xlabel('loss evaluation index')
plt.show()
#%%
if __name__ == "__main__":
from preprocess import Datasets
import torch.utils.data
from preprocess.transforms import SpectrogramTransform
from preprocess.fusion import separate_sensors_collate
from param import fs, duration_window, duration_overlap, spectro_batch_size
spectrogram_transform = SpectrogramTransform(["Acc_norm", "Gyr_y"], fs, duration_window, duration_overlap,
spectro_batch_size, interpolation='linear', log_power=True, out_size=(48,48))
collate_fn = separate_sensors_collate
try : # do not reload the datasets if they already exist
train_dataset
val_dataset
except NameError:
train_dataset = Datasets.SignalsDataSet(mode='train', split='balanced', comp_preprocess_first=True, transform=spectrogram_transform)
val_dataset = Datasets.SignalsDataSet(mode='val', split='balanced', comp_preprocess_first=True, transform=spectrogram_transform)
train_dataloader = torch.utils.data.DataLoader(train_dataset, batch_size=64, collate_fn=collate_fn, num_workers=0, shuffle=True)
val_dataloader = torch.utils.data.DataLoader(val_dataset, batch_size=64, collate_fn=collate_fn, num_workers=0)
str_result = ""
# axis = freq
collate_concat = ConcatCollate(axis_collate='freq')
dataloader.collate_fn = collate_concat
X_batch, _ = next(iter(dataloader))
signal = X_batch[0, 0, :]
plt.subplot(3, 1, 3)
plt.plot(signal.to(torch.device('cpu')).numpy())
plt.title("'frequency' concat")
# ---------------------- spectrogram ----------------------------
spectrogram_transform = SpectrogramTransform(example_signals,
fs,
duration_window,
duration_overlap,
spectro_batch_size,
interpolation='none',
log_power=True)
DS.transform = spectrogram_transform
# axis = freq
plt.figure()
collate_concat = ConcatCollate(axis_collate='freq')
dataloader.collate_fn = collate_concat
X_batch, _ = next(iter(dataloader))
signal = X_batch[0, 0, :, :]
plt.subplot(1, 2, 1)