new load (in tb.py) & obligatory debugging

.gitignore

@@ -17,7 +17,6 @@ wav2vec2-demo/
 testing.py
 *.lock
 wav2vec2-pretrained-demo/
-tb.py
 runs/
 logging_events/
 

bids_load.py

@@ -70,19 +70,21 @@
 import torch
 from pathlib import Path
 from torch.utils.data import Dataset
+from datasets import DatasetDicct, concatenate_datasets
+from transformers import Wav2Vec2FeatureExtractor
 import numpy
 
 class BIDSBrainVisionDataset(Dataset):
-    def __init__(self, directory, channel_names, target_name, window_size=2.0, overlap=0.0, preload=True): #preload=False for goin easy on the RAM
+    def __init__(self, directory, channel_names, target_name, window_size=2.0, overlap=0.0, preload=True, feature_extractor=None): #preload=False for goin easy on the RAM
         self.directory = Path(directory)
         self.channel_names = channel_names
         self.target_name = target_name
         self.window_size = window_size
         self.overlap = overlap
         self.preload = preload
+        self.feature_extractor = feature_extractor
 
         self.filepaths = list(self.directory.glob("*.vhdr"))
-
         self.windows = []
         self._prepare_dataset()
 
@@ -96,26 +98,26 @@ def _load_brainvision_file(self, filepath):
 
         target, _ = raw[self.target_name, :]
 
-        x_data = torch.stack(ecogs, dim=1).unsqueeze(0)  ####maybe worng stack (after permuting?)
+        x_data = torch.stack(ecogs, dim=0).unsqueeze(0)  ####maybe worng stack (after permuting?)
         x_data = (x_data - x_data.mean()) / x_data.std()
-        x_data = x_data.squeeze(1)
+        # x_data = x_data.squeeze(1)
         # x_data = x_data.permute(0, 2, 1)
         # x_data = x_data.mean(dim=1, keepdim=True) ####################################################
 
-        y_data = torch.tensor(target.T, dtype=torch.float32).reshape(1, 1, -1)
+        y_data = torch.tensor(target.T, dtype=torch.float32).reshape(1, -1)
 #ydata: torch.Size([1, 1, 130001]), xdata: torch.Size([1, 6, 130001])
         return x_data, y_data, raw.info['sfreq']
 
     def _sliding_windows(self, data, window_size, overlap, sfreq):
         step = int(window_size * sfreq)
         overlap_step = int(overlap * sfreq)
-        data_length = data.shape[2]
+        data_length = data.shape[1]
         windows = []
 
         for x in range(0, data_length - step + 1, step - overlap_step):
             stop = x + step
-            print(f"window from {x} to {stop}")
-            windows.append(data[:, :, x:stop])
+            # print(f"window from {x} to {stop}")
+            windows.append(data[:, x:stop])
             # print(f"window size: {window_size}")
         print(f"number of windows={len(windows)}")
         return windows
@@ -136,7 +138,7 @@ def __len__(self):
         return len(self.windows)
 
     def __getitem__(self, idx):
-        return self.windows[idx]
+        x_window, y_window = self.windows[idx]
 
 channel_names = ['ECOG_RIGHT_0', 'ECOG_RIGHT_1', 'ECOG_RIGHT_2', 'ECOG_RIGHT_3', 'ECOG_RIGHT_4', 'ECOG_RIGHT_5']
 target_name = 'MOV_LEFT_CLEAN'