(self, eeg_signals_path, imagenet_path, image_transform=identity, subject = 4)
| 239 | |
| 240 | # Constructor |
| 241 | def __init__(self, eeg_signals_path, imagenet_path, image_transform=identity, subject = 4): |
| 242 | # Load EEG signals |
| 243 | loaded = torch.load(eeg_signals_path) |
| 244 | # if opt.subject!=0: |
| 245 | # self.data = [loaded['dataset'][i] for i in range(len(loaded['dataset']) ) if loaded['dataset'][i]['subject']==opt.subject] |
| 246 | # else: |
| 247 | # print(loaded) |
| 248 | if subject!=0: |
| 249 | self.data = [loaded['dataset'][i] for i in range(len(loaded['dataset']) ) if loaded['dataset'][i]['subject']==subject] |
| 250 | else: |
| 251 | self.data = loaded['dataset'] |
| 252 | self.labels = loaded["labels"] |
| 253 | self.images = loaded["images"] |
| 254 | self.imagenet = imagenet_path |
| 255 | self.image_transform = image_transform |
| 256 | self.num_voxels = 440 |
| 257 | self.data_len = 512 |
| 258 | # Compute size |
| 259 | self.size = len(self.data) |
| 260 | self.processor = AutoProcessor.from_pretrained("openai/clip-vit-large-patch14") |
| 261 | |
| 262 | # Get size |
| 263 | def __len__(self): |
nothing calls this directly
no outgoing calls
no test coverage detected