(self, preds, labels=None)
| 225 | self.running_start_idx = 0 |
| 226 | |
| 227 | def update(self, preds, labels=None): |
| 228 | |
| 229 | # Expect preds in shape B x C |
| 230 | |
| 231 | if torch.is_tensor(preds): |
| 232 | preds = preds.detach().cpu().numpy() |
| 233 | |
| 234 | b, c = preds.shape |
| 235 | |
| 236 | if self.all_preds is None: |
| 237 | self.all_preds = np.zeros((self.length, c)) |
| 238 | |
| 239 | self.all_preds[self.running_start_idx: self.running_start_idx + b] = preds |
| 240 | |
| 241 | if labels is not None: |
| 242 | if torch.is_tensor(labels): |
| 243 | labels = labels.detach().cpu().numpy() |
| 244 | |
| 245 | if self.all_labels is None: |
| 246 | self.all_labels = np.zeros((self.length,)) |
| 247 | |
| 248 | self.all_labels[self.running_start_idx: self.running_start_idx + b] = labels |
| 249 | |
| 250 | # Maintain running index on dataset being evaluated |
| 251 | self.running_start_idx += b |
| 252 | |
| 253 | def save(self): |
| 254 |
nothing calls this directly
no outgoing calls
no test coverage detected