(self, ids, layers, interventions)
| 163 | for layer, feature in t_features.items() } |
| 164 | |
| 165 | def get_featuremaps(self, ids, layers, interventions): |
| 166 | zs = self.get_zs_for_ids(ids) |
| 167 | z_tensor = torch.tensor(zs).float().to(self.tester.device) |
| 168 | # Quantilized features are returned. |
| 169 | q_features = self.tester.feature_maps(z_tensor, |
| 170 | decode_intervention_array(interventions, |
| 171 | self.tester.layer_shapes()), layers) |
| 172 | # Scale them 0-255 and return them. |
| 173 | # TODO: turn them into pngs for returning. |
| 174 | return { layer: [ |
| 175 | value.clamp(0, 1).mul(255).byte().cpu().numpy().tolist() |
| 176 | for value in valuelist ] |
| 177 | for layer, valuelist in q_features.items() |
| 178 | if (not layers) or (layer in layers) } |
| 179 | |
| 180 | def get_recipes(self): |
| 181 | recipedir = os.path.join(self.project_dir, 'recipe') |
no test coverage detected