diff options
Diffstat (limited to 'models')
| -rw-r--r-- | models/model.py | 66 |
1 files changed, 53 insertions, 13 deletions
diff --git a/models/model.py b/models/model.py index 79952cb..18896ae 100644 --- a/models/model.py +++ b/models/model.py @@ -59,6 +59,8 @@ class Model: self.in_size: tuple[int, int] = (64, 48) self.pr: Optional[int] = None self.k: Optional[int] = None + self.num_pairs: Optional[int] = None + self.num_pos_pairs: Optional[int] = None self._gallery_dataset_meta: Optional[dict[str, list]] = None self._probe_datasets_meta: Optional[dict[str, dict[str, list]]] = None @@ -216,7 +218,7 @@ class Model: y = batch_c1['label'].to(self.device) # Duplicate labels for each part y = y.repeat(self.rgb_pn.num_total_parts, 1) - trip_loss, dist, non_zero_counts = self.triplet_loss(embedding, y) + trip_loss, dist, num_non_zero = self.triplet_loss(embedding, y) losses = torch.cat(( ae_losses, torch.stack(( @@ -240,18 +242,36 @@ class Model: 'HPM': losses[3], 'PartNet': losses[4] }, self.curr_iter) - self.writer.add_scalars('Loss/non-zero counts', { - 'HPM': non_zero_counts[:self.rgb_pn.hpm_num_parts].mean(), - 'PartNet': non_zero_counts[self.rgb_pn.hpm_num_parts:].mean() - }, self.curr_iter) - self.writer.add_scalars('Embedding/distance', { - 'HPM': dist[:self.rgb_pn.hpm_num_parts].mean(), - 'PartNet': dist[self.rgb_pn.hpm_num_parts].mean() - }, self.curr_iter) - self.writer.add_scalars('Embedding/2-norm', { - 'HPM': embedding[:self.rgb_pn.hpm_num_parts].norm(), - 'PartNet': embedding[self.rgb_pn.hpm_num_parts].norm() - }, self.curr_iter) + # None-zero losses in batch + if num_non_zero: + self.writer.add_scalars('Loss/non-zero counts', { + 'HPM': num_non_zero[:self.rgb_pn.hpm_num_parts].mean(), + 'PartNet': num_non_zero[self.rgb_pn.hpm_num_parts:].mean() + }, self.curr_iter) + # Embedding distance + mean_hpm_dist = dist[:self.rgb_pn.hpm_num_parts].mean(0) + self._add_ranked_scalars( + 'Embedding/HPM distance', mean_hpm_dist, + self.num_pos_pairs, self.num_pairs, self.curr_iter + ) + mean_pa_dist = dist[self.rgb_pn.hpm_num_parts:].mean(0) + self._add_ranked_scalars( + 'Embedding/ParNet distance', mean_pa_dist, + self.num_pos_pairs, self.num_pairs, self.curr_iter + ) + # Embedding norm + mean_hpm_embedding = embedding[:self.rgb_pn.hpm_num_parts].mean(0) + mean_hpm_norm = mean_hpm_embedding.norm(dim=-1) + self._add_ranked_scalars( + 'Embedding/HPM norm', mean_hpm_norm, + self.k, self.pr * self.k, self.curr_iter + ) + mean_pa_embedding = embedding[self.rgb_pn.hpm_num_parts:].mean(0) + mean_pa_norm = mean_pa_embedding.norm(dim=-1) + self._add_ranked_scalars( + 'Embedding/PartNet norm', mean_pa_norm, + self.k, self.pr * self.k, self.curr_iter + ) if self.curr_iter % 100 == 0: lrs = self.scheduler.get_last_lr() @@ -303,6 +323,24 @@ class Model: self.writer.close() break + def _add_ranked_scalars( + self, + main_tag: str, + metric: torch.Tensor, + num_pos: int, + num_all: int, + global_step: int + ): + rank = metric.argsort() + pos_ile = 100 - (num_pos - 1) * 100 // num_all + self.writer.add_scalars(main_tag, { + '0%-ile': metric[rank[-1]], + f'{100 - pos_ile}%-ile': metric[rank[-num_pos]], + '50%-ile': metric[rank[num_all // 2 - 1]], + f'{pos_ile}%-ile': metric[rank[num_pos - 1]], + '100%-ile': metric[rank[0]] + }, global_step) + def predict_all( self, iters: tuple[int], @@ -524,6 +562,8 @@ class Model: ) -> DataLoader: config: dict = dataloader_config.copy() (self.pr, self.k) = config.pop('batch_size', (8, 16)) + self.num_pairs = (self.pr*self.k-1) * (self.pr*self.k) // 2 + self.num_pos_pairs = (self.k*(self.k-1)//2) * self.pr if self.is_train: triplet_sampler = TripletSampler(dataset, (self.pr, self.k)) return DataLoader(dataset, |