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| author | Jordan Gong <jordan.gong@protonmail.com> | 2021-03-01 11:26:34 +0800 |
|---|---|---|
| committer | Jordan Gong <jordan.gong@protonmail.com> | 2021-03-01 11:26:34 +0800 |
| commit | 8afcd7659fba5f15e221eee0158237edda749317 (patch) | |
| tree | 8f629415e958e299168752e1462bccb95258b0a5 /models | |
| parent | 0d2b643d7e04eba872e8b1fc9b04478a026bb3b0 (diff) | |
| parent | 2f3a7fbef70efd2cf91b7d77b3b71ffb4de907e2 (diff) | |
Merge branch 'python3.8' into python3.7
Diffstat (limited to 'models')
| -rw-r--r-- | models/model.py | 11 |
1 files changed, 5 insertions, 6 deletions
diff --git a/models/model.py b/models/model.py index aef5302..61470d9 100644 --- a/models/model.py +++ b/models/model.py @@ -56,8 +56,6 @@ 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 @@ -171,6 +169,9 @@ class Model: triplet_is_hard, triplet_is_mean, None ) + num_pairs = (self.pr*self.k-1) * (self.pr*self.k) // 2 + num_pos_pairs = (self.k*(self.k-1)//2) * self.pr + # Try to accelerate computation using CUDA or others self.rgb_pn = self.rgb_pn.to(self.device) self.triplet_loss = self.triplet_loss.to(self.device) @@ -253,12 +254,12 @@ class Model: 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 + num_pos_pairs, 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 + num_pos_pairs, num_pairs, self.curr_iter ) # Embedding norm mean_hpm_embedding = embedding[:self.rgb_pn.hpm_num_parts].mean(0) @@ -563,8 +564,6 @@ 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, |