From db0564967d8cfc03b2d3fe4f7d10eff0867e1771 Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Mon, 1 Mar 2021 11:22:16 +0800
Subject: Move pairs variable to local

---
 models/model.py | 11 +++++------
 1 file changed, 5 insertions(+), 6 deletions(-)

(limited to 'models/model.py')

diff --git a/models/model.py b/models/model.py
index 34cb816..b942eb8 100644
--- a/models/model.py
+++ b/models/model.py
@@ -59,8 +59,6 @@ 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
@@ -174,6 +172,9 @@ 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)
@@ -256,12 +257,12 @@ 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)
@@ -566,8 +567,6 @@ 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,
-- 
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