Merge branch 'disentangling_only' into disentangling_only_py3.8

# Conflicts:
#	models/model.py
#	utils/configuration.py
#	utils/sampler.py

1 files changed, 13 insertions, 15 deletions

diff --git a/models/model.py b/models/model.py
index 667a0a7..46987ca 100644
--- a/models/model.py
+++ b/models/model.py
@@ -17,7 +17,7 @@ from utils.configuration import DataloaderConfiguration, \
     HyperparameterConfiguration, DatasetConfiguration, ModelConfiguration, \
     SystemConfiguration
 from utils.dataset import CASIAB, ClipConditions, ClipViews, ClipClasses
-from utils.sampler import TripletSampler
+from utils.sampler import DisentanglingSampler
 
 
 class Model:
@@ -56,8 +56,7 @@ class Model:
         self.is_train: bool = True
         self.in_channels: int = 3
         self.in_size: Tuple[int, int] = (64, 48)
-        self.pr: Optional[int] = None
-        self.k: Optional[int] = None
+        self.batch_size: Optional[int] = None
 
         self._gallery_dataset_meta: Optional[Dict[str, List]] = None
         self._probe_datasets_meta: Optional[Dict[str, Dict[str, List]]] = None
@@ -91,7 +90,7 @@ class Model:
     @property
     def _checkpoint_sig(self) -> str:
         return '_'.join((self._model_sig, self._hp_sig, self._dataset_sig,
-                         str(self.pr), str(self.k)))
+                         str(self.batch_size)))
 
     @property
     def _checkpoint_name(self) -> str:
@@ -100,7 +99,7 @@ class Model:
     @property
     def _log_sig(self) -> str:
         return '_'.join((self._model_name, str(self.total_iter), self._hp_sig,
-                         self._dataset_sig, str(self.pr), str(self.k)))
+                         self._dataset_sig, str(self.batch_size)))
 
     @property
     def _log_name(self) -> str:
@@ -441,11 +440,11 @@ class Model:
             dataloader_config: DataloaderConfiguration
     ) -> DataLoader:
         config: Dict = dataloader_config.copy()
-        (self.pr, self.k) = config.pop('batch_size', (8, 16))
+        self.batch_size = config.pop('batch_size', 16)
         if self.is_train:
-            triplet_sampler = TripletSampler(dataset, (self.pr, self.k))
+            dis_sampler = DisentanglingSampler(dataset, self.batch_size)
             return DataLoader(dataset,
-                              batch_sampler=triplet_sampler,
+                              batch_sampler=dis_sampler,
                               collate_fn=self._batch_splitter,
                               **config)
         else:  # is_test
@@ -458,15 +457,14 @@ class Model:
                Dict[str, Union[List[str], torch.Tensor]]]:
         """
         Disentanglement need two random conditions, this function will
-        split pr * k * 2 samples to 2 dicts each containing pr * k
-        samples. labels and clip data are tensor, and others are list.
+        split batch_size * 2 samples to 2 dicts each containing
+        batch_size samples. labels and clip data are tensor, and others
+        are list.
         """
-        _batch = [[], []]
-        for i in range(0, self.pr * self.k * 2, self.k * 2):
-            _batch[0] += batch[i:i + self.k]
-            _batch[1] += batch[i + self.k:i + self.k * 2]
+        batch_0 = batch[slice(0, self.batch_size * 2, 2)]
+        batch_1 = batch[slice(1, self.batch_size * 2, 2)]
 
-        return default_collate(_batch[0]), default_collate(_batch[1])
+        return default_collate(batch_0), default_collate(batch_1)
 
     def _make_signature(self,
                         config: Dict,