fix: fix a bug and refactor dataloading

scar/main/_scar.py

@@ -325,7 +325,7 @@ def __init__(
         raw_count = raw_count.fillna(0)  # missing vals -> zeros
 
         # Loading numpy to tensor on GPU
-        self.raw_count = torch.from_numpy(raw_count.values).int().to(self.device)
+        self.raw_count = raw_count.values
         """raw_count : np.ndarray, raw count matrix.
         """
         self.n_features = raw_count.shape[1]
@@ -358,7 +358,7 @@ def __init__(
             self.batch_id = torch.zeros(raw_count.shape[0]).int().to(self.device)
             self.n_batch = 1
 
-        self.ambient_profile = torch.from_numpy(ambient_profile).float().to(self.device)
+        self.ambient_profile = ambient_profile
         """ambient_profile : np.ndarray, the probability of occurrence of each ambient transcript.
         """
 
@@ -442,11 +442,11 @@ def train(
         train_ids, test_ids = train_test_split(list_ids, train_size=train_size)
 
         # Generators
-        training_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, train_ids)
+        training_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, train_ids, device=self.device)
         training_generator = torch.utils.data.DataLoader(
             training_set, batch_size=batch_size, shuffle=shuffle
         )
-        val_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, test_ids)
+        val_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, test_ids, device=self.device)
         val_generator = torch.utils.data.DataLoader(
             val_set, batch_size=batch_size, shuffle=shuffle
         )
@@ -588,7 +588,7 @@ def inference(
             native_frequencies, and noise_ratio. \
                 A feature_assignment will be added in 'sgRNA' or 'tag' or 'CMO' feature type.   
         """
-        total_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id)
+        total_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, device=self.device)
         n_features = self.n_features
         sample_size = self.raw_count.shape[0]
         self.native_counts = np.empty([sample_size, n_features])
@@ -707,12 +707,12 @@ def assignment(self, cutoff=3, moi=None):
 class UMIDataset(torch.utils.data.Dataset):
     """Characterizes dataset for PyTorch"""
 
-    def __init__(self, raw_count, ambient_profile, batch_id, list_ids=None):
+    def __init__(self, raw_count, ambient_profile, batch_id, device, list_ids=None):
         """Initialization"""
-        self.raw_count = raw_count
-        self.ambient_profile = ambient_profile
-        self.batch_id = batch_id
-        self.batch_onehot = self._onehot(batch_id)
+        self.raw_count = torch.from_numpy(raw_count).int().to(device)
+        self.ambient_profile = torch.from_numpy(ambient_profile).float().to(device)
+        self.batch_id = batch_id.to(torch.int64).to(device)
+        self.batch_onehot = self._onehot(batch_id.to(torch.int64)).to(device)
 
         if list_ids:
             self.list_ids = list_ids
@@ -735,6 +735,6 @@ def __getitem__(self, index):
     def _onehot(self, batch_id):
         """One-hot encoding"""
         n_batch = batch_id.unique().size()[0]
-        x_onehot = torch.zeros(n_batch, n_batch).to(batch_id.device)
+        x_onehot = torch.zeros(n_batch, n_batch)
         x_onehot.scatter_(1, batch_id.unique().unsqueeze(1), 1)
         return x_onehot