cast tensors to float32 automatically

.vscode/settings.json

@@ -0,0 +1,4 @@
+{
+    "python.linting.pylintEnabled": true,
+    "python.linting.enabled": true
+}

scarches/dataset/scpoli/anndata.py

@@ -38,7 +38,7 @@ def __init__(self,
         self.cell_type_keys = cell_type_keys
         self.cell_type_encoder = cell_type_encoder
         self._is_sparse = sparse.issparse(adata.X)
-        self.data = adata.X if self._is_sparse else torch.tensor(adata.X)
+        self.data = adata.X if self._is_sparse else torch.tensor(adata.X, dtype=torch.float32)
 
         size_factors = np.ravel(adata.X.sum(1))
 
@@ -80,7 +80,7 @@ def __getitem__(self, index):
         outputs = dict()
 
         if self._is_sparse:
-            x = torch.tensor(np.squeeze(self.data[index].toarray()))
+            x = torch.tensor(np.squeeze(self.data[index].toarray()), dtype=torch.float32)
         else:
             x = self.data[index]
         outputs["x"] = x

scarches/models/scpoli/scpoli_model.py

@@ -344,7 +344,7 @@ def get_latent(
             c = torch.tensor(label_tensor, device=device).T
         if sparse.issparse(x):
             x = x.A
-        x = torch.tensor(x, device=device)
+        x = torch.tensor(x, device=device, dtype=torch.float32)
 
         latents = []
         # batch the latent transformation process