refactor: load entire data to GPU

Novartis · Jul 25, 2024 · cab3872 · cab3872
1 parent 7ada773
commit cab3872
Showing 1 changed file with 15 additions and 15 deletions.
diff --git a/scar/main/_scar.py b/scar/main/_scar.py
@@ -228,7 +228,7 @@ def __init__(
             elif torch.backends.mps.is_available() and torch.backends.mps.is_built():
                 self.device = torch.device("mps")
                 self.logger.info("MPS is detected and will be used.")
-                self.logger.warning("PyTorch is slower on MPS than on the CPU; we recommend using the CPU by specifying device='cpu' on Mac.")
+                # self.logger.warning("PyTorch is slower on MPS than on the CPU; we recommend using the CPU by specifying device='cpu' on Mac.")
             else:
                 self.device = torch.device("cpu")
                 self.logger.info("No GPU detected. Use CPU instead.")
@@ -441,11 +441,11 @@ 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, list_ids=train_ids)
+        training_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, device=self.device, list_ids=train_ids)
         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, list_ids=test_ids)
+        val_set = UMIDataset(self.raw_count, self.ambient_profile, self.batch_id, device=self.device, list_ids=test_ids)
         val_generator = torch.utils.data.DataLoader(
             val_set, batch_size=batch_size, shuffle=shuffle
         )
@@ -495,9 +495,9 @@ def train(
                 vae_nets.train()
                 for x_batch, ambient_freq, batch_id_onehot in training_generator:
                     # Move data to device
-                    x_batch = x_batch.to(self.device)
-                    ambient_freq = ambient_freq.to(self.device)
-                    batch_id_onehot = batch_id_onehot.to(self.device)
+                    # x_batch = x_batch.to(self.device)
+                    # ambient_freq = ambient_freq.to(self.device)
+                    # batch_id_onehot = batch_id_onehot.to(self.device)
 
                     optim.zero_grad()
                     dec_nr, dec_prob, means, var, dec_dp = vae_nets(x_batch, batch_id_onehot)
@@ -592,7 +592,7 @@ 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])
@@ -609,9 +609,9 @@ def inference(
 
         for x_batch_tot, ambient_freq_tot, x_batch_id_onehot_tot in generator_full_data:
             # Move data to device
-            x_batch_tot = x_batch_tot.to(self.device)
-            x_batch_id_onehot_tot = x_batch_id_onehot_tot.to(self.device)
-            ambient_freq_tot = ambient_freq_tot.to(self.device)
+            # x_batch_tot = x_batch_tot.to(self.device)
+            # x_batch_id_onehot_tot = x_batch_id_onehot_tot.to(self.device)
+            # ambient_freq_tot = ambient_freq_tot.to(self.device)
 
             minibatch_size = x_batch_tot.shape[
                 0
@@ -716,12 +716,12 @@ 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 = torch.from_numpy(raw_count).int()
-        self.ambient_profile = torch.from_numpy(ambient_profile).float()
-        self.batch_id = torch.from_numpy(batch_id).to(torch.int64)
-        self.batch_onehot = torch.from_numpy(np.eye(len(np.unique(batch_id)))).to(torch.int64)
+        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 = torch.from_numpy(batch_id).to(torch.int64).to(device)
+        self.batch_onehot = torch.from_numpy(np.eye(len(np.unique(batch_id)))).to(torch.int64).to(device)
 
         if list_ids:
             self.list_ids = list_ids