ajout d'un paramètre device à HateSpeechCollater

data.py

@@ -46,9 +46,11 @@ class HateSpeechDataset(Dataset):
 
 
 class HateSpeechCollater:
-    def __init__(self, tokenizer):
+    def __init__(self, tokenizer, device=None):
         self.tokenizer = tokenizer
         self.labels2id = {"Hate": 0, "Offensive": 1, "Other": 2}
+        self.device = torch.device(
+            "cpu") if device is None else torch.device(device)
 
     def __call__(self, input):
         sentences = []
@@ -56,19 +58,22 @@ class HateSpeechCollater:
         for sent, lab in input:
             sentences.append(sent)
             labels.append(self.labels2id[lab])
-        labels_tensor = torch.tensor(labels, dtype=torch.long)
+        labels_tensor = torch.tensor(
+            labels, dtype=torch.long, device=self.device)
         # labels_onehot = one_hot(labels_tensor, num_classes=3)
 
         try:
             inputs = self.tokenizer(
                 sentences, return_tensors="pt", padding=True, truncation=True)
+            inputs["input_ids"] = inputs["input_ids"].to(self.device)
+            inputs["attention_mask"] = inputs["attention_mask"].to(self.device)
         except TypeError:
             pad_id = self.tokenizer.token_to_id("<pad>")
             self.tokenizer.enable_padding(pad_id=pad_id)
             encoded = self.tokenizer.encode_batch(sentences)
             inputs = {
-                "input_ids": torch.tensor([x.ids for x in encoded]),
-                "attention_mask": torch.tensor([x.attention_mask for x in encoded])
+                "input_ids": torch.tensor([x.ids for x in encoded], device=self.device),
+                "attention_mask": torch.tensor([x.attention_mask for x in encoded], device=self.device)
             }
 
         return inputs, labels_tensor

train_roberta.py

@@ -19,7 +19,7 @@ if __name__ == "__main__":
 
         tokenizer = RobertaTokenizerFast.from_pretrained('roberta-base')
         dataset = HateSpeechDataset(args.input)
-        collater = HateSpeechCollater(tokenizer)
+        collater = HateSpeechCollater(tokenizer, device)
         class_names = collater.class_names()
 
         for fold, (train, test) in enumerate(dataset.iter_folds(args.folds, True), 1):