- data[0] not supported on Pytorch V.2.4.1 replaced by item()

franck-armand · Oct 11, 2024 · 19dd90c · 19dd90c
1 parent 96f2689
commit 19dd90c
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Showing 3 changed files with 10 additions and 10 deletions.
diff --git a/core/adapt.py b/core/adapt.py
@@ -107,9 +107,9 @@ def train_tgt(src_encoder, tgt_encoder, critic,
                               params.num_epochs,
                               step + 1,
                               len_data_loader,
-                              loss_critic.data[0],
-                              loss_tgt.data[0],
-                              acc.data[0]))
+                              loss_critic.item(),
+                              loss_tgt.item(),
+                              acc.item()))
 
         #############################
         # 2.4 save model parameters #

diff --git a/core/pretrain.py b/core/pretrain.py
@@ -52,7 +52,7 @@ def train_src(encoder, classifier, data_loader):
                               params.num_epochs_pre,
                               step + 1,
                               len(data_loader),
-                              loss.data[0]))
+                              loss.item()))
 
         # eval model on test set
         if ((epoch + 1) % params.eval_step_pre == 0):
@@ -78,8 +78,8 @@ def eval_src(encoder, classifier, data_loader):
     classifier.eval()
 
     # init loss and accuracy
-    loss = 0
-    acc = 0
+    loss = 0.0
+    acc = 0.0
 
     # set loss function
     criterion = nn.CrossEntropyLoss()
@@ -90,7 +90,7 @@ def eval_src(encoder, classifier, data_loader):
         labels = make_variable(labels)
 
         preds = classifier(encoder(images))
-        loss += criterion(preds, labels).data[0]
+        loss += criterion(preds, labels).item()
 
         pred_cls = preds.data.max(1)[1]
         acc += pred_cls.eq(labels.data).cpu().sum()

diff --git a/core/test.py b/core/test.py
@@ -13,8 +13,8 @@ def eval_tgt(encoder, classifier, data_loader):
     classifier.eval()
 
     # init loss and accuracy
-    loss = 0
-    acc = 0
+    loss = 0.0
+    acc = 0.0
 
     # set loss function
     criterion = nn.CrossEntropyLoss()
@@ -25,7 +25,7 @@ def eval_tgt(encoder, classifier, data_loader):
         labels = make_variable(labels).squeeze_()
 
         preds = classifier(encoder(images))
-        loss += criterion(preds, labels).data[0]
+        loss += criterion(preds, labels).item()
 
         pred_cls = preds.data.max(1)[1]
         acc += pred_cls.eq(labels.data).cpu().sum()