storing different losses added

data/weibo/config.py

 
     image_model_name = '../../../../../media/external_10TB/10TB/ghorbanpoor/vit-base-patch16-224'
     image_embedding = 768
-    text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/bert-base-uncased"
+    text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/chinese-xlnet-base"
     # text_encoder_model = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
-    text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/bert-base-uncased"
+    text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/chinese-xlnet-base"
     # text_tokenizer = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
     text_embedding = 768
     max_length = 200

learner.py

 
 def train_epoch(config, model, train_loader, optimizer, scalar):
     loss_meter = AvgMeter('train')
+    c_loss_meter = AvgMeter('train')
+    s_loss_meter = AvgMeter('train')
+
     # tqdm_object = tqdm(train_loader, total=len(train_loader))
 
     targets = []
 
         with torch.cuda.amp.autocast():
             output, score = model(batch)
-            loss = calculate_loss(model, score, batch['label'])
+            loss, c_loss, s_loss = calculate_loss(model, score, batch['label'])
         scalar.scale(loss).backward()
         torch.nn.utils.clip_grad_norm_(model.parameters(), config.max_grad_norm)
 
 
         count = batch["id"].size(0)
         loss_meter.update(loss.detach(), count)
+        c_loss_meter.update(c_loss.detach(), count)
+        s_loss_meter.update(s_loss.detach(), count)
 
         # tqdm_object.set_postfix(train_loss=loss_meter.avg, lr=get_lr(optimizer))
 
         target = batch['label'].detach()
         targets.append(target)
 
-    return loss_meter, targets, predictions
+    losses = (loss_meter, s_loss_meter, c_loss_meter)
+
+    return losses, targets, predictions
 
 
 def validation_epoch(config, model, validation_loader):
     loss_meter = AvgMeter('validation')
+    c_loss_meter = AvgMeter('validation')
+    s_loss_meter = AvgMeter('validation')
 
     targets = []
     predictions = []
         batch = batch_constructor(config, batch)
         with torch.no_grad():
             output, score = model(batch)
-            loss = calculate_loss(model, score, batch['label'])
+            loss, c_loss, s_loss = calculate_loss(model, score, batch['label'])
 
             count = batch["id"].size(0)
             loss_meter.update(loss.detach(), count)
+            c_loss_meter.update(c_loss.detach(), count)
+            s_loss_meter.update(s_loss.detach(), count)
 
             # tqdm_object.set_postfix(validation_loss=loss_meter.avg)
 
             target = batch['label'].detach()
             targets.append(target)
 
-    return loss_meter, targets, predictions
+    losses = (loss_meter, s_loss_meter, c_loss_meter)
+
+    return losses, targets, predictions
 
 
 def supervised_train(config, train_loader, validation_loader, trial=None):
         train_accuracy = multiclass_acc(train_truth, train_pred)
         validation_accuracy = multiclass_acc(validation_truth, validation_pred)
         print_lr(optimizer)
-        print('Training Loss:', train_loss, 'Training Accuracy:', train_accuracy)
+        print('Training Loss:', train_loss[0], 'Training Accuracy:', train_accuracy)
         print('Validation Loss', validation_loss, 'Validation Accuracy:', validation_accuracy)
 
         if lr_scheduler:
-            lr_scheduler.step(validation_loss.avg)
+            lr_scheduler.step(validation_loss[0].avg)
         if early_stopping:
-            early_stopping(validation_loss.avg, model)
+            early_stopping(validation_loss[0].avg, model)
             if early_stopping.early_stop:
                 print("Early stopping")
                 break

main.py

 
     make_directory(config.output_path)
 
-    config.writer = SummaryWriter(config.output_path)
+    # config.writer = SummaryWriter(config.output_path)
 
     if args.use_optuna and use_optuna:
         optuna_main(config, args.use_optuna)
     else:
         torch_main(config)
 
-    config.writer.close()
+    # config.writer.close()

model.py

     # fake = (label == 1).nonzero()
     # real = (label == 0).nonzero()
     # s_loss = 0 * similarity[fake].mean() + similarity[real].mean()
+    s_loss = similarity.mean()
     c_loss = model.classifier_loss_function(score, label)
-    loss = model.config.loss_weight * c_loss + similarity.mean()
-    return loss
+    loss = model.config.loss_weight * c_loss + s_loss
+    return loss, c_loss, s_loss
 
 
 def calculate_similarity_loss(config, image_embeddings, text_embeddings):