bug in evaluation fixed

data/weibo/config.py

     projection_size = 64
     dropout = 0.5
 
-    device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+    device = torch.device("cuda:1" if torch.cuda.is_available() else "cpu")
 
     image_model_name = '../../../../../media/external_10TB/10TB/ghorbanpoor/vit-base-patch16-224'
     image_embedding = 768

evaluation.py

     plt.show()
 
 
-def save_embedding(config, x, fname='embedding.tsv'):
+def save_loss(ids, predictions, targets, l, path):
+    ids = [i.cpu().numpy() for i in ids]
+    predictions = [i.cpu().numpy() for i in predictions]
+    targets = [i.cpu().numpy() for i in targets]
+    losses = [i[0].cpu().numpy() for i in l]
+    classifier_losses = [i[1].cpu().numpy() for i in l]
+    similarity_losses = [i[2].cpu().numpy() for i in l]
+
+    pd.DataFrame({'id': ids, 'predicted_label': predictions, 'real_label': targets, 'losses': losses,
+                  'classifier_losses': classifier_losses, 'similarity_losses': similarity_losses}).to_csv(path)
+
+
+def save_embedding(x, fname='embedding.tsv'):
     x = [i.cpu().numpy() for i in x]
 
     x = np.concatenate(x, axis=0)

test_main.py

 
 from data_loaders import make_dfs, build_loaders
 from evaluation import metrics, report_per_class, roc_auc_plot, precision_recall_plot, plot_tsne, plot_pca, \
-    save_embedding
+    save_embedding, save_loss
 from learner import batch_constructor
 from model import FakeNewsModel, calculate_loss
 
     roc_auc_plot(targets, scores, fname=str(config.output_path) + "/roc.png")
     precision_recall_plot(targets, scores, fname=str(config.output_path) + "/pr.png")
 
-    save_embedding(config, image_features, fname=str(config.output_path) + '/new_image_features.tsv')
-    save_embedding(config, text_features, fname=str(config.output_path) + '/new_text_features.tsv')
-    save_embedding(config, multimodal_features, fname=str(config.output_path) + '/new_multimodal_features_.tsv')
-    save_embedding(config, concat_features, fname=str(config.output_path) + '/new_concat_features.tsv')
+    save_embedding(image_features, fname=str(config.output_path) + '/new_image_features.tsv')
+    save_embedding(text_features, fname=str(config.output_path) + '/new_text_features.tsv')
+    save_embedding(multimodal_features, fname=str(config.output_path) + '/new_multimodal_features_.tsv')
+    save_embedding(concat_features, fname=str(config.output_path) + '/new_concat_features.tsv')
 
     config_parameters = str(config)
     with open(config.output_path + '/new_parameters.txt', 'w') as f:
         f.write(config_parameters)
-    print(config)
 
-    pd.DataFrame({'id': ids, 'predicted_label': predictions, 'real_label': targets, 'losses': losses}).to_csv(
-        str(config.output_path) + '/new_text_label.csv')
+    save_loss(ids, predictions, targets, losses, str(config.output_path) + '/new_text_label.csv')
+
 
 
 def test_main(config, trial_number=None):