some changes to store losses and embeddings

.gitignore

 
 .idea/
 env/
-venv/
+venv/
+results/

data/weibo/config.py

     validation_text_path = data_path + 'weibo_train.csv'
     test_text_path = data_path + 'weibo_test.csv'
 
-    batch_size = 64
+    batch_size = 100
     epochs = 100
-    num_workers = 4
+    num_workers = 1
     head_lr = 1e-03
     image_encoder_lr = 1e-02
     text_encoder_lr = 1e-05
 
     image_model_name = '../../../../../media/external_10TB/10TB/ghorbanpoor/vit-base-patch16-224'
     image_embedding = 768
-    text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/chinese-xlnet-base"
+    text_encoder_model = "../../../../../media/external_10TB/10TB/ghorbanpoor/xlnet-base-chinese"
     # text_encoder_model = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
-    text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/chinese-xlnet-base"
+    text_tokenizer = "../../../../../media/external_10TB/10TB/ghorbanpoor/xlnet-base-chinese"
     # text_tokenizer = "/home/faeze/PycharmProjects/new_fake_news_detectioin/bert/bert-base-uncased"
     text_embedding = 768
     max_length = 200
 
     labels = ['real', 'fake']
 
-    wanted_accuracy = 0.80
+    wanted_accuracy = 0.85
 
     def optuna(self, trial):
         self.head_lr = trial.suggest_loguniform('head_lr', 1e-5, 1e-1)

evaluation.py

 
     # config.writer.add_embedding(mat=x, label_img=y, metadata=z, tag=fname)
 
+
 def plot_tsne(config, x, y, fname='tsne.png'):
     x = [i.cpu().numpy() for i in x]
     y = [i.cpu().numpy() for i in y]
     ax.legend(fontsize='large', markerscale=2)
     plt.title('tsne of ' + str(fname.split('/')[-1].split('.')[0]))
     plt.savefig(fname=fname)
-    # plt.show()
+    plt.show()
+
+
+def save_embedding(config, x, fname='embedding.tsv'):
+    x = [i.cpu().numpy() for i in x]
+
+    x = np.concatenate(x, axis=0)
+
+    embedding_df = pd.DataFrame(x)
+
+    embedding_df.to_csv(fname, sep='\t', index=False, header=False)
 
 
 def plot_pca(config, x, y, fname='pca.png'):

learner.py

 
 def batch_constructor(config, batch):
     b = {}
-    for k, v in batch.items():
-        if k != 'text':
-            b[k] = v.to(config.device)
+    for key, value in batch.items():
+        if key != 'text':
+            b[key] = value.to(config.device)
+        else:
+            b[key] = value
     return b
 
 

test_main.py

 import random
 
 import numpy as np
+import pandas as pd
 import torch
 from tqdm import tqdm
 
 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
+from evaluation import metrics, report_per_class, roc_auc_plot, precision_recall_plot, plot_tsne, plot_pca, \
+    save_embedding
 from learner import batch_constructor
-from model import FakeNewsModel
+from model import FakeNewsModel, calculate_loss
 
 
 def test(config, test_loader, trial_number=None):
     targets = []
     predictions = []
     scores = []
+    ids = []
+    losses = []
     tqdm_object = tqdm(test_loader, total=len(test_loader))
     for i, batch in enumerate(tqdm_object):
         batch = batch_constructor(config, batch)
         with torch.no_grad():
             output, score = model(batch)
-
-            prediction = output.detach()
-            predictions.append(prediction)
-
-            score = score.detach()
-            scores.append(score)
-
-            target = batch['label'].detach()
-            targets.append(target)
-
-            image_feature = model.image_embeddings.detach()
-            image_features.append(image_feature)
-
-            text_feature = model.text_embeddings.detach()
-            text_features.append(text_feature)
-
-            multimodal_feature = model.multimodal_embeddings.detach()
-            multimodal_features.append(multimodal_feature)
-
-            concat_feature = model.classifier.embeddings.detach()
-            concat_features.append(concat_feature)
-
-    # config.writer.add_graph(model, input_to_model=batch, verbose=True)
+            loss, c_loss, s_loss = calculate_loss(model, score, batch['label'])
+
+            predictions.append(output.detach())
+            scores.append(score.detach())
+            targets.append(batch['label'].detach())
+            ids.append(batch['id'].detach())
+            image_features.append(model.image_embeddings.detach())
+            text_features.append(model.text_embeddings.detach())
+            multimodal_features.append(model.multimodal_embeddings.detach())
+            concat_features.append(model.classifier.embeddings.detach())
+            losses.append((loss.detach(), c_loss.detach(), s_loss.detach()))
 
     s = ''
     s += report_per_class(targets, predictions) + '\n'
-    s += metrics(targets, predictions, scores, file_path=str(config.output_path) + '/fpr_tpr.csv') + '\n'
-    with open(config.output_path + '/results.txt', 'w') as f:
+    s += metrics(targets, predictions, scores, file_path=str(config.output_path) + '/new_fpr_tpr.csv') + '\n'
+    with open(config.output_path + '/new_results.txt', 'w') as f:
         f.write(s)
 
     roc_auc_plot(targets, scores, fname=str(config.output_path) + "/roc.png")
     precision_recall_plot(targets, scores, fname=str(config.output_path) + "/pr.png")
 
-    # saving_in_tensorboard(config, image_features, targets, 'image_features')
-    plot_tsne(config, image_features, targets, fname=str(config.output_path) + '/image_features_tsne.png')
-    plot_pca(config, image_features, targets, fname=str(config.output_path) + '/image_features_pca.png')
-
-    # saving_in_tensorboard(config, text_features, targets, 'text_features')
-    plot_tsne(config, text_features, targets, fname=str(config.output_path) + '/text_features_tsne.png')
-    plot_pca(config, text_features, targets, fname=str(config.output_path) + '/text_features_pca.png')
-    #
-    # saving_in_tensorboard(config, multimodal_features, targets, 'multimodal_features')
-    plot_tsne(config, multimodal_features, targets, fname=str(config.output_path) + '/multimodal_features_tsne.png')
-    plot_pca(config, multimodal_features, targets, fname=str(config.output_path) + '/multimodal_features_pca.png')
-
-    # saving_in_tensorboard(config, concat_features, targets, 'concat_features')
-    plot_tsne(config, concat_features, targets, fname=str(config.output_path) + '/concat_features_tsne.png')
-    plot_pca(config, concat_features, targets, fname=str(config.output_path) + '/concat_features_pca.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')
 
     config_parameters = str(config)
-    with open(config.output_path + '/parameters.txt', 'w') as f:
+    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')
+
 
 def test_main(config, trial_number=None):
     train_df, test_df, validation_df = make_dfs(config, )