clean up

Doku/Squat_down_old_vs_new_single_sample.txt

-Sample 9
-
-NEW TRAININGS SESSION with NTU_RelationNet_multi_label 
-
-Properties: 
- Dataset:  stgcn_40_class_epoch80_features_reindex_norm 
- Language embedding:  ['sentence_40_mean_ver2_norm', 'sentence_40_mean_ver4_norm', 'sentence_40_mean_ver5_norm'] 
- Unseen Classes:  [5, 17, 19, 24, 27, 45, 57, 59, 64, 67, 85, 97, 99, 104, 107] 
- Seed:  23 
- Batchsize:  128 
- Dropout:  0.5 
- Bias:  True 
- Batchnorm:  False 
- Weight init with xavier:  False 
- Multi hot:  False 
- Inferenz with sum of relation scores:  False 
-
-Dimension vom Language Embedding:  (30720,)
-Dimension vom Language Embedding:  (61440,)
-Dimension vom Language Embedding:  (92160,)
-Dimension vom Language Embedding:  (120, 768)
- Unseen Classes:  [5, 17, 19, 24, 27, 45, 57, 59, 64, 67, 85, 97, 99, 104, 107] 
-
-Using Model:  500k
-
- GZSL Unseen Class Accuracy: 
-Label:  [[27]]
-Top 5:  [ 27   2  42  82 107]
-[0.4149502  0.35621354 0.26102823 0.25498113 0.1207521]
-Top 15 Modulo unique:  [27.  2. 36. 35. 12. 39.]
-Adapted relation scores:  [0.91782318 0.9121652  0.79746223 0.71359468 0.66594639 0.66278358]
-Relation scores:  [0.41495019 0.35621354 0.02195648 0.00315389 0.00105174 0.00097783]        
-top 5 acc for class:  27 1.0
-Accuracy for class  27 :  1.0
-Confusion Scores for Class  27  (>10%):
-
- GZSL Seen Class Accuracy:
-Label:  [[22]]
-Top 5:  [ 22 102  62  24  25]
-Top 15 Modulo unique:  [22. 24. 25. 19. 20. 23. 10.]
-Adapted relation scores:  [0.89612739 0.88690615 0.88533399 0.82322471 0.81586078 0.81375734
- 0.81168559]
-Relation scores:  [0.23443718 0.18568955 0.17853129 0.04008907 0.03373242 0.03211224
- 0.03059368]
-top 5 acc for class:  22 1.0
-Accuracy for class  22 :  1.0
-Confusion Scores for Class  22  (>10%):
-gzsl: seen=1.0000, unseen=1.0000, h=1.0000
-gzsl top5: seen=1.0000, unseen top5=1.0000, h=1.0000
-
-
-
-
-
-
-NEW TRAININGS SESSION with NTU_zsar40 
-
-Properties: 
- Dataset:  stgcn_40_class_epoch80_features_reindex_norm 
- Language embedding:  language_embedding_40_bert_mean_norm 
- Unseen Classes:  [5, 17, 19, 24, 27] 
- Seed:  23 
- Dropout:  0.5 
-
-(env_st-gcn) C:\Users\XPS15\Documents\Eigene Dokumente\Uni\Master\zero-shot-action-recognition\LearningToCompare_ZSL>"c:/Users/XPS15/Documents/Eigene Dokumente/Uni/Master/zero-shot-action-recognition/st-gcn/env_st-gcn/Scripts/python.exe" "c:/Users/XPS15/Documents/Eigene Dokumente/Uni/Master/zero-shot-action-recognition/LearningToCompare_ZSL/NTU_RelationNet_test_single_sample.py"
- Unseen Classes:  [5, 17, 19, 24, 27] 
-
- Language embedding:  language_embedding_40_bert_mean_norm
-
-Using Model:  500k
-
- GZSL Unseen Class Accuracy: 
-Label:  [[27]]
-[[ 2 27 36 35 17]]
-Adapted relation scores:  [0.94392101 0.81732137 0.68269    0.66622576 0.6623002 ]
-Relation scores:  [0.88331246 0.03490609 0.00154686 0.00105853 0.000967  ]
-top 5 acc for class:  27 1.0
-Accuracy for class  27 :  0.0
-Confusion Scores for Class  27  (>10%):
-     Confusion with class: 2 (proportion):  1.0
-
- GZSL Seen Class Accuracy:
-Label:  [[22]]
-[[22 24 25  5 19]]
-Adapted relation scores:  [0.94284933 0.92247968 0.90267141 0.86377904 0.80653934]
-Relation scores:  [0.85447633 0.47164091 0.27745107 0.10517248 0.02712905]
-top 5 acc for class:  22 1.0
-Accuracy for class  22 :  1.0
-Confusion Scores for Class  22  (>10%):
-gzsl: seen=1.0000, unseen=0.0000, h=0.0000
-gzsl top5: seen=1.0000, unseen top5=1.0000, h=1.0000
-
-
-
-__________________
-
-Descriptive
-
-__________________
-
-nseen Classes:  [5, 17, 19, 24, 27] 
-
-Using Model:  500k
-
- GZSL Unseen Class Accuracy: 
-Label:  [[27]]
-[[ 2 27 35 12 13]]
-Adapted relation scores:  [0.94597788 0.92846303 0.85434461 0.7689299  0.72443867]
-Relation scores:  [0.9420315  0.55807549 0.08380191 0.01132301 0.00405024]
-top 5 acc for class:  27 1.0
-Accuracy for class  27 :  0.0
-Confusion Scores for Class  27  (>10%):
-     Confusion with class: 2 (proportion):  1.0
\ No newline at end of file

LearningToCompare_ZSL/NTU_RelationNet_extract_features.py

@@ -15,11 +15,11 @@ import torch.nn.functional as F
 parser = argparse.ArgumentParser(description="Zero Shot Learning")
 parser.add_argument("-g","--gpu",type=int, default=0)
 parser.add_argument("-u","--unseen_classes", type = int, nargs = '*', default = [32, 9, 34])
-parser.add_argument("-s","--semantic_input",type = str, nargs = '*', default = ['sentence_40_mean_ver2_norm', 'sentence_40_mean_ver3_norm', 'sentence_40_mpnet_ver4n1_norm' ])
-parser.add_argument("-d","--dropout",type=float, default=0.25)
+parser.add_argument("-s","--semantic_input",type = str, nargs = '*', default = ['sentence_40_mean_ver2_norm', 'class_sentences_40_ver2_roberta_aug_1_norm', 'class_sentences_40_ver2_roberta_aug_2_norm', 'class_sentences_40_ver2_roberta_aug_3_norm', 'class_sentences_40_ver2_roberta_aug_4_norm', 'sentence_40_mean_ver5_norm', 'class_sentences_40_ver5_roberta_aug_1_norm', 'class_sentences_40_ver5_roberta_aug_2_norm', 'class_sentences_40_ver5_roberta_aug_3_norm', 'class_sentences_40_ver5_roberta_aug_4_norm', 'sentence_40_mean_ver4_norm', 'class_sentences_40_ver4_roberta_aug_1_norm', 'class_sentences_40_ver4_roberta_aug_2_norm', 'class_sentences_40_ver4_roberta_aug_3_norm', 'class_sentences_40_ver4_roberta_aug_4_norm'])
+parser.add_argument("-d","--dropout",type=float, default=0)
 parser.add_argument("--log_filename",type=str, default="log.txt")
-parser.add_argument("--input_dir",type=str, default='session_2021-07-05_13_55_42')
-parser.add_argument("--input_model",type=str, default='best_zsl')
+parser.add_argument("--input_dir",type=str, default='session_2021-07-14_23_08_50')
+parser.add_argument("--input_model",type=str, default='500k')
 args = parser.parse_args()
 
 
@@ -130,8 +130,8 @@ def main():
 
     elif INPUT_MODEL == "500k":
         print("Using Model: ", INPUT_MODEL)
-        attribute_network.load_state_dict(torch.load(INPUT_DIR + "/NTU_zsar40_attribute_network_500000.pkl"))
-    
+        attribute_network.load_state_dict(torch.load(INPUT_DIR + "/NTU_zsar55_5_attribute_network_500000.pkl", map_location=torch.device('cpu')))
+            
     else:
         print("Input Model ----",INPUT_MODEL,"---- not existing!")
         exit()