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jac99 · Jan 31, 2024 · 9f3a43a · 9f3a43a
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Showing 4 changed files with 9 additions and 9 deletions.
diff --git a/datasets/pointnetvlad/generate_test_sets.py b/datasets/pointnetvlad/generate_test_sets.py
@@ -102,8 +102,8 @@ def construct_query_and_database_sets(base_path, runs_folder, folders, pointclou
                 # indices of the positive matches in database i of each query (key) in test set j
                 test_sets[j][key][i] = index[0].tolist()
 
-    output_to_file(database_sets, base_path, "minkloc_" + output_name + '_evaluation_database.pickle')
-    output_to_file(test_sets, base_path, "minkloc_" + output_name + '_evaluation_query.pickle')
+    output_to_file(database_sets, base_path, output_name + '_evaluation_database_v2.pickle')
+    output_to_file(test_sets, base_path, output_name + '_evaluation_query_v2.pickle')
 
 
 if __name__ == '__main__':

diff --git a/datasets/pointnetvlad/generate_training_tuples_baseline.py b/datasets/pointnetvlad/generate_training_tuples_baseline.py
@@ -93,5 +93,5 @@ def construct_query_dict(df_centroids, base_path, filename, ind_nn_r, ind_r_r=50
     print("Number of training submaps: " + str(len(df_train['file'])))
     print("Number of non-disjoint test submaps: " + str(len(df_test['file'])))
     # ind_nn_r is a threshold for positive elements - 10 is in original PointNetVLAD code for refined dataset
-    construct_query_dict(df_train, base_path, "training_queries_baseline2.pickle", ind_nn_r=10)
-    construct_query_dict(df_test, base_path, "test_queries_baseline2.pickle", ind_nn_r=10)
+    construct_query_dict(df_train, base_path, "training_queries_baseline_v2.pickle", ind_nn_r=10)
+    construct_query_dict(df_test, base_path, "test_queries_baseline_v2.pickle", ind_nn_r=10)
diff --git a/datasets/pointnetvlad/generate_training_tuples_refine.py b/datasets/pointnetvlad/generate_training_tuples_refine.py
@@ -77,4 +77,4 @@
 
     print("Number of training submaps: " + str(len(df_train['file'])))
     # ind_nn_r is a threshold for positive elements - 12.5 is in original PointNetVLAD code for refined dataset
-    construct_query_dict(df_train, base_path, "training_queries_refine2.pickle", ind_nn_r=12.5)
+    construct_query_dict(df_train, base_path, "training_queries_refine_v2.pickle", ind_nn_r=12.5)
diff --git a/eval/pnv_evaluate.py b/eval/pnv_evaluate.py
@@ -21,11 +21,11 @@
 def evaluate(model, device, params: TrainingParams, log: bool = False, show_progress: bool = False):
     # Run evaluation on all eval datasets
 
-    eval_database_files = ['oxford_evaluation_database.pickle', 'university_evaluation_database.pickle',
-                           'residential_evaluation_database.pickle', 'business_evaluation_database.pickle']
+    eval_database_files = ['oxford_evaluation_database_v2.pickle', 'university_evaluation_database_v2.pickle',
+                           'residential_evaluation_database_v2.pickle', 'business_evaluation_database_v2.pickle']
 
-    eval_query_files = ['oxford_evaluation_query.pickle', 'university_evaluation_query.pickle',
-                        'residential_evaluation_query.pickle', 'business_evaluation_query.pickle']
+    eval_query_files = ['oxford_evaluation_query_v2.pickle', 'university_evaluation_query_v2.pickle',
+                        'residential_evaluation_query_v2.pickle', 'business_evaluation_query_v2.pickle']
 
     assert len(eval_database_files) == len(eval_query_files)