refine func

app.py

@@ -25,14 +25,6 @@ from src.display.utils import (
 )
 from src.envs import API, EVAL_REQUESTS_PATH, EVAL_RESULTS_PATH, H4_TOKEN, IS_PUBLIC, QUEUE_REPO, REPO_ID, RESULTS_REPO
 from PIL import Image
-# from src.populate import get_evaluation_queue_df, get_leaderboard_df
-# from src.submission.submit import add_new_eval
-# from src.tools.collections import update_collections
-# from src.tools.plots import (
-#     create_metric_plot_obj,
-#     create_plot_df,
-#     create_scores_df,
-# )
 from dummydatagen import dummy_data_for_plot, create_metric_plot_obj_1, dummydf
 import copy
 
@@ -40,49 +32,45 @@ import copy
 def restart_space():
     API.restart_space(repo_id=REPO_ID, token=H4_TOKEN)
 
-
-
 gtbench_raw_data = dummydf()
 methods = list(set(gtbench_raw_data['Method']))
 
-
-
-
 # Searching and filtering
-
+metrics = ["Style-UA", "Style-IRA", "Style-CRA", "Object-UA", "Object-IRA", "Object-CRA", "FID", "run-time", "storage", "memory"]
 
 def update_table(
     hidden_df: pd.DataFrame,
-    columns: list,
+    columns_1: list,
+    columns_2: list,
+    columns_3: list,
     model1: list,
 ):
 
-    filtered_df = select_columns(hidden_df, columns)
+    filtered_df = select_columns(hidden_df, columns_1, columns_2, columns_3)
 
     filtered_df = filter_model1(filtered_df, model1)
 
     return filtered_df
 
 
-def select_columns(df: pd.DataFrame, columns: list) -> pd.DataFrame:
+def select_columns(df: pd.DataFrame, columns_1: list, columns_2: list,  columns_3: list) -> pd.DataFrame:
     always_here_cols = [
-        "Model", "Agent", "Opponent Model", "Opponent Agent"
+        "Method"
     ]
     # We use COLS to maintain sorting
     all_columns = metrics
 
-    if len(columns) == 0:
+    if (len(columns_1)+len(columns_2) + len(columns_3)) == 0:
         filtered_df = df[
             always_here_cols +
             [c for c in all_columns if c in df.columns]
         ]
 
-        return filtered_df
-
-    filtered_df = df[
-        always_here_cols +
-        [c for c in all_columns if c in df.columns and c in columns]
-    ]
+    else:
+        filtered_df = df[
+            always_here_cols +
+            [c for c in all_columns if c in df.columns and (c in columns_1 or c in columns_2 or c in columns_3 ) ]
+        ]
 
     return filtered_df
 
@@ -101,9 +89,6 @@ def filter_model1(
 
 
 
-metrics = ["Style-UA", "Style-IRA", "Style-CRA", "Object-UA", "Object-IRA", "Object-CRA", "FID", "run-time", "storage", "memory"]
-
-
 demo = gr.Blocks(css=custom_css)
 
 
@@ -135,7 +120,7 @@ with demo:
                         )
                         
                     with gr.Row():
-                        shown_columns_3 = gr.CheckboxGroup(
+                        shown_columns_2 = gr.CheckboxGroup(
                             choices=["FID"],
                             label="Image Quality",
                             elem_id="column-select",
@@ -143,7 +128,7 @@ with demo:
                         )
                     
                     with gr.Row():
-                        shown_columns_4 = gr.CheckboxGroup(
+                        shown_columns_3 = gr.CheckboxGroup(
                             choices=["Time (s)", "Memory (GB)", "Storage (GB)"],
                             label="Resource Costs",
                             elem_id="column-select",
@@ -168,13 +153,15 @@ with demo:
             )
 
 
-            for selector in [model1_column]:
+            for selector in [shown_columns_1,shown_columns_2, shown_columns_3, model1_column]:
                 selector.change(
                     update_table,
                     [   
-                        model1_column,
                         game_bench_df_for_search,
-                        
+                        shown_columns_1,
+                        shown_columns_2,
+                        shown_columns_3,
+                        model1_column, 
                     ],
                     leaderboard_table,
                     queue=True,

src/display/about.py

@@ -7,11 +7,13 @@ INTRODUCTION_TEXT = """
 
 paper: https://arxiv.org/abs/2402.11846
 
-The rapid advancement of diffusion models (DMs) has not only transformed various real- world industries but has also introduced negative societal concerns, including the generation of harmful content, copyright disputes, and the rise of stereotypes and biases. To mitigate these issues, machine unlearning (MU) has emerged as a potential solution, demonstrating its ability to remove undesired generative capabilities of DMs in various applications. However, by examining existing MU evaluation methods, we uncover several key challenges that can result in incomplete, inaccurate, or biased evaluations for MU in DMs. 
+Code: https://github.com/OPTML-Group/UnlearnCanvas
 
-To address them, we enhance the evaluation metrics for MU, including the introduction of an often-overlooked retainability measurement for DMs post-unlearning. Additionally, we introduce UnlearnCanvas, a comprehensive high-resolution stylized image dataset that facilitates us to evaluate the unlearning of artistic painting styles in conjunction with associated image objects. 
+The rapid advancement of diffusion models (DMs) has not only transformed various real- world industries but has also introduced negative societal concerns, including the generation of harmful content, copyright disputes, and the rise of stereotypes and biases. <strong>To mitigate these issues, machine unlearning (MU) has emerged as a potential solution, demonstrating its ability to remove undesired generative capabilities of DMs in various applications.</strong> However, by examining existing MU evaluation methods, we uncover several key challenges that can result in incomplete, inaccurate, or biased evaluations for MU in DMs. 
 
-We show that this dataset plays a pivotal role in establishing a standardized and automated evaluation framework for MU techniques on DMs, featuring 7 quantitative metrics to address various aspects of unlearning effectiveness. Through extensive experiments, we benchmark 5 state-of- the-art MU methods, revealing novel insights into their pros and cons, and the underlying unlearning mechanisms. Furthermore, we demonstrate the potential of UnlearnCanvas to benchmark other generative modeling tasks, such as style transfer. The UnlearnCanvas dataset, benchmark, and the codes to reproduce all the results in this work can be found at https://github.com/OPTML-Group/UnlearnCanvas.
+To address them, we enhance the evaluation metrics for MU, including the introduction of an often-overlooked retainability measurement for DMs post-unlearning. Additionally, we introduce <strong>UnlearnCanvas, a comprehensive high-resolution stylized image dataset</strong> that facilitates us to evaluate the unlearning of artistic painting styles in conjunction with associated image objects. 
+
+We show that this dataset plays a pivotal role in establishing a standardized and automated evaluation framework for MU techniques on DMs, featuring 7 quantitative metrics to address various aspects of unlearning effectiveness. Through extensive experiments, we benchmark 5 state-of- the-art MU methods, revealing novel insights into their pros and cons, and the underlying unlearning mechanisms. Furthermore, we demonstrate the potential of UnlearnCanvas to benchmark other generative modeling tasks, such as style transfer.
 
 """