Update app.py

app.py

@@ -5,8 +5,11 @@ import plotly.express as px
 from rapidfuzz import fuzz
 import re
 
+
 def load_leaderboard():
     # Load validation / test CSV files
+    pd.set_option('display.float_format', '{:.2f}'.format)
+
     results_csv_files = {
         'imagenet': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet.csv',
         'real': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-real.csv',
@@ -26,51 +29,41 @@ def load_leaderboard():
         'fp32-nchw-pt240-cpu-i9_10940x-dynamo': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/benchmark-infer-fp32-nchw-pt240-cpu-i9_10940x-dynamo.csv',
         'fp32-nchw-pt240-cpu-i7_12700h-dynamo': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/benchmark-infer-fp32-nchw-pt240-cpu-i7_12700h-dynamo.csv',
     }
-    
+
     dataframes = {name: pd.read_csv(url) for name, url in results_csv_files.items()}
     bench_dataframes = {name: pd.read_csv(url) for name, url in benchmark_csv_files.items()}
     bench_dataframes = {name: df for name, df in bench_dataframes.items() if 'infer_gmacs' in df.columns}
-    main_bench_dataframe = bench_dataframes[main_bench]
-    
+    print(bench_dataframes.keys())
+
     # Clean up dataframes
     remove_column_names = ["top1_err", "top5_err", "top1_diff", "top5_diff", "rank_diff", "param_count"]
     for df in dataframes.values():
         for col in remove_column_names:
             if col in df.columns:
-                df.drop(columns=[col], inplace=True)    
+                df.drop(columns=[col], inplace=True)
 
     # Rename / process results columns
     for name, df in dataframes.items():
         df.rename(columns={"top1": f"{name}_top1", "top5": f"{name}_top5"}, inplace=True)
-        df['arch_name'] = df['model'].apply(lambda x: x.split('.')[0])    
-    
-    # Process benchmark dataframe
-    main_bench_dataframe['arch_name'] = main_bench_dataframe['model']
-    main_bench_dataframe.rename(columns={'infer_img_size': 'img_size'}, inplace=True)
-    
+        df['arch_name'] = df['model'].apply(lambda x: x.split('.')[0])
+
+    # Process benchmark dataframes
+    for name, df in bench_dataframes.items():
+        df['arch_name'] = df['model']
+        df.rename(columns={'infer_img_size': 'img_size'}, inplace=True)
+
     # Merge all result dataframes
     result = dataframes['imagenet']
     for name, df in dataframes.items():
         if name != 'imagenet':
             result = pd.merge(result, df, on=['arch_name', 'model', 'img_size', 'crop_pct', 'interpolation'], how='outer')
-    
-    # Merge with benchmark data
-    result = pd.merge(result, main_bench_dataframe, on=['arch_name', 'img_size'], how='left', suffixes=('', '_benchmark'))
 
-    # Calculate TFLOP/s
-    result['infer_tflop_s'] = result['infer_samples_per_sec'] * result['infer_gmacs'] * 2 / 1000
-    
     # Calculate average scores
     top1_columns = [col for col in result.columns if col.endswith('_top1') and not col == 'a_top1']
     top5_columns = [col for col in result.columns if col.endswith('_top5') and not col == 'a_top5']
     result['avg_top1'] = result[top1_columns].mean(axis=1)
     result['avg_top5'] = result[top5_columns].mean(axis=1)
-    
-    # Reorder columns
-    first_columns = ['model', 'img_size', 'avg_top1', 'avg_top5']
-    other_columns = [col for col in result.columns if col not in first_columns and col != 'model_benchmark']
-    result = result[first_columns + other_columns]
-    
+
     # Create fully merged dataframes for each benchmark set
     merged_dataframes = {}
     for bench_name, bench_df in bench_dataframes.items():
@@ -82,7 +75,7 @@ def load_leaderboard():
         # Reorder columns
         first_columns = ['model', 'img_size', 'avg_top1', 'avg_top5']
         other_columns = [col for col in merged_df.columns if col not in first_columns]
-        merged_df = merged_df[first_columns + other_columns].copy()
+        merged_df = merged_df[first_columns + other_columns].copy(deep=True)
 
         # Drop columns that are no longer needed / add too much noise
         merged_df.drop('arch_name', axis=1, inplace=True)
@@ -97,7 +90,6 @@ def load_leaderboard():
 
     return merged_dataframes
 
-
 REGEX_PREFIX = "re:"
 
 def auto_match(pattern, text):