Derive new field "infer_tflop_s"

app.py

@@ -52,6 +52,9 @@ def load_leaderboard():
     
     # 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')]
@@ -151,8 +154,8 @@ def create_scatter_plot(df, x_axis, y_axis, model_filter, highlight_filter):
 full_df = load_leaderboard()
 
 # Define the available columns for sorting and plotting
-sort_columns = ['avg_top1', 'avg_top5', 'infer_samples_per_sec', 'param_count', 'infer_gmacs', 'infer_macts']
-plot_columns = ['infer_samples_per_sec', 'infer_gmacs', 'infer_macts', 'param_count', 'avg_top1', 'avg_top5']
+sort_columns = ['avg_top1', 'avg_top5', 'infer_samples_per_sec', 'param_count', 'infer_gmacs', 'infer_macts', 'infer_tflop_s']
+plot_columns = ['infer_samples_per_sec', 'infer_gmacs', 'infer_macts', 'infer_tflop_s', 'param_count', 'avg_top1', 'avg_top5']
 
 DEFAULT_SEARCH = ""
 DEFAULT_SORT = "avg_top1"