Create app.py

app.py

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+import fnmatch
+import gradio as gr
+import pandas as pd
+import plotly.express as px
+from rapidfuzz import fuzz
+import re
+
+def load_leaderboard():
+ # Load validation / test CSV files
+ 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',
+ 'v2': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenetv2-matched-frequency.csv',
+ 'sketch': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-sketch.csv',
+ 'a': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-a.csv',
+ 'r': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/results-imagenet-r.csv',
+ }
+
+ # Load benchmark CSV files
+ main_bench = 'amp-nhwc-pt210-cu121-rtx3090'
+ benchmark_csv_files = {
+ 'amp-nhwc-pt210-cu121-rtx3090': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/benchmark-infer-amp-nhwc-pt210-cu121-rtx3090.csv',
+ 'fp32-nchw-pt221-cpu-i9_10940x-dynamo': 'https://raw.githubusercontent.com/huggingface/pytorch-image-models/main/results/benchmark-infer-fp32-nchw-pt221-cpu-i9_10940x-dynamo.csv',
+ }
+ # FIXME support selecting benchmark 'infer_samples_per_sec' / 'infer_step_time' from different benchmark files.
+ 
+ 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()}
+ main_bench_dataframe = bench_dataframes[main_bench]
+ 
+ # 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) 
+
+ # 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)
+ 
+ # 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 average scores
+ top1_columns = [col for col in result.columns if col.endswith('_top1')]
+ top5_columns = [col for col in result.columns if col.endswith('_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]
+ 
+ # Drop columns that are no longer needed / add too much noise
+ result.drop('arch_name', axis=1, inplace=True)
+ result.drop('crop_pct', axis=1, inplace=True)
+ result.drop('interpolation', axis=1, inplace=True)
+ 
+ # Round numerical values
+ result = result.round(2)
+ 
+ return result
+
+
+REGEX_PREFIX = "re:"
+
+def auto_match(pattern, text):
+ # Check if it's a regex pattern (starts with 're:')
+ if pattern.startswith(REGEX_PREFIX):
+ regex_pattern = pattern[len(REGEX_PREFIX):].strip()
+ try:
+ return bool(re.match(regex_pattern, text, re.IGNORECASE))
+ except re.error:
+ # If it's an invalid regex, return False
+ return False
+ 
+ # Check if it's a wildcard pattern
+ elif any(char in pattern for char in ['*', '?']):
+ return fnmatch.fnmatch(text.lower(), pattern.lower())
+ 
+ # If not regex or wildcard, use fuzzy matching
+ else:
+ return fuzz.partial_ratio(
+ pattern.lower(), text.lower(), score_cutoff=90) > 0
+
+
+def filter_leaderboard(df, model_name, sort_by):
+ if not model_name:
+ return df.sort_values(by=sort_by, ascending=False)
+ 
+ mask = df['model'].apply(lambda x: auto_match(model_name, x))
+ filtered_df = df[mask].sort_values(by=sort_by, ascending=False)
+ 
+ return filtered_df
+
+def create_scatter_plot(df, x_axis, y_axis):
+ fig = px.scatter(
+ df,
+ x=x_axis,
+ y=y_axis,
+ log_x=True,
+ log_y=True,
+ hover_data=['model'],
+ trendline='ols',
+ trendline_options=dict(log_x=True, log_y=True),
+ title=f'{y_axis} vs {x_axis}'
+ )
+ return fig
+
+# Load the leaderboard data
+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']
+
+DEFAULT_SEARCH = ""
+DEFAULT_SORT = "avg_top1"
+DEFAULT_X = "infer_samples_per_sec"
+DEFAULT_Y = "avg_top1"
+
+def update_leaderboard_and_plot(model_name=DEFAULT_SEARCH, sort_by=DEFAULT_SORT, x_axis=DEFAULT_X, y_axis=DEFAULT_Y):
+ filtered_df = filter_leaderboard(
+ full_df, # in outer scope
+ model_name,
+ sort_by,
+ )
+ fig = create_scatter_plot(filtered_df, x_axis, y_axis)
+ return filtered_df, fig
+
+
+with gr.Blocks(title="The timm Leaderboard") as app:
+ gr.HTML("<center><h1>PyTorch Image Models Leaderboard</h1></center>")
+ gr.HTML("<p>This leaderboard is based on the results of the models from <a href='https://github.com/huggingface/pytorch-image-models'>PyTorch Image Models</a>.</p>")
+ gr.HTML("<p>Search tips:<br>- Use wildcards (* or ?) for pattern matching<br>- Use 're:' prefix for regex search<br>- Otherwise, fuzzy matching will be used</p>")
+ 
+ with gr.Row():
+ search_bar = gr.Textbox(lines=1, label="Search Model", placeholder="e.g. resnet*, re:^vit, efficientnet", scale=3)
+ sort_dropdown = gr.Dropdown(choices=sort_columns, label="Sort by", value=DEFAULT_SORT, scale=1)
+ 
+ with gr.Row():
+ x_axis = gr.Dropdown(choices=plot_columns, label="X-axis", value=DEFAULT_X)
+ y_axis = gr.Dropdown(choices=plot_columns, label="Y-axis", value=DEFAULT_Y)
+ 
+ update_btn = gr.Button(value="Update", variant="primary")
+
+ leaderboard = gr.Dataframe()
+ plot = gr.Plot()
+ 
+ app.load(update_leaderboard_and_plot, outputs=[leaderboard, plot])
+
+ update_btn.click(
+ update_leaderboard_and_plot,
+ inputs=[search_bar, sort_dropdown, x_axis, y_axis],
+ outputs=[leaderboard, plot]
+ )
+
+app.launch()