app.py

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 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')]
    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)

    result['highlighted'] = False
    
    # 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, model_filter, highlight_filter):
    selected_color = 'orange'
    
    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),
        color='highlighted',
        color_discrete_map={True: selected_color, False: 'blue'},
        title=f'{y_axis} vs {x_axis}'
    )

    # Create legend labels
    legend_labels = {}
    if highlight_filter:
        legend_labels[True] = f'{highlight_filter}'
        legend_labels[False] = f'{model_filter or "all models"}'
    else:
        legend_labels[False] = f'{model_filter or "all models"}'
    
    # Update legend
    for trace in fig.data:
        if isinstance(trace.marker.color, str):  # This is for the scatter traces
            trace.name = legend_labels.get(trace.marker.color == selected_color, '')
    
    fig.update_layout(
        showlegend=True,
        legend_title_text='Model Selection'
    )
    
    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', '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"
DEFAULT_X = "infer_samples_per_sec"
DEFAULT_Y = "avg_top1"

def update_leaderboard_and_plot(
        model_name=DEFAULT_SEARCH,
        highlight_name=None,
        sort_by=DEFAULT_SORT,
        x_axis=DEFAULT_X,
        y_axis=DEFAULT_Y,
):
    filtered_df = filter_leaderboard(full_df, model_name, sort_by)    
    
    # Apply the highlight filter to the entire dataset so the output will be union (comparison) if the filters are disjoint
    highlight_df = filter_leaderboard(full_df, highlight_name, sort_by) if highlight_name else None
    
    # Combine filtered_df and highlight_df, removing duplicates
    if highlight_df is not None:
        combined_df = pd.concat([filtered_df, highlight_df]).drop_duplicates().reset_index(drop=True)
        combined_df = combined_df.sort_values(by=sort_by, ascending=False)
        combined_df['highlighted'] = combined_df['model'].isin(highlight_df['model'])
    else:
        combined_df = filtered_df
           
    fig = create_scatter_plot(combined_df, x_axis, y_axis, model_name, highlight_name)
    display_df = combined_df.drop(columns=['highlighted'])
    display_df = display_df.style.apply(lambda x: ['background-color: #FFA500' if combined_df.loc[x.name, 'highlighted'] else '' for _ in x], axis=1).format(precision=2)
    return display_df, fig


with gr.Blocks(title="The timm Leaderboard") as app:
    gr.HTML("<center><h1>The timm (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'>timm</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="Model Filter", 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():
        highlight_bar = gr.Textbox(lines=1, label="Model Highlight/Compare Filter", placeholder="e.g. convnext*, re:^efficient")
    
    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])
    
    search_bar.submit(
        update_leaderboard_and_plot,
        inputs=[search_bar, highlight_bar, sort_dropdown, x_axis, y_axis],
        outputs=[leaderboard, plot]
    )
    highlight_bar.submit(
        update_leaderboard_and_plot,
        inputs=[search_bar, highlight_bar, sort_dropdown, x_axis, y_axis],
        outputs=[leaderboard, plot]
    )
    update_btn.click(
        update_leaderboard_and_plot,
        inputs=[search_bar, highlight_bar, sort_dropdown, x_axis, y_axis],
        outputs=[leaderboard, plot]
    )

app.launch()