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CPU Upgrade
| import abc | |
| import gradio as gr | |
| from gen_table import * | |
| from meta_data import * | |
| with gr.Blocks() as demo: | |
| struct = load_results() | |
| timestamp = struct['time'] | |
| EVAL_TIME = format_timestamp(timestamp) | |
| results = struct['results'] | |
| N_MODEL = len(results) | |
| N_DATA = len(results['LLaVA-v1.5-7B']) - 1 | |
| DATASETS = list(results['LLaVA-v1.5-7B']) | |
| DATASETS.remove('META') | |
| print(DATASETS) | |
| gr.Markdown(LEADERBORAD_INTRODUCTION.format(N_MODEL, N_DATA, EVAL_TIME)) | |
| structs = [abc.abstractproperty() for _ in range(N_DATA)] | |
| with gr.Tabs(elem_classes='tab-buttons') as tabs: | |
| with gr.TabItem('🏅 OpenVLM Main Leaderboard', elem_id='main', id=0): | |
| gr.Markdown(LEADERBOARD_MD['MAIN']) | |
| _, check_box = BUILD_L1_DF(results, MAIN_FIELDS) | |
| table = generate_table(results, DEFAULT_BENCH) | |
| type_map = check_box['type_map'] | |
| checkbox_group = gr.CheckboxGroup( | |
| choices=check_box['all'], | |
| value=check_box['required'], | |
| label='Evaluation Dimension', | |
| interactive=True, | |
| ) | |
| headers = check_box['essential'] + checkbox_group.value | |
| with gr.Row(): | |
| model_size = gr.CheckboxGroup( | |
| choices=MODEL_SIZE, | |
| value=MODEL_SIZE, | |
| label='Model Size', | |
| interactive=True | |
| ) | |
| model_type = gr.CheckboxGroup( | |
| choices=MODEL_TYPE, | |
| value=MODEL_TYPE, | |
| label='Model Type', | |
| interactive=True | |
| ) | |
| data_component = gr.components.DataFrame( | |
| value=table[headers], | |
| type='pandas', | |
| datatype=[type_map[x] for x in headers], | |
| interactive=False, | |
| visible=True) | |
| def filter_df(fields, model_size, model_type): | |
| filter_list = ['Avg Score', 'Avg Rank', 'OpenSource', 'Verified'] | |
| headers = check_box['essential'] + fields | |
| new_fields = [field for field in fields if field not in filter_list] | |
| df = generate_table(results, new_fields) | |
| df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']] | |
| df = df[df['flag']] | |
| df.pop('flag') | |
| if len(df): | |
| df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))] | |
| df = df[df['flag']] | |
| df.pop('flag') | |
| comp = gr.components.DataFrame( | |
| value=df[headers], | |
| type='pandas', | |
| datatype=[type_map[x] for x in headers], | |
| interactive=False, | |
| visible=True) | |
| return comp | |
| for cbox in [checkbox_group, model_size, model_type]: | |
| cbox.change(fn=filter_df, inputs=[checkbox_group, model_size, model_type], outputs=data_component) | |
| with gr.TabItem('🔍 About', elem_id='about', id=1): | |
| gr.Markdown(urlopen(VLMEVALKIT_README).read().decode()) | |
| for i, dataset in enumerate(DATASETS): | |
| with gr.TabItem(f'📊 {dataset} Leaderboard', elem_id=dataset, id=i + 2): | |
| if dataset in LEADERBOARD_MD: | |
| gr.Markdown(LEADERBOARD_MD[dataset]) | |
| s = structs[i] | |
| s.table, s.check_box = BUILD_L2_DF(results, dataset) | |
| s.type_map = s.check_box['type_map'] | |
| s.checkbox_group = gr.CheckboxGroup( | |
| choices=s.check_box['all'], | |
| value=s.check_box['required'], | |
| label=f'{dataset} CheckBoxes', | |
| interactive=True, | |
| ) | |
| s.headers = s.check_box['essential'] + s.checkbox_group.value | |
| with gr.Row(): | |
| s.model_size = gr.CheckboxGroup( | |
| choices=MODEL_SIZE, | |
| value=MODEL_SIZE, | |
| label='Model Size', | |
| interactive=True | |
| ) | |
| s.model_type = gr.CheckboxGroup( | |
| choices=MODEL_TYPE, | |
| value=MODEL_TYPE, | |
| label='Model Type', | |
| interactive=True | |
| ) | |
| s.data_component = gr.components.DataFrame( | |
| value=s.table[s.headers], | |
| type='pandas', | |
| datatype=[s.type_map[x] for x in s.headers], | |
| interactive=False, | |
| visible=True) | |
| s.dataset = gr.Textbox(value=dataset, label=dataset, visible=False) | |
| def filter_df_l2(dataset_name, fields, model_size, model_type): | |
| s = structs[DATASETS.index(dataset_name)] | |
| headers = s.check_box['essential'] + fields | |
| df = cp.deepcopy(s.table) | |
| df['flag'] = [model_size_flag(x, model_size) for x in df['Param (B)']] | |
| df = df[df['flag']] | |
| df.pop('flag') | |
| if len(df): | |
| df['flag'] = [model_type_flag(df.iloc[i], model_type) for i in range(len(df))] | |
| df = df[df['flag']] | |
| df.pop('flag') | |
| comp = gr.components.DataFrame( | |
| value=df[headers], | |
| type='pandas', | |
| datatype=[s.type_map[x] for x in headers], | |
| interactive=False, | |
| visible=True) | |
| return comp | |
| for cbox in [s.checkbox_group, s.model_size, s.model_type]: | |
| cbox.change( | |
| fn=filter_df_l2, | |
| inputs=[s.dataset, s.checkbox_group, s.model_size, s.model_type], | |
| outputs=s.data_component) | |
| with gr.Row(): | |
| with gr.Accordion('Citation', open=False): | |
| citation_button = gr.Textbox( | |
| value=CITATION_BUTTON_TEXT, | |
| label=CITATION_BUTTON_LABEL, | |
| elem_id='citation-button') | |
| if __name__ == '__main__': | |
| demo.launch(server_name='0.0.0.0') | |