File size: 6,372 Bytes
3c75092
a6e43e6
3c75092
a6e43e6
 
 
3c75092
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a6e43e6
3c75092
 
 
 
 
a6e43e6
 
3c75092
 
 
 
a6e43e6
 
3c75092
 
 
 
a6e43e6
 
3c75092
a6e43e6
3c75092
a6e43e6
3c75092
a570ac2
3c75092
a570ac2
3c75092
 
 
 
 
 
 
 
a6e43e6
a570ac2
3c75092
a6e43e6
 
3c75092
a6e43e6
3c75092
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
a6e43e6
3c75092
 
 
 
 
a6e43e6
 
3c75092
 
 
 
a6e43e6
 
3c75092
 
 
 
a6e43e6
 
3c75092
a6e43e6
3c75092
 
a6e43e6
3c75092
 
 
 
 
 
 
 
 
 
 
a6e43e6
3c75092
a6e43e6
 
3c75092
a6e43e6
3c75092
 
 
 
a6e43e6
 
 
 
3c75092
 
a6e43e6
3c75092
a6e43e6
3c75092
 
 
 
a6e43e6
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
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('πŸ… OpenVLM Main Leaderboard', elem_id='main', id=0):
            gr.Markdown(LEADERBOARD_MD['MAIN'])
            table, check_box = BUILD_L1_DF(results, MAIN_FIELDS)
            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 = cp.deepcopy(table)
                df['flag'] = [model_size_flag(x, model_size) for x in df['Parameters (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')

                df = generate_table(results, new_fields, df)
                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('πŸ” About', elem_id='about', id=1):
            gr.Markdown(urlopen(VLMEVALKIT_README).read().decode())

        for i, dataset in enumerate(DATASETS):
            with gr.TabItem(f'πŸ“Š {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['Parameters (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')