|
import gradio as gr |
|
import pandas as pd |
|
import json |
|
from constants import BANNER, INTRODUCTION_TEXT, CITATION_TEXT, METRICS_TAB_TEXT, DIR_OUTPUT_REQUESTS |
|
from init import is_model_on_hub, upload_file, load_all_info_from_dataset_hub |
|
from utils_display import AutoEvalColumn, fields, make_clickable_model, styled_error, styled_message |
|
from datetime import datetime, timezone |
|
|
|
LAST_UPDATED = "September, 7th 2023" |
|
GPU_MODEL = "NVIDIA Tesla M60" |
|
|
|
column_names = {"model": "model", |
|
"AP-IoU=0.50:0.95-area=all-maxDets=100": "AP", |
|
"AP-IoU=0.50-area=all-maxDets=100": "AP@.50", |
|
"AP-IoU=0.75-area=all-maxDets=100": "AP@.75", |
|
"AP-IoU=0.50:0.95-area=small-maxDets=100" : "AP-S", |
|
"AP-IoU=0.50:0.95-area=medium-maxDets=100": "AP-M", |
|
"AP-IoU=0.50:0.95-area=large-maxDets=100": "AP-L", |
|
"AR-IoU=0.50:0.95-area=all-maxDets=1": "AR1", |
|
"AR-IoU=0.50:0.95-area=all-maxDets=10": "AR10", |
|
"AR-IoU=0.50:0.95-area=all-maxDets=100": "AR100", |
|
"AR-IoU=0.50:0.95-area=small-maxDets=100": "AR-S", |
|
"AR-IoU=0.50:0.95-area=medium-maxDets=100": "AR-M", |
|
"AR-IoU=0.50:0.95-area=large-maxDets=100": "AR-L", |
|
"estimated_fps": "FPS(*)", |
|
"hub_license": "hub license", |
|
} |
|
|
|
eval_queue_repo, requested_models, csv_results = load_all_info_from_dataset_hub() |
|
|
|
if not csv_results.exists(): |
|
raise Exception(f"CSV file {csv_results} does not exist locally") |
|
|
|
|
|
original_df = pd.read_csv(csv_results) |
|
lst_evaluated_models = original_df["model"].tolist() |
|
lst_evaluated_models = list(map(str.lower, lst_evaluated_models)) |
|
|
|
|
|
def decimal_formatter(x): |
|
x = "{:.2f}".format(x) |
|
return x |
|
|
|
def perc_formatter(x): |
|
x = "{:.2%}".format(x) |
|
while len(x) < 6: |
|
x = f"0{x}" |
|
return x |
|
|
|
|
|
cols_to_drop = [col for col in original_df.columns if col not in column_names] |
|
original_df.drop(cols_to_drop, axis=1, inplace=True) |
|
|
|
for col in original_df.columns: |
|
if col == "model": |
|
original_df[col] = original_df[col].apply(lambda x: x.replace(x, make_clickable_model(x))) |
|
elif col == "estimated_fps": |
|
original_df[col] = original_df[col].apply(decimal_formatter) |
|
elif col == "hub_license": |
|
continue |
|
else: |
|
original_df[col] = original_df[col].apply(perc_formatter) |
|
|
|
original_df.rename(columns=column_names, inplace=True) |
|
|
|
COLS = [c.name for c in fields(AutoEvalColumn)] |
|
TYPES = [c.type for c in fields(AutoEvalColumn)] |
|
|
|
def request_model(model_text, chbcoco2017): |
|
|
|
|
|
dataset_selection = [] |
|
if chbcoco2017: |
|
dataset_selection.append("COCO validation 2017 dataset") |
|
|
|
if len(dataset_selection) == 0: |
|
return styled_error("You need to select at least one dataset") |
|
|
|
|
|
base_model_on_hub, error_msg = is_model_on_hub(model_text) |
|
if not base_model_on_hub: |
|
return styled_error(f"Base model '{model_text}' {error_msg}") |
|
|
|
|
|
model_text = model_text.replace(" ","") |
|
if model_text.lower() in lst_evaluated_models: |
|
return styled_error(f"Results of the model '{model_text}' are now ready and available.") |
|
|
|
|
|
current_time = datetime.now(timezone.utc).strftime("%Y-%m-%dT%H:%M:%SZ") |
|
required_datasets = ', '.join(dataset_selection) |
|
eval_entry = { |
|
"date": current_time, |
|
"model": model_text, |
|
"datasets_selected": required_datasets |
|
} |
|
|
|
|
|
DIR_OUTPUT_REQUESTS.mkdir(parents=True, exist_ok=True) |
|
|
|
fn_datasets = '@ '.join(dataset_selection) |
|
filename = model_text.replace("/","@") + "@@" + fn_datasets |
|
if filename in requested_models: |
|
return styled_error(f"A request for this model '{model_text}' and dataset(s) was already made.") |
|
try: |
|
filename_ext = filename + ".txt" |
|
out_filepath = DIR_OUTPUT_REQUESTS / filename_ext |
|
|
|
|
|
with open(out_filepath, "w") as f: |
|
f.write(json.dumps(eval_entry)) |
|
|
|
upload_file(filename, out_filepath) |
|
|
|
|
|
requested_models.append(filename) |
|
|
|
|
|
out_filepath.unlink() |
|
|
|
return styled_message("🤗 Your request has been submitted and will be evaluated soon!</p>") |
|
except Exception as e: |
|
return styled_error(f"Error submitting request!") |
|
|
|
with gr.Blocks() as demo: |
|
gr.HTML(BANNER, elem_id="banner") |
|
gr.Markdown(INTRODUCTION_TEXT, elem_classes="markdown-text") |
|
|
|
with gr.Tabs(elem_classes="tab-buttons") as tabs: |
|
with gr.TabItem("🏅 COCO val 2017", elem_id="od-benchmark-tab-table", id=0): |
|
leaderboard_table = gr.components.Dataframe( |
|
value=original_df, |
|
datatype=TYPES, |
|
max_rows=None, |
|
elem_id="leaderboard-table", |
|
interactive=False, |
|
visible=True, |
|
) |
|
|
|
with gr.TabItem("📈 Metrics", elem_id="od-benchmark-tab-table", id=1): |
|
gr.Markdown(METRICS_TAB_TEXT, elem_classes="markdown-text") |
|
|
|
with gr.TabItem("✉️✨ Request a model here!", elem_id="od-benchmark-tab-table", id=2): |
|
with gr.Column(): |
|
gr.Markdown("# ✉️✨ Request results for a new model here!", elem_classes="markdown-text") |
|
with gr.Column(): |
|
gr.Markdown("Select a dataset:", elem_classes="markdown-text") |
|
with gr.Column(): |
|
model_name_textbox = gr.Textbox(label="Model name (user_name/model_name)") |
|
chb_coco2017 = gr.Checkbox(label="COCO validation 2017 dataset", visible=False, value=True, interactive=False) |
|
with gr.Column(): |
|
mdw_submission_result = gr.Markdown() |
|
btn_submitt = gr.Button(value="🚀 Request") |
|
btn_submitt.click(request_model, |
|
[model_name_textbox, chb_coco2017], |
|
mdw_submission_result) |
|
|
|
gr.Markdown(f"(*) FPS was measured using *{GPU_MODEL}* processing 1 image per batch. Refer to the 📈 \"Metrics\" tab for further details.", elem_classes="markdown-text") |
|
gr.Markdown(f"Last updated on **{LAST_UPDATED}**", elem_classes="markdown-text") |
|
|
|
with gr.Row(): |
|
with gr.Accordion("📙 Citation", open=False): |
|
gr.Textbox( |
|
value=CITATION_TEXT, lines=7, |
|
label="Copy the BibTeX snippet to cite this source", |
|
elem_id="citation-button", |
|
).style(show_copy_button=True) |
|
|
|
demo.launch() |
|
|
