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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 7h 2023"
GPU_MODEL = "NVIDIA Tesla M60"
column_names = {"AP-IoU=0.50:0.95-area=all-maxDets=100": "AP",
"AP-IoU=0.50-area=all-maxDets=100": "[email protected]",
"AP-IoU=0.75-area=all-maxDets=100": "[email protected]",
"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")
# Get csv with data and parse columns
original_df = pd.read_csv(csv_results)
# Formats the columns
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
# Drop columns not specified in dictionary
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) # For decimal values
elif col == "hub_license":
continue
else:
original_df[col] = original_df[col].apply(perc_formatter) # For % values
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):
# Determine the selected checkboxes
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}")
# Construct the output dictionary
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
}
# Prepare file path
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
# Write the results to a text file
with open(out_filepath, "w") as f:
f.write(json.dumps(eval_entry))
upload_file(filename, out_filepath)
# Include file in the list of uploaded files
requested_models.append(filename)
# Remove the local file
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()
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