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Zero
File size: 6,426 Bytes
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import fire
import json
import os
import datasets
import datetime
from pathlib import Path
from datetime import datetime
from PIL import Image
datasets.config.DEFAULT_MAX_BATCH_SIZE = 500
def create_hf_dataset(data_file: str, split="test"):
hf_dataset = datasets.Dataset.from_list(
data_file,
features=datasets.Features(
{
"question_id": datasets.Value("string"),
"model": datasets.Value("string"),
"conversation": [
{
"role": datasets.Value("string"),
"content": datasets.Value("string"),
}
],
"language": datasets.Value("string"),
"image": datasets.Image(),
"turn": datasets.Value("int32"),
}
),
split=split,
)
return hf_dataset
def create_hf_battle_dataset(data_file: str, split="test"):
hf_dataset = datasets.Dataset.from_list(
data_file,
features=datasets.Features(
{
"question_id": datasets.Value("string"),
"model_a": datasets.Value("string"),
"model_b": datasets.Value("string"),
"conversation_a": [
{
"role": datasets.Value("string"),
"content": datasets.Value("string"),
}
],
"conversation_b": [
{
"role": datasets.Value("string"),
"content": datasets.Value("string"),
}
],
"language": datasets.Value("string"),
"image": datasets.Image(),
"turn": datasets.Value("int32"),
"anony": datasets.Value("bool"),
}
),
split=split,
)
return hf_dataset
def load_image(path:str):
try:
return Image.open(path)
except Exception as e:
print(f"Error loading image {path}: {e}")
return None
def get_date_from_time_stamp(unix_timestamp: int):
# Create a datetime object from the Unix timestamp
dt = datetime.fromtimestamp(unix_timestamp)
# Convert the datetime object to a string with the desired format
date_str = dt.strftime("%Y-%m-%d")
return date_str
def load_battle_image(battle, log_dir):
image_path = Path(log_dir) / f"{get_date_from_time_stamp(battle['tstamp'])}-convinput_images" / f"input_image_{battle['question_id']}.png"
return load_image(image_path)
def main(
data_file: str = "./results/latest/clean_battle_conv.json",
repo_id: str = "DongfuTingle/wildvision-bench",
log_dir: str = os.getenv("LOGDIR", "./vision-arena-logs/"),
mode="battle",
token = os.environ.get("HUGGINGFACE_TOKEN", None)
):
with open(data_file, "r") as f:
data = json.load(f)
has_image_stats = {
"has_image": 0,
"no_image": 0,
}
if mode == "keep_bad_only":
# anony only
data = [d for d in data if d["anony"]]
new_data = []
for battle in data:
image = load_battle_image(battle, log_dir)
if image is None:
has_image_stats["no_image"] += 1
# we don't keep the data without image
continue
has_image_stats["has_image"] += 1
if battle["winner"] in ["model_a", "model_b"]:
if battle["winner"] == "model_a":
worse_model = "model_b"
worse_conv = "conversation_b"
if battle["winner"] == "model_b":
worse_model = "model_a"
worse_conv = "conversation_a"
new_data.append({
"question_id": battle["question_id"],
"model": battle[worse_model],
"conversation": battle[worse_conv],
"language": battle["language"],
"image": image,
"turn": battle["turn"],
})
elif battle["winner"] == "tie (bothbad)":
new_data.append({
"question_id": battle["question_id"],
"model": battle["model_a"],
"conversation": battle["conversation_a"],
"language": battle["language"],
"image": image,
"turn": battle["turn"],
})
new_data.append({
"question_id": battle["question_id"],
"model": battle["model_b"],
"conversation": battle["conversation_b"],
"language": battle["language"],
"image": image,
"turn": battle["turn"],
})
split = "test"
hf_dataset = create_hf_dataset(new_data, "test")
elif mode == "battle":
new_data = []
for battle in data:
image = load_battle_image(battle, log_dir)
if image is None:
has_image_stats["no_image"] += 1
continue
has_image_stats["has_image"] += 1
new_data.append({
"question_id": battle["question_id"],
"model_a": battle["model_a"],
"model_b": battle["model_b"],
"conversation_a": battle["conversation_a"],
"conversation_b": battle["conversation_b"],
"language": battle["language"],
"image": image,
"turn": battle["turn"],
"anony": battle["anony"],
})
split = "test"
hf_dataset = create_hf_battle_dataset(new_data, "test")
else:
raise ValueError(f"Invalid mode: {mode}")
print(f"Stats: {has_image_stats}")
print(hf_dataset)
print(f"Uploading to part {repo_id}:{split}...")
hf_dataset.push_to_hub(
repo_id=repo_id,
config_name=mode,
split=split,
token=token,
commit_message=f"Add vision-arena {split} dataset",
)
print("Done!")
if __name__ == "__main__":
fire.Fire(main) |