olympiad/MLAgentBench/benchmarks/fathomnet/scripts/prepare.py

import subprocess
import pandas as pd
import json

taskname = "fathomnet-out-of-sample-detection"
# download_dir = f"benchmarks/{taskname}/env"
download_dir = "benchmarks/" + taskname + "/env"

input(f"Consent to the competition at https://www.kaggle.com/competitions/{taskname}/data; Press any key after you have accepted the rules online.")

subprocess.run(["kaggle", "competitions", "download", "-c", taskname], cwd=download_dir) 
subprocess.run(["unzip", "-n", f"{taskname}.zip"], cwd=download_dir) 
subprocess.run(["rm", f"{taskname}.zip"], cwd=download_dir) 

### download images
input(f"""Download large amount of images to current directory by doing this:

conda create -n fgvc_test python=3.9 pip
conda activate fgvc_test
pip install -r requirements.txt
python download_images.py ../env/object_detection/train.json --outpath ../env/images

Press any key after done""")


subprocess.run(["rm", "download_images.py"], cwd=download_dir)
subprocess.run(["rm", "demo_download.ipynb"], cwd=download_dir)
subprocess.run(["rm", "requirements.txt"], cwd=download_dir)

# ## split train to train and test in env
trainset = pd.read_csv(f"{download_dir}/multilabel_classification/train.csv")
trainset = trainset.sample(frac=1, random_state=42)
trainset = trainset.reset_index(drop=True)
trainset.iloc[:int(len(trainset)*0.98)].to_csv(f"{download_dir}/multilabel_classification/train.csv", index=False)
testset = trainset.iloc[int(len(trainset)*0.98):]
# split testset to only full_text and labels
testset.to_csv(f"answer.csv", index=False)

# split train json
orig_train_json = json.load(open(f"{download_dir}/object_detection/train.json"))
test_json = json.load(open(f"{download_dir}/object_detection/eval.json"))

# split train_json according to trainset
train_json = orig_train_json.copy()
train_json["images"] = [x for x in orig_train_json["images"] if x["file_name"][:-4] not in testset["id"].values]
images_ids = [x["id"] for x in train_json["images"]]
train_json["annotations"] = [x for x in orig_train_json["annotations"] if x["image_id"] in images_ids]

test_json["images"] = [x for x in orig_train_json["images"] if x["file_name"][:-4] in testset["id"].values]
# relabel ids
for i, x in enumerate(train_json["images"]):
    for y in train_json["annotations"]:
        if y["image_id"] == x["id"]:
            y["image_id"] = i + 1
    x["id"] = i + 1

for i, x in enumerate(train_json["annotations"]):
    x["id"] = i + 1

for i, x in enumerate(test_json["images"]):
    x["id"] = i + 1



# write train_json and test_json
with open(f"{download_dir}/object_detection/train.json", "w") as f:
    json.dump(train_json, f, indent=4)
    
with open(f"{download_dir}/object_detection/eval.json", "w") as f:
    json.dump(test_json, f, indent=4)