Delete IR_benchmark

IR_benchmark/run_for_laion.py

@@ -1,75 +0,0 @@
-# CUDA_VISIBLE_DEVICES=1 python run_for_laion.py
-import os, sys 
-from src import Scorer
-from PIL import Image, ImageOps 
-import pyarrow.parquet as pq 
-import requests 
-from io import BytesIO
-import pandas as pd 
-import tarfile
-from tqdm import tqdm 
-import json
-
-# [0]: dataset 
-# ====================================================================
-def iter_tar_images(tar):
-    buffer = {}
-    for member in tar:
-        base, ext = os.path.splitext(member.name)
-        ext = ext.lower()
-        if base not in buffer:
-            buffer[base] = {}
-            
-        if ext == ".json":
-            f = tar.extractfile(member)
-            if f is None:
-                continue
-            meta = json.load(f)
-            buffer[base]["meta"] = meta
-            buffer[base]["meta_file"] = member.name
-        elif member.name.lower().endswith((".jpg", ".jpeg", ".png")):
-            f = tar.extractfile(member)
-            if f is None:
-                continue           
-            img = Image.open(f).convert("RGB")
-            img = ImageOps.exif_transpose(img)
-            buffer[base]["img"] = img 
-            buffer[base]["img_file"] = member.name
-
-        if base in buffer and "img" in buffer[base] and "meta" in buffer[base]:
-            yield buffer[base]["img"], buffer[base]["meta"], buffer[base]["img_file"], buffer[base]["meta_file"], base
-            del buffer[base]
-
-subset = "00000"
-data_rt = f"/scratch1/datasets/LAION/{subset}"
-filename = [
-    f  #     os.path.splitext(f)[0]
-    for f in os.listdir(data_rt)
-    if os.path.isfile(os.path.join(data_rt, f)) and ".tar" in f
-]
-filename = sorted(filename)
-
-save_rt = f"/scratch1/users/hsiang.chen/IR_Benchmark/data/LAION_label/{subset}"
-os.makedirs(save_rt, exist_ok=True)
-scorer = Scorer()
-for idx, tar_file in enumerate(filename):
-    results = []
-    ct = 0
-    tar = tarfile.open(os.path.join(data_rt, tar_file), "r")
-    print(f"data:{idx}/{len(filename)}, filename: {tar_file}, # of files: {len(tar.getmembers())}")
-    for img, meta, img_file, meta_file, filename in tqdm(iter_tar_images(tar)):
-        img_list = [img] # can be a list of multiple PIL images
-        output_score = scorer(img_list).tolist()
-        # print(img, meta, filename, output_score)
-        meta["depa_score"] = output_score[0]
-        meta["source"] = os.path.join(data_rt, tar_file)
-        meta["img_file"] = img_file
-        meta["meta_file"] = meta_file
-        results.append(meta)
-        ct += 1
-    tar.close()
-
-    splitname, splitext = os.path.splitext(tar_file)
-    df = pd.DataFrame(results)
-    df.to_parquet(os.path.join(save_rt, f"{splitname}.parquet"))
-