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# Authors: Hui Ren (rhfeiyang.github.io)
import os
import sys
import numpy as np
from PIL import Image
import pickle
sys.path.append(os.path.join(os.path.dirname(__file__), "../../../"))
from custom_datasets import get_dataset
from utils.art_filter import Art_filter
import torch
from matplotlib import pyplot as plt
import math
import argparse
import socket
import time
from tqdm import tqdm
import torch
def parse_args():
parser = argparse.ArgumentParser(description="Filter the coco dataset")
parser.add_argument("--check", action="store_true", help="Check the complete")
parser.add_argument("--mode", default="clip_logit", help="Filter mode: clip_logit, clip_filt, caption_filt")
parser.add_argument("--split" , default="val", help="Dataset split, val/train")
# parser.add_argument("--start_idx", default=0, type=int, help="Start index")
args = parser.parse_args()
return args
def get_feat(save_path, dataloader, filter):
clip_feat_file = save_path
# compute_new = False
clip_feat={}
if os.path.exists(clip_feat_file):
with open(clip_feat_file, 'rb') as f:
clip_feat = pickle.load(f)
else:
print(f"computing clip feat",flush=True)
clip_feature_ret = filter.clip_feature(dataloader)
clip_feat["image_features"] = clip_feature_ret["clip_features"]
clip_feat["ids"] = clip_feature_ret["ids"]
with open(clip_feat_file, 'wb') as f:
pickle.dump(clip_feat, f)
print(f"clip_feat_result saved to {clip_feat_file}",flush=True)
return clip_feat
def get_clip_logit(save_root, dataloader, filter):
feat_path = os.path.join(save_root, "clip_feat.pickle")
clip_feat = get_feat(feat_path, dataloader, filter)
clip_logits_file = os.path.join(save_root, "clip_logits.pickle")
# if clip_logit:
if os.path.exists(clip_logits_file):
with open(clip_logits_file, 'rb') as f:
clip_logits = pickle.load(f)
else:
clip_logits = filter.clip_logit_by_feat(clip_feat["image_features"])
clip_logits["ids"] = clip_feat["ids"]
with open(clip_logits_file, 'wb') as f:
pickle.dump(clip_logits, f)
print(f"clip_logits_result saved to {clip_logits_file}",flush=True)
return clip_logits
def clip_filt(save_root, dataloader, filter):
clip_filt_file = os.path.join(save_root, "clip_filt_result.pickle")
if os.path.exists(clip_filt_file):
with open(clip_filt_file, 'rb') as f:
clip_filt_result = pickle.load(f)
else:
clip_logits = get_clip_logit(save_root, dataloader, filter)
clip_filt_result = filter.clip_filt(clip_logits)
with open(clip_filt_file, 'wb') as f:
pickle.dump(clip_filt_result, f)
print(f"clip_filt_result saved to {clip_filt_file}",flush=True)
return clip_filt_result
def caption_filt(save_root, dataloader, filter):
caption_filt_file = os.path.join(save_root, "caption_filt_result.pickle")
if os.path.exists(caption_filt_file):
with open(caption_filt_file, 'rb') as f:
caption_filt_result = pickle.load(f)
else:
caption_filt_result = filter.caption_filt(dataloader)
with open(caption_filt_file, 'wb') as f:
pickle.dump(caption_filt_result, f)
print(f"caption_filt_result saved to {caption_filt_file}",flush=True)
return caption_filt_result
def gather_result(save_dir, dataloader, filter):
all_remain_ids=[]
all_remain_ids_train=[]
all_remain_ids_val=[]
all_filtered_id_num = 0
clip_filt_result = clip_filt(save_dir, dataloader, filter)
caption_filt_result = caption_filt(save_dir, dataloader, filter)
caption_filtered_ids = [i[0] for i in caption_filt_result["filtered_ids"]]
all_filtered_id_num += len(set(clip_filt_result["filtered_ids"]) | set(caption_filtered_ids) )
remain_ids = set(clip_filt_result["remain_ids"]) & set(caption_filt_result["remain_ids"])
remain_ids = list(remain_ids)
remain_ids.sort()
with open(os.path.join(save_dir, "remain_ids.pickle"), 'wb') as f:
pickle.dump(remain_ids, f)
print(f"remain_ids saved to {save_dir}/remain_ids.pickle",flush=True)
return remain_ids
@torch.no_grad()
def main(args):
filter = Art_filter()
if args.mode == "caption_filt" or args.mode == "gather_result":
filter.clip_filter = None
torch.cuda.empty_cache()
# caption_folder_path = "/vision-nfs/torralba/scratch/jomat/sam_dataset/PixArt-alpha/captions"
# image_folder_path = "/vision-nfs/torralba/scratch/jomat/sam_dataset/images"
# id_dict_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/images/id_dict"
# filt_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/filt_result"
def collate_fn(examples):
# {"image": image, "id":id}
ret = {}
if "image" in examples[0]:
pixel_values = [example["image"] for example in examples]
ret["images"] = pixel_values
if "caption" in examples[0]:
# prompts = [example["caption"] for example in examples]
prompts = []
for example in examples:
if isinstance(example["caption"][0], list):
prompts.append([" ".join(example["caption"][0])])
else:
prompts.append(example["caption"])
ret["text"] = prompts
id = [example["id"] for example in examples]
ret["ids"] = id
return ret
if args.split == "val":
dataset = get_dataset("coco_val")["val"]
elif args.split == "train":
dataset = get_dataset("coco_train", get_val=False)["train"]
dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=False, num_workers=8, collate_fn=collate_fn)
error_files=[]
save_root = f"/vision-nfs/torralba/scratch/jomat/sam_dataset/coco/filt/{args.split}"
os.makedirs(save_root, exist_ok=True)
if args.mode == "clip_feat":
feat_path = os.path.join(save_root, "clip_feat.pickle")
clip_feat = get_feat(feat_path, dataloader, filter)
if args.mode == "clip_logit":
clip_logit = get_clip_logit(save_root, dataloader, filter)
if args.mode == "clip_filt":
# if os.path.exists(clip_filt_file):
# with open(clip_filt_file, 'rb') as f:
# ret = pickle.load(f)
# else:
clip_filt_result = clip_filt(save_root, dataloader, filter)
if args.mode == "caption_filt":
caption_filt_result = caption_filt(save_root, dataloader, filter)
if args.mode == "gather_result":
filtered_result = gather_result(save_root, dataloader, filter)
print("finished",flush=True)
for file in error_files:
# os.remove(file)
print(file,flush=True)
if __name__ == "__main__":
args = parse_args()
log_file = "sam_filt"
idx=0
hostname = socket.gethostname()
now_time = time.strftime("%Y-%m-%d-%H-%M-%S", time.localtime())
while os.path.exists(f"{log_file}_{hostname}_check{args.check}_{now_time}_{idx}.log"):
idx+=1
main(args)
# clip_logits_analysis()
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