Spaces:
Running
on
Zero
Running
on
Zero
File size: 14,458 Bytes
262b155 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 |
# 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.sam import SamDataset
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
def parse_args():
parser = argparse.ArgumentParser(description="Filter the sam dataset")
parser.add_argument("--check", action="store_true", help="Check the complete")
parser.add_argument("--mode", default="clip_logit", choices=["clip_logit_update","clip_logit", "clip_filt", "caption_filt", "gather_result","caption_flit_append"])
parser.add_argument("--start_idx", default=0, type=int, help="Start index")
parser.add_argument("--end_idx", default=9e10, type=int, help="Start index")
args = parser.parse_args()
return args
@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 = "/afs/csail.mit.edu/u/h/huiren/code/diffusion/stable_diffusion/clip_dissection/SAM/subset/captions"
image_folder_path = "/vision-nfs/torralba/scratch/jomat/sam_dataset/nfs-data/sam/images"
id_dict_dir = "/vision-nfs/torralba/scratch/jomat/sam_dataset/sam_ids/8.16/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 "text" in examples[0]:
prompts = [example["text"] for example in examples]
ret["text"] = prompts
id = [example["id"] for example in examples]
ret["ids"] = id
return ret
error_files=[]
val_set = ["sa_000000"]
result_check_set = ["sa_000020"]
all_remain_ids=[]
all_remain_ids_train=[]
all_remain_ids_val=[]
all_filtered_id_num = 0
remain_feat_num = 0
remain_caption_num = 0
filter_feat_num = 0
filter_caption_num = 0
for idx,file in tqdm(enumerate(sorted(os.listdir(id_dict_dir)))):
if idx < args.start_idx or idx >= args.end_idx:
continue
if file.endswith(".pickle") and not file.startswith("all"):
print("=====================================")
print(file,flush=True)
save_dir = os.path.join(filt_dir, file.replace("_id_dict.pickle", ""))
if not os.path.exists(save_dir):
os.makedirs(save_dir, exist_ok=True)
id_dict_file = os.path.join(id_dict_dir, file)
with open(id_dict_file, 'rb') as f:
id_dict = pickle.load(f)
ids = list(id_dict.keys())
dataset = SamDataset(image_folder_path, caption_folder_path, id_file=ids, id_dict_file=id_dict_file)
# dataset = SamDataset(image_folder_path, caption_folder_path, id_file=[10061410, 10076945, 10310013,1042012, 4487809, 4541052], id_dict_file="/vision-nfs/torralba/scratch/jomat/sam_dataset/images/id_dict/all_id_dict.pickle")
dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=False, num_workers=8, collate_fn=collate_fn)
clip_logits = None
clip_logits_file = os.path.join(save_dir, "clip_logits_result.pickle")
clip_filt_file = os.path.join(save_dir, "clip_filt_result.pickle")
caption_filt_file = os.path.join(save_dir, "caption_filt_result.pickle")
if args.mode == "clip_feat":
compute_new = False
clip_logits = {}
if os.path.exists(clip_logits_file):
with open(clip_logits_file, 'rb') as f:
clip_logits = pickle.load(f)
if "image_features" not in clip_logits:
compute_new = True
else:
compute_new=True
if compute_new:
if clip_logits == '':
clip_logits = {}
print(f"compute clip_feat {file}",flush=True)
clip_feature_ret = filter.clip_feature(dataloader)
clip_logits["image_features"] = clip_feature_ret["clip_features"]
if "ids" in clip_logits:
assert clip_feature_ret["ids"] == clip_logits["ids"]
else:
clip_logits["ids"] = clip_feature_ret["ids"]
with open(clip_logits_file, 'wb') as f:
pickle.dump(clip_logits, f)
print(f"clip_feat_result saved to {clip_logits_file}",flush=True)
else:
print(f"skip {clip_logits_file}",flush=True)
if args.mode == "clip_logit":
# if clip_logit:
if os.path.exists(clip_logits_file):
try:
with open(clip_logits_file, 'rb') as f:
clip_logits = pickle.load(f)
except:
continue
skip = True
if args.check and clip_logits=="":
skip = False
else:
skip = False
# skip = False
if not skip:
# os.makedirs(os.path.join(save_dir, "tmp"), exist_ok=True)
with open(clip_logits_file, 'wb') as f:
pickle.dump("", f)
try:
clip_logits = filter.clip_logit(dataloader)
except:
print(f"Error in clip_logit {file}",flush=True)
continue
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)
else:
print(f"skip {clip_logits_file}",flush=True)
if args.mode == "clip_logit_update":
if os.path.exists(clip_logits_file):
with open(clip_logits_file, 'rb') as f:
clip_logits = pickle.load(f)
else:
print(f"{clip_logits_file} not exist",flush=True)
continue
if clip_logits == "":
print(f"skip {clip_logits_file}",flush=True)
continue
ret = filter.clip_logit_by_feat(clip_logits["clip_features"])
# assert (clip_logits["clip_logits"] - ret["clip_logits"]).abs().max() < 0.01
clip_logits["clip_logits"] = ret["clip_logits"]
clip_logits["text"] = ret["text"]
with open(clip_logits_file, 'wb') as f:
pickle.dump(clip_logits, f)
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:
if clip_logits is None:
try:
with open(clip_logits_file, 'rb') as f:
clip_logits = pickle.load(f)
except:
print(f"Error in loading {clip_logits_file}",flush=True)
error_files.append(clip_logits_file)
continue
if clip_logits == "":
print(f"skip {clip_logits_file}",flush=True)
error_files.append(clip_logits_file)
continue
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)
if args.mode == "caption_filt":
if os.path.exists(caption_filt_file):
try:
with open(caption_filt_file, 'rb') as f:
ret = pickle.load(f)
except:
continue
skip = True
if args.check and ret=="":
skip = False
# os.remove(caption_filt_file)
print(f"empty {caption_filt_file}",flush=True)
# skip = True
else:
skip = False
if not skip:
with open(caption_filt_file, 'wb') as f:
pickle.dump("", f)
# try:
ret = filter.caption_filt(dataloader)
# except:
# print(f"Error in filtering {file}",flush=True)
# continue
with open(caption_filt_file, 'wb') as f:
pickle.dump(ret, f)
print(f"caption_filt_result saved to {caption_filt_file}",flush=True)
else:
print(f"skip {caption_filt_file}",flush=True)
if args.mode == "caption_flit_append":
if not os.path.exists(caption_filt_file):
print(f"{caption_filt_file} not exist",flush=True)
continue
with open(caption_filt_file, 'rb') as f:
old_caption_filt_result = pickle.load(f)
skip = True
for i in filter.caption_filter.filter_prompts:
if i not in old_caption_filt_result["filter_prompts"]:
skip = False
break
if skip:
print(f"skip {caption_filt_file}",flush=True)
continue
old_remain_ids = old_caption_filt_result["remain_ids"]
new_dataset = SamDataset(image_folder_path, caption_folder_path, id_file=old_remain_ids, id_dict_file=id_dict_file)
new_dataloader = torch.utils.data.DataLoader(new_dataset, batch_size=64, shuffle=False, num_workers=8, collate_fn=collate_fn)
ret = filter.caption_filt(new_dataloader)
old_caption_filt_result["remain_ids"] = ret["remain_ids"]
old_caption_filt_result["filtered_ids"].extend(ret["filtered_ids"])
new_filter_count = ret["filter_count"].copy()
for i in range(len(old_caption_filt_result["filter_count"])):
new_filter_count[i] += old_caption_filt_result["filter_count"][i]
old_caption_filt_result["filter_count"] = new_filter_count
old_caption_filt_result["filter_prompts"] = ret["filter_prompts"]
with open(caption_filt_file, 'wb') as f:
pickle.dump(old_caption_filt_result, f)
if args.mode == "gather_result":
with open(clip_filt_file, 'rb') as f:
clip_filt_result = pickle.load(f)
with open(caption_filt_file, 'rb') as f:
caption_filt_result = pickle.load(f)
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_feat_num += len(clip_filt_result["remain_ids"])
remain_caption_num += len(caption_filt_result["remain_ids"])
filter_feat_num += len(clip_filt_result["filtered_ids"])
filter_caption_num += len(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)
all_remain_ids.extend(remain_ids)
if file.replace("_id_dict.pickle","") in val_set:
all_remain_ids_val.extend(remain_ids)
else:
all_remain_ids_train.extend(remain_ids)
if args.mode == "gather_result":
print(f"filtered ids: {all_filtered_id_num}",flush=True)
print(f"remain feat num: {remain_feat_num}",flush=True)
print(f"remain caption num: {remain_caption_num}",flush=True)
print(f"filter feat num: {filter_feat_num}",flush=True)
print(f"filter caption num: {filter_caption_num}",flush=True)
all_remain_ids.sort()
with open(os.path.join(filt_dir, "all_remain_ids.pickle"), 'wb') as f:
pickle.dump(all_remain_ids, f)
with open(os.path.join(filt_dir, "all_remain_ids_train.pickle"), 'wb') as f:
pickle.dump(all_remain_ids_train, f)
with open(os.path.join(filt_dir, "all_remain_ids_val.pickle"), 'wb') as f:
pickle.dump(all_remain_ids_val, f)
print(f"all_remain_ids saved to {filt_dir}/all_remain_ids.pickle",flush=True)
print(f"all_remain_ids_train saved to {filt_dir}/all_remain_ids_train.pickle",flush=True)
print(f"all_remain_ids_val saved to {filt_dir}/all_remain_ids_val.pickle",flush=True)
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()
|