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inference.py
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inference.py
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# Copyright(c) 2022 Liang Zhang
# E-Mail: <zhangliang00@ruc.edu.cn>
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import clip
import json
import torch
import random
import pickle
import logging
import argparse
import transformers
import numpy as np
import torch.nn as nn
from collections import OrderedDict
from tqdm import tqdm
from dataloader import get_it_loader
from evaluate import eval_epoch
# from sentence_transformers import SentenceTransformer
from train import merge_dict
os.environ["TOKENIZERS_PARALLELISM"] = "false"
# torch.distributed.init_process_group(backend="nccl")
def get_logger(filename=None):
logger = logging.getLogger('logger')
logger.setLevel(logging.DEBUG)
logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
level=logging.INFO)
if filename is not None:
handler = logging.FileHandler(filename)
handler.setLevel(logging.DEBUG)
handler.setFormatter(logging.Formatter('%(asctime)s:%(levelname)s: %(message)s'))
logging.getLogger().addHandler(handler)
return logger
def set_seed(args):
random.seed(args.seed)
os.environ['PYTHONHASHSEED'] = str(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed) # if you are using multi-GPU.
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def main(args):
set_seed(args)
os.makedirs(args.output_dir, exist_ok=True)
logger = get_logger(filename=args.output_dir+'/log.txt')
clip_ckpt = args.clip_ckpt
langs = args.langs.split(',')
clip_model, preprocess = clip.load(clip_ckpt, device='cuda', acquirer=args.acquirer, d_acquirer_hidden=args.acquirer_hidden, m_acquirer=args.m_acquirer, langs=langs, skip=args.skip)
src_toker = clip.tokenize
clip_model.eval()
trg_toker = transformers.AutoTokenizer.from_pretrained('pretrained_model/bert-base-multilingual-cased')
assert args.init_model is not None or (args.is_clip or args.is_mclip or args.sentence_transformer)
if args.acquirer_ckpt:
logger.info(f'Loading acquirer weight from {args.acquirer_ckpt}')
acquirer_state_dict = torch.load(args.acquirer_ckpt)
acquirer_keys = ['acquirer', 'multilingual_embedding', 'multilingual_embedding_linear']
acquirer_state_dict = OrderedDict({
k:v for k,v in acquirer_state_dict.items() if any(n in k for n in acquirer_keys)
})
clip_model.load_state_dict(acquirer_state_dict, strict=False)
if args.embedding_ckpt:
logger.info(f'Loading embedding weight from {args.embedding_ckpt}')
embedding_state_dict = torch.load(args.embedding_ckpt)
embedding_keys = ['multilingual_embedding', 'multilingual_embedding_linear']
embedding_state_dict = OrderedDict({
k:v for k,v in embedding_state_dict.items() if any(n in k for n in embedding_keys)
})
clip_model.load_state_dict(embedding_state_dict, strict=False)
test_loaders = []
if args.img_type == 'm30k+coco':
path_dict_m30k = json.load(open('./dataset/Multi30k/multi30k_4lang_path.json'))
path_dict_coco = json.load(open('./dataset/MSCOCO/COCO_en_path.json'))
path_dict = merge_dict(path_dict_m30k, path_dict_coco)
elif args.img_type == 'm30k':
path_dict = json.load(open('./dataset/Multi30k/multi30k_4lang_path.json'))
anno_files = path_dict[args.eval_split]['anno_file']
image_dirs = path_dict[args.eval_split]['feature']
name_files = path_dict[args.eval_split]['name_file']
langs = path_dict[args.eval_split]['langs']
print(langs)
split_test_loaders = []
split_test_lang = []
none_split_lang = []
for anno, image_dir, name_file, lang in zip(anno_files, image_dirs, name_files, langs):
if isinstance(name_file, list):
temp_loader = []
temp_split = []
for i, name in enumerate(name_file):
_, loader_split = get_it_loader(args, name, anno, image_dir, preprocess, src_toker, is_train=False, st=args.sentence_transformer, return_type='dict')
temp_loader.append(loader_split)
split_test_loaders.append(temp_loader)
split_test_lang.append(lang)
else:
_, loader = get_it_loader(args, name_file, anno, image_dir, preprocess, src_toker, is_train=False, st=args.sentence_transformer, return_type='dict')
test_loaders.append(loader)
none_split_lang.append(lang)
for split_test_loader, lang in zip(split_test_loaders, split_test_lang):
if 'ja' not in lang:
continue
metrics = eval_epoch(clip_model, split_test_loader, 'cuda', 1, [f'{lang}_split_{i}' for i in range(len(split_test_loader))], trg_toker, logger, is_clip=args.is_clip, return_all_metrics=True, sentence_transformer=args.sentence_transformer,
acquirer=args.acquirer, new_embed=args.new_embed, save_matrix=True, output_root=os.path.join(args.output_dir, 'sim_matrix')
)
# logger.info(f' Lang: {lang} mR1: {mR1}')
metrics = eval_epoch(clip_model, test_loaders, 'cuda', 1, none_split_lang, trg_toker, logger, is_clip=args.is_clip, return_all_metrics=True, sentence_transformer=args.sentence_transformer,
acquirer=args.acquirer, new_embed=args.new_embed, save_matrix=True, output_root=os.path.join(args.output_dir, 'sim_matrix')
)
# logger.info(f'mR1: {mR1}')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--output_dir', default='', type=str)
parser.add_argument('--batch_size', default=128, type=int)
parser.add_argument('--n_workers', default=4, type=int)
parser.add_argument('--lr', default=5e-5, type=float)
parser.add_argument('--epochs', default=50, type=int)
parser.add_argument('--seed', default=42, type=int)
parser.add_argument('--init_model', default=None, type=str)
parser.add_argument('--contrastive', action='store_true')
parser.add_argument('--itm', action='store_true')
parser.add_argument('--itm_nce', action='store_true')
parser.add_argument('--kd', default=True, type=int)
parser.add_argument('--optim', choices=['Adam', 'BertAdam'], type=str, default='Adam')
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--img_type', type=str, default='m30k+coco')
parser.add_argument('--eval_split', type=str, default='test')
parser.add_argument('--is_clip', action='store_true')
parser.add_argument('--is_mclip', action='store_true')
parser.add_argument('--clip_ckpt', type=str, default='pretrained_model/clip/ViT-B-32.pt')
parser.add_argument('--sentence_transformer', action='store_true')
parser.add_argument('--acquirer', action='store_true')
parser.add_argument('--new_embed', action='store_true')
parser.add_argument('--m_acquirer', action='store_true')
parser.add_argument('--langs', type=str, default='de,fr,cs,ja,zh,it,es,ru,pl,tr,ko')
parser.add_argument('--acquirer_ckpt', type=str, default=None)
parser.add_argument('--embedding_ckpt', type=str, default=None)
parser.add_argument('--acquirer_hidden', type=int, default=256)
parser.add_argument('--mbert_acquirer', action='store_true')
parser.add_argument('--skip', action='store_true')
parser.add_argument('--acquirer_config', type=str, default='./expr/mbert_acquirer/acquirer_config.json')
parser.add_argument('--video', action='store_true')
parser.add_argument('--path_file', type=str, default=None)
args = parser.parse_args()
os.makedirs(args.output_dir, exist_ok=True)
with open(f'{args.output_dir}/eval_config.json', 'w', encoding='utf-8') as f:
json.dump(args.__dict__, f, indent=1, ensure_ascii=False)
main(args)