Semantic Element Consistency Learning. This is the evaluation code for SECNet using PyTorch.
This code is designed to run on Ubuntu. You will need to prepare the following:
- Visit Link (Due to anonymous submission, link will be available soon) to download
ALPRO_model.pt
,SECNet_model.pt
,bert-base-uncased.zip
,vit_base_patch16_224.zip
, andext.zip
to the root directory. Unzip the corresponding files. - Download
videos.pt
and place it in thedata/msrvtt_ret/
.
The final directory structure should look like this:
- SECNet
- bert-base-uncased
- config_release
- data
- msrvtt
- txt
- videos
- ext
- output
- run_scripts
- src
- vit_base_patch16_224
- ALPRO_model.pt
- SECNet_model.pt
conda create -n SECNet python=3.7
pip install cmake==3.13.0
conda install pytorch torchvision torchaudio pytorch-cuda=11.7 -c pytorch -c nvidia
pip install horovod
pip install -r requirements.txt
conda activate SECNet
cd run_scripts
bash pt_SECNet.sh
conda activate SECNet
cd run_scripts
bash inf_msrvtt_ret.sh
You can modify e2e_weights_path
in config_release/msrvtt_ret.json
to ALPRO_model
to obtain test results for ALPRO.