We recommended the following dependencies.
- Python 3.7
- Pytorch 1.6+
- Numpy
- nltk
Download the dataset files. We use the image feature created by SCAN, downloaded here. All the data needed for reproducing the experiments in the paper, including image features and vocabularies, can be downloaded from:
wget https://scanproject.blob.core.windows.net/scan-data/data.zip
wget https://scanproject.blob.core.windows.net/scan-data/vocab.zip - Train new BCAN models: Run
train.py:
python train.py --data_path "$DATA_PATH" --data_name "$DATA_NAME" --logger_name "$LOGGER_NAME" --model_name "$MODEL_NAME"Argument used to train Flickr30K models and MSCOCO models are similar with those of SCAN:
For Flickr30K:
| Method | Arguments |
|---|---|
| BCAN-equal | --num_epochs=20 --lr_update=15 --correct_type=equal |
| BCAN-prob | --num_epochs=20 --lr_update=15 --correct_type=prob |
For MSCOCO:
| Method | Arguments |
|---|---|
| BCAN-equal | --num_epochs=15 --lr_update=8 --correct_type=equal |
| BCAN-prob | --num_epochs=15 --lr_update=8 --correct_type=prob |
from vocab import Vocabulary
import evaluation
evaluation.evalrank("$RUN_PATH/coco_scan/model_best.pth.tar", data_path="$DATA_PATH", split="test")