/visdial

Visual Dialog: Light-weight Transformer for Many Inputs (ECCV 2020)

Primary LanguagePython

This is the code implementation for the paper titled: "Efficient Attention Mechanism for Visual Dialog that can Handle All the Interactions between Multiple Inputs" (Accepted to ECCV 2020).

Table of content

If you find this code useful or use our method as the baseline for comparison, please kindly cite the paper with the following bibtex or the plain citation:

@inproceedings{nguyen2020efficient,
  title={Efficient attention mechanism for visual dialog that can handle all the interactions between multiple inputs},
  author={Nguyen, Van-Quang and Suganuma, Masanori and Okatani, Takayuki},
  booktitle={Computer Vision--ECCV 2020: 16th European Conference, Glasgow, UK, August 23--28, 2020, Proceedings, Part XXIV 16},
  pages={223--240},
  year={2020},
  organization={Springer}
}

or as simply plain as:

Nguyen, Van-Quang, Masanori Suganuma, and Takayuki Okatani. "Efficient attention mechanism for visual dialog that can handle all the interactions between multiple inputs." Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XXIV 16. Springer International Publishing, 2020.

Setup and Environment

This code is implemented using the following environment configurations:

Component Details
Pytorch version 1.2
Python version 3.7
GPU Tesla V100-SXM2 (16GB)
No. of GPUs 4
CUDA 10.0
GPU Driver 410.104
RAM 376GB
CPU Xeon(R) Gold 6148 CPU @ 2.40GHz

To set up the environment, we recommend you to set up a virtual environment using Anaconda.

  1. Install Anaconda or Miniconda distribution based on Python3+ from their downloads' site.
  2. Clone this repository and create an environment
  3. Install all the dependencies
conda create -n visdial python=3.7

# activate the environment and install all dependencies
conda activate visdial

# Install the dependencies
export PROJ_ROOT='/path/to/visualdialog/'
cd $PROJ_ROOT/
pip install -r requirements.txt

Download Data

  1. Download the following json files for VisDial v1.0 and put them in $PRO_ROOT/dataset/annotations/:

  2. Download the following json files for VisDial v0.9 and also put them in $PROJ_ROOT/dataset/annotations/:

    • For training set here.
    • For validation set here.

  3. Get the word counts for VisDial v1.0 train split here and put it in $PROJ_ROOT/dataset/annotations/. They are used to build the vocabulary.

  4. Get the image features. We use the extracted features for VisDial v1.0 images using a Faster-RCNN pre-trained on Visual Genome.

    • First download images for training set of Visdial v1.0 from COCO train2014 and val2014, which are available here and also download the images for validation and test sets of Visdial v1.0 from here and here.
    • Then follow the instruction here to extract the bottom-up-attention features for images based on the pretrained Faster-RCNN:
      • First, clone the code provided by the authors at https://github.com/peteanderson80/bottom-up-attention.
      • Second, setup the environment as here.
      • Then, extract the features as mentioned in our paper. We provide our code for extraction; please copy the code $PROJ_ROOT/others/generate_visdial.py from our project to bottom-up-attention/tools.
      • Run the following command to extract:
      # Estimate 10 hours
# Extract the image features for the training split
/usr/bin/python generate_visdial.py \
--split "train" \
--topNattr 20 \
--num_images 123287 \
--data_path '/path_to_the_image_dir/trainval2014' \
--out_path '$PROJ_ROOT/datasets/bottom-up-attention/trainval_resnet101_faster_rcnn_genome_num_boxes_100.h5' \
--prototxt 'models/vg/ResNet-101/faster_rcnn_end2end_final/test.prototxt' \
--weights '/path_to_bottom_up_attention_checkpoints/bottom-up-attention/resnet101_faster_rcnn_final.caffemodel'

# Estimate 35 minutes
# Extract the image features for the validation split
/usr/bin/python generate_visdial.py \
--split "val" \
--topNattr 20 \
--num_images 2064 \
--data_path '/path_to_the_image_dir/VisualDialog_val2018' \
--out_path '/$PROJ_ROOT/datasets/bottom-up-attention/val2018_resnet101_faster_rcnn_genome_num_boxes_100.h5' \
--prototxt 'models/vg/ResNet-101/faster_rcnn_end2end_final/test.prototxt' \
--weights '/path_to_bottom_up_attention_checkpoints/bottom-up-attention/resnet101_faster_rcnn_final.caffemodel'     

# Estimate 2 hours
# Extract the image features for the test split
/usr/bin/python generate_visdial.py \
--split "test" \
--topNattr 20 \
--num_images 8000 \
--data_path '/path_to_the_image_dir/VisualDialog_test2018' \
--out_path '/$PROJ_ROOT/datasets/bottom-up-attention/test2018_resnet101_faster_rcnn_genome_num_boxes_100.h5' \
--prototxt 'models/vg/ResNet-101/faster_rcnn_end2end_final/test.prototxt' \
--weights '/path_to_bottom_up_attention_checkpoints/bottom-up-attention/resnet101_faster_rcnn_final.caffemodel'
      • At the end, the directory $PROJ_ROOT/datasets/bottom-up-attention/ should have the following files:
      ./trainval_resnet101_faster_rcnn_genome_100.h5
./val2018_resnet101_faster_rcnn_genome_100.h5
./test2018_resnet101_faster_rcnn_genome_100.h5
    • In the $PROJ_ROOT/datasets/, we also provide the available data that you need: 
      $PROJ_ROOT/datasets/glove/embedding_Glove_840_300d.pkl
$PROJ_ROOT/datasets/genome/1600-400-20/attributes_vocab.txt
$PROJ_ROOT/datasets/genome/1600-400-20/objects_vocab.txt

Training

Our code supports both generative and discriminative decoders (and both of them that we call misc). We also provide the training script which supports Visdial v1.0 and Visdial v0.9.

Note: If the CUDA is out of memory, please consider to decrease the batch_size.

Training on Visdial v1.0

To reproduce our results on Visdial v1.0, please run the following command (the other hyperparameters will be considered as default as our paper's):

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
--config_name model_v10 \
--save_dir checkpoints \
--batch_size 8 \
--decoder_type misc \
--init_lr 0.001 \
--scheduler_type "LinearLR" \
--num_epochs 15 \
--num_samples 123287 \
--milestone_steps 3 5 7 9 11 13 \
--encoder_out 'img' 'ques' \
--dropout 0.1 \
--img_has_bboxes \
--ca_has_layer_norm \
--ca_num_attn_stacks 2 \
--ca_has_residual \
--ca_has_self_attns \
--txt_has_layer_norm \
--txt_has_decoder_layer_norm \
--txt_has_pos_embedding \

Note 1: The batch_size is set per each GPU. If you have 4 GPUs, the number of actual batch_size is 32 as ours.

Note 2: You can also train a discriminative model or a generative model by specifying --decoder_type as disc and gen, respectively.

Training on Visdial v0.9

To reproduce our results on Visdial v0.9, please run the following command (the other hyperparameters will be considered as default as our paper's):

CUDA_VISIBLE_DEVICES=0,1,2,3 python train.py \
--config_name misc_v0.9 \
--save_dir checkpoints \
--v0.9 \
--batch_size 8 \
--decoder_type misc \
--init_lr 0.001 \
--scheduler_type "LinearLR" \
--num_epochs 5 \
--num_samples 123287 \
--milestone_steps 3 5 \
--encoder_out 'img' 'ques' \
--dropout 0.1 \
--img_has_bboxes \
--ca_has_layer_norm \
--ca_num_attn_stacks 2 \
--ca_has_residual \
--ca_has_self_attns \
--txt_has_layer_norm \
--txt_has_decoder_layer_norm \
--txt_has_pos_embedding \
--val_feat_img_path "datasets/bottom-up-attention/trainval_resnet101_faster_rcnn_genome_num_boxes_100.h5" \
--train_feat_img_path "datasets/bottom-up-attention/trainval_resnet101_faster_rcnn_genome_num_boxes_100.h5" \
--val_json_dialog_path "datasets/annotations/visdial_0.9_val.json" \
--train_json_dialog_path "datasets/annotations/visdial_0.9_val.json"

Note 1: You must turn the flag --v0.9 on, then the corresponding VisdialDataset for Visdial v0.9 will be generated.

Note 2: val_json_dialog_path is the same as train_feat_img_path since the v0.9 validation split is part of trainval split in Visdial v1.0. It will not cause any confliction since the validation split v0.9 will be generated based on the image_ids we get from val_json_dialog_path.

As the original testbed, we also provide an --overfit flag, which can be useful for debugging.

Saving model checkpoints

The checkpoint is saved at every epoch at the directory you specify with --save_dir. The default directory is checkpoint/.

Logging

Tensorboard is used for logging training progress. Please go to checkpoints/tensorboard directory execute the following

tensorboard --logdir ./ --port 8008
#  and open `localhost:8008` in the browser.

Finetuning for Ensemble

Run the following command to perform fintuning:

python finetune.py \
--model_path path/to/checkpoint/model_v10.pth \
--save_path path/to/saved/checkpoint \

Evaluation

The evaluation of a trained model checkpoint on the validation set can be done as follows:

python evaluate.py \
--model_path 'checkpoints/model_v10.pth' \
--split val \
--decoder_type disc \
--device 'cuda:0' \
--output_path 'checkpoints/val_v1_disc.json'

Note 1: You can evaluate on three kinds of decoders: disc, gen, and misc.

Note 2: The above script is also applicable for the test split by changing the value of --split to test. After that, please submit the test_v1_disc.json to the server for further evaluation.

Note 3: The above script is also applicable for the evaluation on Visdial v0.9.

This will generate an EvalAI submission file, and report metrics (Mean reciprocal rank, R@{1, 5, 10}, Mean rank), and Normalized Discounted Cumulative Gain (NDCG), introduced in the first Visual Dialog Challenge (in 2018).

Result of Checkpoints

The overall architecture

The get the summary of the overall architecture, run the following python code:

import torch

model = torch.load('checkpoints/model_v10.pth')
print(model)

The number of the stack of attention blocks

To compute the number of parameters in our proposed attention stacks, run the python code as follows:

import torch
from visdial.utils import get_num_params

model = torch.load('checkpoints/model_v10.pth')
# The number of parameters per one stack
print(get_num_params(model.encoder.attn_encoder.cross_attn_encoder[0]))

# The number of parameters of the attention encoder
print(get_num_params(model.encoder.attn_encoder))

Performance on v1.0 validation split (trained on v1.0 train + val):

Model R@1 R@5 R@10 MeanR MRR NDCG
[model-v1.0] with outputs from disc 0.4894 0.7865 0.8788 4.8589 0.6232 0.6272
[model-v1.0] with outputs from gen 0.4044 0.6161 0.6971 14.9274 0.5074 0.6358
[model-v1.0] with outputs from the avg of two 0.4303 0.6663 0.7567 10.6030 0.5436 0.6575

Performance on v1.0 test split (trained on v1.0 train + val):

Model R@1 R@5 R@10 MeanR MRR NDCG
[disc-model-v1.0] 0.4700 0.7703 0.8775 4.90 0.6065 0.6092

Performance on v0.9 validation split (trained on v0.9 train):

Model R@1 R@5 R@10 MeanR MRR
[disc-model-v0.9] 55.05 0.83.98 91.58 3.69 67.94

Acknowledgements