This code is in development to solve action identification described in this [work]. It is developed based on MacNet: Compositional Attention Networks for Machine Reasoning (ICLR 2018).
MacNet is a fully differentiable model that learns to perform multi-step reasoning and explore it in the context of the CLEVR dataset. This code is meant to explore in the context of the action-agumented CLEVR_dataset. You can download the dataset here.
In particular, two methods are explored in this work. First of all, a naive approach is implemented in thebaseline
branch, where the two images are stacked together and the resulted vector is fed into MacNet as signle input. A second approch is to redesign the MAC cell at mac_cell.py
in intraActionCell
branch. Details of the design is shown in the images below. Images are fed separately into the network.
Note: Run python main.py -h
or see config.py
for the complete list of options. To run action-based model, see --incluAction
, --actionOnlyTrain
and --alterActionTrain
.
- Tensorflow (originally has been developed with 1.3 but should work for later versions as well).
- We have performed experiments on Maxwell Titan X GPU. We assume 12GB of GPU memory.
- See
requirements.txt
for the required python packages and runpip install -r requirements.txt
to install them.
Before training the model, we first have to download the CLEVR dataset and extract features for the images:
To download and unpack the data, run the following commands, where * is v1.0
for regular CLEVR dataset and action
for action-based CLEVR dataset. Use the following code to download CLEVR dataset and prepare it for training:
wget https://s3-us-west-1.amazonaws.com/clevr/CLEVR_*.zip
unzip CLEVR_*.zip
mv CLEVR_* CLEVR_v1
mkdir CLEVR_v1/data
mv CLEVR_v1/questions/* CLEVR_v1/data/
You can find action-based data here: ... dropbox link
The final command moves the dataset questions into the data
directory, where we will put all the data files we use during training.
Extract ResNet-101 features for the CLEVR train, val, and test images with the following commands:
To extract data for intraActionCell, use argument --mode action_sep
. To extract data for stacked images, use argument --mode stack_action
.
python extract_features.py --input_image_dir CLEVR_v1/images/train --output_h5_file CLEVR_v1/data/train.h5
python extract_features.py --input_image_dir CLEVR_v1/images/val --output_h5_file CLEVR_v1/data/val.h5
python extract_features.py --input_image_dir CLEVR_v1/images/test --output_h5_file CLEVR_v1/data/test.h5
To train the model, run the following command:
python main.py --expName "clevrExperiment" --train --testedNum 10000 --epochs 25 --netLength 16 @configs/args.txt
First, the program preprocesses the CLEVR questions. It tokenizes them and maps them to integers to prepare them for the network. It then stores a JSON with that information about them as well as word-to-integer dictionaries in the ./CLEVR_v1/data
directory.
Then, the program trains the model. Weights are saved by default to ./weights/{expName}
and statistics about the training are collected in ./results/{expName}
, where expName
is the name we choose to give to the current experiment.
- The number of examples used for training and evaluation can be set by
--trainedNum
and--testedNum
respectively. - You can use the
-r
flag to restore and continue training a previously pre-trained model. - We recommend you to try out varying the number of MAC cells used in the network through the
--netLength
option to explore different lengths of reasoning processes. - Good lengths for CLEVR are in the range of 4-16 (using more cells tends to converge faster and achieves a bit higher accuracy, while lower number of cells usually results in more easily interpretable attention maps).
We have explored several variants of our model. We provide a few examples in configs/args1-4.txt
. For instance, you can run the first by:
python main.py --expName "experiment1" --train --testedNum 10000 --epochs 25 --netLength 6 @configs/args1.txt
args1
is the standard recurrent-control-memory cell. Leads to the most interpretable results among the configs.args2
uses a non-recurrent variant of the control unit that converges faster.args3
incorporates self-attention into the write unit.args4
adds control-based gating over the memory.
See config.py
for further available options (Note that some of them are still in an experimental stage).
To evaluate the trained model, and get predictions and attention maps, run the following:
python main.py --expName "clevrExperiment" --finalTest --testedNum 10000 --netLength 16 -r --getPreds --getAtt @configs/args.txt
The command will restore the model we have trained, and evaluate it on the validation set. JSON files with predictions and the attention distributions resulted by running the model are saved by default to ./preds/{expName}
.
- In case you are interested in getting attention maps (
--getAtt
), and to avoid having large prediction files, we advise you to limit the number of examples evaluated to 5,000-20,000.
After we evaluate the model with the command above, we can visualize the attention maps generated by running:
python visualization.py --expName "clevrExperiment" --tier val
(Tier can be set to train
or test
as well). The script supports filtering of the visualized questions by various ways. See visualization.py
for further details.
To get more interpretable visualizations, it is highly recommended to reduce the number of cells to 4-8 (--netLength
). Using more cells allows the network to learn more effective ways to approach the task but these tend to be less interpretable compared to a shorter networks (with less cells).
Optionally, to make the image attention maps look a little bit nicer, you can do the following (using imagemagick):
for x in preds/clevrExperiment/*Img*.png; do magick convert $x -brightness-contrast 20x35 $x; done;
To run different experiments use the following commands:
git checkout baseline`
python3 main.py --expName "pretrained" --train --epochs 6 --netLength 6 --gpus 1 --workers 1 --taskSize 8 --batchSize 64 --weightsToKeep 10 --earlyStopping 5 --incluAction --trainedNum 350000 --testedNum 10000 @configs/args1.txt --debug
python main.py --expName "pretrained" --finalTest --netLength 6 -r --getPreds --getAtt @configs/args1.txt --incluAction --debug --gpus 1
We need to copy pretrained file to action_only_finetuned folder first
cp ./weights/pretrained/config-pretrained.json ./weights/action_only_finetuned/config-pretrained.csv
cp ./results/pretrained/results-pretrained.csv ./results/action_only_finetuned/results-action_only_finetuned.csv
python3 main.py --expName "action_only_finetuned" --train --epochs 200 --netLength 6 --gpus 1 --workers 1 --taskSize 8 --batchSize 64 --weightsToKeep 20 --earlyStopping 10 -r --incluAction --actionOnlyTrain @configs/args1.txt --debug
python main.py --expName "action_only_finetuned" --finalTest --netLength 6 -r --getPreds --getAtt @configs/args1.txt --incluAction --actionOnlyTrain --gpus 1 --debug
python visualization.py --expName "action_only_finetuned" --tier val
python3 main.py --expName "action_only_scratch" --train --epochs 200 --netLength 6 --gpus 0 --workers 1 --taskSize 8 --batchSize 64 --weightsToKeep 20 --earlyStopping 20 --incluAction --actionOnlyTrain @configs/args1.txt --debug
python main.py --expName "action_only_scratch" --finalTest --netLength 6 -r --getPreds --getAtt @configs/args1.txt --debug --incluAction --gpus 0
python visualization.py --expName "action_only_scratch" --tier val
git checkout intraActionCell
python3 main.py --expName "intraActionCell" --train --epochs 200 --netLength 6 --gpus 0 --workers 1 --taskSize 8 --batchSize 64 --weightsToKeep 20 --earlyStopping 20 --dataBasedir ./CLEVR_action @configs/args1.txt
python main.py --expName "intraActionCell" --finalTest --netLength 6 -r --getPreds --getAtt @configs/args1.txt --gpus 0`
python visualization.py --expName "intraActionCell" --tier val
If you find this work useful in your research, please cite
@misc{celinew1221_action_mac_network_2018,
title={# Compostional Attention Networks with Action Identification for Machine Reasoning},
author={Celine Wei},
year={2018},
publisher={Github},
journal={GitHub repository},
howpublished={\url{https://github.com/celinew1221/mac-network/}},
}