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Text To Layout

Text-to-layout using graph convolutional neural network (GCNN) or recurrent neural network (RNN).

Further information of the project can be found on: https://drive.google.com/file/d/1HZIBCutbvoDOcpghDzUTmfn9fKN1kr_s/view?usp=sharing

Libraries

The code has been tested using Python 3.8.8 and pytorch 1.7.1.

Other libraries needed:

  • tqdm (tested on 4.58.0)
  • NumPy (tested on 1.19)
  • nltk (tested on 3.5)

Dataset

Download and save the data in ./GCN2LY or ./RNN2LY folder:

https://drive.google.com/file/d/1FQC2yEV6--yM2ejsOsILR7pOTeTknLmE/view?usp=sharing

The data contains:

  • datasets: A folder containing the following datasets:
    • AMR2014: Training and Testing datasets with the captions unprocessed.
    • AMR2014train-dev-test: Training, development and testing datasets with the captions processed.
    • COCO-annotations: MSCOCO2014 training and testing annotations.
  • text_encoder100.pth: Pretrained text encoder (DAMSM).
  • captions.pickle: Vocabulary of the pretrained encoder.

Training

To train the model you need to set up the following variables in main.py file:

  • IS_TRAINING: True.
  • EPOCHS: Number of epochs to train.
  • CHECKPOINTS_PATH: The path to save the checkpoints (remember to create the folder before).

Additionally, you can set up the following variables to save the outputs of the development dataset.

  • SAVE_OUTPUT: True.
  • VALIDATION_OUTPUT: Path to store the output (remember to create the folder before).

For testing the training process is recommended to set the variable UQ_CAP in main.py to True.

Testing

To test the model you need to set up the following variables in main.py file:

  • IS_TRAINING: False.
  • EPOCH_VALIDATION: The epoch number to validate.
  • VALIDATION_OUTPUT: Path to store the output (remember to create the folder before).
  • SAVE_OUTPUT: True.

Pretrained model

GCN2LY and RNN2LY checkpoints

https://drive.google.com/file/d/18_R-d3fPoqEKypZPI8O4TOYoixT9uOkb/view?usp=sharing

All of them trained using a hidden size of 256 and 10 objects for 30 epochs.

PT stands for pretrained RNN encoder.

FT stands for pretrained and fine-tuned RNN encoder.

Metrics

To understand the metrics check chapter 5: https://drive.google.com/file/d/1HZIBCutbvoDOcpghDzUTmfn9fKN1kr_s/view?usp=sharing

System RSCP↑ AR↓ RS↓ P↑ F1↑ R↑ [email protected]↑ [email protected]↑ [email protected]↑ [email protected]↑
Obj-GAN 0.348 0.246 2216.491 0.866 0.566 0.499 0.257 0.094 0.227 0.073
GCN2LY 0.449 0.191 7.540 0.893 0.594 0.520 0.362 0.172 0.286 0.159
RNN2LY 0.459 0.191 7.503 0.910 0.606 0.529 0.360 0.171 0.295 0.165
RNN2LY_PT 0.464 0.188 7.587 0.907 0.609 0.534 0.358 0.171 0.291 0.164
RNN2LY_FT 0.459 0.190 7.478 0.911 0.606 0.529 0.364 0.173 0.296 0.167

PT stands for pretrained RNN encoder.

FT stands for pretrained and fine-tuned RNN encoder.

Examples

The ground truth (GT) and Obj-Gan are used to compare the results.

A group of people sit around a table. MSCOCO image ID: 10986.

alt text

A man sliding into a base next to another baseball player. MSCOCO image ID: 515982.

alt text

A woman sitting with a boy cutting a cake. MSCOCO image ID: 194097.

alt text

Information about the project

  • Type of project: End of degree project.
  • Author: Carlos Domínguez Becerril.
  • Supervisors: Gorka Azkune Galparsoro and Oier López de Lacalle Lecuona.