Image Captioning

Usage

Define the configuration for your experiment. See default.json to see the structure and available options. You are free to modify and restructure the configuration as per your needs.
Implement factories to return project specific models, datasets based on config. Add more flags as per requirement in the config.
Implement experiment.py based on the project requirements.
After defining the configuration (say my_exp.json) - simply run python3 main.py my_exp to start the experiment
The logs, stats, plots and saved models would be stored in ./experiment_data/my_exp dir. This can be configured in contants.py
To resume an ongoing experiment, simply run the same command again. It will load the latest stats and models and resume training pr evaluate performance.

main.py: Main driver class
experiment_final.py: Main experiment class. Initialized based on config - takes care of training, saving stats and plots, logging and resuming experiments. Implemented the training and testing functions.
dataset_factory: Factory to build datasets based on config
model_factory.py: Factory to build models based on config
constants.py: constants used across the project
file_utils.py: utility functions for handling files
caption_utils.py: utility functions to generate bleu scores
vocab.py: A simple Vocabulary wrapper
coco_dataset: A simple implementation of torch.utils.data.Dataset the Coco Dataset
get_datasets.ipynb: A helper notebook to set up the dataset in your workspace
architecture_1_final.py: Contains the architecture for the architecture 1 mentioned in the assignment. Also contains the code for generation used during testing.
architecture_2_final.py: Contains the architecture for the architecture 2 mentioned in the assignment. Also contains the code for generation used during testing.
default.json: Contains the config of the hyperparameters used in our experiments.
get_datasets.ipynb: Contains the code to create the dataset.
coco_dataset.py: Contains the helper class to convert our data to coco format.
test_ids.csv, train_ids.csv, val_ids.csv: Contains the image ids for test, train and val datasets respectively.