gladiator07 / bristol-myers-squibb-translation-kaggle

50th place solution of a Kaggle competition where we were given the task to translate old chemical images to its Inchi

Bristol-Myers Squibb – Molecular Translation

Introduction

This repository is the 50th place solution for the Bristol-Myers Squibb – Molecular Translation competition.

Solution writeup: https://www.kaggle.com/c/bms-molecular-translation/discussion/243820

Requirements

This project requires the below libraries:

• torch==1.8.1+cu111
• torchvision==0.9.1+cu111
• numpy
• albumentations
• pytorch_lightning
• wandb
• pandas
• opencv_python
• tokenizers
• python_Levenshtein
• PyYAML

You can simply install them by using:

pip install -r requirements.txt -f https://download.pytorch.org/whl/torch_stable.html

Usage

This repository contains training and prediction scripts. You can reproduce our results by using this project. Note that the subword tokenization is performed with huggingface tokenizers and the detailed training code is in this notebook. Of course, do not forget to download the dataset from the competition. You can download both the dataset and the tokenizer by using:

kaggle competitions download -c bms-molecular-translation
unzip -qq bms-molecular-translation.zip -d res
rm bms-molecular-translation.zip

kaggle kernels output bms-molecular-translation-train-inchi-tokenizer
mv tokenizer.json res/

Train a model

First of all, you need to make a training configuration file. For example:

data:
  datasets:
    main:
      image_dir: res/train
      label_csv_path: res/train_labels.csv

  tokenizer_path: res/tokenizer.json
  val_ratio: 0.01

model:
  image_size: 224
  patch_size: 16
  max_seq_len: 256

  num_encoder_layers: 6
  num_decoder_layers: 6

  hidden_dim: 512
  num_attn_heads: 8
  expansion_ratio: 4

  encoder_dropout_rate: 0.1
  decoder_dropout_rate: 0.1

train:
  epochs: 10
  warmup_steps: 10000

  accumulate_grads: 8
  train_batch_size: 128
  val_batch_size: 128

  learning_rate: 1.e-4
  learning_rate_decay: linear

  weight_decay: 0.05
  max_grad_norm: 1.0

  grad_ckpt_ratio: 0.0

environ:
  name: [your model name]

  num_gpus: 1
  precision: 16

After writing your own training configuration file, login to the wandb by using the below command to log the training and validation metrics.

wandb login [your wandb key]

Now you can train the model by:

python src/train.py [your config file]

If you want to use apex in training, use --use_apex_amp option. Note that the apex should be installed in your system. It also supports resuming from checkpoint file and using pretrained weights. The detailed usage is as follows:

usage: train.py [-h] [--use_apex_amp] [--resume RESUME] [--checkpoint CHECKPOINT]
                [--pretrained PRETRAINED]
                config

positional arguments:
  config

optional arguments:
  -h, --help            show this help message and exit
  --use_apex_amp
  --resume RESUME
  --checkpoint CHECKPOINT
  --pretrained PRETRAINED

Make a prediction

After training the model, you can make a prediction.

data:
  image_dir: res/test
  label_csv_path: res/sample_submission.csv
  tokenizer_path: res/tokenizer.json

model:
  image_size: 224
  patch_size: 16
  max_seq_len: 256

  num_encoder_layers: 6
  num_decoder_layers: 6

  hidden_dim: 512
  num_attn_heads: 8
  expansion_ratio: 4

predict:
  batch_size: 1024
  weight_path: [trained model weight]

environ:
  name: [your model name]
  precision: 16

Create a configuration and run the below command:

python src/predict.py [your config file]

Some utility scripts

This project also contains useful utility scripts.

weight averaging

python scripts/average_weights.py model1.pth model2.pth model3.pth ... --output averaged.pth

combining encoder and decoder transformers

python scripts/combine_encoder_and_decoder.py --encoder vit.pth --decoder gpt2.pth --output model.pth

download pretrained ViT encoder

python scripts/download_pretrained_encoder.py vit-large --output ViT-encoder.pth --include_embeddings

resize input image resolution

python scripts/resize_encoder_resolution.py model-224.pth --output model-384.pth --image_size 384

stack transformer layers

python scripts/stack_transformer_layers.py model-12.pth --output model-24.pth --num_encoder_layers 24 --num_decoder_layers 6 --modify_mode repeat-first

visualize embedding and projection layers

python scripts/visualize_embeddings.py model.pth

create external dataset

python scripts/create_external_dataset.py res/extra_inchi.csv --output_path . --num_folds 4 --fold_index 0