7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle

Related tags

Deep Learningdeep-learningkagglekaggle-competitionhpa-challenge-2021
Overview

kaggle-hpa-2021-7th-place-solution

Code for 7th place solution of Human Protein Atlas - Single Cell Classification on Kaggle.

A description of the method can be found in this post in the kaggle discussion.

Dataset Preparation

Resize Images

# Resize train images to 768x768
python scripts/hap_segmenter/create_cell_mask.py resize_image \
    --input_directory data/input/hpa-single-cell-image-classification.zip/train \
    --output_directory data/input/hpa-768768.zip \
    --image_size 768
# Resize train images to 1536x1536
python scripts/hap_segmenter/create_cell_mask.py resize_image \
    --input_directory data/input/hpa-single-cell-image-classification.zip/train \
    --output_directory data/input/hpa-1536.zip \
    --image_size 1536

# Resize test images to 768x768
python scripts/hpa_segmenter/create_cell_mask.py resize_image \
    --input_directory /kaggle/input/hpa-single-cell-image-classification/test \
    --output_directory data/input/hpa-768-test.zip \
    --image_size 768
# Resize test images to 1536x1536
python scripts/hpa_segmenter/create_cell_mask.py resize_image \
    --input_directory /kaggle/input/hpa-single-cell-image-classification/test \
    --output_directory data/input/hpa-1536-test.zip \
    --image_size 1536

You can specify a directory in a zip file in the same way as a normal directory.

Download Public HPA

Download all images in kaggle_2021.tsv in this dataset, resize them into 768x768 and 1536x1536, and archive them as data/input/hpa-public-768.zip and data/input/hpa-public-1536.zip.

Create Cell Mask

# Create cell masks for the Kaggle train set with 1536x1536
python scripts/hpa_segmenter/create_cell_mask.py create_cell_mask \
    --input_directory data/input/hpa-1536.zip \
    --output_directory data/input/hpa-1536-mask-v2.zip \
    --label_cell_scale_factor 1.0

# Resize the masks to 768x768
python scripts/hpa_segmenter/create_cell_mask.py resize_cell_mask \
    --input_directory data/input/hpa-1536-mask-v2.zip \
    --output_directory data/input/hpa-768-mask-v2-from-1536.zip \
    --image_size 768

# Create cell masks for the Public HPA dataset with 1536x1536
python scripts/hpa_segmenter/create_cell_mask.py create_cell_mask \
    --input_directory data/input/hpa-public-1536.zip/hpa-public-1536 \
    --output_directory data/input/hpa-public-1536-mask-v2.zip \
    --label_cell_scale_factor 1.0

# Resize the masks to 768x768
python scripts/hpa_segmenter/create_cell_mask.py resize_cell_mask \
    --input_directory data/input/hpa-public-1536-mask-v2.zip \
    --output_directory data/input/hpa-public-768-mask-v2-from-1536.zip \
    --image_size 768

# Create cell masks for the test set with the original resolution
# Run with `--label_cell_scale_factor = 0.5` to save inference time
python scripts/hpa_segmenter/create_cell_mask.py create_cell_mask \
    --input_directory /kaggle/input/hpa-single-cell-image-classification/test \
    --output_directory data/input/hpa-test-mask-v2.zip \
    --label_cell_scale_factor 0.5

# Resize the masks to 1536x1536
python scripts/hpa_segmenter/create_cell_mask.py resize_cell_mask \
    --input_directory data/input/hpa-test-mask-v2.zip \
    --output_directory data/input/hpa-test-mask-v2-1536.zip \
    --image_size 1536

# Resize the masks to 768x768
python scripts/hpa_segmenter/create_cell_mask.py resize_cell_mask \
    --input_directory data/input/hpa-test-mask-v2.zip \
    --output_directory data/input/hpa-test-mask-v2-768.zip \
    --image_size 768

Create Input for Cell-level Classifier

# Create cell-level inputs for the Kaggle train set using 768x768 images as fixed scale image.
python scripts/hap_segmenter/create_cell_mask.py crop_and_resize_cell \
    --image_directory data/input/hpa-768768.zip \
    --cell_mask_directory data/input/hpa-768-mask-v2-from-1536.zip \
    --output_directory data/input/hpa-cell-crop-v2-192-from-768.zip \
    --image_size 192

# Create cell-level inputs for the Public HPA dataset using 768x768 images as fixed scale image.
python scripts/hap_segmenter/create_cell_mask.py crop_and_resize_cell \
    --image_directory data/input/hpa-public-768.zip \
    --cell_mask_directory data/input/hpa-public-768-mask-v2-from-1536.zip \
    --output_directory data/input/hpa-public-cell-crop-v2-192-from-768.zip \
    --image_size 192

# Create cell-level inputs for the Kaggle train set using 1536x1536 images as fixed scale image.
python scripts/hap_segmenter/create_cell_mask.py crop_and_resize_cell \
    --image_directory data/input/hpa-1536.zip \
    --cell_mask_directory data/input/hpa-1536-mask-v2.zip \
    --output_directory data/input/hpa-cell-crop-v2-192-from-1536.zip \
    --image_size 192

# Create cell-level inputs for the Public HPA dataset using 1536x1536 images as fixed scale image.
python scripts/hap_segmenter/create_cell_mask.py crop_and_resize_cell \
    --image_directory data/input/hpa-public-1536.zip \
    --cell_mask_directory data/input/hpa-public-1536-mask-v2.zip \
    --output_directory data/input/hpa-public-cell-crop-v2-192-from-1536.zip \
    --image_size 192

# Create cell-level inputs for the test set using 768x768 images as fixed scale image.
python scripts/hpa_segmenter/create_cell_mask.py crop_and_resize_cell \
    --image_directory data/input/hpa-768768-test.zip \
    --cell_mask_directory data/input/hpa-test-mask-v2-768.zip \
    --output_directory data/input/hpa-test-cell-crop-v2-192-from-768.zip \
    --image_size 192

# Create cell-level inputs for the test set using 1536x1536 images as fixed scale image.
python scripts/hpa_segmenter/create_cell_mask.py crop_and_resize_cell \
    --image_directory data/input/hpa-1536-test.zip \
    --cell_mask_directory data/input/hpa-test-mask-v2-1536.zip \
    --output_directory data/input/hpa-test-cell-crop-v2-192-from-1536.zip \
    --image_size 192

Training

# Train image-level classifier
python scripts/cam_consistency_training/run.py train \
    --config_path scripts/cam_consistency_training/configs/${CONFIG_NAME}.yaml

# Train cell-level classifier
python scripts/cell_crop/run.py train \
    --config_path scripts/cell_crop/configs/${CONFIG_NAME}.yaml

If you want to train on multiple GPUs, use a launcher like torch.distributed.launch and pass --local_rank option. You can override the fields in the config by passing an argument like field_name=${value} (e.g. fold_index=1). We trained 5 folds for all models used in the final submission pipeline. The config files are located in scripts/cam_consistency_training/configs and scripts/cell_crop/configs. We trained the models in the following order.

  1. scripts/cam_consistency_training/configs/eff-b2-focal-alpha1-cutmix-pubhpa-maskv2.yaml
  2. scripts/cam_consistency_training/configs/eff-b5-focal-alpha1-cutmix-pubhpa-maskv2.yaml
  3. scripts/cam_consistency_training/configs/eff-b7-focal-alpha1-cutmix-pubhpa-maskv2.yaml
  4. scripts/cam_consistency_training/configs/eff-b2-cutmix-pubhpa-768-to-1536.yaml
  5. Do predict_valid and concat_valid_predictions (described below) for each model and save the average of the output files under data/working/consistency_training/b2-1536-b2-b5-b7-768-avg/.
  6. scripts/cam_consistency_training/configs/eff-b2-focal-stage2-b2b2b5b7avg.yaml
  7. scripts/cell_crop/configs/resnest50-bce-from768-cutmix-softpl.yaml
  8. Do predict_valid and concat_valid_predictions for each model and save the average of the output files under data/working/image-level-and-cell-crop-both-5folds/.
  9. scripts/cam_consistency_training/configs/eff-b2-focal-stage3.yaml
  10. scripts/cam_consistency_training/configs/eff-b2-focal-stage3-cos.yaml
  11. scripts/cell_crop/configs/resnest50-bce-from768-stage3.yaml
  12. scripts/cell_crop/configs/resnest50-bce-from1536-stage3-cos.yaml

Inference

Validation Set

# Image-level classifier inference
python scripts/cam_consistency_training/run.py predict_valid \
    --config_path scripts/cam_consistency_training/configs/${CONFIG_NAME}.yaml

# Cell-level classifier inference
python scripts/cell_crop/run.py predict_valid \
    --config_path scripts/cell_crop/configs/${CONFIG_NAME}.yaml

# Concatenate the predictions for each fold to obtain the OOF prediction for the entire training data
python scripts/cam_consistency_training/run.py concat_valid_predictions \
    --config_path scripts/cam_consistency_training/configs/${CONFIG_NAME}.yaml
python scripts/cell_crop/run.py concat_valid_predictions \
    --config_path scripts/cell_crop/configs/${CONFIG_NAME}.yaml

Test Set

# Image-level classifier inference
python scripts/cam_consistency_training/run.py predict_test \
    --config_path scripts/cam_consistency_training/configs/${CONFIG_NAME}.yaml

# Cell-level classifier inference
python scripts/cell_crop/run.py predict_test \
    --config_path scripts/cell_crop/configs/${CONFIG_NAME}.yaml

# Make our final submission with post-processing
python scripts/average_predictions.py \
    --orig_size_cell_mask_directory data/input/hpa-test-mask-v2.zip \
    "data/working/consistency_training/eff-b2-focal-stage3/0" \
    "data/working/consistency_training/eff-b2-focal-stage3/1" \
    "data/working/consistency_training/eff-b2-focal-stage3/2" \
    "data/working/consistency_training/eff-b2-focal-stage3/3" \
    "data/working/consistency_training/eff-b2-focal-stage3/4" \
    "data/working/consistency_training/eff-b2-focal-stage3-cos/0" \
    "data/working/consistency_training/eff-b2-focal-stage3-cos/1" \
    "data/working/consistency_training/eff-b2-focal-stage3-cos/2" \
    "data/working/consistency_training/eff-b2-focal-stage3-cos/3" \
    "data/working/consistency_training/eff-b2-focal-stage3-cos/4" \
    "data/working/cell_crop/resnest50-bce-from768-stage3/0" \
    "data/working/cell_crop/resnest50-bce-from768-stage3/1" \
    "data/working/cell_crop/resnest50-bce-from768-stage3/2" \
    "data/working/cell_crop/resnest50-bce-from768-stage3/3" \
    "data/working/cell_crop/resnest50-bce-from768-stage3/4" \
    "data/working/cell_crop/resnest50-bce-from1536-stage3-cos/0" \
    "data/working/cell_crop/resnest50-bce-from1536-stage3-cos/1" \
    "data/working/cell_crop/resnest50-bce-from1536-stage3-cos/2" \
    "data/working/cell_crop/resnest50-bce-from1536-stage3-cos/3" \
    "data/working/cell_crop/resnest50-bce-from1536-stage3-cos/4" \
    --edge_area_threshold 80000 --center_area_threshold 32000

Use the code on Kaggle Notebook

Use docker to zip the source code and the wheels of the dependencies and upload them as a dataset.

docker run --rm -it -v /path/to/this/repo:/tmp/workspace -w /tmp/workspace/ gcr.io/kaggle-images/python bash ./build_zip.sh

In Kaggle Notebook, when you copy the code as shown below, you can run it the same way as your local environment.

# Make a working directory
!mkdir -p /kaggle/tmp

# Change the current directory
cd /kaggle/tmp

# Copy source code from the uploaded dataset
!cp -r /kaggle/input/<your-dataset-name>/* .

# You can use it as well as local environment
!python scripts/hpa_segmenter/create_cell_mask.py create_cell_mask ...
Owner
GitHub Repository
A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".

An Introduction to Deep Learning for the Physical Layer An usable PyTorch implementation of the noisy autoencoder infrastructure in the paper "An Intr

Gram.AI 120 Nov 21, 2022
KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control

KeypointDeformer: Unsupervised 3D Keypoint Discovery for Shape Control Tomas Jakab, Richard Tucker, Ameesh Makadia, Jiajun Wu, Noah Snavely, Angjoo Ka

Tomas Jakab 87 Nov 30, 2022
AAAI-22 paper: SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning

SimSR Code and dataset for the paper SimSR: Simple Distance-based State Representationfor Deep Reinforcement Learning (AAAI-22). Requirements We assum

7 Dec 19, 2022
Source code of our work: "Benchmarking Deep Models for Salient Object Detection"

SALOD Source code of our work: "Benchmarking Deep Models for Salient Object Detection". In this works, we propose a new benchmark for SALient Object D

22 Dec 30, 2022
This repo contains source code and materials for the TEmporally COherent GAN SIGGRAPH project.

TecoGAN This repository contains source code and materials for the TecoGAN project, i.e. code for a TEmporally COherent GAN for video super-resolution

Nils Thuerey 5.2k Jan 02, 2023
This is the repo for the paper "Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement".

Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement This is the repository for the paper "Improving the Accuracy-Memory Trad

3 Dec 29, 2022
Rainbow DQN implementation that outperforms the paper's results on 40% of games using 20x less data 🌈

Rainbow 🌈 An implementation of Rainbow DQN which reaches a median HNS of 205.7 after only 10M frames (the original Rainbow from Hessel et al. 2017 re

Dominik Schmidt 31 Dec 21, 2022
PyTorch implementations of the paper: "DR.VIC: Decomposition and Reasoning for Video Individual Counting, CVPR, 2022"

DRNet for Video Indvidual Counting (CVPR 2022) Introduction This is the official PyTorch implementation of paper: DR.VIC: Decomposition and Reasoning

tao han 35 Nov 22, 2022
Large scale PTM - PPI relation extraction

Large-scale protein-protein post-translational modification extraction with distant supervision and confidence calibrated BioBERT The silver standard

1 Feb 25, 2022
Easy genetic ancestry predictions in Python

ezancestry Easily visualize your direct-to-consumer genetics next to 2500+ samples from the 1000 genomes project. Evaluate the performance of a custom

Kevin Arvai 38 Jan 02, 2023
Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method

C++/ROS Source Codes for "Autonomous Driving on Curvy Roads without Reliance on Frenet Frame: A Cartesian-based Trajectory Planning Method" published in IEEE Trans. Intelligent Transportation Systems

Bai Li 88 Dec 23, 2022
GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models

GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Model This repository is the official PyTorch implementation of GraphRNN, a graph gene

Jiaxuan 568 Dec 29, 2022
SAMO: Streaming Architecture Mapping Optimisation

SAMO: Streaming Architecture Mapping Optimiser The SAMO framework provides a method of optimising the mapping of a Convolutional Neural Network model

Alexander Montgomerie-Corcoran 20 Dec 10, 2022
Table-Extractor 表格抽取

(t)able-(ex)tractor 本项目旨在实现pdf表格抽取。 Models 版面分析模块（Yolo） 表格结构抽取（ResNet + Transformer） 文字识别模块（CRNN + CTC Loss） Acknowledgements TableMaster attention-i

2 Jan 15, 2022
[NIPS 2021] UOTA: Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration.

UOTA: Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration This repository is the official PyTorch implementation of UOT

6 Jun 29, 2022
Header-only library for using Keras models in C++.

frugally-deep Use Keras models in C++ with ease Table of contents Introduction Usage Performance Requirements and Installation FAQ Introduction Would

Tobias Hermann 927 Jan 05, 2023
RRxIO - Robust Radar Visual/Thermal Inertial Odometry: Robust and accurate state estimation even in challenging visual conditions.

RRxIO - Robust Radar Visual/Thermal Inertial Odometry RRxIO offers robust and accurate state estimation even in challenging visual conditions. RRxIO c

Christopher Doer 64 Dec 29, 2022
PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identification in Symbolic Scores.

Symbolic Melody Identification This repository is an unofficial PyTorch implementation of the paper:A Convolutional Approach to Melody Line Identifica

Sophia Y. Chou 3 Feb 21, 2022
Complex-Valued Neural Networks (CVNN)Complex-Valued Neural Networks (CVNN)

Complex-Valued Neural Networks (CVNN) Done by @NEGU93 - J. Agustin Barrachina Using this library, the only difference with a Tensorflow code is that y

youceF 1 Nov 12, 2021
Spectrum is an AI that uses machine learning to generate Rap song lyrics

Spectrum Spectrum is an AI that uses deep learning to generate rap song lyrics. View Demo Report Bug Request Feature Open In Colab About The Project S

39 Dec 16, 2022