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
Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks

Self-Correcting Quantum Many-Body Control using Reinforcement Learning with Tensor Networks This repository contains the code and data for the corresp

Friederike Metz 7 Apr 23, 2022
AQP is a modular pipeline built to enable the comparison and testing of different quality metric configurations.

Audio Quality Platform - AQP An Open Modular Python Platform for Objective Speech and Audio Quality Metrics AQP is a highly modular pipeline designed

Jack Geraghty 24 Oct 01, 2022
Implement face detection, and age and gender classification, and emotion classification.

YOLO Keras Face Detection Implement Face detection, and Age and Gender Classification, and Emotion Classification. (image from wider face dataset) Ove

Chloe 10 Nov 14, 2022
An implementation of chunked, compressed, N-dimensional arrays for Python.

Zarr Latest Release Package Status License Build Status Coverage Downloads Gitter Citation What is it? Zarr is a Python package providing an implement

Zarr Developers 1.1k Dec 30, 2022
use machine learning to recognize gesture on raspberrypi

Raspberrypi_Gesture-Recognition use machine learning to recognize gesture on raspberrypi 說明 利用 tensorflow lite 訓練手部辨識模型 分辨 "剪刀"、"石頭"、"布" 之手勢 再將訓練模型匯入

1 Dec 10, 2021
This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems.

This repository contain code on Novelty-Driven Binary Particle Swarm Optimisation for Truss Optimisation Problems. The main directory include the code

0 Dec 23, 2021
Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-wise Distributed Data based on Pytorch Framework

VFedPCA+VFedAKPCA This is the official source code for the Paper: Vertical Federated Principal Component Analysis and Its Kernel Extension on Feature-

John 9 Sep 18, 2022
This repo includes our code for evaluating and improving transferability in domain generalization (NeurIPS 2021)

Transferability for domain generalization This repo is for evaluating and improving transferability in domain generalization (NeurIPS 2021), based on

gordon 9 Nov 29, 2022
A high-performance anchor-free YOLO. Exceeding yolov3~v5 with ONNX, TensorRT, NCNN, and Openvino supported.

YOLOX is an anchor-free version of YOLO, with a simpler design but better performance! It aims to bridge the gap between research and industrial communities. For more details, please refer to our rep

7.7k Jan 06, 2023
BLEURT is a metric for Natural Language Generation based on transfer learning.

BLEURT: a Transfer Learning-Based Metric for Natural Language Generation BLEURT is an evaluation metric for Natural Language Generation. It takes a pa

Google Research 492 Jan 05, 2023
Code for the IJCAI 2021 paper "Structure Guided Lane Detection"

SGNet Project for the IJCAI 2021 paper "Structure Guided Lane Detection" Abstract Recently, lane detection has made great progress with the rapid deve

Jinming Su 27 Dec 08, 2022
(ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning"

CLNet (ICCV 2021) PyTorch implementation of Paper "Progressive Correspondence Pruning by Consensus Learning" [project page] [paper] Citing CLNet If yo

Chen Zhao 22 Aug 26, 2022
Optimizers-visualized - Visualization of different optimizers on local minimas and saddle points.

Optimizers Visualized Visualization of how different optimizers handle mathematical functions for optimization. Contents Installation Usage Functions

Gautam J 1 Jan 01, 2022
Model Quantization Benchmark

Introduction MQBench is an open-source model quantization toolkit based on PyTorch fx. The envision of MQBench is to provide: SOTA Algorithms. With MQ

500 Jan 06, 2023
PyTorch implementation for SDEdit: Image Synthesis and Editing with Stochastic Differential Equations

SDEdit: Image Synthesis and Editing with Stochastic Differential Equations Project | Paper | Colab PyTorch implementation of SDEdit: Image Synthesis a

536 Jan 05, 2023
CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer

CSAW-M This repository contains code for CSAW-M: An Ordinal Classification Dataset for Benchmarking Mammographic Masking of Cancer. Source code for tr

Yue Liu 7 Oct 11, 2022
Binary classification for arrythmia detection with ECG datasets.

HEART DISEASE AI DATATHON 2021 [Eng] / [Kor] #English This is an AI diagnosis modeling contest that uses the heart disease echocardiography and electr

HY_Kim 3 Jul 14, 2022
This repository contains a set of codes to run (i.e., train, perform inference with, evaluate) a diarization method called EEND-vector-clustering.

EEND-vector clustering The EEND-vector clustering (End-to-End-Neural-Diarization-vector clustering) is a speaker diarization framework that integrates

45 Dec 26, 2022
Toontown: Galaxy, a new Toontown game based on Disney's Toontown Online

Toontown: Galaxy The official archive repo for Toontown: Galaxy, a new Toontown

1 Feb 15, 2022
Using this codebase as a tool for my own research. Making some modifications to the original repo for my own purposes.

For SwapNet Create a list.txt file containing all the images to process. This can be done with the GNU find command: find path/to/input/folder -name '

Andrew Jong 2 Nov 10, 2021