2. Train.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# [Angle closure Glaucoma Evaluation Challenge](https://age.grand-challenge.org/Details/)\n",
    "## Scleral spur localization Baseline （RCNN)\n",
    "\n",
    "- To keep model training stable, images with coordinate == -1, were removed.\n",
    "- For real inference, you MIGHT keep all images in val_file_path file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Training\n",
    "\n",
    "- Assume `Training100.zip` and `Validation_ASOCT_Image.zip` are stored @ `./AGE_challenge Baseline/datasets/`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Download PaddleDetection\n",
    " https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection\n",
    " \n",
    "- To use Origin PaddleDetection for AGE loc task :\n",
    "- Replace `PaddleDetection/configs/cascade_rcnn_r50_fpn_1x.yml` with `./cascade_rcnn_r50_fpn_1x.yml`\n",
    "- Or, you might edit configs/cascade_rcnn_r50_fpn_1x.yml\n",
    "\n",
    "```\n",
    "max_iters: 12960\n",
    "snapshot_iter: 2000\n",
    "LearningRate:\n",
    "  milestones: [6000, 8000]\n",
    "```\n",
    "\n",
    "for more details, see [PaddleDetection Docs](https://github.com/PaddlePaddle/models/tree/develop/PaddleCV/PaddleDetection/docs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom dataset (coco type)\n",
    "\n",
    "- coco type json files and folder architecture was constructed in pervious cell.\n",
    "- Under data10461/Training100/, you need these three folders:\n",
    "\n",
    "```\n",
    "annotations\n",
    "\tinstances_train2017.json\n",
    "\tinstances_val2017.json\n",
    "train2017\n",
    "\t***.jpg\n",
    "val2017\n",
    "\t***.jpg\n",
    "```\n",
    "\n",
    "for more details, see [Data.md](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/DATA.md), [Data.md中文版](https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleDetection/docs/DATA_cn.md)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/home/aiib-mia/Desktop/shangfangxin/AGE_challenge Baseline/LocalizationRCNN/PaddleDetection\n"
     ]
    }
   ],
   "source": [
    "!rm -rf ./PaddleDetection/dataset/coco\n",
    "# you might replace this path to absolute path\n",
    "!ln -sf ../../../datasets/Training100/ ./PaddleDetection/dataset/coco\n",
    "%cd ./PaddleDetection"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "env: PYTHONPATH=./\n",
      "./\r\n"
     ]
    }
   ],
   "source": [
    "%set_env PYTHONPATH=./\n",
    "!echo $PYTHONPATH"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# too many lines of training log, set print frequence to per 1000 steps. 12960 steps in total\n",
    "!python tools/train.py -c configs/cascade_rcnn_r50_fpn_1x.yml -o log_iter=1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}