[FIX] resolve some comments (cont.)

examples/hwf/datasets/test.ipynb

@@ -1,70 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "metadata": {},
-   "outputs": [
-    {
-     "ename": "NameError",
-     "evalue": "name '__file__' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [1]\u001b[0m, in \u001b[0;36m<cell line: 10>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mPIL\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Image\n\u001b[1;32m      8\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtorchvision\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mtransforms\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m transforms\n\u001b[0;32m---> 10\u001b[0m CURRENT_DIR \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mabspath(os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mdirname(\u001b[38;5;18;43m__file__\u001b[39;49m))\n\u001b[1;32m     12\u001b[0m img_transform \u001b[38;5;241m=\u001b[39m transforms\u001b[38;5;241m.\u001b[39mCompose([transforms\u001b[38;5;241m.\u001b[39mToTensor(), transforms\u001b[38;5;241m.\u001b[39mNormalize((\u001b[38;5;241m0.5\u001b[39m,), (\u001b[38;5;241m1\u001b[39m,))])\n\u001b[1;32m     14\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mrequests\u001b[39;00m\n",
-      "\u001b[0;31mNameError\u001b[0m: name '__file__' is not defined"
-     ]
-    }
-   ],
-   "source": [
-    "import json\n",
-    "import os\n",
-    "import requests\n",
-    "import os\n",
-    "import zipfile\n",
-    "\n",
-    "from PIL import Image\n",
-    "from torchvision.transforms import transforms\n",
-    "\n",
-    "CURRENT_DIR = os.path.abspath(os.path.dirname(__file__))\n",
-    "\n",
-    "img_transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5,), (1,))])\n",
-    "\n",
-    "import requests\n",
-    "import os\n",
-    "import zipfile\n",
-    "\n",
-    "\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "kernelspec": {
-   "display_name": "abl",
-   "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": "3.8.13"
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}

examples/hwf/hwf_example.ipynb → examples/hwf/hwf.ipynb

@@ -11,11 +11,11 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "This example shows a simple implementation of Handwritten Formula, which was first introduced in [Li et al., 2020](https://arxiv.org/abs/2006.06649). In this task, the inputs are images of decimal formulas, and the outputs are their computed results.\n",
-    "\n",
+    "This example shows a simple implementation of [Handwritten Formula](https://arxiv.org/abs/2006.06649). In this task, xxx.\n",
+    "<!-- \n",
     "In Abductive Learning, we hope to first use learning part to map the input images to their symbols (we call them pseudo labels), and then use reasoning part to calculate the summation of these pseudo labels to get the final result.\n",
     "\n",
-    "The HWF dataset ontains images of decimal formulas and their computed results. "
+    "The HWF dataset ontains images of decimal formulas and their computed results.  -->"
    ]
   },
   {
@@ -41,17 +41,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "12/18 12:48:19 - abl - INFO - Abductive Learning on the HWF example.\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Initialize logger and print basic information\n",
     "print_log(\"Abductive Learning on the HWF example.\", logger=\"current\")\n",
@@ -185,22 +177,9 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "FileNotFoundError",
-     "evalue": "[Errno 2] 没有那个文件或目录: '/home/huwc/ABL-Package/examples/hwf/datasets/data/expr_train.json'",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
-      "Input \u001b[0;32mIn [4]\u001b[0m, in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;66;03m# Get training and testing data\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m train_data \u001b[38;5;241m=\u001b[39m \u001b[43mget_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mget_pseudo_label\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mTrue\u001b[39;49;00m\u001b[43m)\u001b[49m\n\u001b[1;32m      3\u001b[0m test_data \u001b[38;5;241m=\u001b[39m get_dataset(train\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mFalse\u001b[39;00m, get_pseudo_label\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n",
-      "File \u001b[0;32m~/ABL-Package/examples/hwf/datasets/get_dataset.py:21\u001b[0m, in \u001b[0;36mget_dataset\u001b[0;34m(train, get_pseudo_label)\u001b[0m\n\u001b[1;32m     19\u001b[0m Y \u001b[38;5;241m=\u001b[39m []\n\u001b[1;32m     20\u001b[0m img_dir \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(CURRENT_DIR, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata/Handwritten_Math_Symbols/\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m---> 21\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28;43mopen\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mfile\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[1;32m     22\u001b[0m     data \u001b[38;5;241m=\u001b[39m json\u001b[38;5;241m.\u001b[39mload(f)\n\u001b[1;32m     23\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m idx \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;28mlen\u001b[39m(data)):\n",
-      "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] 没有那个文件或目录: '/home/huwc/ABL-Package/examples/hwf/datasets/data/expr_train.json'"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
     "# Get training and testing data\n",
     "train_data = get_dataset(train=True, get_pseudo_label=True)\n",

examples/hwf/requirements.txt

@@ -1 +1,2 @@
+abl
 gdown

requirements.txt

@@ -2,8 +2,5 @@ numpy
 pyswip==0.2.9
 torch
 torchvision
-torchaudio
 zoopt
-termcolor
-openml
-z3-solver 
+termcolor