test.ipynb

{
 "metadata": {
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3-final"
  },
  "orig_nbformat": 2,
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3.8.3 64-bit (conda)",
   "metadata": {
    "interpreter": {
     "hash": "07efdcd4b820c98a756949507a4d29d7862823915ec7477944641bea022f4f62"
    }
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2,
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "True\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import os\n",
    "from PIL import Image\n",
    "import random\n",
    "import torch\n",
    "import torch.utils.data as data\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "\n",
    "\n",
    "def load_fixed_set(root):\n",
    "    # Load the fixed dataset\n",
    "    filename = 'testA.npy'\n",
    "    path = os.path.join(root, filename)\n",
    "    dataset = np.load(path)\n",
    "    return dataset\n",
    "\n",
    "dataset = load_fixed_set(root='data/')\n",
    "print((dataset[0,:,:,:,:]==dataset[1,:,:,:,:]).all())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "(4, 4)\n"
     ]
    }
   ],
   "source": [
    "x=np.zeros((5,5))\n",
    "t=x[1:,1:]\n",
    "print(t.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}