exp-dataset-exploration.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import numpy as np\n",
    "import argparse\n",
    "from dataset import get_dataset\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.autograd import Variable\n",
    "from torchvision import transforms\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Namespace:\n",
    "    def __init__(self, **kwargs):\n",
    "        self.__dict__.update(kwargs)\n",
    "        \n",
    "args = Namespace(data_dir='./data/sort-of-clevr.pickle', batch_size=64, num_workers=20, no_img_norm=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "train-dataset loaded. 180000 rel+nonrel samples\n",
      "val-dataset loaded. 10000/10000 rel/nonrel samples\n"
     ]
    }
   ],
   "source": [
    "train_set, val_set = get_dataset(args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([3, 75, 75])\n",
      "tensor([0., 1., 0., 0., 0., 0., 0., 1., 0., 0., 1.])\n",
      "tensor(8)\n",
      "torch.Size([3, 75, 75])\n",
      "tensor([1., 0., 0., 0., 0., 0., 0., 1., 1., 0., 0.])\n",
      "tensor(2)\n",
      "torch.Size([3, 75, 75])\n",
      "tensor([0., 0., 1., 0., 0., 0., 0., 1., 0., 0., 1.])\n",
      "tensor(4)\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i, data in enumerate(val_set):\n",
    "    img_rel = data[0][0]\n",
    "    qst_rel = data[1][0]\n",
    "    ans_rel = data[2][0]\n",
    "    img_nonrel = data[0][1]\n",
    "    qst_nonrel = data[1][1]\n",
    "    ans_nonrel = data[2][1]\n",
    "    \n",
    "    plt.imshow(img_rel.permute(1,2,0))\n",
    "    print(img_rel.shape)\n",
    "    print(qst_rel)\n",
    "    print(ans_rel)\n",
    "    \n",
    "    if i == 2:\n",
    "        break\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7102"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "torch.backends.cudnn.version()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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