visualize.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "D:\\data\\PyProject\\ConvLSTM.pytorch\\.\n"
     ]
    }
   ],
   "source": [
    "from configs.config_3x3_16_3x3_32_3x3_64 import config\n",
    "from utils.dataset import MovingMNISTDataset\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import torch\n",
    "from torch.utils.data import DataLoader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loaded 10000 samples (train)\n"
     ]
    }
   ],
   "source": [
    "vis_dataset = MovingMNISTDataset(config)\n",
    "vis_loader = DataLoader(vis_dataset, batch_size=5, \n",
    "                       num_workers=config.num_workers, shuffle=True, pin_memory=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([5, 10, 1, 64, 64]) torch.Size([5, 10, 1, 64, 64]) tensor(1.) tensor(0.)\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 7200x1800 with 100 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for batch_idx, (inputs, targets) in enumerate(vis_loader):\n",
    "    inputs = inputs.float().to(config.device)\n",
    "    targets = targets.float().to(config.device)\n",
    "    print(inputs.shape, targets.shape, inputs.max(), inputs.min())\n",
    "    images = torch.cat([inputs, targets], dim=1)\n",
    "    _, axarr = plt.subplots(images.shape[0], images.shape[1], figsize=(images.shape[1]*5, images.shape[0]*5))\n",
    "    for b in range(images.shape[0]):\n",
    "        for n in range(images.shape[1]):\n",
    "            axarr[b][n].imshow(images[b, n, 0], cmap='gray')\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "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": 4
}