lab-10_3_1_visdom-example.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 10-3-1 Visdom Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "\n",
    "import torchvision\n",
    "import torchvision.datasets as dsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## import visdom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](figs/turn_on_terminal.png)\n",
    "\n",
    "Jupyter Notebook > Terminal 를 새로 켜서 `python -m visdom.server` 를 입력하세요!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import visdom\n",
    "vis = visdom.Visdom()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "vis.text(\"Hello, world!\",env=\"main\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=torch.randn(3,200,200)\n",
    "vis.image(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "vis.images(torch.Tensor(3,3,28,28))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## example (using MNIST and CIFAR10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 시간이 좀 걸립니다.\n",
    "MNIST = dsets.MNIST(root=\"./MNIST_data\",train = True,transform=torchvision.transforms.ToTensor(), download=True)\n",
    "cifar10 = dsets.CIFAR10(root=\"./cifar10\",train = True, transform=torchvision.transforms.ToTensor(),download=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### CIFAR10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = cifar10.__getitem__(0)\n",
    "print(data[0].shape)\n",
    "vis.images(data[0],env=\"main\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### MNIST"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "data = MNIST.__getitem__(0)\n",
    "print(data[0].shape)\n",
    "vis.images(data[0],env=\"main\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_loader = torch.utils.data.DataLoader(dataset = MNIST,\n",
    "                                          batch_size = 32,\n",
    "                                          shuffle = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "for num, value in enumerate(data_loader):\n",
    "    value = value[0]\n",
    "    print(value.shape)\n",
    "    vis.images(value)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis.close(env=\"main\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Line Plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "Y_data = torch.randn(5)\n",
    "plt = vis.line (Y=Y_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_data = torch.Tensor([1,2,3,4,5])\n",
    "plt = vis.line(Y=Y_data, X=X_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line update"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "Y_append = torch.randn(1)\n",
    "X_append = torch.Tensor([6])\n",
    "\n",
    "vis.line(Y=Y_append, X=X_append, win=plt, update='append')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### multiple Line on single windows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num = torch.Tensor(list(range(0,10)))\n",
    "num = num.view(-1,1)\n",
    "num = torch.cat((num,num),dim=1)\n",
    "\n",
    "plt = vis.line(Y=torch.randn(10,2), X = num)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Line info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = vis.line(Y=Y_data, X=X_data, opts = dict(title='Test', showlegend=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = vis.line(Y=Y_data, X=X_data, opts = dict(title='Test', legend = ['1번'],showlegend=True))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = vis.line(Y=torch.randn(10,2), X = num, opts=dict(title='Test', legend=['1번','2번'],showlegend=True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make function for update line"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_tracker(loss_plot, loss_value, num):\n",
    "    '''num, loss_value, are Tensor'''\n",
    "    vis.line(X=num,\n",
    "             Y=loss_value,\n",
    "             win = loss_plot,\n",
    "             update='append'\n",
    "             )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt = vis.line(Y=torch.Tensor(1).zero_())\n",
    "\n",
    "for i in range(500):\n",
    "    loss = torch.randn(1) + i\n",
    "    loss_tracker(plt, loss, torch.Tensor([i]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## close the window"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "vis.close(env=\"main\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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