mirror of
https://github.com/yunjey/pytorch-tutorial.git
synced 2025-07-24 18:45:33 +08:00
tutorials are added
This commit is contained in:
yunjey
397
tutorials/00 - PyTorch Basics/basics.ipynb
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397
tutorials/00 - PyTorch Basics/basics.ipynb
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 19,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"import torch \n",
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"import torchvision\n",
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"import torch.nn as nn\n",
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"import torch.utils.data as data\n",
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"import numpy as np\n",
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"import torchvision.transforms as transforms\n",
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"import torchvision.datasets as dsets\n",
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"from torch.autograd import Variable"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Simple Example"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"-1.2532 -1.1120 0.9717\n",
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"-2.3617 0.1516 1.1280\n",
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"-2.1599 0.0828 -1.4305\n",
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" 0.5265 0.5020 -2.1852\n",
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"-0.9197 0.1772 -1.1378\n",
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"[torch.FloatTensor of size 5x3]\n",
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"\n"
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]
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}
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],
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"source": [
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"# random normal\n",
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"x = torch.randn(5, 3)\n",
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"print (x)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# build a layer\n",
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"linear = nn.Linear(3, 2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Parameter containing:\n",
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" 0.3884 -0.3335 -0.5146\n",
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"-0.3692 0.1977 -0.4081\n",
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"[torch.FloatTensor of size 2x3]\n",
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"\n",
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"Parameter containing:\n",
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"-0.4826\n",
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"-0.0038\n",
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"[torch.FloatTensor of size 2]\n",
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"\n"
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]
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}
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],
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"source": [
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"# Sess weight and bias\n",
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"print (linear.weight)\n",
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"print (linear.bias)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Variable containing:\n",
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"-1.0986 -0.1575\n",
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"-2.0311 0.4378\n",
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"-0.6131 1.3938\n",
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" 0.6790 0.7929\n",
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"-0.3134 0.8351\n",
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"[torch.FloatTensor of size 5x2]\n",
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"\n"
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]
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}
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],
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"source": [
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"# forward propagate\n",
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"y = linear(Variable(x))\n",
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"print (y)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Convert numpy array to torch tensor"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"# convert numpy array to tensor\n",
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"a = np.array([[1,2], [3,4]])\n",
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"b = torch.from_numpy(a)\n",
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"print (b)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Input pipeline"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"#### (1) Preprocessing"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 18,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Image Preprocessing \n",
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"transform = transforms.Compose([\n",
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" transforms.Scale(40),\n",
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" transforms.RandomHorizontalFlip(),\n",
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" transforms.RandomCrop(32),\n",
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" transforms.ToTensor()])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### (2) Define Dataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Files already downloaded and verified\n",
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"torch.Size([3, 32, 32])\n",
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"6\n"
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]
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}
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],
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"source": [
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"# download and loading dataset f\n",
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"train_dataset = dsets.CIFAR10(root='./data/',\n",
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" train=True, \n",
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" transform=transform,\n",
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" download=True)\n",
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"\n",
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"image, label = train_dataset[0]\n",
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"print (image.size())\n",
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"print (label)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### (3) Data Loader"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 16,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# data loader provides queue and thread in a very simple way\n",
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"train_loader = data.DataLoader(dataset=train_dataset,\n",
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" batch_size=100, \n",
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" shuffle=True,\n",
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" num_workers=2)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# iteration start then queue and thread start\n",
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"data_iter = iter(train_loader)\n",
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"\n",
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"# mini-batch images and labels\n",
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"images, labels = data_iter.next()\n",
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"\n",
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"for images, labels in train_loader:\n",
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" # your training code will be written here\n",
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" pass"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### (4) What about custom dataset not cifar10?"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 25,
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"metadata": {
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"collapsed": false
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},
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"outputs": [],
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"source": [
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"class CustomDataset(data.Dataset):\n",
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" def __init__(self):\n",
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" pass\n",
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" def __getitem__(self, index):\n",
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" # You should build this function to return one data for given index\n",
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" pass\n",
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" def __len__(self):\n",
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" pass"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 26,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"ename": "TypeError",
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"evalue": "'NoneType' object cannot be interpreted as an integer",
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"output_type": "error",
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"traceback": [
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"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
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"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
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"\u001b[0;32m<ipython-input-26-a76c7b5c92c3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mbatch_size\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m100\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m num_workers=2)\n\u001b[0m",
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"\u001b[0;32m/home/yunjey/anaconda3/lib/python3.5/site-packages/torch/utils/data/dataloader.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, dataset, batch_size, shuffle, sampler, num_workers, collate_fn, pin_memory)\u001b[0m\n\u001b[1;32m 250\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 251\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 252\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mRandomSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 253\u001b[0m \u001b[0;32melif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mshuffle\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 254\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msampler\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mSequentialSampler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdataset\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;32m/home/yunjey/anaconda3/lib/python3.5/site-packages/torch/utils/data/sampler.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data_source)\u001b[0m\n\u001b[1;32m 45\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 47\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnum_samples\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_source\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 49\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0m__iter__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
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"\u001b[0;31mTypeError\u001b[0m: 'NoneType' object cannot be interpreted as an integer"
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]
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}
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],
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"source": [
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"custom_dataset = CustomDataset()\n",
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"data.DataLoader(dataset=custom_dataset,\n",
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" batch_size=100, \n",
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" shuffle=True,\n",
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" num_workers=2)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Using Pretrained Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"metadata": {
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"collapsed": false
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},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"Downloading: \"https://s3.amazonaws.com/pytorch/models/resnet18-5c106cde.pth\" to /home/yunjey/.torch/models/resnet18-5c106cde.pth\n",
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"100%|██████████| 46827520/46827520 [07:48<00:00, 99907.53it/s] \n"
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]
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}
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],
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"source": [
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"# Download and load pretrained model\n",
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"resnet = torchvision.models.resnet18(pretrained=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# delete top layer for finetuning\n",
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"sub_model = nn.Sequentialtial(*list(resnet.children()[:-1]))\n",
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"\n",
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"# for test\n",
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"images = Variable(torch.randn(10, 3, 256, 256))\n",
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"print (resnet(images).size())\n",
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"print (sub_model(images).size())"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Save and Load Model"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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},
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"outputs": [],
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"source": [
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"# Save and load the trained model\n",
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"torch.save(sub_model, 'model.pkl')\n",
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"\n",
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"model = torch.load('model.pkl')"
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]
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}
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],
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python [conda root]",
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"language": "python",
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"name": "conda-root-py"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.5.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}
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