Repository: systemml Updated Branches: refs/heads/master 7e3c03609 -> 0ee8800b8
[SYSTEMML-1742] Transfer Learning using Caffe VGG-19 model Project: http://git-wip-us.apache.org/repos/asf/systemml/repo Commit: http://git-wip-us.apache.org/repos/asf/systemml/commit/0ee8800b Tree: http://git-wip-us.apache.org/repos/asf/systemml/tree/0ee8800b Diff: http://git-wip-us.apache.org/repos/asf/systemml/diff/0ee8800b Branch: refs/heads/master Commit: 0ee8800b8e10b65983c61677b00c2bfb185c1d38 Parents: 7e3c036 Author: Arvind Surve <[email protected]> Authored: Thu Aug 24 01:09:26 2017 -0700 Committer: Arvind Surve <[email protected]> Committed: Thu Aug 24 01:11:01 2017 -0700 ---------------------------------------------------------------------- ...lassify_Using_VGG_19_Transfer_Learning.ipynb | 520 +++++++++++++++++++ 1 file changed, 520 insertions(+) ---------------------------------------------------------------------- http://git-wip-us.apache.org/repos/asf/systemml/blob/0ee8800b/samples/jupyter-notebooks/Image_Classify_Using_VGG_19_Transfer_Learning.ipynb ---------------------------------------------------------------------- diff --git a/samples/jupyter-notebooks/Image_Classify_Using_VGG_19_Transfer_Learning.ipynb b/samples/jupyter-notebooks/Image_Classify_Using_VGG_19_Transfer_Learning.ipynb new file mode 100644 index 0000000..048308a --- /dev/null +++ b/samples/jupyter-notebooks/Image_Classify_Using_VGG_19_Transfer_Learning.ipynb @@ -0,0 +1,520 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Image Classification using Caffe VGG-19 model (Transfer Learning)\n", + "\n", + "This notebook demonstrates importing VGG-19 model from Caffe to SystemML and use that model to do an image classification. VGG-19 model has been trained using ImageNet dataset (1000 classes with ~ 14M images). If an image to be predicted is in one of the class VGG-19 has trained on then accuracy will be higher.\n", + "We expect prediction of any image through SystemML using VGG-19 model will be similar to that of image predicted through Caffe using VGG-19 model directly." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Prerequisite:\n", + "1. SystemML Python Package\n", + "To run this notebook you need to install systeml 1.0 (Master Branch code as of 08/24/2017 or later) python package.\n", + "2. Download Dogs-vs-Cats Kaggle dataset from https://www.kaggle.com/c/dogs-vs-cats/data location to a directory.\n", + " Unzip the train.zip directory to some location and update the variable \"train_dir\" in bottom two cells in which classifyImagesWTransfLearning() and classifyImages() methods are called to test this change. " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### SystemML Python Package information" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Name: systemml\r\n", + "Version: 1.0.0\r\n", + "Summary: Apache SystemML is a distributed and declarative machine learning platform.\r\n", + "Home-page: http://systemml.apache.org/\r\n";, + "Author: Apache SystemML\r\n", + "Author-email: [email protected]\r\n", + "License: Apache 2.0\r\n", + "Location: /home/asurve/src/anaconda2/lib/python2.7/site-packages\r\n", + "Requires: Pillow, numpy, scipy, pandas\r\n" + ] + } + ], + "source": [ + "!pip show systemml" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### SystemML Build information\n", + "Following code will show SystemML information which is installed in the environment." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SystemML Built-Time:2017-08-17 19:20:41 UTC\n", + "Archiver-Version: Plexus Archiver\n", + "Artifact-Id: systemml\n", + "Build-Jdk: 1.8.0_121\n", + "Build-Time: 2017-08-17 19:20:41 UTC\n", + "Built-By: asurve\n", + "Created-By: Apache Maven 3.3.9\n", + "Group-Id: org.apache.systemml\n", + "Main-Class: org.apache.sysml.api.DMLScript\n", + "Manifest-Version: 1.0\n", + "Minimum-Recommended-Spark-Version: 2.1.0\n", + "Version: 1.0.0-SNAPSHOT\n", + "\n" + ] + } + ], + "source": [ + "from systemml import MLContext\n", + "ml = MLContext(sc)\n", + "print (\"SystemML Built-Time:\"+ ml.buildTime())\n", + "print(ml.info())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "# Workaround for Python 2.7.13 to avoid certificate validation issue while downloading any file.\n", + "\n", + "import ssl\n", + "\n", + "try:\n", + " _create_unverified_https_context = ssl._create_unverified_context\n", + "except AttributeError:\n", + " # Legacy Python that doesn't verify HTTPS certificates by default\n", + " pass\n", + "else:\n", + " # Handle target environment that doesn't support HTTPS verification\n", + " ssl._create_default_https_context = _create_unverified_https_context" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# Create label.txt file\n", + "\n", + "def createLabelFile(fileName):\n", + " file = open(fileName, 'w')\n", + " file.write('1,\"Cat\" \\n')\n", + " file.write('2,\"Dog\" \\n')\n", + " file.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Download model, proto files and convert them to SystemML format.\n", + "\n", + "1. Download Caffe Model (VGG-19), proto files (deployer, network and solver) and label file.\n", + "2. Convert the Caffe model into SystemML input format.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Download caffemodel and proto files \n", + "\n", + "\n", + "def downloadAndConvertModel(downloadDir='.', trained_vgg_weights='trained_vgg_weights'):\n", + " \n", + " # Step 1: Download the VGG-19 model and other files.\n", + " import errno\n", + " import os\n", + " import urllib\n", + "\n", + " # Create directory, if exists don't error out\n", + " try:\n", + " os.makedirs(os.path.join(downloadDir,trained_vgg_weights))\n", + " except OSError as exc: # Python >2.5\n", + " if exc.errno == errno.EEXIST and os.path.isdir(trained_vgg_weights):\n", + " pass\n", + " else:\n", + " raise\n", + " \n", + " # Download deployer, network, solver proto and label files.\n", + " urllib.urlretrieve('https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/imagenet/vgg19/VGG_ILSVRC_19_layers_deploy.proto', os.path.join(downloadDir,'VGG_ILSVRC_19_layers_deploy.proto'))\n", + " urllib.urlretrieve('https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/imagenet/vgg19/VGG_ILSVRC_19_layers_network.proto',os.path.join(downloadDir,'VGG_ILSVRC_19_layers_network.proto'))\n", + " #TODO: After downloading network file (VGG_ILSVRC_19_layers_network.proto) , change num_output from 1000 to 2\n", + " \n", + " urllib.urlretrieve('https://raw.githubusercontent.com/apache/systemml/master/scripts/nn/examples/caffe2dml/models/imagenet/vgg19/VGG_ILSVRC_19_layers_solver.proto',os.path.join(downloadDir,'VGG_ILSVRC_19_layers_solver.proto'))\n", + " # TODO: set values as descrived below in VGG_ILSVRC_19_layers_solver.proto (Possibly through APIs whenever available)\n", + " # test_iter: 100\n", + " # stepsize: 40\n", + " # max_iter: 200\n", + " \n", + " # Create labels for data\n", + " ### 1,\"Cat\"\n", + " ### 2,\"Dog\"\n", + " createLabelFile(os.path.join(downloadDir, trained_vgg_weights, 'labels.txt'))\n", + "\n", + " # TODO: Following line commented as its 500MG file, if u need to download it please uncomment it and run.\n", + " # urllib.urlretrieve('http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_19_layers.caffemodel', os.path.join(downloadDir,'VGG_ILSVRC_19_layers.caffemodel'))\n", + "\n", + " # Step 2: Convert the caffemodel to trained_vgg_weights directory\n", + " import systemml as sml\n", + " sml.convert_caffemodel(sc, os.path.join(downloadDir,'VGG_ILSVRC_19_layers_deploy.proto'), os.path.join(downloadDir,'VGG_ILSVRC_19_layers.caffemodel'), os.path.join(downloadDir,trained_vgg_weights))\n", + " \n", + " return" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "##### PrintTopK\n", + "This function will print top K probabilities and indices from the result." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Print top K indices and probability\n", + "\n", + "def printTopK(prob, label, k):\n", + " print(label, 'Top ', k, ' Index : ', np.argsort(-prob)[0, :k])\n", + " print(label, 'Top ', k, ' Probability : ', prob[0,np.argsort(-prob)[0, :k]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Classify images\n", + "\n", + "This function classify images from images specified through urls.\n", + "\n", + "###### Input Parameters: \n", + " urls: List of urls\n", + " printTokKData (default False): Whether to print top K indices and probabilities\n", + " topK: Top K elements to be displayed. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import urllib\n", + "from systemml.mllearn import Caffe2DML\n", + "import systemml as sml\n", + "\n", + "\n", + "def classifyImages(urls,img_shape=(3, 224, 224), printTokKData=False, topK=5, downloadDir='.', trained_vgg_weights='trained_vgg_weights'):\n", + "\n", + " size = (img_shape[1], img_shape[2])\n", + " \n", + " vgg = Caffe2DML(sqlCtx, solver=os.path.join(downloadDir,'VGG_ILSVRC_19_layers_solver.proto'), input_shape=img_shape)\n", + " vgg.load(trained_vgg_weights)\n", + "\n", + " for url in urls:\n", + " outFile = 'inputTest.jpg'\n", + " urllib.urlretrieve(url, outFile)\n", + " \n", + " from IPython.display import Image, display\n", + " display(Image(filename=outFile))\n", + " \n", + " print (\"Prediction of above image to ImageNet Class using\");\n", + "\n", + " ## Do image classification through SystemML processing\n", + " from PIL import Image\n", + " input_image = sml.convertImageToNumPyArr(Image.open(outFile), img_shape=img_shape\n", + " , color_mode='BGR', mean=sml.getDatasetMean('VGG_ILSVRC_19_2014'))\n", + " print (\"Image preprocessed through SystemML :: \", vgg.predict(input_image)[0])\n", + " if(printTopKData == True):\n", + " sysml_proba = vgg.predict_proba(input_image)\n", + " printTopK(sysml_proba, 'SystemML BGR', topK)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pyspark.ml.linalg import Vectors\n", + "import os\n", + "import systemml as sml\n", + "\n", + "\n", + "def getLabelFeatures(filename, train_dir, img_shape):\n", + " from PIL import Image\n", + "\n", + " vec = Vectors.dense(sml.convertImageToNumPyArr(Image.open(os.path.join(train_dir, filename)), img_shape=img_shape)[0,:])\n", + " if filename.lower().startswith('cat'):\n", + " return (1, vec)\n", + " elif filename.lower().startswith('dog'):\n", + " return (2, vec)\n", + " else:\n", + " raise ValueError('Expected the filename to start with either cat or dog')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from pyspark.sql.functions import rand\n", + "import os\n", + "\n", + "def createTrainingDF(train_dir, train_data_file, img_shape):\n", + " list_jpeg_files = os.listdir(train_dir)\n", + " # 10 files per partition\n", + " train_df = sc.parallelize(list_jpeg_files, int(len(list_jpeg_files)/10)).map(lambda filename : getLabelFeatures(filename, train_dir, img_shape)).toDF(['label', 'features']).orderBy(rand())\n", + " # Optional: but helps seperates conversion-related from training\n", + " # train_df.write.parquet(train_data_file) # 'kaggle-cats-dogs.parquet'\n", + " return train_df" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def readTrainingDF(train_dir, train_data_file):\n", + " train_df = sqlContext.read.parquet(train_data_file)\n", + " return train_df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# downloadAndConvertModel(downloadDir, trained_vgg_weights)\n", + "# TODO: Take \"TODO\" actions mentioned in the downloadAndConvertModel() function after calling downloadAndConvertModel() function." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def retrainModel(img_shape, downloadDir, trained_vgg_weights, train_dir, train_data_file, vgg_new_model):\n", + "\n", + " # Let downloadAndConvertModel() functon be commented out, as it needs to be called separately (which is done in cell above) and manual action to be taken after calling it.\n", + " # downloadAndConvertModel(downloadDir, trained_vgg_weights)\n", + " # TODO: Take \"TODO\" actions mentioned in the downloadAndConvertModel() function after calling that function.\n", + " \n", + " train_df = createTrainingDF(train_dir, train_data_file, img_shape)\n", + " ## Write from input files OR read if its already written/converted\n", + " # train_df = readTrainingDF(train_dir, train_data_file)\n", + " \n", + " # Load the model\n", + " vgg = Caffe2DML(sqlCtx, solver=os.path.join(downloadDir,'VGG_ILSVRC_19_layers_solver.proto'), input_shape=img_shape)\n", + " vgg.load(weights=os.path.join(downloadDir,trained_vgg_weights), ignore_weights=['fc8'])\n", + " vgg.set(debug=True).setExplain(True)\n", + "\n", + " # Train the model using new data\n", + " vgg.fit(train_df)\n", + " \n", + " # Save the trained model\n", + " vgg.save(vgg_new_model)\n", + " \n", + " return vgg" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import urllib\n", + "from systemml.mllearn import Caffe2DML\n", + "import systemml as sml\n", + "\n", + "\n", + "def classifyImagesWTransfLearning(urls, model, img_shape=(3, 224, 224), printTokKData=False, topK=5):\n", + "\n", + " size = (img_shape[1], img_shape[2])\n", + " # vgg.load(trained_vgg_weights)\n", + "\n", + " for url in urls:\n", + " outFile = 'inputTest.jpg'\n", + " urllib.urlretrieve(url, outFile)\n", + " \n", + " from IPython.display import Image, display\n", + " display(Image(filename=outFile))\n", + " \n", + " print (\"Prediction of above image to ImageNet Class using\");\n", + "\n", + " ## Do image classification through SystemML processing\n", + " from PIL import Image\n", + " input_image = sml.convertImageToNumPyArr(Image.open(outFile), img_shape=img_shape\n", + " , color_mode='BGR', mean=sml.getDatasetMean('VGG_ILSVRC_19_2014'))\n", + "\n", + " print (\"Image preprocessed through SystemML :: \", model.predict(input_image)[0])\n", + " if(printTopKData == True):\n", + " sysml_proba = model.predict_proba(input_image)\n", + " printTopK(sysml_proba, 'SystemML BGR', topK)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Sample code to retrain the model and use it to classify image through two different way\n", + "\n", + "There are couple of parameters to set based on what you are looking for.\n", + "1. printTopKData (default False): If this parameter gets set to True, then top K results (probabilities and indices) will be displayed. \n", + "2. topK (default 5): How many entities (K) to be displayed.\n", + "3. Directories, data file name, model name and directory where data has donwloaded." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": false + }, + "outputs": [], + "source": [ + "# ImageNet specific parameters\n", + "img_shape = (3, 224, 224)\n", + "\n", + "# Setting other than current directory causes \"network file not found\" issue, as network file\n", + "# location is defined in solver file which does not have a path, so it searches in current dir.\n", + "downloadDir = '.' # /home/asurve/caffe_models' \n", + "trained_vgg_weights = 'trained_vgg_weights'\n", + "\n", + "train_dir = '/home/asurve/data/keggle/dogs_vs_cats_2/train'\n", + "train_data_file = 'kaggle-cats-dogs.parquet'\n", + " \n", + "vgg_new_model = 'kaggle-cats-dogs-model_2'\n", + " \n", + "printTopKData=True\n", + "topK=5\n", + "\n", + "urls = ['http://cdn3-www.dogtime.com/assets/uploads/gallery/goldador-dog-breed-pictures/puppy-1.jpg','https://lh3.googleusercontent.com/-YdeAa1Ff4Ac/VkUnQ4vuZGI/AAAAAAAAAEg/nBiUn4pp6aE/w800-h800/images-6.jpeg','https://upload.wikimedia.org/wikipedia/commons/thumb/5/58/MountainLion.jpg/312px-MountainLion.jpg']\n", + "\n", + "vgg = retrainModel(img_shape, downloadDir, trained_vgg_weights, train_dir, train_data_file, vgg_new_model)\n", + "classifyImagesWTransfLearning(urls, vgg, img_shape, printTopKData, topK)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "scrolled": true + }, + "outputs": [], + "source": [ + "img_shape = (3, 224, 224)\n", + "\n", + "printTopKData=True\n", + "topK=5\n", + "\n", + "# Setting other than current directory causes \"network file not found\" issue, as network file\n", + "# location is defined in solver file which does not have a path, so it searches in current dir.\n", + "downloadDir = '.' # /home/asurve/caffe_models' \n", + "trained_vgg_weights = 'kaggle-cats-dogs-model_2'\n", + "\n", + "urls = ['http://cdn3-www.dogtime.com/assets/uploads/gallery/goldador-dog-breed-pictures/puppy-1.jpg','https://lh3.googleusercontent.com/-YdeAa1Ff4Ac/VkUnQ4vuZGI/AAAAAAAAAEg/nBiUn4pp6aE/w800-h800/images-6.jpeg','https://upload.wikimedia.org/wikipedia/commons/thumb/5/58/MountainLion.jpg/312px-MountainLion.jpg']\n", + "\n", + "classifyImages(urls,img_shape, printTopKData, topK, downloadDir, trained_vgg_weights)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.13" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +}
