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Repository: stratospark/keras-multiprocess-image-data-generator
Branch: master
Commit: 1d938c66e4da
Files: 10
Total size: 6.3 MB

Directory structure:
gitextract_98h8luz3/

├── .gitignore
├── Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.ipynb
├── Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.md
├── LICENSE
├── nvidia-gpu-monitor.ipynb
├── requirements.txt
└── tools/
    ├── __init__.py
    ├── image.py
    ├── image_gen_extended.py
    └── sysmonitor.py

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FILE CONTENTS
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FILE: .gitignore
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*.pyc
.ipynb_checkpoints/*
*.zip
test1
train
data/


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FILE: Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.ipynb
================================================
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accelerating Deep Learning with Multiprocess Image Augmentation in Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![Benchmark Results](./results.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Code available @ https://github.com/stratospark/keras-multiprocess-image-data-generator**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* [Introduction](#Introduction)\n",
    "* [Benchmark: CIFAR10 - In Memory Performance, Image Generation Only](#Benchmark:-CIFAR10---In-Memory-Performance,-Image-Generation-Only)\n",
    "* [Benchmark: CIFAR10 - In Memory Performance, Image Generation with GPU Training](#Benchmark:-CIFAR10---In-Memory-Performance,-Image-Generation-with-GPU-Training)\n",
    "* [Benchmark: Dogs vs. Cats - On Disk Performance, Image Generation witih GPU Training](#Benchmark:-Dogs-vs.-Cats---On-Disk-Performance,-Image-Generation-witih-GPU-Training)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**TLDR: By adding multiprocessing support to Keras ImageDataGenerator, benchmarking on a 6-core i7-6850K and 12GB TITAN X Pascal: 3.5x speedup of training with image augmentation on in memory datasets, 3.9x speedup of training with image augmentation on datasets streamed from disk.**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "When exploring Deep Learning models, it isn't only beneficial to have good performance for the final training run. Accelerating training speed means more network models can be tried and more hyperparameter settings can be explored in the same amount of time. **The more that we can experiment, the better our results can become.**\n",
    "\n",
    "In my experience with [training a moderately sized network](http://blog.stratospark.com/deep-learning-applied-food-classification-deep-learning-keras.html) on my home desktop, I found one bottleneck to be creating additional images to augment my dataset. Keras provides an [ImageDataGenerator](https://keras.io/preprocessing/image/) class that can take images, in memory or on disk, and create many different variations based on a set of parameters: rotations, flips, zooms, altering colors, etc. For reference, here is a [great tutorial](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) on improving network accuracy with image augmentation.\n",
    "\n",
    "![cat images augmented](https://blog.keras.io/img/imgclf/cat_data_augmentation.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While training my initial models, I was waiting upwards of an entire day to see enough results to decide what to change. I saw that I was taking nowhere near full advantage of my CPU or GPU. As a result, I decided to add some Python multiprocessing support to a fork of ImageDataGenerator. I was able to drastically cut my training time and was finally able to steer my experiments in the right direction!\n",
    "\n",
    "For reference, I am using:\n",
    "* Intel Core i7-6850K\n",
    "* NVIDIA TITAN X Pascal 12GB\n",
    "* 96GB RAM\n",
    "* 64-bit Ubuntu 16.04\n",
    "* Python 2.7.13 :: Continuum Analytics, Inc.\n",
    "* Keras 1.2.1\n",
    "* Tensorflow 0.12.1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the multiprocessing-enabled ImageDataGenerator that is included with this repo as a drop-in replacement for the version that currently ships with Keras. If it makes sense, the code may get incorporated into the main branch at some point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import keras as K\n",
    "import matplotlib.pyplot as plt\n",
    "import multiprocessing\n",
    "import time\n",
    "import collections\n",
    "import sys\n",
    "import signal\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# The original class can be imported like this:\n",
    "# from keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "# We access the modified version through T.ImageDataGenerator\n",
    "import tools.image as T\n",
    "\n",
    "# Useful for checking the output of the generators after code change\n",
    "try:\n",
    "    from importlib import reload\n",
    "    reload(T)\n",
    "except:\n",
    "    reload(T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are helper methods used throughout the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def preprocess_img(img):\n",
    "    img = img.astype(np.float32) / 255.0\n",
    "    img -= 0.5\n",
    "    return img * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot_images(img_gen, title):\n",
    "    fig, ax = plt.subplots(6, 6, figsize=(10, 10))\n",
    "    plt.suptitle(title, size=32)\n",
    "    plt.setp(ax, xticks=[], yticks=[])\n",
    "    plt.tight_layout(rect=[0, 0.03, 1, 0.95])\n",
    "    for (imgs, labels) in img_gen:\n",
    "        for i in range(6):\n",
    "            for j in range(6):\n",
    "                if i*6 + j < 32:\n",
    "                    ax[i][j].imshow(imgs[i*6 + j])\n",
    "        break    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Benchmark: CIFAR10 - In Memory Performance, Image Generation Only"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[CIFAR10](https://www.cs.toronto.edu/~kriz/cifar.html) is a toy dataset that includes 50,000 training images and 10,000 test images of shape 32x32x3.\n",
    "\n",
    "It includes the following 10 classes: **airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from keras.datasets.cifar10 import load_data\n",
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = load_data()\n",
    "\n",
    "y_train_cat = to_categorical(y_train)\n",
    "y_test_cat = to_categorical(y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here is an example of how to set up a `multiprocessing.Pool` and add it as an argument to the ImageDataGenerator constructor. This is the only change to the class' public interface. If you leave out the `pool` parameter or set it to `None`, the generator will operate in its original single process mode."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4 process, duration: 0.0404160022736\n"
     ]
    },
    {
     "data": {
      "image/png": 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VKfVR0OQ/AZrA3wb285MGLSxm9Ndaa62Ueido4b4B1AbuU0o9C6IuDYFMJW8A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99sH1wbHn8c4X3fh0zkxNkJW/6FL0AWNNZjQlfnLyIsOjtFLi7k6DG1AvlPoQ8SoXOnTCKxviG4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\nOw0Ohm6zTgmtskn87tAY1BWY0tOMvHfIb728BBdcIkGJtXJwiaXTUGnp7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FRxZqfhzvZb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ycRi+3RRs6xnAuU88edbGYUFKuacD1/zoKygC9upLz4iIyMAw/r5n5wEbZ3NIuebzSIPnsuM2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\n19ODscES2nQC4+HCFbTPuQvmYlmWuZ2DHCdPy3OSP9tIkS6TrDG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QR3ab9t4dLJHwbSLL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WhQFEVRFEVRFGVH9pA8yYrkQD3DvKARFAPjqV/24B1MUVZJuAgRB5RKvJ1FSLyTRT2oI4QV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\nIiIiIqJYvGggIiIiIqJY/x8NYu48OgNWFQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcd66276790>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "try:\n",
    "    pool.terminate()\n",
    "except:\n",
    "    pass\n",
    "n_process = 4\n",
    "    \n",
    "pool = multiprocessing.Pool(processes=n_process)\n",
    "start = time.time()\n",
    "gen = T.ImageDataGenerator(\n",
    "     featurewise_center=False,\n",
    "     samplewise_center=False,\n",
    "     featurewise_std_normalization=False,\n",
    "     samplewise_std_normalization=False,\n",
    "     zca_whitening=False,\n",
    "     rotation_range=45,\n",
    "     width_shift_range=.1,\n",
    "     height_shift_range=.1,\n",
    "     shear_range=0.,\n",
    "     zoom_range=0,\n",
    "     channel_shift_range=0,\n",
    "     fill_mode='nearest',\n",
    "     cval=0.,\n",
    "     horizontal_flip=True,\n",
    "     vertical_flip=False,\n",
    "     rescale=1/255.,\n",
    "     #preprocessing_function=preprocess_img, # disable for nicer visualization\n",
    "     dim_ordering='default',\n",
    "     pool=pool # <-------------- Only change needed!\n",
    ")\n",
    "\n",
    "gen.fit(X_train)\n",
    "X_train_aug = gen.flow(X_train, y_train_cat, seed=0)\n",
    "\n",
    "print('{} process, duration: {}'.format(4, time.time() - start))\n",
    "plot_images(X_train_aug, 'Augmented Images generated with {} processes'.format(n_process))\n",
    "\n",
    "pool.terminate()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now that we have verified that the images are being properly generated with multiple processes, we want to benchmark how the number of processes affects performance. Idealy, we would like to see speedups scale linearly with the number of processes added. However, as explained by [Amdahl's Law](https://en.wikipedia.org/wiki/Amdahl%27s_law), there are diminishing returns due to additional overhead.\n",
    "\n",
    "The following benchmark will first test image augmentation without multiprocessing, then do a test for an increasing number of processes, up to a max of the number of logical CPUs your system has. It does multiple rounds of these tests so that we may average the results."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('Round', 0)\n",
      "0: 6.84576511383 ... 1: 9.6486890316 ... 2: 6.03799390793 ... 3: 4.88081693649 ... 4: 4.66870999336 ... 5: 3.70913481712 ... 6: 3.27630805969 ... 7: 3.48509907722 ... 8: 3.64657878876 ... 9: 3.74150896072 ... 10: 3.57441878319 ... 11: 3.60130214691 ... 12: 3.47499299049 ... ('Round', 1)\n",
      "0: 6.75701498985 ... 1: 9.94960093498 ... 2: 5.64250087738 ... 3: 5.06900811195 ... 4: 4.61409282684 ... 5: 4.57506585121 ... 6: 3.48270392418 ... 7: 3.51494693756 ... 8: 3.88235402107 ... 9: 3.62926697731 ... 10: 3.91224503517 ... 11: 3.59025716782 ... 12: 3.5045068264 ... ('Round', 2)\n",
      "0: 6.90472793579 ... 1: 9.55179905891 ... 2: 6.57418012619 ... 3: 5.2566280365 ... 4: 4.55560803413 ... 5: 4.45380306244 ... 6: 3.54513192177 ... 7: 3.21149206161 ... 8: 3.78789710999 ... 9: 3.67751908302 ... 10: 3.74882698059 ... 11: 3.98700881004 ... 12: 3.64187002182 ... ('Round', 3)\n",
      "0: 6.82807612419 ... 1: 9.48674917221 ... 2: 5.57596802711 ... 3: 4.74470591545 ... 4: 4.18711090088 ... 5: 3.89195489883 ... 6: 3.22924613953 ... 7: 3.17622900009 ... 8: 4.07523298264 ... 9: 3.59954690933 ... 10: 3.7366130352 ... 11: 3.52489495277 ... 12: 3.82451415062 ... ('Round', 4)\n",
      "0: 6.73704409599 ... 1: 9.2156291008 ... 2: 6.23566198349 ... 3: 5.13580393791 ... 4: 4.71229195595 ... 5: 3.35283398628 ... 6: 3.24846291542 ... 7: 3.79010605812 ... 8: 3.74294400215 ... 9: 3.76095604897 ... 10: 3.7142059803 ... 11: 3.54178500175 ... 12: 3.72024703026 ... ('Round', 5)\n",
      "0: 6.75245904922 ... 1: 10.7912859917 ... 2: 6.79878306389 ... 3: 4.67795395851 ... 4: 4.7692129612 ... 5: 3.99766302109 ... 6: 3.45177388191 ... 7: 3.30268979073 ... 8: 3.92767882347 ... 9: 3.69342398643 ... 10: 3.52480602264 ... 11: 3.46998000145 ... 12: 3.60531187057 ... ('Round', 6)\n",
      "0: 6.94973492622 ... 1: 9.72229290009 ... 2: 6.76698184013 ... 3: 5.28792905807 ... 4: 4.44634389877 ... 5: 4.34274101257 ... 6: 3.94904899597 ... 7: 3.34885692596 ... 8: 3.69488501549 ... 9: 3.87995219231 ... 10: 3.78279495239 ... 11: 3.49752092361 ... 12: 3.56351184845 ... ('Round', 7)\n",
      "0: 6.71522402763 ... 1: 10.2026801109 ... 2: 6.04175400734 ... 3: 5.20836210251 ... 4: 4.35653805733 ... 5: 4.39560294151 ... 6: 3.74392104149 ... 7: 3.19262504578 ... 8: 3.89874505997 ... 9: 3.41301083565 ... 10: 3.79124188423 ... 11: 3.90449810028 ... 12: 3.74271798134 ... ('Round', 8)\n",
      "0: 6.8355588913 ... 1: 9.49789810181 ... 2: 5.33640003204 ... 3: 5.41973185539 ... 4: 4.42942810059 ... 5: 4.30604100227 ... 6: 3.22810721397 ... 7: 3.24005103111 ... 8: 3.61394405365 ... 9: 3.50949716568 ... 10: 3.62207698822 ... 11: 3.84033894539 ... 12: 3.85311603546 ... ('Round', 9)\n",
      "0: 6.74057507515 ... 1: 10.3358399868 ... 2: 6.02810311317 ... 3: 5.41968894005 ... 4: 4.69001197815 ... 5: 3.6060628891 ... 6: 3.84348988533 ... 7: 3.67217493057 ... 8: 4.02522802353 ... 9: 3.74887800217 ... 10: 4.08099198341 ... 11: 3.81078886986 ... 12: 3.46359109879 ... "
     ]
    }
   ],
   "source": [
    "durs = collections.defaultdict(list)\n",
    "num_cores = 2\n",
    "try:\n",
    "    num_cores = multiprocessing.cpu_count()\n",
    "except:\n",
    "    pass\n",
    "\n",
    "for j in range(10):\n",
    "    print('Round', j)\n",
    "    \n",
    "    for num_p in range(0, num_cores + 1):\n",
    "        pool = None\n",
    "        if num_p > 0:\n",
    "            pool = multiprocessing.Pool(processes=num_p)\n",
    "            \n",
    "        start = time.time()\n",
    "        gen = T.ImageDataGenerator(\n",
    "             featurewise_center=False,\n",
    "             samplewise_center=False,\n",
    "             featurewise_std_normalization=False,\n",
    "             samplewise_std_normalization=False,\n",
    "             zca_whitening=False,\n",
    "             rotation_range=45,\n",
    "             width_shift_range=.1,\n",
    "             height_shift_range=.1,\n",
    "             shear_range=0.,\n",
    "             zoom_range=0,\n",
    "             channel_shift_range=0,\n",
    "             fill_mode='nearest',\n",
    "             cval=0.,\n",
    "             horizontal_flip=True,\n",
    "             vertical_flip=False,\n",
    "             rescale=None,\n",
    "             preprocessing_function=preprocess_img,\n",
    "             dim_ordering='default',\n",
    "             pool=pool\n",
    "        )\n",
    "\n",
    "        gen.fit(X_train)\n",
    "        X_train_aug = gen.flow(X_train, y_train_cat, seed=0)\n",
    "\n",
    "        for i, (imgs, labels) in enumerate(X_train_aug):\n",
    "            if i == 1000:\n",
    "                break\n",
    "\n",
    "        dur = time.time() - start\n",
    "        #print(num_p, dur)\n",
    "        sys.stdout.write('{}: {} ... '.format(num_p, dur))\n",
    "        sys.stdout.flush()\n",
    "        \n",
    "        durs[num_p].append(dur)\n",
    "\n",
    "        if pool:\n",
    "            pool.terminate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6.845765</td>\n",
       "      <td>9.648689</td>\n",
       "      <td>6.037994</td>\n",
       "      <td>4.880817</td>\n",
       "      <td>4.668710</td>\n",
       "      <td>3.709135</td>\n",
       "      <td>3.276308</td>\n",
       "      <td>3.485099</td>\n",
       "      <td>3.646579</td>\n",
       "      <td>3.741509</td>\n",
       "      <td>3.574419</td>\n",
       "      <td>3.601302</td>\n",
       "      <td>3.474993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.757015</td>\n",
       "      <td>9.949601</td>\n",
       "      <td>5.642501</td>\n",
       "      <td>5.069008</td>\n",
       "      <td>4.614093</td>\n",
       "      <td>4.575066</td>\n",
       "      <td>3.482704</td>\n",
       "      <td>3.514947</td>\n",
       "      <td>3.882354</td>\n",
       "      <td>3.629267</td>\n",
       "      <td>3.912245</td>\n",
       "      <td>3.590257</td>\n",
       "      <td>3.504507</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6.904728</td>\n",
       "      <td>9.551799</td>\n",
       "      <td>6.574180</td>\n",
       "      <td>5.256628</td>\n",
       "      <td>4.555608</td>\n",
       "      <td>4.453803</td>\n",
       "      <td>3.545132</td>\n",
       "      <td>3.211492</td>\n",
       "      <td>3.787897</td>\n",
       "      <td>3.677519</td>\n",
       "      <td>3.748827</td>\n",
       "      <td>3.987009</td>\n",
       "      <td>3.641870</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6.828076</td>\n",
       "      <td>9.486749</td>\n",
       "      <td>5.575968</td>\n",
       "      <td>4.744706</td>\n",
       "      <td>4.187111</td>\n",
       "      <td>3.891955</td>\n",
       "      <td>3.229246</td>\n",
       "      <td>3.176229</td>\n",
       "      <td>4.075233</td>\n",
       "      <td>3.599547</td>\n",
       "      <td>3.736613</td>\n",
       "      <td>3.524895</td>\n",
       "      <td>3.824514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6.737044</td>\n",
       "      <td>9.215629</td>\n",
       "      <td>6.235662</td>\n",
       "      <td>5.135804</td>\n",
       "      <td>4.712292</td>\n",
       "      <td>3.352834</td>\n",
       "      <td>3.248463</td>\n",
       "      <td>3.790106</td>\n",
       "      <td>3.742944</td>\n",
       "      <td>3.760956</td>\n",
       "      <td>3.714206</td>\n",
       "      <td>3.541785</td>\n",
       "      <td>3.720247</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6.752459</td>\n",
       "      <td>10.791286</td>\n",
       "      <td>6.798783</td>\n",
       "      <td>4.677954</td>\n",
       "      <td>4.769213</td>\n",
       "      <td>3.997663</td>\n",
       "      <td>3.451774</td>\n",
       "      <td>3.302690</td>\n",
       "      <td>3.927679</td>\n",
       "      <td>3.693424</td>\n",
       "      <td>3.524806</td>\n",
       "      <td>3.469980</td>\n",
       "      <td>3.605312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6.949735</td>\n",
       "      <td>9.722293</td>\n",
       "      <td>6.766982</td>\n",
       "      <td>5.287929</td>\n",
       "      <td>4.446344</td>\n",
       "      <td>4.342741</td>\n",
       "      <td>3.949049</td>\n",
       "      <td>3.348857</td>\n",
       "      <td>3.694885</td>\n",
       "      <td>3.879952</td>\n",
       "      <td>3.782795</td>\n",
       "      <td>3.497521</td>\n",
       "      <td>3.563512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.715224</td>\n",
       "      <td>10.202680</td>\n",
       "      <td>6.041754</td>\n",
       "      <td>5.208362</td>\n",
       "      <td>4.356538</td>\n",
       "      <td>4.395603</td>\n",
       "      <td>3.743921</td>\n",
       "      <td>3.192625</td>\n",
       "      <td>3.898745</td>\n",
       "      <td>3.413011</td>\n",
       "      <td>3.791242</td>\n",
       "      <td>3.904498</td>\n",
       "      <td>3.742718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.835559</td>\n",
       "      <td>9.497898</td>\n",
       "      <td>5.336400</td>\n",
       "      <td>5.419732</td>\n",
       "      <td>4.429428</td>\n",
       "      <td>4.306041</td>\n",
       "      <td>3.228107</td>\n",
       "      <td>3.240051</td>\n",
       "      <td>3.613944</td>\n",
       "      <td>3.509497</td>\n",
       "      <td>3.622077</td>\n",
       "      <td>3.840339</td>\n",
       "      <td>3.853116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6.740575</td>\n",
       "      <td>10.335840</td>\n",
       "      <td>6.028103</td>\n",
       "      <td>5.419689</td>\n",
       "      <td>4.690012</td>\n",
       "      <td>3.606063</td>\n",
       "      <td>3.843490</td>\n",
       "      <td>3.672175</td>\n",
       "      <td>4.025228</td>\n",
       "      <td>3.748878</td>\n",
       "      <td>4.080992</td>\n",
       "      <td>3.810789</td>\n",
       "      <td>3.463591</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         0          1         2         3         4         5         6   \\\n",
       "0  6.845765   9.648689  6.037994  4.880817  4.668710  3.709135  3.276308   \n",
       "1  6.757015   9.949601  5.642501  5.069008  4.614093  4.575066  3.482704   \n",
       "2  6.904728   9.551799  6.574180  5.256628  4.555608  4.453803  3.545132   \n",
       "3  6.828076   9.486749  5.575968  4.744706  4.187111  3.891955  3.229246   \n",
       "4  6.737044   9.215629  6.235662  5.135804  4.712292  3.352834  3.248463   \n",
       "5  6.752459  10.791286  6.798783  4.677954  4.769213  3.997663  3.451774   \n",
       "6  6.949735   9.722293  6.766982  5.287929  4.446344  4.342741  3.949049   \n",
       "7  6.715224  10.202680  6.041754  5.208362  4.356538  4.395603  3.743921   \n",
       "8  6.835559   9.497898  5.336400  5.419732  4.429428  4.306041  3.228107   \n",
       "9  6.740575  10.335840  6.028103  5.419689  4.690012  3.606063  3.843490   \n",
       "\n",
       "         7         8         9         10        11        12  \n",
       "0  3.485099  3.646579  3.741509  3.574419  3.601302  3.474993  \n",
       "1  3.514947  3.882354  3.629267  3.912245  3.590257  3.504507  \n",
       "2  3.211492  3.787897  3.677519  3.748827  3.987009  3.641870  \n",
       "3  3.176229  4.075233  3.599547  3.736613  3.524895  3.824514  \n",
       "4  3.790106  3.742944  3.760956  3.714206  3.541785  3.720247  \n",
       "5  3.302690  3.927679  3.693424  3.524806  3.469980  3

Download .txt

gitextract_98h8luz3/

├── .gitignore
├── Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.ipynb
├── Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.md
├── LICENSE
├── nvidia-gpu-monitor.ipynb
├── requirements.txt
└── tools/
    ├── __init__.py
    ├── image.py
    ├── image_gen_extended.py
    └── sysmonitor.py

Download .txt

SYMBOL INDEX (90 symbols across 3 files)

FILE: tools/image.py
  function random_rotation (line 26) | def random_rotation(x, rg, row_axis=1, col_axis=2, channel_axis=0,
  function random_shift (line 56) | def random_shift(x, wrg, hrg, row_axis=1, col_axis=2, channel_axis=0,
  function random_shear (line 88) | def random_shear(x, intensity, row_axis=1, col_axis=2, channel_axis=0,
  function random_zoom (line 118) | def random_zoom(x, zoom_range, row_axis=1, col_axis=2, channel_axis=0,
  function random_channel_shift (line 158) | def random_channel_shift(x, intensity, channel_axis=0):
  function transform_matrix_offset_center (line 168) | def transform_matrix_offset_center(matrix, x, y):
  function apply_transform (line 177) | def apply_transform(x, transform_matrix, channel_axis=0, fill_mode='near...
  function flip_axis (line 188) | def flip_axis(x, axis):
  function standardize (line 194) | def standardize(x,
  function random_transform (line 246) | def random_transform(x,
  function array_to_img (line 340) | def array_to_img(x, dim_ordering='default', scale=True):
  function img_to_array (line 390) | def img_to_array(img, dim_ordering='default'):
  function load_img (line 424) | def load_img(path, grayscale=False, target_size=None):
  function list_pictures (line 452) | def list_pictures(directory, ext='jpg|jpeg|bmp|png'):
  class ImageDataGenerator (line 458) | class ImageDataGenerator(object):
    method __init__ (line 501) | def __init__(self,
    method flow (line 569) | def flow(self, X, y=None, batch_size=32, shuffle=True, seed=None,
    method flow_from_directory (line 582) | def flow_from_directory(self, directory,
    method pipeline (line 603) | def pipeline(self):
    method standardize (line 638) | def standardize(self, x):
    method random_transform (line 652) | def random_transform(self, x):
    method fit (line 668) | def fit(self, x,
  class Iterator (line 733) | class Iterator(object):
    method __init__ (line 735) | def __init__(self, n, batch_size, shuffle, seed):
    method reset (line 751) | def reset(self):
    method _flow_index (line 754) | def _flow_index(self, n, batch_size=32, shuffle=False, seed=None):
    method __iter__ (line 776) | def __iter__(self):
    method __next__ (line 781) | def __next__(self, *args, **kwargs):
  function process_image_pipeline (line 785) | def process_image_pipeline(tup):
  function process_image_pipeline_dir (line 794) | def process_image_pipeline_dir(tup):
  class NumpyArrayIterator (line 807) | class NumpyArrayIterator(Iterator):
    method __init__ (line 809) | def __init__(self, x, y, image_data_generator,
    method next (line 847) | def next(self):
  class DirectoryIterator (line 888) | class DirectoryIterator(Iterator):
    method __init__ (line 890) | def __init__(self, directory, image_data_generator,
    method next (line 978) | def next(self):

FILE: tools/image_gen_extended.py
  function random_rotation (line 23) | def random_rotation(x, rg, row_index=1, col_index=2, channel_index=0,
  function random_shift (line 36) | def random_shift(x, wrg, hrg, row_index=1, col_index=2, channel_index=0,
  function random_shear (line 50) | def random_shear(x, intensity, row_index=1, col_index=2, channel_index=0,
  function random_zoom (line 63) | def random_zoom(x, zoom_range, row_index=1, col_index=2, channel_index=0,
  function random_barrel_transform (line 83) | def random_barrel_transform(x, intensity):
  function random_channel_shift (line 88) | def random_channel_shift(x, intensity, channel_index=0, rng=None):
  function transform_matrix_offset_center (line 98) | def transform_matrix_offset_center(matrix, x, y):
  function apply_transform (line 107) | def apply_transform(x, transform_matrix, channel_index=0, fill_mode='nea...
  function flip_axis (line 118) | def flip_axis(x, axis):
  function array_to_img (line 125) | def array_to_img(x, dim_ordering=K.image_dim_ordering(), mode=None, scal...
  function img_to_array (line 144) | def img_to_array(img, dim_ordering=K.image_dim_ordering()):
  function load_img (line 162) | def load_img(path, target_mode=None, target_size=None):
  function list_pictures (line 171) | def list_pictures(directory, ext='jpg|jpeg|bmp|png'):
  function pil_image_reader (line 175) | def pil_image_reader(filepath, target_mode=None, target_size=None, dim_o...
  function standardize (line 179) | def standardize(x,
  function center_crop (line 288) | def center_crop(x, center_crop_size, **kwargs):
  function random_crop (line 293) | def random_crop(x, random_crop_size, sync_seed=None, rng=None, **kwargs):
  function preprocess_input (line 305) | def preprocess_input(x, rng=None, **kwargs):
  function random_transform (line 308) | def random_transform(x,
  class ImageDataGenerator (line 427) | class ImageDataGenerator(object):
    method __init__ (line 465) | def __init__(self,
    method sync_seed (line 510) | def sync_seed(self):
    method fitting (line 514) | def fitting(self):
    method pipeline (line 518) | def pipeline(self):
    method sync (line 521) | def sync(self, image_data_generator):
    method set_pipeline (line 525) | def set_pipeline(self, p):
    method flow (line 533) | def flow(self, X, y=None, batch_size=32, shuffle=True, seed=None,
    method flow_from_directory (line 544) | def flow_from_directory(self, directory,
    method process (line 563) | def process(self, x, rng):
    method fit_generator (line 580) | def fit_generator(self, generator, nb_iter):
    method fit (line 595) | def fit(self, X, rounds=1):
  class Iterator (line 612) | class Iterator(object):
    method __init__ (line 614) | def __init__(self, N, batch_size, shuffle, seed):
    method reset (line 624) | def reset(self):
    method _flow_index (line 627) | def _flow_index(self, N, batch_size=32, shuffle=False, seed=None):
    method __add__ (line 651) | def __add__(self, it):
    method __iter__ (line 666) | def __iter__(self):
    method __next__ (line 671) | def __next__(self, *args, **kwargs):
  function process_image_worker (line 674) | def process_image_worker(tup):
  class NumpyArrayIterator (line 679) | class NumpyArrayIterator(Iterator):
    method __init__ (line 681) | def __init__(self, X, y, image_data_generator,
    method close (line 704) | def close(self):
    method __add__ (line 713) | def __add__(self, it):
    method next (line 721) | def next(self):
  class DirectoryIterator (line 759) | class DirectoryIterator(Iterator):
    method __init__ (line 761) | def __init__(self, directory, image_data_generator,
    method __add__ (line 859) | def __add__(self, it):
    method next (line 873) | def next(self):

FILE: tools/sysmonitor.py
  function gpu_info (line 15) | def gpu_info():
  class SysMonitor (line 29) | class SysMonitor(threading.Thread):
    method __init__ (line 32) | def __init__(self):
    method run (line 38) | def run(self):
    method stop (line 47) | def stop(self):
    method plot (line 51) | def plot(self, title, vert=False):

Download .json

Condensed preview — 10 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (6,671K chars).

[
  {
    "path": ".gitignore",
    "chars": 51,
    "preview": "*.pyc\n.ipynb_checkpoints/*\n*.zip\ntest1\ntrain\ndata/\n"
  },
  {
    "path": "Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.ipynb",
    "chars": 5490642,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"# Accelerating Deep Learning with M"
  },
  {
    "path": "Accelerating Deep Learning with Multiprocess Image Augmentation in Keras.md",
    "chars": 54708,
    "preview": "\n# Accelerating Deep Learning with Multiprocess Image Augmentation in Keras\n\n![Benchmark Results](./results.png)\n\n**Code"
  },
  {
    "path": "LICENSE",
    "chars": 1074,
    "preview": "MIT License\n\nCopyright (c) 2017 Patrick Rodriguez\n\nPermission is hereby granted, free of charge, to any person obtaining"
  },
  {
    "path": "nvidia-gpu-monitor.ipynb",
    "chars": 946187,
    "preview": "{\n \"cells\": [\n  {\n   \"cell_type\": \"markdown\",\n   \"metadata\": {},\n   \"source\": [\n    \"## Live plot of Nvidia GPU utilizat"
  },
  {
    "path": "requirements.txt",
    "chars": 1320,
    "preview": "apipkg==1.4\nautopep8==1.2.4\nbackports-abc==0.5\nbackports.shutil-get-terminal-size==1.0.0\nbackports.ssl-match-hostname==3"
  },
  {
    "path": "tools/__init__.py",
    "chars": 0,
    "preview": ""
  },
  {
    "path": "tools/image.py",
    "chars": 41706,
    "preview": "\"\"\"Fairly basic set of tools for real-time data augmentation on image data.\nCan easily be extended to include new transf"
  },
  {
    "path": "tools/image_gen_extended.py",
    "chars": 38682,
    "preview": "'''Fairly basic set of tools for real-time data augmentation on image data.\nCan easily be extended to include new transf"
  },
  {
    "path": "tools/sysmonitor.py",
    "chars": 1936,
    "preview": "import time\nimport datetime\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import OrderedDict\nimpor"
  }
]

About this extraction

This page contains the full source code of the stratospark/keras-multiprocess-image-data-generator GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 10 files (6.3 MB), approximately 1.6M tokens, and a symbol index with 90 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.

Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.

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