Speed Tests.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Speed Comparisons between the Numpy an TF implementations of the DTCWT\n",
    "Operations were performed on a system with a GTX 1080 GPU and Intel Xeon CPU E5-2660 CPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:30:49.826397Z",
     "start_time": "2017-08-07T11:30:48.221518Z"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import datasets\n",
    "import dtcwt\n",
    "import dtcwt.tf\n",
    "import tensorflow as tf\n",
    "from time import time\n",
    "import numpy as np\n",
    "import os\n",
    "import py3nvml\n",
    "plt.style.use('seaborn')\n",
    "py3nvml.grab_gpus(1);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Small Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "## DTCWT on a single small image (64x64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:30:51.360177Z",
     "start_time": "2017-08-07T11:30:49.828104Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create the input\n",
    "h, w = 64, 64\n",
    "in_ = np.random.randn(1,h,w)\n",
    "\n",
    "# Set up the transforms\n",
    "nlevels = 3\n",
    "tf.reset_default_graph()\n",
    "fwd = dtcwt.Transform2d()        # Numpy Transform\n",
    "fwd_tf = dtcwt.tf.Transform2d()  # Tensorflow Transform\n",
    "\n",
    "in_placeholder = tf.placeholder(tf.float32, [None, h, w])\n",
    "out_tf = fwd_tf.forward(in_placeholder, nlevels=nlevels)\n",
    "out_fft = tf.fft2d(tf.cast(in_placeholder, tf.complex64))\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:30:56.101356Z",
     "start_time": "2017-08-07T11:30:51.362870Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.63 ms ± 592 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "small_np = %timeit -o for i in in_: fwd.forward(i, nlevels=nlevels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:30:56.989200Z",
     "start_time": "2017-08-07T11:30:56.103634Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.26 ms ± 444 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "small_tf = %timeit -o sess.run(out_tf.lowpass_op, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Comparison) Using an FFT in Tensorflow\n",
    "We can safely assume that something like the FFT is an optimized, fast operation to do. This is a good yardstick to gauge the overheads with working on a GPU"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:30:57.470088Z",
     "start_time": "2017-08-07T11:30:56.992097Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.82 ms ± 68.2 µs per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sess.run(out_fft, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTCWT on a batch of small images (100x64x64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:30:57.510947Z",
     "start_time": "2017-08-07T11:30:57.473103Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_ = np.random.randn(100,h,w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:02.709545Z",
     "start_time": "2017-08-07T11:30:57.513113Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "642 ms ± 68.7 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "small_np_batch = %timeit -o for i in in_: fwd.forward(i, nlevels=nlevels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:04.972253Z",
     "start_time": "2017-08-07T11:31:02.713718Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.77 ms ± 126 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "small_tf_batch = %timeit -o sess.run(out_tf.lowpass_op, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Comparison) Using an FFT in Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:10.279027Z",
     "start_time": "2017-08-07T11:31:04.975413Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.53 ms ± 439 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sess.run(out_fft, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Small Image Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:10.504659Z",
     "start_time": "2017-08-07T11:31:10.282293Z"
    }
   },
   "outputs": [
    {
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hl1J2u4e7R4CT2FalB9uqdGA7XRxBBwDAAAQdAAADEHQAAAxA0AEAMABBBwDAAAQdAAAD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\nQNABADAAQQcAwAD/DzOv+htPD7E0AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3e3e9d8d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('np single', 'tf single', 'np batch', 'tf batch')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [small_np.average, small_tf.average, small_np_batch.average, small_tf_batch.average]\n",
    "performance = [i*1000 for i in performance]\n",
    "fig, ax = plt.subplots(1, figsize=(8,8))\n",
    "ax.bar(y_pos, performance, align='center', alpha=0.5, color=['red', 'blue'])\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Time (ms)')\n",
    "plt.title('Times to perform DTCWT on small images')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analysis of Large Images"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTCWT on a single large image (512x512)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:11.373219Z",
     "start_time": "2017-08-07T11:31:10.506792Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Create the input\n",
    "h, w = 512, 512\n",
    "in_ = np.random.randn(1,h,w)\n",
    "\n",
    "# Set up the transforms\n",
    "nlevels = 3\n",
    "tf.reset_default_graph()\n",
    "fwd = dtcwt.Transform2d()        # Numpy Transform\n",
    "fwd_tf = dtcwt.tf.Transform2d()  # Tensorflow Transform\n",
    "\n",
    "in_placeholder = tf.placeholder(tf.float32, [None, h, w])\n",
    "out_tf = fwd_tf.forward(in_placeholder, nlevels=nlevels)\n",
    "out_fft = tf.fft2d(tf.cast(in_placeholder, tf.complex64))\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:16.031492Z",
     "start_time": "2017-08-07T11:31:11.375442Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "57.3 ms ± 5.27 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "large_np = %timeit -o for i in in_: fwd.forward(i, nlevels=nlevels)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:18.058548Z",
     "start_time": "2017-08-07T11:31:16.033601Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.39 ms ± 109 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "large_tf = %timeit -o sess.run(out_tf.lowpass_op, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Comparison) Using an FFT in Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:23.107977Z",
     "start_time": "2017-08-07T11:31:18.061652Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6.19 ms ± 325 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sess.run(out_fft, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTCWT on a batch of large images (100x512x512)\n",
    "Batches are something tensorflow naturally handles. This should widen the gap as we only have to copy data to the GPU once for multiple images"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:31:24.473872Z",
     "start_time": "2017-08-07T11:31:23.112698Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "in_ = np.random.randn(100,512,512)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:08.472588Z",
     "start_time": "2017-08-07T11:31:24.477445Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.44 s ± 189 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "large_np_batch = %timeit -o for i in in_: fwd.forward(i, nlevels=nlevels).lowpass"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:09.632071Z",
     "start_time": "2017-08-07T11:32:08.474983Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124 ms ± 4.32 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "large_tf_batch = %timeit -o  sess.run(out_tf.lowpass_op, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### (Comparison) Using an FFT in Tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:10.702518Z",
     "start_time": "2017-08-07T11:32:09.634609Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "113 ms ± 2.6 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit sess.run(out_fft, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DTCWT on a batch of large images with a convolution afterwards\n",
    "This again should widen the gap, as having already calculated something on the GPU, we don't need to transfer the data there again"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:12.112465Z",
     "start_time": "2017-08-07T11:32:10.706380Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "h, w = 512, 512\n",
    "in_ = np.random.randn(100, h, w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:12.156396Z",
     "start_time": "2017-08-07T11:32:12.114544Z"
    }
   },
   "outputs": [],
   "source": [
    "fwd = dtcwt.Transform2d()\n",
    "tf.reset_default_graph()\n",
    "sess = tf.Session()\n",
    "highs = tf.placeholder(tf.float32, [None, h>>3, w>>3, 6])\n",
    "weights = tf.get_variable('weights', shape=(5,5,6,64))\n",
    "step = tf.nn.conv2d(highs, weights, strides=[1,1,1,1], padding='SAME')\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:57.965489Z",
     "start_time": "2017-08-07T11:32:12.159253Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.7 s ± 139 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "large_np_conv = %timeit -o sess.run(step, {highs: [abs(fwd.forward(i, nlevels=3).highpasses[2]) for i in in_]})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF Implementation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:32:58.860531Z",
     "start_time": "2017-08-07T11:32:57.968404Z"
    },
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "tf.reset_default_graph()\n",
    "in_placeholder = tf.placeholder(tf.float32, [None, h, w])\n",
    "fwd_tf = dtcwt.tf.Transform2d() \n",
    "out_tf = fwd_tf.forward(in_placeholder, nlevels=3)\n",
    "p = tf.abs(out_tf.highpasses_ops[2])\n",
    "weights = tf.get_variable('weights', shape=(5,5,6,64))\n",
    "out = tf.nn.conv2d(p, weights, strides=[1,1,1,1], padding='SAME')\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T11:33:10.477651Z",
     "start_time": "2017-08-07T11:32:58.863436Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "143 ms ± 1.61 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "large_tf_conv = %timeit -o sess.run(out, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Running TF backed on a CPU\n",
    "Perhaps there is still a speed-up when using a CPU and tensorflow?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T12:03:40.293069Z",
     "start_time": "2017-08-07T12:03:28.964177Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12.8 ms ± 389 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n"
     ]
    }
   ],
   "source": [
    "# Create the input\n",
    "h, w = 512, 512\n",
    "in_ = np.random.randn(1,h,w)\n",
    "\n",
    "# Set up the transforms\n",
    "nlevels = 3\n",
    "tf.reset_default_graph()\n",
    "with tf.device(\"/cpu:0\"):\n",
    "    fwd_tf = dtcwt.tf.Transform2d()  # Tensorflow Transform\n",
    "    in_placeholder = tf.placeholder(tf.float32, [None, h, w])\n",
    "    out_tf = fwd_tf.forward(in_placeholder, nlevels=nlevels)\n",
    "\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "large_tf_cpu = %timeit -o  sess.run(out_tf.lowpass_op, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T12:03:47.646900Z",
     "start_time": "2017-08-07T12:03:42.065636Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "516 ms ± 17.8 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "in_ = np.random.randn(100,h,w)\n",
    "large_tf_batch_cpu = %timeit -o  sess.run(out_tf.lowpass_op, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T12:04:18.946481Z",
     "start_time": "2017-08-07T12:04:13.202643Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "610 ms ± 27.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "tf.reset_default_graph()\n",
    "with tf.device(\"/cpu:0\"):\n",
    "    in_placeholder = tf.placeholder(tf.float32, [None, h, w])\n",
    "    fwd_tf = dtcwt.tf.Transform2d() \n",
    "    out_tf = fwd_tf.forward(in_placeholder, nlevels=3)\n",
    "    p = tf.abs(out_tf.highpasses_ops[2])\n",
    "    weights = tf.get_variable('weights', shape=(5,5,6,64))\n",
    "    out = tf.nn.conv2d(p, weights, strides=[1,1,1,1], padding='SAME')\n",
    "sess = tf.Session()\n",
    "sess.run(tf.global_variables_initializer())\n",
    "large_tf_conv_cpu = %timeit -o sess.run(out, {in_placeholder: in_})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Large Image Conclusion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2017-08-07T12:05:41.444267Z",
     "start_time": "2017-08-07T12:05:40.930019Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Kv7BssskmtX1yypQp+f73v5/x48ene/fumThx4mrvYtOW+Xj7Nt76ftfkyiuvzI033ljb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\nF/wfAOvqbvm2/GkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3dec478208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "objects = ('np one img', 'tf one img\\n(gpu)', 'tf one img\\n(cpu)', 'np batch', 'tf batch\\n(gpu)', \n",
    "           'tf batch\\n(cpu)', 'np batch\\n+conv', 'tf batch\\n+conv\\n(gpu)', 'tf batch\\n+conv\\n(cpu)')\n",
    "y_pos = np.arange(len(objects))\n",
    "performance = [large_np.average, large_tf.average, large_tf_cpu.average, \n",
    "               large_np_batch.average, large_tf_batch.average, large_tf_batch_cpu.average,\n",
    "               large_np_conv.average, large_tf_conv.average, large_tf_conv_cpu.average]\n",
    "performance = [i*1000 for i in performance]\n",
    "fig, ax = plt.subplots(1, figsize=(12,12))\n",
    "ax.bar(y_pos, performance, align='center', alpha=0.5, color=['red', 'blue', 'green'])\n",
    "plt.xticks(y_pos, objects)\n",
    "plt.ylabel('Time (ms)')\n",
    "plt.title('Times to perform different DTCWT operations on large images')\n",
    "plt.show()"
   ]
  }
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